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Merge pull request zxcalc#66 from rkruegs123/master
Added annealing and genetic algorithms for local search
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@@ -12,3 +12,4 @@ pyzx.egg-info | |
*.class | ||
*.ipynb_checkpoints | ||
pyzx/js/d3.v5.js | ||
*~ |
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 1, | ||
"id": "welcome-louisville", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import sys; sys.path.append('..')\n", | ||
"import pyzx as zx\n", | ||
"from pyzx.generate import CNOT_HAD_PHASE_circuit" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "passive-variation", | ||
"metadata": {}, | ||
"source": [ | ||
"We often want to extract a circuit from a ZX-diagram. However, equivalent ZX-diagrams can induce drastically different circuits upon extraction even if they have similar numbers of spiders and edges. We can apply two optimization techniques, simulated annealing and genetic algorithms, to search this local space for a ZX-diagram that is more amenable to extraction." | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "aging-prairie", | ||
"metadata": {}, | ||
"source": [ | ||
"Given a circuit, the typical approach to searching this space is to first fully simplify the associated ZX-diagram and then search over ZX-diagrams that are equivalent to this fully simplified version. So, let's first generate a random circuit and convert it to a ZX-diagram:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 2, | ||
"id": "searching-astronomy", | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"Circuit on 5 qubits with 100 gates.\n", | ||
" 20 is the T-count\n", | ||
" 80 Cliffords among which \n", | ||
" 58 2-qubit gates (58 CNOT, 0 other) and\n", | ||
" 22 Hadamard gates.\n" | ||
] | ||
} | ||
], | ||
"source": [ | ||
"N_QUBITS = 5\n", | ||
"DEPTH = 100\n", | ||
"c = CNOT_HAD_PHASE_circuit(qubits=N_QUBITS, depth=DEPTH, clifford=False)\n", | ||
"print(c.stats())\n", | ||
"g = c.to_graph()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "hindu-virus", | ||
"metadata": {}, | ||
"source": [ | ||
"Then, we want to fully simplify this ZX-diagram. There are two ways of doing this: applying `full_reduce` or applying `teleport_reduce` followed by circuit-level optimizations via `basic_optimization`. Note that if you go with the latter, you have to put the resulting ZX-diagram in graph-like form with `zx.simplify.to_graph_like()`. We'll go with the former:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 3, | ||
"id": "color-split", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"g_simp = g.copy()\n", | ||
"zx.full_reduce(g_simp)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "answering-flower", | ||
"metadata": {}, | ||
"source": [ | ||
"Now, we can search the space of equivalent ZX-diagrams. First lets do it with annealing:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 4, | ||
"id": "surgical-birth", | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stderr", | ||
"output_type": "stream", | ||
"text": [ | ||
"annealing...: 100%|██████████| 1000/1000 [00:18<00:00, 53.52it/s]\n" | ||
] | ||
} | ||
], | ||
"source": [ | ||
"N_ITERS = 1000\n", | ||
"\n", | ||
"g_anneal, _ = zx.anneal(g_simp, iters=N_ITERS)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "level-sunday", | ||
"metadata": {}, | ||
"source": [ | ||
"Let's convert this new ZX-diagram back to a circuit and see how we did. We can apply some optimizations along the way:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 5, | ||
"id": "healthy-toner", | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"Circuit on 5 qubits with 82 gates.\n", | ||
" 18 is the T-count\n", | ||
" 64 Cliffords among which \n", | ||
" 37 2-qubit gates (30 CNOT, 7 other) and\n", | ||
" 19 Hadamard gates.\n" | ||
] | ||
} | ||
], | ||
"source": [ | ||
"zx.full_reduce(g_anneal)\n", | ||
"c_anneal = zx.extract_circuit(g_anneal.copy()).to_basic_gates()\n", | ||
"c_anneal = zx.basic_optimization(c_anneal)\n", | ||
"print(c_anneal.stats())" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "native-highway", | ||
"metadata": {}, | ||
"source": [ | ||
"We can do the same with genetic algorithms" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 6, | ||
"id": "massive-simon", | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stderr", | ||
"output_type": "stream", | ||
"text": [ | ||
"Generations: 100%|██████████| 40/40 [00:14<00:00, 2.76it/s]" | ||
] | ||
}, | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"Circuit on 5 qubits with 80 gates.\n", | ||
" 18 is the T-count\n", | ||
" 62 Cliffords among which \n", | ||
" 36 2-qubit gates (29 CNOT, 7 other) and\n", | ||
" 15 Hadamard gates.\n" | ||
] | ||
}, | ||
{ | ||
"name": "stderr", | ||
"output_type": "stream", | ||
"text": [ | ||
"\n" | ||
] | ||
} | ||
], | ||
"source": [ | ||
"ga_opt = zx.GeneticOptimizer()\n", | ||
"g_evolve = ga_opt.evolve(g_simp, n_mutants=20, n_generations=40, quiet=False)\n", | ||
"zx.full_reduce(g_evolve)\n", | ||
"c_evolve = zx.extract_circuit(g_evolve.copy()).to_basic_gates()\n", | ||
"c_evolve = zx.basic_optimization(c_evolve)\n", | ||
"print(c_evolve.stats())" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "modified-johns", | ||
"metadata": {}, | ||
"source": [ | ||
"Typically, this approach works well for small circuits (i.e., < 10 qubits) but it depends on the structure of the circuit. Also, annealing tends to do better than the genetic approach." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "qualified-impact", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.8.5" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 5 | ||
} |
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# PyZX - Python library for quantum circuit rewriting | ||
# and optimization using the ZX-calculus | ||
# Copyright (C) 2021 - Aleks Kissinger and John van de Wetering | ||
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# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
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# http://www.apache.org/licenses/LICENSE-2.0 | ||
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# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. |
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