{
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  {
   "cell_type": "markdown",
   "id": "b65b5bd8",
   "metadata": {
    "cq.autogen": "title_cell"
   },
   "source": [
    "# Tensor Hypercontraction\n",
    "\n",
    "SELECT and PREPARE for the second quantized Tensor Hypercontracted (THC) chemistry Hamtiltonian."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1936da8a",
   "metadata": {
    "cq.autogen": "top_imports"
   },
   "outputs": [],
   "source": [
    "from qualtran import Bloq, CompositeBloq, BloqBuilder, Signature, Register\n",
    "from qualtran import QBit, QInt, QUInt, QAny\n",
    "from qualtran.drawing import show_bloq, show_call_graph, show_counts_sigma\n",
    "from typing import *\n",
    "import numpy as np\n",
    "import sympy\n",
    "import cirq"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c848ea",
   "metadata": {
    "cq.autogen": "UniformSuperpositionTHC.bloq_doc.md"
   },
   "source": [
    "## `UniformSuperpositionTHC`\n",
    "Prepare uniform superposition state for THC.\n",
    "\n",
    "$$\n",
    "    |0\\rangle^{\\otimes 2\\log(M+1)} \\rightarrow \\sum_{\\mu\\le\\nu}^{M} |\\mu\\rangle|\\nu\\rangle\n",
    "    + \\sum_{\\mu}^{N/2}|\\mu\\rangle|\\nu=M+1\\rangle,\n",
    "$$\n",
    "\n",
    "where $M$ is the THC auxiliary dimension, and $N$ is the number of spin orbitals.\n",
    "\n",
    "The toffoli complexity of this gate should be $10 \\log(M+1) + 2 b_r - 9$.\n",
    "Currently it is a good deal larger due to:\n",
    " 1. inverting inequality tests should not need more toffolis.\n",
    " 2. We are not using phase-gradient gate toffoli cost for Ry rotations.\n",
    " 3. Small differences in quoted vs implemented comparator costs.\n",
    "\n",
    "See: https://github.com/quantumlib/Qualtran/issues/390\n",
    "\n",
    "#### Parameters\n",
    " - `num_mu`: THC auxiliary index dimension $M$\n",
    " - `num_spin_orb`: number of spin orbitals $N$ \n",
    "\n",
    "#### Registers\n",
    " - `mu`: $\\mu$ register.\n",
    " - `nu`: $\\nu$ register.\n",
    " - `succ`: ancilla flagging success of amplitude amplification.\n",
    " - `nu_eq_mp1`: ancillas for flagging if $\\nu = M+1$.\n",
    " - `rot`: The ancilla to be rotated for amplitude amplification. \n",
    "\n",
    "#### References\n",
    " - [Even more efficient quantum computations of chemistry through     tensor hypercontraction](https://arxiv.org/pdf/2011.03494.pdf). Eq. 29.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5ff7633",
   "metadata": {
    "cq.autogen": "UniformSuperpositionTHC.bloq_doc.py"
   },
   "outputs": [],
   "source": [
    "from qualtran.bloqs.chemistry.thc import UniformSuperpositionTHC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1fddec5",
   "metadata": {
    "cq.autogen": "UniformSuperpositionTHC.example_instances.md"
   },
   "source": [
    "### Example Instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fdce0654",
   "metadata": {
    "cq.autogen": "UniformSuperpositionTHC.thc_uni"
   },
   "outputs": [],
   "source": [
    "num_mu = 10\n",
    "num_spin_orb = 4\n",
    "thc_uni = UniformSuperpositionTHC(num_mu=num_mu, num_spin_orb=num_spin_orb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eca0582",
   "metadata": {
    "cq.autogen": "UniformSuperpositionTHC.graphical_signature.md"
   },
   "source": [
    "#### Graphical Signature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41eefb04",
   "metadata": {
    "cq.autogen": "UniformSuperpositionTHC.graphical_signature.py"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<IPython.core.display.M…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qualtran.drawing import show_bloqs\n",
    "show_bloqs([thc_uni],\n",
    "           ['`thc_uni`'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ea50cb",
   "metadata": {
    "cq.autogen": "UniformSuperpositionTHC.call_graph.md"
   },
   "source": [
    "### Call Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "134616a6",
   "metadata": {
    "cq.autogen": "UniformSuperpositionTHC.call_graph.py"
   },
   "outputs": [
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      "text/markdown": [
       "#### Counts totals:\n",
       " - `Allocate`: 1\n",
       " - `C^3X`: 1\n",
       " - `EqualsAConstant`: 3\n",
       " - `Free`: 1\n",
       " - `GreaterThanConstant`: 4\n",
       " - `H⨂4`: 6\n",
       " - `LessThanConstant`: 4\n",
       " - `LessThanEqual`: 4\n",
       " - `ReflectionUsingPrepare`: 1\n",
       " - `ReflectionUsingPrepare`: 1\n",
       " - `Ry(-1.1167599872823721)`: 1\n",
       " - `Ry(1.1167599872823721)`: 1\n",
       " - `Toffoli`: 4\n",
       " - `X`: 1"
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      "text/plain": [
       "<IPython.core.display.Markdown object>"
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     },
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   ],
   "source": [
    "from qualtran.resource_counting.generalizers import ignore_split_join\n",
    "thc_uni_g, thc_uni_sigma = thc_uni.call_graph(max_depth=1, generalizer=ignore_split_join)\n",
    "show_call_graph(thc_uni_g)\n",
    "show_counts_sigma(thc_uni_sigma)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "176027fd-e452-40d4-a526-9e1b9e86896a",
   "metadata": {},
   "source": [
    "### Flame Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "65f3c175-c87d-4bf5-8e5f-8bde322152f4",
   "metadata": {},
   "outputs": [
    {
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       "<!-- Flame graph stack visualization. See https://github.com/brendangregg/FlameGraph for latest version, and http://www.brendangregg.com/flamegraphs.html for examples. -->\n",
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       "<defs>\n",
       "\t<linearGradient id=\"background\" y1=\"0\" y2=\"1\" x1=\"0\" x2=\"0\">\n",
       "\t\t<stop stop-color=\"#eeeeee\" offset=\"5%\"/>\n",
       "\t\t<stop stop-color=\"#eeeeb0\" offset=\"95%\"/>\n",
       "\t</linearGradient>\n",
       "</defs>\n",
       "<style type=\"text/css\">\n",
       "\ttext { font-family:Verdana; font-size:12px; fill:rgb(0,0,0); }\n",
       "\t#search, #ignorecase { opacity:0.1; cursor:pointer; }\n",
       "\t#search:hover, #search.show, #ignorecase:hover, #ignorecase.show { opacity:1; }\n",
       "\t#subtitle { text-anchor:middle; font-color:rgb(160,160,160); }\n",
       "\t#title { text-anchor:middle; font-size:17px}\n",
       "\t#unzoom { cursor:pointer; }\n",
       "\t#frames &gt; *:hover { stroke:black; stroke-width:0.5; cursor:pointer; }\n",
       "\t.hide { display:none; }\n",
       "\t.parent { opacity:0.5; }\n",
       "</style>\n",
       "<script type=\"text/ecmascript\">\n",
       "<![CDATA[\n",
       "\t\"use strict\";\n",
       "\tvar details, searchbtn, unzoombtn, matchedtxt, svg, searching, currentSearchTerm, ignorecase, ignorecaseBtn;\n",
       "\tfunction init(evt) {\n",
       "\t\tdetails = document.getElementById(\"details\").firstChild;\n",
       "\t\tsearchbtn = document.getElementById(\"search\");\n",
       "\t\tignorecaseBtn = document.getElementById(\"ignorecase\");\n",
       "\t\tunzoombtn = document.getElementById(\"unzoom\");\n",
       "\t\tmatchedtxt = document.getElementById(\"matched\");\n",
       "\t\tsvg = document.getElementsByTagName(\"svg\")[0];\n",
       "\t\tsearching = 0;\n",
       "\t\tcurrentSearchTerm = null;\n",
       "\n",
       "\t\t// use GET parameters to restore a flamegraphs state.\n",
       "\t\tvar params = get_params();\n",
       "\t\tif (params.x && params.y)\n",
       "\t\t\tzoom(find_group(document.querySelector('[x=\"' + params.x + '\"][y=\"' + params.y + '\"]')));\n",
       "                if (params.s) search(params.s);\n",
       "\t}\n",
       "\n",
       "\t// event listeners\n",
       "\twindow.addEventListener(\"click\", function(e) {\n",
       "\t\tvar target = find_group(e.target);\n",
       "\t\tif (target) {\n",
       "\t\t\tif (target.nodeName == \"a\") {\n",
       "\t\t\t\tif (e.ctrlKey === false) return;\n",
       "\t\t\t\te.preventDefault();\n",
       "\t\t\t}\n",
       "\t\t\tif (target.classList.contains(\"parent\")) unzoom(true);\n",
       "\t\t\tzoom(target);\n",
       "\t\t\tif (!document.querySelector('.parent')) {\n",
       "\t\t\t\t// we have basically done a clearzoom so clear the url\n",
       "\t\t\t\tvar params = get_params();\n",
       "\t\t\t\tif (params.x) delete params.x;\n",
       "\t\t\t\tif (params.y) delete params.y;\n",
       "\t\t\t\thistory.replaceState(null, null, parse_params(params));\n",
       "\t\t\t\tunzoombtn.classList.add(\"hide\");\n",
       "\t\t\t\treturn;\n",
       "\t\t\t}\n",
       "\n",
       "\t\t\t// set parameters for zoom state\n",
       "\t\t\tvar el = target.querySelector(\"rect\");\n",
       "\t\t\tif (el && el.attributes && el.attributes.y && el.attributes._orig_x) {\n",
       "\t\t\t\tvar params = get_params()\n",
       "\t\t\t\tparams.x = el.attributes._orig_x.value;\n",
       "\t\t\t\tparams.y = el.attributes.y.value;\n",
       "\t\t\t\thistory.replaceState(null, null, parse_params(params));\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\telse if (e.target.id == \"unzoom\") clearzoom();\n",
       "\t\telse if (e.target.id == \"search\") search_prompt();\n",
       "\t\telse if (e.target.id == \"ignorecase\") toggle_ignorecase();\n",
       "\t}, false)\n",
       "\n",
       "\t// mouse-over for info\n",
       "\t// show\n",
       "\twindow.addEventListener(\"mouseover\", function(e) {\n",
       "\t\tvar target = find_group(e.target);\n",
       "\t\tif (target) details.nodeValue = \"Function: \" + g_to_text(target);\n",
       "\t}, false)\n",
       "\n",
       "\t// clear\n",
       "\twindow.addEventListener(\"mouseout\", function(e) {\n",
       "\t\tvar target = find_group(e.target);\n",
       "\t\tif (target) details.nodeValue = ' ';\n",
       "\t}, false)\n",
       "\n",
       "\t// ctrl-F for search\n",
       "\t// ctrl-I to toggle case-sensitive search\n",
       "\twindow.addEventListener(\"keydown\",function (e) {\n",
       "\t\tif (e.keyCode === 114 || (e.ctrlKey && e.keyCode === 70)) {\n",
       "\t\t\te.preventDefault();\n",
       "\t\t\tsearch_prompt();\n",
       "\t\t}\n",
       "\t\telse if (e.ctrlKey && e.keyCode === 73) {\n",
       "\t\t\te.preventDefault();\n",
       "\t\t\ttoggle_ignorecase();\n",
       "\t\t}\n",
       "\t}, false)\n",
       "\n",
       "\t// functions\n",
       "\tfunction get_params() {\n",
       "\t\tvar params = {};\n",
       "\t\tvar paramsarr = window.location.search.substr(1).split('&');\n",
       "\t\tfor (var i = 0; i < paramsarr.length; ++i) {\n",
       "\t\t\tvar tmp = paramsarr[i].split(\"=\");\n",
       "\t\t\tif (!tmp[0] || !tmp[1]) continue;\n",
       "\t\t\tparams[tmp[0]]  = decodeURIComponent(tmp[1]);\n",
       "\t\t}\n",
       "\t\treturn params;\n",
       "\t}\n",
       "\tfunction parse_params(params) {\n",
       "\t\tvar uri = \"?\";\n",
       "\t\tfor (var key in params) {\n",
       "\t\t\turi += key + '=' + encodeURIComponent(params[key]) + '&';\n",
       "\t\t}\n",
       "\t\tif (uri.slice(-1) == \"&\")\n",
       "\t\t\turi = uri.substring(0, uri.length - 1);\n",
       "\t\tif (uri == '?')\n",
       "\t\t\turi = window.location.href.split('?')[0];\n",
       "\t\treturn uri;\n",
       "\t}\n",
       "\tfunction find_child(node, selector) {\n",
       "\t\tvar children = node.querySelectorAll(selector);\n",
       "\t\tif (children.length) return children[0];\n",
       "\t}\n",
       "\tfunction find_group(node) {\n",
       "\t\tvar parent = node.parentElement;\n",
       "\t\tif (!parent) return;\n",
       "\t\tif (parent.id == \"frames\") return node;\n",
       "\t\treturn find_group(parent);\n",
       "\t}\n",
       "\tfunction orig_save(e, attr, val) {\n",
       "\t\tif (e.attributes[\"_orig_\" + attr] != undefined) return;\n",
       "\t\tif (e.attributes[attr] == undefined) return;\n",
       "\t\tif (val == undefined) val = e.attributes[attr].value;\n",
       "\t\te.setAttribute(\"_orig_\" + attr, val);\n",
       "\t}\n",
       "\tfunction orig_load(e, attr) {\n",
       "\t\tif (e.attributes[\"_orig_\"+attr] == undefined) return;\n",
       "\t\te.attributes[attr].value = e.attributes[\"_orig_\" + attr].value;\n",
       "\t\te.removeAttribute(\"_orig_\"+attr);\n",
       "\t}\n",
       "\tfunction g_to_text(e) {\n",
       "\t\tvar text = find_child(e, \"title\").firstChild.nodeValue;\n",
       "\t\treturn (text)\n",
       "\t}\n",
       "\tfunction g_to_func(e) {\n",
       "\t\tvar func = g_to_text(e);\n",
       "\t\t// if there's any manipulation we want to do to the function\n",
       "\t\t// name before it's searched, do it here before returning.\n",
       "\t\treturn (func);\n",
       "\t}\n",
       "\tfunction update_text(e) {\n",
       "\t\tvar r = find_child(e, \"rect\");\n",
       "\t\tvar t = find_child(e, \"text\");\n",
       "\t\tvar w = parseFloat(r.attributes.width.value) -3;\n",
       "\t\tvar txt = find_child(e, \"title\").textContent.replace(/\\([^(]*\\)$/,\"\");\n",
       "\t\tt.attributes.x.value = parseFloat(r.attributes.x.value) + 3;\n",
       "\n",
       "\t\t// Smaller than this size won't fit anything\n",
       "\t\tif (w < 2 * 12 * 0.59) {\n",
       "\t\t\tt.textContent = \"\";\n",
       "\t\t\treturn;\n",
       "\t\t}\n",
       "\n",
       "\t\tt.textContent = txt;\n",
       "\t\tvar sl = t.getSubStringLength(0, txt.length);\n",
       "\t\t// check if only whitespace or if we can fit the entire string into width w\n",
       "\t\tif (/^ *$/.test(txt) || sl < w)\n",
       "\t\t\treturn;\n",
       "\n",
       "\t\t// this isn't perfect, but gives a good starting point\n",
       "\t\t// and avoids calling getSubStringLength too often\n",
       "\t\tvar start = Math.floor((w/sl) * txt.length);\n",
       "\t\tfor (var x = start; x > 0; x = x-2) {\n",
       "\t\t\tif (t.getSubStringLength(0, x + 2) <= w) {\n",
       "\t\t\t\tt.textContent = txt.substring(0, x) + \"..\";\n",
       "\t\t\t\treturn;\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\tt.textContent = \"\";\n",
       "\t}\n",
       "\n",
       "\t// zoom\n",
       "\tfunction zoom_reset(e) {\n",
       "\t\tif (e.attributes != undefined) {\n",
       "\t\t\torig_load(e, \"x\");\n",
       "\t\t\torig_load(e, \"width\");\n",
       "\t\t}\n",
       "\t\tif (e.childNodes == undefined) return;\n",
       "\t\tfor (var i = 0, c = e.childNodes; i < c.length; i++) {\n",
       "\t\t\tzoom_reset(c[i]);\n",
       "\t\t}\n",
       "\t}\n",
       "\tfunction zoom_child(e, x, ratio) {\n",
       "\t\tif (e.attributes != undefined) {\n",
       "\t\t\tif (e.attributes.x != undefined) {\n",
       "\t\t\t\torig_save(e, \"x\");\n",
       "\t\t\t\te.attributes.x.value = (parseFloat(e.attributes.x.value) - x - 10) * ratio + 10;\n",
       "\t\t\t\tif (e.tagName == \"text\")\n",
       "\t\t\t\t\te.attributes.x.value = find_child(e.parentNode, \"rect[x]\").attributes.x.value + 3;\n",
       "\t\t\t}\n",
       "\t\t\tif (e.attributes.width != undefined) {\n",
       "\t\t\t\torig_save(e, \"width\");\n",
       "\t\t\t\te.attributes.width.value = parseFloat(e.attributes.width.value) * ratio;\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\n",
       "\t\tif (e.childNodes == undefined) return;\n",
       "\t\tfor (var i = 0, c = e.childNodes; i < c.length; i++) {\n",
       "\t\t\tzoom_child(c[i], x - 10, ratio);\n",
       "\t\t}\n",
       "\t}\n",
       "\tfunction zoom_parent(e) {\n",
       "\t\tif (e.attributes) {\n",
       "\t\t\tif (e.attributes.x != undefined) {\n",
       "\t\t\t\torig_save(e, \"x\");\n",
       "\t\t\t\te.attributes.x.value = 10;\n",
       "\t\t\t}\n",
       "\t\t\tif (e.attributes.width != undefined) {\n",
       "\t\t\t\torig_save(e, \"width\");\n",
       "\t\t\t\te.attributes.width.value = parseInt(svg.width.baseVal.value) - (10 * 2);\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\tif (e.childNodes == undefined) return;\n",
       "\t\tfor (var i = 0, c = e.childNodes; i < c.length; i++) {\n",
       "\t\t\tzoom_parent(c[i]);\n",
       "\t\t}\n",
       "\t}\n",
       "\tfunction zoom(node) {\n",
       "\t\tvar attr = find_child(node, \"rect\").attributes;\n",
       "\t\tvar width = parseFloat(attr.width.value);\n",
       "\t\tvar xmin = parseFloat(attr.x.value);\n",
       "\t\tvar xmax = parseFloat(xmin + width);\n",
       "\t\tvar ymin = parseFloat(attr.y.value);\n",
       "\t\tvar ratio = (svg.width.baseVal.value - 2 * 10) / width;\n",
       "\n",
       "\t\t// XXX: Workaround for JavaScript float issues (fix me)\n",
       "\t\tvar fudge = 0.0001;\n",
       "\n",
       "\t\tunzoombtn.classList.remove(\"hide\");\n",
       "\n",
       "\t\tvar el = document.getElementById(\"frames\").children;\n",
       "\t\tfor (var i = 0; i < el.length; i++) {\n",
       "\t\t\tvar e = el[i];\n",
       "\t\t\tvar a = find_child(e, \"rect\").attributes;\n",
       "\t\t\tvar ex = parseFloat(a.x.value);\n",
       "\t\t\tvar ew = parseFloat(a.width.value);\n",
       "\t\t\tvar upstack;\n",
       "\t\t\t// Is it an ancestor\n",
       "\t\t\tif (0 == 0) {\n",
       "\t\t\t\tupstack = parseFloat(a.y.value) > ymin;\n",
       "\t\t\t} else {\n",
       "\t\t\t\tupstack = parseFloat(a.y.value) < ymin;\n",
       "\t\t\t}\n",
       "\t\t\tif (upstack) {\n",
       "\t\t\t\t// Direct ancestor\n",
       "\t\t\t\tif (ex <= xmin && (ex+ew+fudge) >= xmax) {\n",
       "\t\t\t\t\te.classList.add(\"parent\");\n",
       "\t\t\t\t\tzoom_parent(e);\n",
       "\t\t\t\t\tupdate_text(e);\n",
       "\t\t\t\t}\n",
       "\t\t\t\t// not in current path\n",
       "\t\t\t\telse\n",
       "\t\t\t\t\te.classList.add(\"hide\");\n",
       "\t\t\t}\n",
       "\t\t\t// Children maybe\n",
       "\t\t\telse {\n",
       "\t\t\t\t// no common path\n",
       "\t\t\t\tif (ex < xmin || ex + fudge >= xmax) {\n",
       "\t\t\t\t\te.classList.add(\"hide\");\n",
       "\t\t\t\t}\n",
       "\t\t\t\telse {\n",
       "\t\t\t\t\tzoom_child(e, xmin, ratio);\n",
       "\t\t\t\t\tupdate_text(e);\n",
       "\t\t\t\t}\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\tsearch();\n",
       "\t}\n",
       "\tfunction unzoom(dont_update_text) {\n",
       "\t\tunzoombtn.classList.add(\"hide\");\n",
       "\t\tvar el = document.getElementById(\"frames\").children;\n",
       "\t\tfor(var i = 0; i < el.length; i++) {\n",
       "\t\t\tel[i].classList.remove(\"parent\");\n",
       "\t\t\tel[i].classList.remove(\"hide\");\n",
       "\t\t\tzoom_reset(el[i]);\n",
       "\t\t\tif(!dont_update_text) update_text(el[i]);\n",
       "\t\t}\n",
       "\t\tsearch();\n",
       "\t}\n",
       "\tfunction clearzoom() {\n",
       "\t\tunzoom();\n",
       "\n",
       "\t\t// remove zoom state\n",
       "\t\tvar params = get_params();\n",
       "\t\tif (params.x) delete params.x;\n",
       "\t\tif (params.y) delete params.y;\n",
       "\t\thistory.replaceState(null, null, parse_params(params));\n",
       "\t}\n",
       "\n",
       "\t// search\n",
       "\tfunction toggle_ignorecase() {\n",
       "\t\tignorecase = !ignorecase;\n",
       "\t\tif (ignorecase) {\n",
       "\t\t\tignorecaseBtn.classList.add(\"show\");\n",
       "\t\t} else {\n",
       "\t\t\tignorecaseBtn.classList.remove(\"show\");\n",
       "\t\t}\n",
       "\t\treset_search();\n",
       "\t\tsearch();\n",
       "\t}\n",
       "\tfunction reset_search() {\n",
       "\t\tvar el = document.querySelectorAll(\"#frames rect\");\n",
       "\t\tfor (var i = 0; i < el.length; i++) {\n",
       "\t\t\torig_load(el[i], \"fill\")\n",
       "\t\t}\n",
       "\t\tvar params = get_params();\n",
       "\t\tdelete params.s;\n",
       "\t\thistory.replaceState(null, null, parse_params(params));\n",
       "\t}\n",
       "\tfunction search_prompt() {\n",
       "\t\tif (!searching) {\n",
       "\t\t\tvar term = prompt(\"Enter a search term (regexp \" +\n",
       "\t\t\t    \"allowed, eg: ^ext4_)\"\n",
       "\t\t\t    + (ignorecase ? \", ignoring case\" : \"\")\n",
       "\t\t\t    + \"\\nPress Ctrl-i to toggle case sensitivity\", \"\");\n",
       "\t\t\tif (term != null) search(term);\n",
       "\t\t} else {\n",
       "\t\t\treset_search();\n",
       "\t\t\tsearching = 0;\n",
       "\t\t\tcurrentSearchTerm = null;\n",
       "\t\t\tsearchbtn.classList.remove(\"show\");\n",
       "\t\t\tsearchbtn.firstChild.nodeValue = \"Search\"\n",
       "\t\t\tmatchedtxt.classList.add(\"hide\");\n",
       "\t\t\tmatchedtxt.firstChild.nodeValue = \"\"\n",
       "\t\t}\n",
       "\t}\n",
       "\tfunction search(term) {\n",
       "\t\tif (term) currentSearchTerm = term;\n",
       "\n",
       "\t\tvar re = new RegExp(currentSearchTerm, ignorecase ? 'i' : '');\n",
       "\t\tvar el = document.getElementById(\"frames\").children;\n",
       "\t\tvar matches = new Object();\n",
       "\t\tvar maxwidth = 0;\n",
       "\t\tfor (var i = 0; i < el.length; i++) {\n",
       "\t\t\tvar e = el[i];\n",
       "\t\t\tvar func = g_to_func(e);\n",
       "\t\t\tvar rect = find_child(e, \"rect\");\n",
       "\t\t\tif (func == null || rect == null)\n",
       "\t\t\t\tcontinue;\n",
       "\n",
       "\t\t\t// Save max width. Only works as we have a root frame\n",
       "\t\t\tvar w = parseFloat(rect.attributes.width.value);\n",
       "\t\t\tif (w > maxwidth)\n",
       "\t\t\t\tmaxwidth = w;\n",
       "\n",
       "\t\t\tif (func.match(re)) {\n",
       "\t\t\t\t// highlight\n",
       "\t\t\t\tvar x = parseFloat(rect.attributes.x.value);\n",
       "\t\t\t\torig_save(rect, \"fill\");\n",
       "\t\t\t\trect.attributes.fill.value = \"rgb(230,0,230)\";\n",
       "\n",
       "\t\t\t\t// remember matches\n",
       "\t\t\t\tif (matches[x] == undefined) {\n",
       "\t\t\t\t\tmatches[x] = w;\n",
       "\t\t\t\t} else {\n",
       "\t\t\t\t\tif (w > matches[x]) {\n",
       "\t\t\t\t\t\t// overwrite with parent\n",
       "\t\t\t\t\t\tmatches[x] = w;\n",
       "\t\t\t\t\t}\n",
       "\t\t\t\t}\n",
       "\t\t\t\tsearching = 1;\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\tif (!searching)\n",
       "\t\t\treturn;\n",
       "\t\tvar params = get_params();\n",
       "\t\tparams.s = currentSearchTerm;\n",
       "\t\thistory.replaceState(null, null, parse_params(params));\n",
       "\n",
       "\t\tsearchbtn.classList.add(\"show\");\n",
       "\t\tsearchbtn.firstChild.nodeValue = \"Reset Search\";\n",
       "\n",
       "\t\t// calculate percent matched, excluding vertical overlap\n",
       "\t\tvar count = 0;\n",
       "\t\tvar lastx = -1;\n",
       "\t\tvar lastw = 0;\n",
       "\t\tvar keys = Array();\n",
       "\t\tfor (k in matches) {\n",
       "\t\t\tif (matches.hasOwnProperty(k))\n",
       "\t\t\t\tkeys.push(k);\n",
       "\t\t}\n",
       "\t\t// sort the matched frames by their x location\n",
       "\t\t// ascending, then width descending\n",
       "\t\tkeys.sort(function(a, b){\n",
       "\t\t\treturn a - b;\n",
       "\t\t});\n",
       "\t\t// Step through frames saving only the biggest bottom-up frames\n",
       "\t\t// thanks to the sort order. This relies on the tree property\n",
       "\t\t// where children are always smaller than their parents.\n",
       "\t\tvar fudge = 0.0001;\t// JavaScript floating point\n",
       "\t\tfor (var k in keys) {\n",
       "\t\t\tvar x = parseFloat(keys[k]);\n",
       "\t\t\tvar w = matches[keys[k]];\n",
       "\t\t\tif (x >= lastx + lastw - fudge) {\n",
       "\t\t\t\tcount += w;\n",
       "\t\t\t\tlastx = x;\n",
       "\t\t\t\tlastw = w;\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\t// display matched percent\n",
       "\t\tmatchedtxt.classList.remove(\"hide\");\n",
       "\t\tvar pct = 100 * count / maxwidth;\n",
       "\t\tif (pct != 100) pct = pct.toFixed(1)\n",
       "\t\tmatchedtxt.firstChild.nodeValue = \"Matched: \" + pct + \"%\";\n",
       "\t}\n",
       "]]>\n",
       "</script>\n",
       "<rect x=\"0.0\" y=\"0\" width=\"1200.0\" height=\"198.0\" fill=\"url(#background)\"/>\n",
       "<text id=\"title\" x=\"600.00\" y=\"24\">Flame Graph</text>\n",
       "<text id=\"details\" x=\"10.00\" y=\"181\"> </text>\n",
       "<text id=\"unzoom\" x=\"10.00\" y=\"24\" class=\"hide\">Reset Zoom</text>\n",
       "<text id=\"search\" x=\"1090.00\" y=\"24\">Search</text>\n",
       "<text id=\"ignorecase\" x=\"1174.00\" y=\"24\">ic</text>\n",
       "<text id=\"matched\" x=\"1090.00\" y=\"181\"> </text>\n",
       "<g id=\"frames\">\n",
       "<g>\n",
       "<title>Ry(-1.1167599872823721)(T:52) (52 TCounts, 11.61%)</title><rect x=\"873.9\" y=\"117\" width=\"137.0\" height=\"15.0\" fill=\"rgb(253,223,53)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"876.93\" y=\"127.5\">Ry(-1.11675998728..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>C^9ZGate(T:32) (32 TCounts, 7.14%)</title><rect x=\"758.0\" y=\"101\" width=\"84.3\" height=\"15.0\" fill=\"rgb(243,178,42)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"761.04\" y=\"111.5\">C^9ZGate(..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>CtrlSpecAnd[CtrlSpec(qdtypes=(QBit(),), cvs=(array([0, 0, 0, 0, 0, 0, 0, 0, 0]),))](T:32) (32 TCounts, 7.14%)</title><rect x=\"758.0\" y=\"69\" width=\"84.3\" height=\"15.0\" fill=\"rgb(243,177,42)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"761.04\" y=\"79.5\">CtrlSpecA..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>And(T:4) (64 TCounts, 14.29%)</title><rect x=\"294.5\" y=\"101\" width=\"168.5\" height=\"15.0\" fill=\"rgb(210,25,6)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"297.46\" y=\"111.5\">And(T:4)</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>C[4][ZGate](T:12) (12 TCounts, 2.68%)</title><rect x=\"842.3\" y=\"85\" width=\"31.6\" height=\"15.0\" fill=\"rgb(219,64,15)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"845.32\" y=\"95.5\">C[..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>C^4X(T:12) (36 TCounts, 8.04%)</title><rect x=\"31.1\" y=\"101\" width=\"94.8\" height=\"15.0\" fill=\"rgb(239,156,37)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"34.07\" y=\"111.5\">C^4X(T:12)</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>And(T:4) (12 TCounts, 2.68%)</title><rect x=\"842.3\" y=\"37\" width=\"31.6\" height=\"15.0\" fill=\"rgb(210,25,6)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"845.32\" y=\"47.5\">An..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>MultiAnd(n=3)(T:8) (8 TCounts, 1.79%)</title><rect x=\"10.0\" y=\"69\" width=\"21.1\" height=\"15.0\" fill=\"rgb(248,201,48)\" rx=\"2\" ry=\"2\"/>\n",
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       "<g>\n",
       "<title>ReflectionUsingPrepare[PrepareIdentity[4]][None][1][0](T:12) (12 TCounts, 2.68%)</title><rect x=\"842.3\" y=\"117\" width=\"31.6\" height=\"15.0\" fill=\"rgb(233,129,30)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"845.32\" y=\"127.5\">Re..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>Toffoli(T:4) (16 TCounts, 3.57%)</title><rect x=\"1147.9\" y=\"117\" width=\"42.1\" height=\"15.0\" fill=\"rgb(216,53,12)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"1150.86\" y=\"127.5\">Tof..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>C^3X(T:8) (8 TCounts, 1.79%)</title><rect x=\"10.0\" y=\"117\" width=\"21.1\" height=\"15.0\" fill=\"rgb(223,84,20)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"13.00\" y=\"127.5\"/>\n",
       "</g>\n",
       "<g>\n",
       "<title>C[4][X](T:12) (36 TCounts, 8.04%)</title><rect x=\"31.1\" y=\"85\" width=\"94.8\" height=\"15.0\" fill=\"rgb(219,64,15)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"34.07\" y=\"95.5\">C[4][X](T:12)</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>C^4ZGate(T:12) (12 TCounts, 2.68%)</title><rect x=\"842.3\" y=\"101\" width=\"31.6\" height=\"15.0\" fill=\"rgb(239,156,37)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"845.32\" y=\"111.5\">C^..</text>\n",
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       "<g>\n",
       "<title>And(T:4) (16 TCounts, 3.57%)</title><rect x=\"715.9\" y=\"85\" width=\"42.1\" height=\"15.0\" fill=\"rgb(210,25,6)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"718.89\" y=\"95.5\">And..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>all (448 TCounts, 100%)</title><rect x=\"10.0\" y=\"149\" width=\"1180.0\" height=\"15.0\" fill=\"rgb(213,39,9)\" rx=\"2\" ry=\"2\"/>\n",
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       "</g>\n",
       "<g>\n",
       "<title>C[3][X](T:8) (8 TCounts, 1.79%)</title><rect x=\"10.0\" y=\"101\" width=\"21.1\" height=\"15.0\" fill=\"rgb(253,221,53)\" rx=\"2\" ry=\"2\"/>\n",
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       "</g>\n",
       "<g>\n",
       "<title>$\\sum_{\\mu &lt; \\nu} |\\mu\\nu\\rangle$(T:448) (448 TCounts, 100.00%)</title><rect x=\"10.0\" y=\"133\" width=\"1180.0\" height=\"15.0\" fill=\"rgb(239,157,37)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"13.00\" y=\"143.5\">$\\sum_{\\mu &lt; \\nu} |\\mu\\nu\\rangle$(T:448)</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>MultiAnd(n=4)(T:12) (12 TCounts, 2.68%)</title><rect x=\"842.3\" y=\"53\" width=\"31.6\" height=\"15.0\" fill=\"rgb(214,44,10)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"845.32\" y=\"63.5\">Mu..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>SingleQubitCompare[False](T:4) (16 TCounts, 3.57%)</title><rect x=\"715.9\" y=\"101\" width=\"42.1\" height=\"15.0\" fill=\"rgb(253,222,53)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"718.89\" y=\"111.5\">Sin..</text>\n",
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       "<g>\n",
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       "</g>\n",
       "<g>\n",
       "<title>LessThanConstant[4][10](T:16) (64 TCounts, 14.29%)</title><rect x=\"294.5\" y=\"117\" width=\"168.5\" height=\"15.0\" fill=\"rgb(247,196,47)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"297.46\" y=\"127.5\">LessThanConstant[4][1..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>BiQubitsMixer[False](T:8) (96 TCounts, 21.43%)</title><rect x=\"463.0\" y=\"101\" width=\"252.9\" height=\"15.0\" fill=\"rgb(223,86,20)\" rx=\"2\" ry=\"2\"/>\n",
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       "</g>\n",
       "<g>\n",
       "<title>EqualsAConstant[4][11](T:12) (36 TCounts, 8.04%)</title><rect x=\"31.1\" y=\"117\" width=\"94.8\" height=\"15.0\" fill=\"rgb(229,112,26)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"34.07\" y=\"127.5\">EqualsACons..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>MultiAnd(n=9)(T:32) (32 TCounts, 7.14%)</title><rect x=\"758.0\" y=\"53\" width=\"84.3\" height=\"15.0\" fill=\"rgb(219,66,15)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"761.04\" y=\"63.5\">MultiAnd(..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>CtrlSpecAnd[CtrlSpec(qdtypes=(QBit(),), cvs=(array([1, 0, 1, 1]),))](T:12) (36 TCounts, 8.04%)</title><rect x=\"31.1\" y=\"69\" width=\"94.8\" height=\"15.0\" fill=\"rgb(242,171,40)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"34.07\" y=\"79.5\">CtrlSpecAnd..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>C[9][ZGate](T:32) (32 TCounts, 7.14%)</title><rect x=\"758.0\" y=\"85\" width=\"84.3\" height=\"15.0\" fill=\"rgb(223,86,20)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"761.04\" y=\"95.5\">C[9][ZGat..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>GreaterThanConstant[4][2](T:16) (64 TCounts, 14.29%)</title><rect x=\"125.9\" y=\"117\" width=\"168.6\" height=\"15.0\" fill=\"rgb(218,60,14)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"128.89\" y=\"127.5\">GreaterThanConstant[4..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>Ry(1.1167599872823721)(T:52) (52 TCounts, 11.61%)</title><rect x=\"1010.9\" y=\"117\" width=\"137.0\" height=\"15.0\" fill=\"rgb(244,180,43)\" rx=\"2\" ry=\"2\"/>\n",
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       "</g>\n",
       "<g>\n",
       "<title>And(T:4) (8 TCounts, 1.79%)</title><rect x=\"10.0\" y=\"53\" width=\"21.1\" height=\"15.0\" fill=\"rgb(210,25,6)\" rx=\"2\" ry=\"2\"/>\n",
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       "</g>\n",
       "<g>\n",
       "<title>And(T:4) (36 TCounts, 8.04%)</title><rect x=\"31.1\" y=\"37\" width=\"94.8\" height=\"15.0\" fill=\"rgb(210,25,6)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"34.07\" y=\"47.5\">And(T:4)</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>CtrlSpecAnd[CtrlSpec(qdtypes=(QBit(),), cvs=(array([0, 0, 0, 0]),))](T:12) (12 TCounts, 2.68%)</title><rect x=\"842.3\" y=\"69\" width=\"31.6\" height=\"15.0\" fill=\"rgb(211,30,7)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"845.32\" y=\"79.5\">Ct..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>CtrlSpecAnd[CtrlSpec(qdtypes=(QBit(),), cvs=(array([1, 1, 1]),))](T:8) (8 TCounts, 1.79%)</title><rect x=\"10.0\" y=\"85\" width=\"21.1\" height=\"15.0\" fill=\"rgb(221,73,17)\" rx=\"2\" ry=\"2\"/>\n",
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       "</g>\n",
       "<g>\n",
       "<title>MultiAnd(n=4)(T:12) (36 TCounts, 8.04%)</title><rect x=\"31.1\" y=\"53\" width=\"94.8\" height=\"15.0\" fill=\"rgb(214,44,10)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"34.07\" y=\"63.5\">MultiAnd(n=..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>LessThanEqual[4][4](T:28) (112 TCounts, 25.00%)</title><rect x=\"463.0\" y=\"117\" width=\"295.0\" height=\"15.0\" fill=\"rgb(252,218,52)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"466.04\" y=\"127.5\">LessThanEqual[4][4](T:28)</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>And(T:4) (64 TCounts, 14.29%)</title><rect x=\"125.9\" y=\"85\" width=\"168.6\" height=\"15.0\" fill=\"rgb(210,25,6)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"128.89\" y=\"95.5\">And(T:4)</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>LessThanConstant[4][2](T:16) (64 TCounts, 14.29%)</title><rect x=\"125.9\" y=\"101\" width=\"168.6\" height=\"15.0\" fill=\"rgb(251,213,51)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"128.89\" y=\"111.5\">LessThanConstant[4][2..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>ReflectionUsingPrepare[PrepareIdentity[3]][None][1][0](T:32) (32 TCounts, 7.14%)</title><rect x=\"758.0\" y=\"117\" width=\"84.3\" height=\"15.0\" fill=\"rgb(226,99,23)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"761.04\" y=\"127.5\">Reflectio..</text>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qualtran.drawing import show_flame_graph\n",
    "show_flame_graph(thc_uni)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2cbc8e6",
   "metadata": {},
   "source": [
    "Let's print out the costs contributing to the TCount."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fa6858a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rotations:      qualtran =    22 vs paper cost =    32.\n",
      "reflection:     qualtran =    44 vs paper cost =    40.\n",
      "arithmetic:     qualtran =   276 vs paper cost =   100.\n",
      "toffoli:        qualtran =    16 vs paper cost =     0.\n",
      "other:          qualtran =     0 vs paper cost =    12.\n",
      "multi_control_pauli: qualtran =     8 vs paper cost =     0.\n"
     ]
    }
   ],
   "source": [
    "from qualtran.resource_counting.classify_bloqs import classify_t_count_by_bloq_type\n",
    "\n",
    "binned_counts = classify_t_count_by_bloq_type(thc_uni)\n",
    "\n",
    "# number of bits for mu register (nm in THC paper)\n",
    "# note this register should range up to num_mu + 1, not num_mu, hence it's just bit_length not (num_mu - 1).bit_length()\n",
    "nm = thc_uni.num_mu.bit_length()\n",
    "# Costs for THC paper\n",
    "# The factor of 4 is for Toffoli -> T conversion\n",
    "paper_costs = {\n",
    "    'arithmetic': 4*(4*(nm - 1) + (4*nm - 3)), # 4 comparitors of cost nm - 1 Toffolis\n",
    "    'rotations': 4*(4 + 4), # Given as br - 3, br = 7 is the number of bits of precision for rotations.\n",
    "    'reflection': 4*(3 + 2*nm-1), # 5 qubit reflection for comparitors and 2*nm + 1 qubits reflect after hadamards\n",
    "    'other': 4*3, # \"Checking the inequality test\" unclear if this is the multi-control not gate.\n",
    "}\n",
    "for k in (paper_costs.keys() | binned_counts.keys()):\n",
    "    print(f\"{k+':':15s} qualtran = {binned_counts.get(k,0):5d} vs paper cost = {paper_costs.get(k,0):5d}.\")\n",
    "\n",
    "assert binned_counts['arithmetic'] == 276"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e92c2265",
   "metadata": {},
   "source": [
    "The discrepancies arise from the following issues:\n",
    "\n",
    "1. Comparators: The paper uncomputes the comparators at zero Toffoli cost, whereas we do not. This is nearly a factor of two difference. This leaves us with 144 vs 100. The extra factor of 44 arises from the different costs of the comparators listed in the paper and those in qualtran. The paper uses a cost of $n_m - 1$ for all the comparitors (with the final $\\nu = M+1$ equality test costing $n_m$), whereas the qualtran comparators assume a cost of $n_m - 1$ for equality testing to a classical value, $n_m$ for comparison to a classical value and $2 n_m - 1$ when comparing two quantum registers.\n",
    "2. Rotations: The paper uses a phase gradient register which has a much lower cost than the qualtran cost which assumes a generic synthesis cost. \n",
    "3. Reflections and other: This discrepancy arises because the paper states the first reflection in between the comparators has Toffoli cost of 3, rather than what we have which is 2 Toffolis and 1 Multi-Controlled Z which costs 2 Toffolis. Our costs for the second reflection match. The other discrepancy is for the operations in between the second set of comparators. Here we count 2 Toffolis and 1 Multi-Controlled-Not gate of cost 2 Toffolis.  \n",
    "\n",
    "The leading order Toffoli cost of this state preparation is 10 $n_m$ in the paper which arises from the comparators and the reflection on the $\\mu$ and $\\nu$ registers, i.e. $4(n_m - 1) + 4n_m - 3 + 2n_m -1 \\approx 10 n_m$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "01436b52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qualtran.drawing.musical_score import draw_musical_score, get_musical_score_data\n",
    "\n",
    "msd = get_musical_score_data(thc_uni.decompose_bloq())\n",
    "fig, ax = draw_musical_score(msd)\n",
    "fig.set_size_inches(20, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c26aa0",
   "metadata": {
    "cq.autogen": "PrepareTHC.bloq_doc.md"
   },
   "source": [
    "## `PrepareTHC`\n",
    "State Preparation for THC Hamilontian.\n",
    "\n",
    "Prepares the state\n",
    "\n",
    "$$\n",
    "    \\frac{1}{\\sqrt{\\lambda}}|+\\rangle|+\\rangle\\left[\n",
    "        \\sum_\\ell^{N/2} \\sqrt{t_\\ell}|\\ell\\rangle|M+1\\rangle\n",
    "        + \\frac{1}{\\sqrt{2}} \\sum_{\\mu\\le\\nu}^M \\sqrt{\\zeta_{\\mu\\nu}} |\\mu\\rangle|\\nu\\rangle\n",
    "    \\right].\n",
    "$$\n",
    "\n",
    "Note we use UniformSuperpositionTHC as a subroutine as part of this bloq in\n",
    "contrast to the reference which keeps them separate.\n",
    "\n",
    "#### Parameters\n",
    " - `num_mu`: THC auxiliary index dimension $M$\n",
    " - `num_spin_orb`: number of spin orbitals $N$\n",
    " - `alt_mu`: Alternate values for mu indices.\n",
    " - `alt_nu`: Alternate values for nu indices.\n",
    " - `alt_theta`: Alternate values for theta indices.\n",
    " - `theta`: Signs of lcu coefficients.\n",
    " - `keep`: keep values.\n",
    " - `keep_bitsize`: number of bits for keep register for coherent alias sampling. \n",
    "\n",
    "#### Registers\n",
    " - `mu`: $\\mu$ register.\n",
    " - `nu`: $\\nu$ register.\n",
    " - `plus_mn`: plus state for controlled swaps on mu/nu.\n",
    " - `plus_a / plus_b`: plus state for controlled swaps on spins.\n",
    " - `sigma`: ancilla register for alias sampling.\n",
    " - `rot`: ancilla register for rotation for uniform superposition state.\n",
    " - `succ`: success flag qubit from uniform state preparation\n",
    " - `nu_eq_mp1`: flag for if $nu = M+1$\n",
    " - `theta`: sign register.\n",
    " - `s`: Contiguous index register.\n",
    " - `alt_mn`: Register to store alt mu and nu values.\n",
    " - `alt_theta`: Register for alternate theta values.\n",
    " - `keep`: keep_bitsize-sized register for the keep values from coherent alias sampling.\n",
    " - `less_than`: Single qubit ancilla for alias sampling.\n",
    " - `extra_ctrl`: An extra control register for producing a multi-controlled CSwap. \n",
    "\n",
    "#### References\n",
    " - [Even more efficient quantum computations of chemistry through     tensor hypercontraction](https://arxiv.org/pdf/2011.03494.pdf). Fig. 2 and Fig. 3.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d6cd1df7",
   "metadata": {
    "cq.autogen": "PrepareTHC.bloq_doc.py"
   },
   "outputs": [],
   "source": [
    "from qualtran.bloqs.chemistry.thc import PrepareTHC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec7c8839",
   "metadata": {
    "cq.autogen": "PrepareTHC.example_instances.md"
   },
   "source": [
    "### Example Instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fc1ebede",
   "metadata": {
    "cq.autogen": "PrepareTHC.thc_prep"
   },
   "outputs": [],
   "source": [
    "from qualtran.bloqs.chemistry.thc.prepare_test import build_random_test_integrals\n",
    "\n",
    "num_spat = 4\n",
    "num_mu = 8\n",
    "t_l, eta, zeta = build_random_test_integrals(num_mu, num_spat, seed=7)\n",
    "thc_prep = PrepareTHC.from_hamiltonian_coeffs(\n",
    "    t_l, eta, zeta, num_bits_state_prep=8, log_block_size=2\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f327eeaf",
   "metadata": {
    "cq.autogen": "PrepareTHC.graphical_signature.md"
   },
   "source": [
    "#### Graphical Signature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "87f90feb",
   "metadata": {
    "cq.autogen": "PrepareTHC.graphical_signature.py"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a39de4726694c69b4c0e421f9fefb9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<IPython.core.display.M…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qualtran.drawing import show_bloqs\n",
    "show_bloqs([thc_prep],\n",
    "           ['`thc_prep`'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b60183fb",
   "metadata": {
    "cq.autogen": "PrepareTHC.call_graph.md"
   },
   "source": [
    "### Call Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d18fe659",
   "metadata": {
    "cq.autogen": "PrepareTHC.call_graph.py"
   },
   "outputs": [
    {
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       "</g>\n",
       "<!-- b0&#45;&gt;b4 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>b0-&gt;b4</title>\n",
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       "</g>\n",
       "<!-- b0&#45;&gt;b5 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>b0-&gt;b5</title>\n",
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       "</g>\n",
       "<!-- b0&#45;&gt;b6 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>b0-&gt;b6</title>\n",
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       "<!-- b0&#45;&gt;b7 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>b0-&gt;b7</title>\n",
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       "</g>\n",
       "<!-- b0&#45;&gt;b8 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>b0-&gt;b8</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M636.17,-87.03C665.93,-74.36 707.68,-56.38 752.59,-36.12\"/>\n",
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       "<!-- b0&#45;&gt;b9 -->\n",
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       "<title>b10</title>\n",
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       "</g>\n",
       "<!-- b0&#45;&gt;b10 -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>b0-&gt;b10</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M636.44,-98.44C693.29,-89.86 799.79,-70.97 896.1,-36.02\"/>\n",
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       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Counts totals:\n",
       " - `$\\sum_{\\mu < \\nu} |\\mu\\nu\\rangle$`: 1\n",
       " - `CCX`: 1\n",
       " - `CSwap`: 3\n",
       " - `CZ`: 2\n",
       " - `H`: 3\n",
       " - `H⨂8`: 1\n",
       " - `LessThanEqual`: 2\n",
       " - `QROAMClean`: 1\n",
       " - `ToContiguousIndex`: 1\n",
       " - `X`: 2"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qualtran.resource_counting.generalizers import ignore_split_join\n",
    "thc_prep_g, thc_prep_sigma = thc_prep.call_graph(max_depth=1, generalizer=ignore_split_join)\n",
    "show_call_graph(thc_prep_g)\n",
    "show_counts_sigma(thc_prep_sigma)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c70647ea-256b-410c-abf7-33c9e2e12ac5",
   "metadata": {},
   "source": [
    "### Flame Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c5e44f2-d481-4e41-b9f3-10e9ec5de498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" width=\"1200\" height=\"214\" onload=\"init(evt)\" viewBox=\"0 0 1200 214\">\n",
       "<!-- Flame graph stack visualization. See https://github.com/brendangregg/FlameGraph for latest version, and http://www.brendangregg.com/flamegraphs.html for examples. -->\n",
       "<!-- NOTES:  -->\n",
       "<defs>\n",
       "\t<linearGradient id=\"background\" y1=\"0\" y2=\"1\" x1=\"0\" x2=\"0\">\n",
       "\t\t<stop stop-color=\"#eeeeee\" offset=\"5%\"/>\n",
       "\t\t<stop stop-color=\"#eeeeb0\" offset=\"95%\"/>\n",
       "\t</linearGradient>\n",
       "</defs>\n",
       "<style type=\"text/css\">\n",
       "\ttext { font-family:Verdana; font-size:12px; fill:rgb(0,0,0); }\n",
       "\t#search, #ignorecase { opacity:0.1; cursor:pointer; }\n",
       "\t#search:hover, #search.show, #ignorecase:hover, #ignorecase.show { opacity:1; }\n",
       "\t#subtitle { text-anchor:middle; font-color:rgb(160,160,160); }\n",
       "\t#title { text-anchor:middle; font-size:17px}\n",
       "\t#unzoom { cursor:pointer; }\n",
       "\t#frames &gt; *:hover { stroke:black; stroke-width:0.5; cursor:pointer; }\n",
       "\t.hide { display:none; }\n",
       "\t.parent { opacity:0.5; }\n",
       "</style>\n",
       "<script type=\"text/ecmascript\">\n",
       "<![CDATA[\n",
       "\t\"use strict\";\n",
       "\tvar details, searchbtn, unzoombtn, matchedtxt, svg, searching, currentSearchTerm, ignorecase, ignorecaseBtn;\n",
       "\tfunction init(evt) {\n",
       "\t\tdetails = document.getElementById(\"details\").firstChild;\n",
       "\t\tsearchbtn = document.getElementById(\"search\");\n",
       "\t\tignorecaseBtn = document.getElementById(\"ignorecase\");\n",
       "\t\tunzoombtn = document.getElementById(\"unzoom\");\n",
       "\t\tmatchedtxt = document.getElementById(\"matched\");\n",
       "\t\tsvg = document.getElementsByTagName(\"svg\")[0];\n",
       "\t\tsearching = 0;\n",
       "\t\tcurrentSearchTerm = null;\n",
       "\n",
       "\t\t// use GET parameters to restore a flamegraphs state.\n",
       "\t\tvar params = get_params();\n",
       "\t\tif (params.x && params.y)\n",
       "\t\t\tzoom(find_group(document.querySelector('[x=\"' + params.x + '\"][y=\"' + params.y + '\"]')));\n",
       "                if (params.s) search(params.s);\n",
       "\t}\n",
       "\n",
       "\t// event listeners\n",
       "\twindow.addEventListener(\"click\", function(e) {\n",
       "\t\tvar target = find_group(e.target);\n",
       "\t\tif (target) {\n",
       "\t\t\tif (target.nodeName == \"a\") {\n",
       "\t\t\t\tif (e.ctrlKey === false) return;\n",
       "\t\t\t\te.preventDefault();\n",
       "\t\t\t}\n",
       "\t\t\tif (target.classList.contains(\"parent\")) unzoom(true);\n",
       "\t\t\tzoom(target);\n",
       "\t\t\tif (!document.querySelector('.parent')) {\n",
       "\t\t\t\t// we have basically done a clearzoom so clear the url\n",
       "\t\t\t\tvar params = get_params();\n",
       "\t\t\t\tif (params.x) delete params.x;\n",
       "\t\t\t\tif (params.y) delete params.y;\n",
       "\t\t\t\thistory.replaceState(null, null, parse_params(params));\n",
       "\t\t\t\tunzoombtn.classList.add(\"hide\");\n",
       "\t\t\t\treturn;\n",
       "\t\t\t}\n",
       "\n",
       "\t\t\t// set parameters for zoom state\n",
       "\t\t\tvar el = target.querySelector(\"rect\");\n",
       "\t\t\tif (el && el.attributes && el.attributes.y && el.attributes._orig_x) {\n",
       "\t\t\t\tvar params = get_params()\n",
       "\t\t\t\tparams.x = el.attributes._orig_x.value;\n",
       "\t\t\t\tparams.y = el.attributes.y.value;\n",
       "\t\t\t\thistory.replaceState(null, null, parse_params(params));\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\telse if (e.target.id == \"unzoom\") clearzoom();\n",
       "\t\telse if (e.target.id == \"search\") search_prompt();\n",
       "\t\telse if (e.target.id == \"ignorecase\") toggle_ignorecase();\n",
       "\t}, false)\n",
       "\n",
       "\t// mouse-over for info\n",
       "\t// show\n",
       "\twindow.addEventListener(\"mouseover\", function(e) {\n",
       "\t\tvar target = find_group(e.target);\n",
       "\t\tif (target) details.nodeValue = \"Function: \" + g_to_text(target);\n",
       "\t}, false)\n",
       "\n",
       "\t// clear\n",
       "\twindow.addEventListener(\"mouseout\", function(e) {\n",
       "\t\tvar target = find_group(e.target);\n",
       "\t\tif (target) details.nodeValue = ' ';\n",
       "\t}, false)\n",
       "\n",
       "\t// ctrl-F for search\n",
       "\t// ctrl-I to toggle case-sensitive search\n",
       "\twindow.addEventListener(\"keydown\",function (e) {\n",
       "\t\tif (e.keyCode === 114 || (e.ctrlKey && e.keyCode === 70)) {\n",
       "\t\t\te.preventDefault();\n",
       "\t\t\tsearch_prompt();\n",
       "\t\t}\n",
       "\t\telse if (e.ctrlKey && e.keyCode === 73) {\n",
       "\t\t\te.preventDefault();\n",
       "\t\t\ttoggle_ignorecase();\n",
       "\t\t}\n",
       "\t}, false)\n",
       "\n",
       "\t// functions\n",
       "\tfunction get_params() {\n",
       "\t\tvar params = {};\n",
       "\t\tvar paramsarr = window.location.search.substr(1).split('&');\n",
       "\t\tfor (var i = 0; i < paramsarr.length; ++i) {\n",
       "\t\t\tvar tmp = paramsarr[i].split(\"=\");\n",
       "\t\t\tif (!tmp[0] || !tmp[1]) continue;\n",
       "\t\t\tparams[tmp[0]]  = decodeURIComponent(tmp[1]);\n",
       "\t\t}\n",
       "\t\treturn params;\n",
       "\t}\n",
       "\tfunction parse_params(params) {\n",
       "\t\tvar uri = \"?\";\n",
       "\t\tfor (var key in params) {\n",
       "\t\t\turi += key + '=' + encodeURIComponent(params[key]) + '&';\n",
       "\t\t}\n",
       "\t\tif (uri.slice(-1) == \"&\")\n",
       "\t\t\turi = uri.substring(0, uri.length - 1);\n",
       "\t\tif (uri == '?')\n",
       "\t\t\turi = window.location.href.split('?')[0];\n",
       "\t\treturn uri;\n",
       "\t}\n",
       "\tfunction find_child(node, selector) {\n",
       "\t\tvar children = node.querySelectorAll(selector);\n",
       "\t\tif (children.length) return children[0];\n",
       "\t}\n",
       "\tfunction find_group(node) {\n",
       "\t\tvar parent = node.parentElement;\n",
       "\t\tif (!parent) return;\n",
       "\t\tif (parent.id == \"frames\") return node;\n",
       "\t\treturn find_group(parent);\n",
       "\t}\n",
       "\tfunction orig_save(e, attr, val) {\n",
       "\t\tif (e.attributes[\"_orig_\" + attr] != undefined) return;\n",
       "\t\tif (e.attributes[attr] == undefined) return;\n",
       "\t\tif (val == undefined) val = e.attributes[attr].value;\n",
       "\t\te.setAttribute(\"_orig_\" + attr, val);\n",
       "\t}\n",
       "\tfunction orig_load(e, attr) {\n",
       "\t\tif (e.attributes[\"_orig_\"+attr] == undefined) return;\n",
       "\t\te.attributes[attr].value = e.attributes[\"_orig_\" + attr].value;\n",
       "\t\te.removeAttribute(\"_orig_\"+attr);\n",
       "\t}\n",
       "\tfunction g_to_text(e) {\n",
       "\t\tvar text = find_child(e, \"title\").firstChild.nodeValue;\n",
       "\t\treturn (text)\n",
       "\t}\n",
       "\tfunction g_to_func(e) {\n",
       "\t\tvar func = g_to_text(e);\n",
       "\t\t// if there's any manipulation we want to do to the function\n",
       "\t\t// name before it's searched, do it here before returning.\n",
       "\t\treturn (func);\n",
       "\t}\n",
       "\tfunction update_text(e) {\n",
       "\t\tvar r = find_child(e, \"rect\");\n",
       "\t\tvar t = find_child(e, \"text\");\n",
       "\t\tvar w = parseFloat(r.attributes.width.value) -3;\n",
       "\t\tvar txt = find_child(e, \"title\").textContent.replace(/\\([^(]*\\)$/,\"\");\n",
       "\t\tt.attributes.x.value = parseFloat(r.attributes.x.value) + 3;\n",
       "\n",
       "\t\t// Smaller than this size won't fit anything\n",
       "\t\tif (w < 2 * 12 * 0.59) {\n",
       "\t\t\tt.textContent = \"\";\n",
       "\t\t\treturn;\n",
       "\t\t}\n",
       "\n",
       "\t\tt.textContent = txt;\n",
       "\t\tvar sl = t.getSubStringLength(0, txt.length);\n",
       "\t\t// check if only whitespace or if we can fit the entire string into width w\n",
       "\t\tif (/^ *$/.test(txt) || sl < w)\n",
       "\t\t\treturn;\n",
       "\n",
       "\t\t// this isn't perfect, but gives a good starting point\n",
       "\t\t// and avoids calling getSubStringLength too often\n",
       "\t\tvar start = Math.floor((w/sl) * txt.length);\n",
       "\t\tfor (var x = start; x > 0; x = x-2) {\n",
       "\t\t\tif (t.getSubStringLength(0, x + 2) <= w) {\n",
       "\t\t\t\tt.textContent = txt.substring(0, x) + \"..\";\n",
       "\t\t\t\treturn;\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\tt.textContent = \"\";\n",
       "\t}\n",
       "\n",
       "\t// zoom\n",
       "\tfunction zoom_reset(e) {\n",
       "\t\tif (e.attributes != undefined) {\n",
       "\t\t\torig_load(e, \"x\");\n",
       "\t\t\torig_load(e, \"width\");\n",
       "\t\t}\n",
       "\t\tif (e.childNodes == undefined) return;\n",
       "\t\tfor (var i = 0, c = e.childNodes; i < c.length; i++) {\n",
       "\t\t\tzoom_reset(c[i]);\n",
       "\t\t}\n",
       "\t}\n",
       "\tfunction zoom_child(e, x, ratio) {\n",
       "\t\tif (e.attributes != undefined) {\n",
       "\t\t\tif (e.attributes.x != undefined) {\n",
       "\t\t\t\torig_save(e, \"x\");\n",
       "\t\t\t\te.attributes.x.value = (parseFloat(e.attributes.x.value) - x - 10) * ratio + 10;\n",
       "\t\t\t\tif (e.tagName == \"text\")\n",
       "\t\t\t\t\te.attributes.x.value = find_child(e.parentNode, \"rect[x]\").attributes.x.value + 3;\n",
       "\t\t\t}\n",
       "\t\t\tif (e.attributes.width != undefined) {\n",
       "\t\t\t\torig_save(e, \"width\");\n",
       "\t\t\t\te.attributes.width.value = parseFloat(e.attributes.width.value) * ratio;\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\n",
       "\t\tif (e.childNodes == undefined) return;\n",
       "\t\tfor (var i = 0, c = e.childNodes; i < c.length; i++) {\n",
       "\t\t\tzoom_child(c[i], x - 10, ratio);\n",
       "\t\t}\n",
       "\t}\n",
       "\tfunction zoom_parent(e) {\n",
       "\t\tif (e.attributes) {\n",
       "\t\t\tif (e.attributes.x != undefined) {\n",
       "\t\t\t\torig_save(e, \"x\");\n",
       "\t\t\t\te.attributes.x.value = 10;\n",
       "\t\t\t}\n",
       "\t\t\tif (e.attributes.width != undefined) {\n",
       "\t\t\t\torig_save(e, \"width\");\n",
       "\t\t\t\te.attributes.width.value = parseInt(svg.width.baseVal.value) - (10 * 2);\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\tif (e.childNodes == undefined) return;\n",
       "\t\tfor (var i = 0, c = e.childNodes; i < c.length; i++) {\n",
       "\t\t\tzoom_parent(c[i]);\n",
       "\t\t}\n",
       "\t}\n",
       "\tfunction zoom(node) {\n",
       "\t\tvar attr = find_child(node, \"rect\").attributes;\n",
       "\t\tvar width = parseFloat(attr.width.value);\n",
       "\t\tvar xmin = parseFloat(attr.x.value);\n",
       "\t\tvar xmax = parseFloat(xmin + width);\n",
       "\t\tvar ymin = parseFloat(attr.y.value);\n",
       "\t\tvar ratio = (svg.width.baseVal.value - 2 * 10) / width;\n",
       "\n",
       "\t\t// XXX: Workaround for JavaScript float issues (fix me)\n",
       "\t\tvar fudge = 0.0001;\n",
       "\n",
       "\t\tunzoombtn.classList.remove(\"hide\");\n",
       "\n",
       "\t\tvar el = document.getElementById(\"frames\").children;\n",
       "\t\tfor (var i = 0; i < el.length; i++) {\n",
       "\t\t\tvar e = el[i];\n",
       "\t\t\tvar a = find_child(e, \"rect\").attributes;\n",
       "\t\t\tvar ex = parseFloat(a.x.value);\n",
       "\t\t\tvar ew = parseFloat(a.width.value);\n",
       "\t\t\tvar upstack;\n",
       "\t\t\t// Is it an ancestor\n",
       "\t\t\tif (0 == 0) {\n",
       "\t\t\t\tupstack = parseFloat(a.y.value) > ymin;\n",
       "\t\t\t} else {\n",
       "\t\t\t\tupstack = parseFloat(a.y.value) < ymin;\n",
       "\t\t\t}\n",
       "\t\t\tif (upstack) {\n",
       "\t\t\t\t// Direct ancestor\n",
       "\t\t\t\tif (ex <= xmin && (ex+ew+fudge) >= xmax) {\n",
       "\t\t\t\t\te.classList.add(\"parent\");\n",
       "\t\t\t\t\tzoom_parent(e);\n",
       "\t\t\t\t\tupdate_text(e);\n",
       "\t\t\t\t}\n",
       "\t\t\t\t// not in current path\n",
       "\t\t\t\telse\n",
       "\t\t\t\t\te.classList.add(\"hide\");\n",
       "\t\t\t}\n",
       "\t\t\t// Children maybe\n",
       "\t\t\telse {\n",
       "\t\t\t\t// no common path\n",
       "\t\t\t\tif (ex < xmin || ex + fudge >= xmax) {\n",
       "\t\t\t\t\te.classList.add(\"hide\");\n",
       "\t\t\t\t}\n",
       "\t\t\t\telse {\n",
       "\t\t\t\t\tzoom_child(e, xmin, ratio);\n",
       "\t\t\t\t\tupdate_text(e);\n",
       "\t\t\t\t}\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\tsearch();\n",
       "\t}\n",
       "\tfunction unzoom(dont_update_text) {\n",
       "\t\tunzoombtn.classList.add(\"hide\");\n",
       "\t\tvar el = document.getElementById(\"frames\").children;\n",
       "\t\tfor(var i = 0; i < el.length; i++) {\n",
       "\t\t\tel[i].classList.remove(\"parent\");\n",
       "\t\t\tel[i].classList.remove(\"hide\");\n",
       "\t\t\tzoom_reset(el[i]);\n",
       "\t\t\tif(!dont_update_text) update_text(el[i]);\n",
       "\t\t}\n",
       "\t\tsearch();\n",
       "\t}\n",
       "\tfunction clearzoom() {\n",
       "\t\tunzoom();\n",
       "\n",
       "\t\t// remove zoom state\n",
       "\t\tvar params = get_params();\n",
       "\t\tif (params.x) delete params.x;\n",
       "\t\tif (params.y) delete params.y;\n",
       "\t\thistory.replaceState(null, null, parse_params(params));\n",
       "\t}\n",
       "\n",
       "\t// search\n",
       "\tfunction toggle_ignorecase() {\n",
       "\t\tignorecase = !ignorecase;\n",
       "\t\tif (ignorecase) {\n",
       "\t\t\tignorecaseBtn.classList.add(\"show\");\n",
       "\t\t} else {\n",
       "\t\t\tignorecaseBtn.classList.remove(\"show\");\n",
       "\t\t}\n",
       "\t\treset_search();\n",
       "\t\tsearch();\n",
       "\t}\n",
       "\tfunction reset_search() {\n",
       "\t\tvar el = document.querySelectorAll(\"#frames rect\");\n",
       "\t\tfor (var i = 0; i < el.length; i++) {\n",
       "\t\t\torig_load(el[i], \"fill\")\n",
       "\t\t}\n",
       "\t\tvar params = get_params();\n",
       "\t\tdelete params.s;\n",
       "\t\thistory.replaceState(null, null, parse_params(params));\n",
       "\t}\n",
       "\tfunction search_prompt() {\n",
       "\t\tif (!searching) {\n",
       "\t\t\tvar term = prompt(\"Enter a search term (regexp \" +\n",
       "\t\t\t    \"allowed, eg: ^ext4_)\"\n",
       "\t\t\t    + (ignorecase ? \", ignoring case\" : \"\")\n",
       "\t\t\t    + \"\\nPress Ctrl-i to toggle case sensitivity\", \"\");\n",
       "\t\t\tif (term != null) search(term);\n",
       "\t\t} else {\n",
       "\t\t\treset_search();\n",
       "\t\t\tsearching = 0;\n",
       "\t\t\tcurrentSearchTerm = null;\n",
       "\t\t\tsearchbtn.classList.remove(\"show\");\n",
       "\t\t\tsearchbtn.firstChild.nodeValue = \"Search\"\n",
       "\t\t\tmatchedtxt.classList.add(\"hide\");\n",
       "\t\t\tmatchedtxt.firstChild.nodeValue = \"\"\n",
       "\t\t}\n",
       "\t}\n",
       "\tfunction search(term) {\n",
       "\t\tif (term) currentSearchTerm = term;\n",
       "\n",
       "\t\tvar re = new RegExp(currentSearchTerm, ignorecase ? 'i' : '');\n",
       "\t\tvar el = document.getElementById(\"frames\").children;\n",
       "\t\tvar matches = new Object();\n",
       "\t\tvar maxwidth = 0;\n",
       "\t\tfor (var i = 0; i < el.length; i++) {\n",
       "\t\t\tvar e = el[i];\n",
       "\t\t\tvar func = g_to_func(e);\n",
       "\t\t\tvar rect = find_child(e, \"rect\");\n",
       "\t\t\tif (func == null || rect == null)\n",
       "\t\t\t\tcontinue;\n",
       "\n",
       "\t\t\t// Save max width. Only works as we have a root frame\n",
       "\t\t\tvar w = parseFloat(rect.attributes.width.value);\n",
       "\t\t\tif (w > maxwidth)\n",
       "\t\t\t\tmaxwidth = w;\n",
       "\n",
       "\t\t\tif (func.match(re)) {\n",
       "\t\t\t\t// highlight\n",
       "\t\t\t\tvar x = parseFloat(rect.attributes.x.value);\n",
       "\t\t\t\torig_save(rect, \"fill\");\n",
       "\t\t\t\trect.attributes.fill.value = \"rgb(230,0,230)\";\n",
       "\n",
       "\t\t\t\t// remember matches\n",
       "\t\t\t\tif (matches[x] == undefined) {\n",
       "\t\t\t\t\tmatches[x] = w;\n",
       "\t\t\t\t} else {\n",
       "\t\t\t\t\tif (w > matches[x]) {\n",
       "\t\t\t\t\t\t// overwrite with parent\n",
       "\t\t\t\t\t\tmatches[x] = w;\n",
       "\t\t\t\t\t}\n",
       "\t\t\t\t}\n",
       "\t\t\t\tsearching = 1;\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\tif (!searching)\n",
       "\t\t\treturn;\n",
       "\t\tvar params = get_params();\n",
       "\t\tparams.s = currentSearchTerm;\n",
       "\t\thistory.replaceState(null, null, parse_params(params));\n",
       "\n",
       "\t\tsearchbtn.classList.add(\"show\");\n",
       "\t\tsearchbtn.firstChild.nodeValue = \"Reset Search\";\n",
       "\n",
       "\t\t// calculate percent matched, excluding vertical overlap\n",
       "\t\tvar count = 0;\n",
       "\t\tvar lastx = -1;\n",
       "\t\tvar lastw = 0;\n",
       "\t\tvar keys = Array();\n",
       "\t\tfor (k in matches) {\n",
       "\t\t\tif (matches.hasOwnProperty(k))\n",
       "\t\t\t\tkeys.push(k);\n",
       "\t\t}\n",
       "\t\t// sort the matched frames by their x location\n",
       "\t\t// ascending, then width descending\n",
       "\t\tkeys.sort(function(a, b){\n",
       "\t\t\treturn a - b;\n",
       "\t\t});\n",
       "\t\t// Step through frames saving only the biggest bottom-up frames\n",
       "\t\t// thanks to the sort order. This relies on the tree property\n",
       "\t\t// where children are always smaller than their parents.\n",
       "\t\tvar fudge = 0.0001;\t// JavaScript floating point\n",
       "\t\tfor (var k in keys) {\n",
       "\t\t\tvar x = parseFloat(keys[k]);\n",
       "\t\t\tvar w = matches[keys[k]];\n",
       "\t\t\tif (x >= lastx + lastw - fudge) {\n",
       "\t\t\t\tcount += w;\n",
       "\t\t\t\tlastx = x;\n",
       "\t\t\t\tlastw = w;\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\t// display matched percent\n",
       "\t\tmatchedtxt.classList.remove(\"hide\");\n",
       "\t\tvar pct = 100 * count / maxwidth;\n",
       "\t\tif (pct != 100) pct = pct.toFixed(1)\n",
       "\t\tmatchedtxt.firstChild.nodeValue = \"Matched: \" + pct + \"%\";\n",
       "\t}\n",
       "]]>\n",
       "</script>\n",
       "<rect x=\"0.0\" y=\"0\" width=\"1200.0\" height=\"214.0\" fill=\"url(#background)\"/>\n",
       "<text id=\"title\" x=\"600.00\" y=\"24\">Flame Graph</text>\n",
       "<text id=\"details\" x=\"10.00\" y=\"197\"> </text>\n",
       "<text id=\"unzoom\" x=\"10.00\" y=\"24\" class=\"hide\">Reset Zoom</text>\n",
       "<text id=\"search\" x=\"1090.00\" y=\"24\">Search</text>\n",
       "<text id=\"ignorecase\" x=\"1174.00\" y=\"24\">ic</text>\n",
       "<text id=\"matched\" x=\"1090.00\" y=\"197\"> </text>\n",
       "<g id=\"frames\">\n",
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       "<text x=\"1101.49\" y=\"127.5\">Toffoli(T:4)</text>\n",
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       "<title>TwoBitCSwap(T:7) (84 TCounts, 8.57%)</title><rect x=\"554.2\" y=\"117\" width=\"101.2\" height=\"15.0\" fill=\"rgb(247,195,46)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"557.24\" y=\"127.5\">TwoBitCSwap(..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>CtrlSpecAnd[CtrlSpec(qdtypes=(QBit(),), cvs=(array([0, 0, 0, 0]),))](T:12) (12 TCounts, 1.22%)</title><rect x=\"390.5\" y=\"69\" width=\"14.4\" height=\"15.0\" fill=\"rgb(211,30,7)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"393.49\" y=\"79.5\"/>\n",
       "</g>\n",
       "<g>\n",
       "<title>BiQubitsMixer[False](T:8) (112 TCounts, 11.43%)</title><rect x=\"655.4\" y=\"117\" width=\"134.8\" height=\"15.0\" fill=\"rgb(223,86,20)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"658.39\" y=\"127.5\">BiQubitsMixer[Fal..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>And(T:4) (4 TCounts, 0.41%)</title><rect x=\"549.4\" y=\"85\" width=\"4.8\" height=\"15.0\" fill=\"rgb(210,25,6)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"552.43\" y=\"95.5\"/>\n",
       "</g>\n",
       "<g>\n",
       "<title>$\\sum_{\\mu &lt; \\nu} |\\mu\\nu\\rangle$(T:448) (448 TCounts, 45.71%)</title><rect x=\"10.0\" y=\"133\" width=\"539.4\" height=\"15.0\" fill=\"rgb(239,157,37)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"13.00\" y=\"143.5\">$\\sum_{\\mu &lt; \\nu} |\\mu\\nu\\rangle$(T:448)</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>C[4][ZGate](T:12) (12 TCounts, 1.22%)</title><rect x=\"390.5\" y=\"85\" width=\"14.4\" height=\"15.0\" fill=\"rgb(219,64,15)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"393.49\" y=\"95.5\"/>\n",
       "</g>\n",
       "<g>\n",
       "<title>Ry(-1.369438406004566)(T:52) (52 TCounts, 5.31%)</title><rect x=\"404.9\" y=\"117\" width=\"62.7\" height=\"15.0\" fill=\"rgb(254,225,53)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"407.94\" y=\"127.5\">Ry(-1...</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>And(T:4) (16 TCounts, 1.63%)</title><rect x=\"332.7\" y=\"85\" width=\"19.3\" height=\"15.0\" fill=\"rgb(210,25,6)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"335.69\" y=\"95.5\"/>\n",
       "</g>\n",
       "<g>\n",
       "<title>SwapWithZero(T:96) (96 TCounts, 9.80%)</title><rect x=\"982.9\" y=\"117\" width=\"115.6\" height=\"15.0\" fill=\"rgb(252,216,51)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"985.90\" y=\"127.5\">SwapWithZero(T..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>CSwapApprox[8](T:32) (96 TCounts, 9.80%)</title><rect x=\"982.9\" y=\"101\" width=\"115.6\" height=\"15.0\" fill=\"rgb(236,146,34)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"985.90\" y=\"111.5\">CSwapApprox[8]..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>all (980 TCounts, 100%)</title><rect x=\"10.0\" y=\"165\" width=\"1180.0\" height=\"15.0\" fill=\"rgb(213,39,9)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"13.00\" y=\"175.5\"/>\n",
       "</g>\n",
       "<g>\n",
       "<title>MultiAnd(n=3)(T:8) (8 TCounts, 0.82%)</title><rect x=\"10.0\" y=\"69\" width=\"9.6\" height=\"15.0\" fill=\"rgb(248,201,48)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"13.00\" y=\"79.5\"/>\n",
       "</g>\n",
       "<g>\n",
       "<title>C^4X(T:12) (36 TCounts, 3.67%)</title><rect x=\"19.6\" y=\"101\" width=\"43.4\" height=\"15.0\" fill=\"rgb(239,156,37)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"22.63\" y=\"111.5\">C^4X..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>GreaterThanConstant[4][4](T:16) (64 TCounts, 6.53%)</title><rect x=\"63.0\" y=\"117\" width=\"77.0\" height=\"15.0\" fill=\"rgb(205,1,0)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"65.98\" y=\"127.5\">GreaterT..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>C[9][ZGate](T:32) (32 TCounts, 3.27%)</title><rect x=\"352.0\" y=\"85\" width=\"38.5\" height=\"15.0\" fill=\"rgb(223,86,20)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"354.96\" y=\"95.5\">C[9..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>And(T:4) (8 TCounts, 0.82%)</title><rect x=\"790.2\" y=\"101\" width=\"9.7\" height=\"15.0\" fill=\"rgb(210,25,6)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"793.24\" y=\"111.5\"/>\n",
       "</g>\n",
       "<g>\n",
       "<title>LessThanConstant[4][4](T:16) (64 TCounts, 6.53%)</title><rect x=\"63.0\" y=\"101\" width=\"77.0\" height=\"15.0\" fill=\"rgb(238,154,36)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"65.98\" y=\"111.5\">LessThan..</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>C[3][X](T:8) (8 TCounts, 0.82%)</title><rect x=\"10.0\" y=\"101\" width=\"9.6\" height=\"15.0\" fill=\"rgb(253,221,53)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"13.00\" y=\"111.5\"/>\n",
       "</g>\n",
       "<g>\n",
       "<title>CSwap[4](T:28) (84 TCounts, 8.57%)</title><rect x=\"554.2\" y=\"133\" width=\"101.2\" height=\"15.0\" fill=\"rgb(225,96,22)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"557.24\" y=\"143.5\">CSwap[4](T:28)</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>And(T:4) (112 TCounts, 11.43%)</title><rect x=\"655.4\" y=\"101\" width=\"134.8\" height=\"15.0\" fill=\"rgb(210,25,6)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"658.39\" y=\"111.5\">And(T:4)</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>CtrlSpecAnd[CtrlSpec(qdtypes=(QBit(),), cvs=(array([0, 1]),))](T:4) (4 TCounts, 0.41%)</title><rect x=\"549.4\" y=\"101\" width=\"4.8\" height=\"15.0\" fill=\"rgb(210,26,6)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"552.43\" y=\"111.5\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qualtran.drawing import show_flame_graph\n",
    "show_flame_graph(thc_prep)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85818cf5",
   "metadata": {},
   "source": [
    "### Paper Comparison\n",
    "\n",
    "Let's compare our costs to those from the paper. Note we will only look at the cost of prepare. Inverting prepare has the same cost up to the cost of the inverse QROM being reduced to\n",
    "\n",
    "$$\n",
    "\\lceil \\frac{d}{k_{s2}} \\rceil + k_{s2}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5be7dcf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arithmetic:          qualtran =   472 vs paper cost =   108.\n",
      "swaps:               qualtran =    48 vs paper cost =    52.\n",
      "data_loading:        qualtran =   248 vs paper cost =   256.\n",
      "Total cost = 862\n"
     ]
    }
   ],
   "source": [
    "binned_counts = classify_t_count_by_bloq_type(thc_prep.decompose_bloq())\n",
    "data_size = thc_prep.num_mu * (thc_prep.num_mu + 1) // 2 + thc_prep.num_spin_orb // 2\n",
    "\n",
    "num_bits_mu = thc_prep.num_mu.bit_length() \n",
    "qrom_bitsize = 2 * num_bits_mu + 2 + thc_prep.keep_bitsize\n",
    "paper_costs = {\n",
    "    'arithmetic': 4*(num_bits_mu ** 2 + num_bits_mu - 1) + 4*thc_prep.keep_bitsize,\n",
    "    'swaps': 4*(2 * num_bits_mu + (num_bits_mu + 1)), # Swaps from inequality and swap from from \n",
    "    'data_loading': 4 * (int(np.ceil(data_size/4) + qrom_bitsize * (4 - 1))), # Eq. 31 from THC paper, k = 4 in this specific case.\n",
    "}\n",
    "for k, v in paper_costs.items():\n",
    "    print(f\"{k+':':20s} qualtran = {binned_counts[k]:5d} vs paper cost = {v:5d}.\")\n",
    "\n",
    "print(f\"Total cost = {sum(v for v in binned_counts.values())}\")\n",
    "assert binned_counts['data_loading'] == 248"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "807674de",
   "metadata": {},
   "source": [
    "The main discrepancies arise the differences in comparator cost seen before. The QROAM cost in qualtran is more accurate as it includes the constant factor of -2 Toffolis arising from uncontrolled QROM. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4dc799bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qualtran.drawing.musical_score import draw_musical_score, get_musical_score_data\n",
    "\n",
    "\n",
    "msd = get_musical_score_data(thc_prep.decompose_bloq())\n",
    "fig, ax = draw_musical_score(msd)\n",
    "fig.set_size_inches(10, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82f5f156",
   "metadata": {
    "cq.autogen": "SelectTHC.bloq_doc.md"
   },
   "source": [
    "## `SelectTHC`\n",
    "SELECT for THC Hamiltonian.\n",
    "\n",
    "#### Parameters\n",
    " - `num_mu`: THC auxiliary index dimension $M$\n",
    " - `num_spin_orb`: number of spin orbitals $N$\n",
    " - `num_bits_theta`: Number of bits of precision for the rotations. Called $\\beth$ in the reference.\n",
    " - `kr1`: block sizes for QROM erasure for outputting rotation angles. See Eq 34.\n",
    " - `kr2`: block sizes for QROM erasure for outputting rotation angles. This is for the second QROM (eq 35)\n",
    " - `control_val`: A control bit for the entire gate.\n",
    " - `keep_bitsize`: number of bits for keep register for coherent alias\n",
    " - `sampling. This can be determined from the PrepareTHC bloq. See`: \n",
    " - `https`: //github.com/quantumlib/Qualtran/issues/549 \n",
    "\n",
    "#### Registers\n",
    " - `succ`: success flag qubit from uniform state preparation\n",
    " - `nu_eq_mp1`: flag for if $nu = M+1$\n",
    " - `mu`: $\\mu$ register.\n",
    " - `nu`: $\\nu$ register.\n",
    " - `theta`: sign register.\n",
    " - `plus_mn`: Flag controlling swaps between mu and nu. Note that as per the Reference, the swaps are NOT performed as part of SELECT as they're acounted for during Prepare.\n",
    " - `plus_a / plus_b`: plus state for controlled swaps on spins.\n",
    " - `sys_a / sys_b`: System registers for (a)lpha/(b)eta orbitals. \n",
    "\n",
    "#### References\n",
    " - [Even more efficient quantum computations of chemistry through     tensor hypercontraction](https://arxiv.org/pdf/2011.03494.pdf). Fig. 7.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6d60ada0",
   "metadata": {
    "cq.autogen": "SelectTHC.bloq_doc.py"
   },
   "outputs": [],
   "source": [
    "from qualtran.bloqs.chemistry.thc import SelectTHC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c68adb1",
   "metadata": {
    "cq.autogen": "SelectTHC.example_instances.md"
   },
   "source": [
    "### Example Instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9ebf4208",
   "metadata": {
    "cq.autogen": "SelectTHC.thc_sel"
   },
   "outputs": [],
   "source": [
    "num_mu = 8\n",
    "num_mu = 10\n",
    "num_spin_orb = 2 * 4\n",
    "thc_sel = SelectTHC(\n",
    "    num_mu=num_mu, num_spin_orb=num_spin_orb, num_bits_theta=12, keep_bitsize=10\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d34aa4b",
   "metadata": {
    "cq.autogen": "SelectTHC.graphical_signature.md"
   },
   "source": [
    "#### Graphical Signature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "436ebbcd",
   "metadata": {
    "cq.autogen": "SelectTHC.graphical_signature.py"
   },
   "outputs": [
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<IPython.core.display.M…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qualtran.drawing import show_bloqs\n",
    "show_bloqs([thc_sel],\n",
    "           ['`thc_sel`'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acd232bb",
   "metadata": {
    "cq.autogen": "SelectTHC.call_graph.md"
   },
   "source": [
    "### Call Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "44fed1ba",
   "metadata": {
    "cq.autogen": "SelectTHC.call_graph.py"
   },
   "outputs": [
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      "text/markdown": [
       "#### Counts totals:\n",
       " - `Allocate`: 1\n",
       " - `ApplyControlledZs`: 1\n",
       " - `ApplyControlledZs`: 1\n",
       " - `CSwap`: 1\n",
       " - `CSwap`: 5\n",
       " - `Free`: 1\n",
       " - `In_mu-R`: 1\n",
       " - `In_mu-R`: 1\n",
       " - `In_mu-R†`: 1\n",
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       " - `X`: 1"
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   "source": [
    "from qualtran.resource_counting.generalizers import ignore_split_join\n",
    "thc_sel_g, thc_sel_sigma = thc_sel.call_graph(max_depth=1, generalizer=ignore_split_join)\n",
    "show_call_graph(thc_sel_g)\n",
    "show_counts_sigma(thc_sel_sigma)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e98a837b-7df6-41b8-9685-f00817b33550",
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   "source": [
    "### Flame Graph"
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  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ec11328c-f2f0-4217-8ad5-00f711d8f78c",
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       "\t\tif (params.x && params.y)\n",
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       "\t\t\tif (target.classList.contains(\"parent\")) unzoom(true);\n",
       "\t\t\tzoom(target);\n",
       "\t\t\tif (!document.querySelector('.parent')) {\n",
       "\t\t\t\t// we have basically done a clearzoom so clear the url\n",
       "\t\t\t\tvar params = get_params();\n",
       "\t\t\t\tif (params.x) delete params.x;\n",
       "\t\t\t\tif (params.y) delete params.y;\n",
       "\t\t\t\thistory.replaceState(null, null, parse_params(params));\n",
       "\t\t\t\tunzoombtn.classList.add(\"hide\");\n",
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       "\t\t\t// set parameters for zoom state\n",
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       "\t\tvar target = find_group(e.target);\n",
       "\t\tif (target) details.nodeValue = \"Function: \" + g_to_text(target);\n",
       "\t}, false)\n",
       "\n",
       "\t// clear\n",
       "\twindow.addEventListener(\"mouseout\", function(e) {\n",
       "\t\tvar target = find_group(e.target);\n",
       "\t\tif (target) details.nodeValue = ' ';\n",
       "\t}, false)\n",
       "\n",
       "\t// ctrl-F for search\n",
       "\t// ctrl-I to toggle case-sensitive search\n",
       "\twindow.addEventListener(\"keydown\",function (e) {\n",
       "\t\tif (e.keyCode === 114 || (e.ctrlKey && e.keyCode === 70)) {\n",
       "\t\t\te.preventDefault();\n",
       "\t\t\tsearch_prompt();\n",
       "\t\t}\n",
       "\t\telse if (e.ctrlKey && e.keyCode === 73) {\n",
       "\t\t\te.preventDefault();\n",
       "\t\t\ttoggle_ignorecase();\n",
       "\t\t}\n",
       "\t}, false)\n",
       "\n",
       "\t// functions\n",
       "\tfunction get_params() {\n",
       "\t\tvar params = {};\n",
       "\t\tvar paramsarr = window.location.search.substr(1).split('&');\n",
       "\t\tfor (var i = 0; i < paramsarr.length; ++i) {\n",
       "\t\t\tvar tmp = paramsarr[i].split(\"=\");\n",
       "\t\t\tif (!tmp[0] || !tmp[1]) continue;\n",
       "\t\t\tparams[tmp[0]]  = decodeURIComponent(tmp[1]);\n",
       "\t\t}\n",
       "\t\treturn params;\n",
       "\t}\n",
       "\tfunction parse_params(params) {\n",
       "\t\tvar uri = \"?\";\n",
       "\t\tfor (var key in params) {\n",
       "\t\t\turi += key + '=' + encodeURIComponent(params[key]) + '&';\n",
       "\t\t}\n",
       "\t\tif (uri.slice(-1) == \"&\")\n",
       "\t\t\turi = uri.substring(0, uri.length - 1);\n",
       "\t\tif (uri == '?')\n",
       "\t\t\turi = window.location.href.split('?')[0];\n",
       "\t\treturn uri;\n",
       "\t}\n",
       "\tfunction find_child(node, selector) {\n",
       "\t\tvar children = node.querySelectorAll(selector);\n",
       "\t\tif (children.length) return children[0];\n",
       "\t}\n",
       "\tfunction find_group(node) {\n",
       "\t\tvar parent = node.parentElement;\n",
       "\t\tif (!parent) return;\n",
       "\t\tif (parent.id == \"frames\") return node;\n",
       "\t\treturn find_group(parent);\n",
       "\t}\n",
       "\tfunction orig_save(e, attr, val) {\n",
       "\t\tif (e.attributes[\"_orig_\" + attr] != undefined) return;\n",
       "\t\tif (e.attributes[attr] == undefined) return;\n",
       "\t\tif (val == undefined) val = e.attributes[attr].value;\n",
       "\t\te.setAttribute(\"_orig_\" + attr, val);\n",
       "\t}\n",
       "\tfunction orig_load(e, attr) {\n",
       "\t\tif (e.attributes[\"_orig_\"+attr] == undefined) return;\n",
       "\t\te.attributes[attr].value = e.attributes[\"_orig_\" + attr].value;\n",
       "\t\te.removeAttribute(\"_orig_\"+attr);\n",
       "\t}\n",
       "\tfunction g_to_text(e) {\n",
       "\t\tvar text = find_child(e, \"title\").firstChild.nodeValue;\n",
       "\t\treturn (text)\n",
       "\t}\n",
       "\tfunction g_to_func(e) {\n",
       "\t\tvar func = g_to_text(e);\n",
       "\t\t// if there's any manipulation we want to do to the function\n",
       "\t\t// name before it's searched, do it here before returning.\n",
       "\t\treturn (func);\n",
       "\t}\n",
       "\tfunction update_text(e) {\n",
       "\t\tvar r = find_child(e, \"rect\");\n",
       "\t\tvar t = find_child(e, \"text\");\n",
       "\t\tvar w = parseFloat(r.attributes.width.value) -3;\n",
       "\t\tvar txt = find_child(e, \"title\").textContent.replace(/\\([^(]*\\)$/,\"\");\n",
       "\t\tt.attributes.x.value = parseFloat(r.attributes.x.value) + 3;\n",
       "\n",
       "\t\t// Smaller than this size won't fit anything\n",
       "\t\tif (w < 2 * 12 * 0.59) {\n",
       "\t\t\tt.textContent = \"\";\n",
       "\t\t\treturn;\n",
       "\t\t}\n",
       "\n",
       "\t\tt.textContent = txt;\n",
       "\t\tvar sl = t.getSubStringLength(0, txt.length);\n",
       "\t\t// check if only whitespace or if we can fit the entire string into width w\n",
       "\t\tif (/^ *$/.test(txt) || sl < w)\n",
       "\t\t\treturn;\n",
       "\n",
       "\t\t// this isn't perfect, but gives a good starting point\n",
       "\t\t// and avoids calling getSubStringLength too often\n",
       "\t\tvar start = Math.floor((w/sl) * txt.length);\n",
       "\t\tfor (var x = start; x > 0; x = x-2) {\n",
       "\t\t\tif (t.getSubStringLength(0, x + 2) <= w) {\n",
       "\t\t\t\tt.textContent = txt.substring(0, x) + \"..\";\n",
       "\t\t\t\treturn;\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\tt.textContent = \"\";\n",
       "\t}\n",
       "\n",
       "\t// zoom\n",
       "\tfunction zoom_reset(e) {\n",
       "\t\tif (e.attributes != undefined) {\n",
       "\t\t\torig_load(e, \"x\");\n",
       "\t\t\torig_load(e, \"width\");\n",
       "\t\t}\n",
       "\t\tif (e.childNodes == undefined) return;\n",
       "\t\tfor (var i = 0, c = e.childNodes; i < c.length; i++) {\n",
       "\t\t\tzoom_reset(c[i]);\n",
       "\t\t}\n",
       "\t}\n",
       "\tfunction zoom_child(e, x, ratio) {\n",
       "\t\tif (e.attributes != undefined) {\n",
       "\t\t\tif (e.attributes.x != undefined) {\n",
       "\t\t\t\torig_save(e, \"x\");\n",
       "\t\t\t\te.attributes.x.value = (parseFloat(e.attributes.x.value) - x - 10) * ratio + 10;\n",
       "\t\t\t\tif (e.tagName == \"text\")\n",
       "\t\t\t\t\te.attributes.x.value = find_child(e.parentNode, \"rect[x]\").attributes.x.value + 3;\n",
       "\t\t\t}\n",
       "\t\t\tif (e.attributes.width != undefined) {\n",
       "\t\t\t\torig_save(e, \"width\");\n",
       "\t\t\t\te.attributes.width.value = parseFloat(e.attributes.width.value) * ratio;\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\n",
       "\t\tif (e.childNodes == undefined) return;\n",
       "\t\tfor (var i = 0, c = e.childNodes; i < c.length; i++) {\n",
       "\t\t\tzoom_child(c[i], x - 10, ratio);\n",
       "\t\t}\n",
       "\t}\n",
       "\tfunction zoom_parent(e) {\n",
       "\t\tif (e.attributes) {\n",
       "\t\t\tif (e.attributes.x != undefined) {\n",
       "\t\t\t\torig_save(e, \"x\");\n",
       "\t\t\t\te.attributes.x.value = 10;\n",
       "\t\t\t}\n",
       "\t\t\tif (e.attributes.width != undefined) {\n",
       "\t\t\t\torig_save(e, \"width\");\n",
       "\t\t\t\te.attributes.width.value = parseInt(svg.width.baseVal.value) - (10 * 2);\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\tif (e.childNodes == undefined) return;\n",
       "\t\tfor (var i = 0, c = e.childNodes; i < c.length; i++) {\n",
       "\t\t\tzoom_parent(c[i]);\n",
       "\t\t}\n",
       "\t}\n",
       "\tfunction zoom(node) {\n",
       "\t\tvar attr = find_child(node, \"rect\").attributes;\n",
       "\t\tvar width = parseFloat(attr.width.value);\n",
       "\t\tvar xmin = parseFloat(attr.x.value);\n",
       "\t\tvar xmax = parseFloat(xmin + width);\n",
       "\t\tvar ymin = parseFloat(attr.y.value);\n",
       "\t\tvar ratio = (svg.width.baseVal.value - 2 * 10) / width;\n",
       "\n",
       "\t\t// XXX: Workaround for JavaScript float issues (fix me)\n",
       "\t\tvar fudge = 0.0001;\n",
       "\n",
       "\t\tunzoombtn.classList.remove(\"hide\");\n",
       "\n",
       "\t\tvar el = document.getElementById(\"frames\").children;\n",
       "\t\tfor (var i = 0; i < el.length; i++) {\n",
       "\t\t\tvar e = el[i];\n",
       "\t\t\tvar a = find_child(e, \"rect\").attributes;\n",
       "\t\t\tvar ex = parseFloat(a.x.value);\n",
       "\t\t\tvar ew = parseFloat(a.width.value);\n",
       "\t\t\tvar upstack;\n",
       "\t\t\t// Is it an ancestor\n",
       "\t\t\tif (0 == 0) {\n",
       "\t\t\t\tupstack = parseFloat(a.y.value) > ymin;\n",
       "\t\t\t} else {\n",
       "\t\t\t\tupstack = parseFloat(a.y.value) < ymin;\n",
       "\t\t\t}\n",
       "\t\t\tif (upstack) {\n",
       "\t\t\t\t// Direct ancestor\n",
       "\t\t\t\tif (ex <= xmin && (ex+ew+fudge) >= xmax) {\n",
       "\t\t\t\t\te.classList.add(\"parent\");\n",
       "\t\t\t\t\tzoom_parent(e);\n",
       "\t\t\t\t\tupdate_text(e);\n",
       "\t\t\t\t}\n",
       "\t\t\t\t// not in current path\n",
       "\t\t\t\telse\n",
       "\t\t\t\t\te.classList.add(\"hide\");\n",
       "\t\t\t}\n",
       "\t\t\t// Children maybe\n",
       "\t\t\telse {\n",
       "\t\t\t\t// no common path\n",
       "\t\t\t\tif (ex < xmin || ex + fudge >= xmax) {\n",
       "\t\t\t\t\te.classList.add(\"hide\");\n",
       "\t\t\t\t}\n",
       "\t\t\t\telse {\n",
       "\t\t\t\t\tzoom_child(e, xmin, ratio);\n",
       "\t\t\t\t\tupdate_text(e);\n",
       "\t\t\t\t}\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\tsearch();\n",
       "\t}\n",
       "\tfunction unzoom(dont_update_text) {\n",
       "\t\tunzoombtn.classList.add(\"hide\");\n",
       "\t\tvar el = document.getElementById(\"frames\").children;\n",
       "\t\tfor(var i = 0; i < el.length; i++) {\n",
       "\t\t\tel[i].classList.remove(\"parent\");\n",
       "\t\t\tel[i].classList.remove(\"hide\");\n",
       "\t\t\tzoom_reset(el[i]);\n",
       "\t\t\tif(!dont_update_text) update_text(el[i]);\n",
       "\t\t}\n",
       "\t\tsearch();\n",
       "\t}\n",
       "\tfunction clearzoom() {\n",
       "\t\tunzoom();\n",
       "\n",
       "\t\t// remove zoom state\n",
       "\t\tvar params = get_params();\n",
       "\t\tif (params.x) delete params.x;\n",
       "\t\tif (params.y) delete params.y;\n",
       "\t\thistory.replaceState(null, null, parse_params(params));\n",
       "\t}\n",
       "\n",
       "\t// search\n",
       "\tfunction toggle_ignorecase() {\n",
       "\t\tignorecase = !ignorecase;\n",
       "\t\tif (ignorecase) {\n",
       "\t\t\tignorecaseBtn.classList.add(\"show\");\n",
       "\t\t} else {\n",
       "\t\t\tignorecaseBtn.classList.remove(\"show\");\n",
       "\t\t}\n",
       "\t\treset_search();\n",
       "\t\tsearch();\n",
       "\t}\n",
       "\tfunction reset_search() {\n",
       "\t\tvar el = document.querySelectorAll(\"#frames rect\");\n",
       "\t\tfor (var i = 0; i < el.length; i++) {\n",
       "\t\t\torig_load(el[i], \"fill\")\n",
       "\t\t}\n",
       "\t\tvar params = get_params();\n",
       "\t\tdelete params.s;\n",
       "\t\thistory.replaceState(null, null, parse_params(params));\n",
       "\t}\n",
       "\tfunction search_prompt() {\n",
       "\t\tif (!searching) {\n",
       "\t\t\tvar term = prompt(\"Enter a search term (regexp \" +\n",
       "\t\t\t    \"allowed, eg: ^ext4_)\"\n",
       "\t\t\t    + (ignorecase ? \", ignoring case\" : \"\")\n",
       "\t\t\t    + \"\\nPress Ctrl-i to toggle case sensitivity\", \"\");\n",
       "\t\t\tif (term != null) search(term);\n",
       "\t\t} else {\n",
       "\t\t\treset_search();\n",
       "\t\t\tsearching = 0;\n",
       "\t\t\tcurrentSearchTerm = null;\n",
       "\t\t\tsearchbtn.classList.remove(\"show\");\n",
       "\t\t\tsearchbtn.firstChild.nodeValue = \"Search\"\n",
       "\t\t\tmatchedtxt.classList.add(\"hide\");\n",
       "\t\t\tmatchedtxt.firstChild.nodeValue = \"\"\n",
       "\t\t}\n",
       "\t}\n",
       "\tfunction search(term) {\n",
       "\t\tif (term) currentSearchTerm = term;\n",
       "\n",
       "\t\tvar re = new RegExp(currentSearchTerm, ignorecase ? 'i' : '');\n",
       "\t\tvar el = document.getElementById(\"frames\").children;\n",
       "\t\tvar matches = new Object();\n",
       "\t\tvar maxwidth = 0;\n",
       "\t\tfor (var i = 0; i < el.length; i++) {\n",
       "\t\t\tvar e = el[i];\n",
       "\t\t\tvar func = g_to_func(e);\n",
       "\t\t\tvar rect = find_child(e, \"rect\");\n",
       "\t\t\tif (func == null || rect == null)\n",
       "\t\t\t\tcontinue;\n",
       "\n",
       "\t\t\t// Save max width. Only works as we have a root frame\n",
       "\t\t\tvar w = parseFloat(rect.attributes.width.value);\n",
       "\t\t\tif (w > maxwidth)\n",
       "\t\t\t\tmaxwidth = w;\n",
       "\n",
       "\t\t\tif (func.match(re)) {\n",
       "\t\t\t\t// highlight\n",
       "\t\t\t\tvar x = parseFloat(rect.attributes.x.value);\n",
       "\t\t\t\torig_save(rect, \"fill\");\n",
       "\t\t\t\trect.attributes.fill.value = \"rgb(230,0,230)\";\n",
       "\n",
       "\t\t\t\t// remember matches\n",
       "\t\t\t\tif (matches[x] == undefined) {\n",
       "\t\t\t\t\tmatches[x] = w;\n",
       "\t\t\t\t} else {\n",
       "\t\t\t\t\tif (w > matches[x]) {\n",
       "\t\t\t\t\t\t// overwrite with parent\n",
       "\t\t\t\t\t\tmatches[x] = w;\n",
       "\t\t\t\t\t}\n",
       "\t\t\t\t}\n",
       "\t\t\t\tsearching = 1;\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\tif (!searching)\n",
       "\t\t\treturn;\n",
       "\t\tvar params = get_params();\n",
       "\t\tparams.s = currentSearchTerm;\n",
       "\t\thistory.replaceState(null, null, parse_params(params));\n",
       "\n",
       "\t\tsearchbtn.classList.add(\"show\");\n",
       "\t\tsearchbtn.firstChild.nodeValue = \"Reset Search\";\n",
       "\n",
       "\t\t// calculate percent matched, excluding vertical overlap\n",
       "\t\tvar count = 0;\n",
       "\t\tvar lastx = -1;\n",
       "\t\tvar lastw = 0;\n",
       "\t\tvar keys = Array();\n",
       "\t\tfor (k in matches) {\n",
       "\t\t\tif (matches.hasOwnProperty(k))\n",
       "\t\t\t\tkeys.push(k);\n",
       "\t\t}\n",
       "\t\t// sort the matched frames by their x location\n",
       "\t\t// ascending, then width descending\n",
       "\t\tkeys.sort(function(a, b){\n",
       "\t\t\treturn a - b;\n",
       "\t\t});\n",
       "\t\t// Step through frames saving only the biggest bottom-up frames\n",
       "\t\t// thanks to the sort order. This relies on the tree property\n",
       "\t\t// where children are always smaller than their parents.\n",
       "\t\tvar fudge = 0.0001;\t// JavaScript floating point\n",
       "\t\tfor (var k in keys) {\n",
       "\t\t\tvar x = parseFloat(keys[k]);\n",
       "\t\t\tvar w = matches[keys[k]];\n",
       "\t\t\tif (x >= lastx + lastw - fudge) {\n",
       "\t\t\t\tcount += w;\n",
       "\t\t\t\tlastx = x;\n",
       "\t\t\t\tlastw = w;\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t\t// display matched percent\n",
       "\t\tmatchedtxt.classList.remove(\"hide\");\n",
       "\t\tvar pct = 100 * count / maxwidth;\n",
       "\t\tif (pct != 100) pct = pct.toFixed(1)\n",
       "\t\tmatchedtxt.firstChild.nodeValue = \"Matched: \" + pct + \"%\";\n",
       "\t}\n",
       "]]>\n",
       "</script>\n",
       "<rect x=\"0.0\" y=\"0\" width=\"1200.0\" height=\"134.0\" fill=\"url(#background)\"/>\n",
       "<text id=\"title\" x=\"600.00\" y=\"24\">Flame Graph</text>\n",
       "<text id=\"details\" x=\"10.00\" y=\"117\"> </text>\n",
       "<text id=\"unzoom\" x=\"10.00\" y=\"24\" class=\"hide\">Reset Zoom</text>\n",
       "<text id=\"search\" x=\"1090.00\" y=\"24\">Search</text>\n",
       "<text id=\"ignorecase\" x=\"1174.00\" y=\"24\">ic</text>\n",
       "<text id=\"matched\" x=\"1090.00\" y=\"117\"> </text>\n",
       "<g id=\"frames\">\n",
       "<g>\n",
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       "<text x=\"915.35\" y=\"63.5\">In_mu-R†(T:380)</text>\n",
       "</g>\n",
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       "<text x=\"123.33\" y=\"47.5\">Toffoli(T:4)</text>\n",
       "</g>\n",
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       "<title>TwoBitCSwap(T:7) (140 TCounts, 8.67%)</title><rect x=\"18.0\" y=\"37\" width=\"102.3\" height=\"15.0\" fill=\"rgb(247,195,46)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"21.04\" y=\"47.5\">TwoBitCSwap(..</text>\n",
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       "<text x=\"21.04\" y=\"63.5\">CSwap[4](T:28)</text>\n",
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       "<text x=\"123.33\" y=\"63.5\">In_mu-R(T:352)</text>\n",
       "</g>\n",
       "<g>\n",
       "<title>SelectTHC[10][8][12][10][1][1][None](T:1615) (1,615 TCounts, 100.00%)</title><rect x=\"10.0\" y=\"69\" width=\"1180.0\" height=\"15.0\" fill=\"rgb(238,154,37)\" rx=\"2\" ry=\"2\"/>\n",
       "<text x=\"13.00\" y=\"79.5\">SelectTHC[10][8][12][10][1][1][None](T:1615)</text>\n",
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       "<IPython.core.display.SVG object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qualtran.drawing import show_flame_graph\n",
    "show_flame_graph(thc_sel)"
   ]
  }
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