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Run notebooks through nbfmt #371

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Run notebooks through nbfmt #371

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158 changes: 91 additions & 67 deletions docs/kpz/kpz.ipynb
View file
Original file line number Diff line number Diff line change
@@ -4,7 +4,9 @@
"cell_type": "code",
"execution_count": 1,
"id": "bfbe98f2",
"metadata": {},
"metadata": {
"id": "a3cdc6c47599"
},
"outputs": [],
"source": [
"##### Copyright 2023 The Cirq Developers"
@@ -14,7 +16,10 @@
"cell_type": "code",
"execution_count": 2,
"id": "161a0876",
"metadata": {},
"metadata": {
"cellView": "form",
"id": "906e07f6e562"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
@@ -33,15 +38,19 @@
{
"cell_type": "markdown",
"id": "9d29cea2",
"metadata": {},
"metadata": {
"id": "d895d2965a7e"
},
"source": [
"# Kardar-Parisi-Zhang Dynamics in the Heisenberg Spin Chain"
]
},
{
"cell_type": "markdown",
"id": "c4a1ff2c",
"metadata": {},
"metadata": {
"id": "c5cb019fcfbc"
},
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
@@ -62,7 +71,9 @@
{
"cell_type": "markdown",
"id": "1de2dd3b",
"metadata": {},
"metadata": {
"id": "70f3cee60e01"
},
"source": [
"Before beginning, we will import the necessary modules into the notebook."
]
@@ -71,7 +82,9 @@
"cell_type": "code",
"execution_count": 3,
"id": "cef7a354",
"metadata": {},
"metadata": {
"id": "68eb3591185f"
},
"outputs": [],
"source": [
"try:\n",
@@ -99,7 +112,9 @@
{
"cell_type": "markdown",
"id": "f4110f55",
"metadata": {},
"metadata": {
"id": "186968984e10"
},
"source": [
"### Motivation and Background\n",
"\n",
@@ -124,7 +139,9 @@
"cell_type": "code",
"execution_count": 4,
"id": "b7477208",
"metadata": {},
"metadata": {
"id": "afd23812124c"
},
"outputs": [],
"source": [
"num_cycles = 2\n",
@@ -139,7 +156,9 @@
{
"cell_type": "markdown",
"id": "af80864e",
"metadata": {},
"metadata": {
"id": "53c75bc5619a"
},
"source": [
"By default, this uses `2*num_cycles` qubits, which is the minimum number of qubits needed for the transferred magnetization to be independent of system size (in the experiment we use 46 qubits and go to cycle 23). The `KPZExperiment` comes with the `num_init_states` initial bitstrings prepared:"
]
@@ -148,7 +167,9 @@
"cell_type": "code",
"execution_count": 5,
"id": "41c5584d",
"metadata": {},
"metadata": {
"id": "4194e9071392"
},
"outputs": [
{
"data": {
@@ -174,7 +195,9 @@
{
"cell_type": "markdown",
"id": "afabba4b",
"metadata": {},
"metadata": {
"id": "b65444f94259"
},
"source": [
"It also prepares the circuits that we need to run. For example, the circuit corresponding to the first initial state is:"
]
@@ -183,7 +206,9 @@
"cell_type": "code",
"execution_count": 6,
"id": "da71a7b2",
"metadata": {},
"metadata": {
"id": "00fc65e3cb43"
},
"outputs": [
{
"data": {
@@ -218,7 +243,9 @@
{
"cell_type": "markdown",
"id": "3fd3828b",
"metadata": {},
"metadata": {
"id": "0be4b1e23a86"
},
"source": [
"To run the experiment, we can either use `KPZExperiment.run_experiment()` or `KPZExperiment.run_experiment_amplitudes()`. In either case, we have to input a Cirq sampler. `run_experiment()` samples bitstrings, closer to what is done in the experiment, whereas `run_experiment_amplitudes()` uses amplitudes from a statevector simulation (and hence requires a `cirq.SimulatesAmplitudes` sampler). In the actual experiment, we apply a post-selection to mitigate against effects of $T_1$; this is not done here because this tutorial uses noiseless simulations.\n",
"\n",
@@ -229,7 +256,9 @@
"cell_type": "code",
"execution_count": 7,
"id": "d3a05a0b",
"metadata": {},
"metadata": {
"id": "1e320cda8aa4"
},
"outputs": [
{
"name": "stderr",
@@ -250,7 +279,9 @@
{
"cell_type": "markdown",
"id": "3df95b0e",
"metadata": {},
"metadata": {
"id": "babe2e86ac00"
},
"source": [
"The measured bitstrings are contained in `res_bitstrings.bitstrs`, which has shape `(num_init_states, reps, num_qubits)`"
]
@@ -259,7 +290,9 @@
"cell_type": "code",
"execution_count": 8,
"id": "b3d73f20",
"metadata": {},
"metadata": {
"id": "f9778320f6a5"
},
"outputs": [
{
"data": {
@@ -327,7 +360,9 @@
{
"cell_type": "markdown",
"id": "944ae2c9",
"metadata": {},
"metadata": {
"id": "b1321a162049"
},
"source": [
"As you can see, the number of excitations is conserved, as expected because fSim gates are number conserving."
]
@@ -336,7 +371,9 @@
"cell_type": "code",
"execution_count": 9,
"id": "0d41941f",
"metadata": {},
"metadata": {
"id": "6271edef408e"
},
"outputs": [],
"source": [
"num_excitations = np.sum(res_bitstrs.bitstrs, axis=2)\n",
@@ -348,7 +385,9 @@
{
"cell_type": "markdown",
"id": "c00b683a",
"metadata": {},
"metadata": {
"id": "9d4e374a5e4d"
},
"source": [
"The transferred magnetization, $\\mathcal{M}$ is computed by counting how many excitations are on the right side of the chain in the final state, contained in `res_bitstrs.num_right_final` (shape `(num_init_states, reps)`), and subtracting the number that were on the right initially, `res_bitstrs.num_right_initial` (shape `(num_init_states,)`), and then multiplying by 2. It is stored in `res_bitstrs.transferred_magnetization` (shape `(num_init_states, reps)`). We can plot a histogram of the transferred magnetization with:"
]
@@ -357,7 +396,9 @@
"cell_type": "code",
"execution_count": 10,
"id": "0627dbd5",
"metadata": {},
"metadata": {
"id": "74bed681e879"
},
"outputs": [
{
"data": {
@@ -387,7 +428,9 @@
{
"cell_type": "markdown",
"id": "a78c9395",
"metadata": {},
"metadata": {
"id": "27f0fda1436d"
},
"source": [
"The same histogram can be obtained without any error from finite `reps` by using `res_amplitudes`:"
]
@@ -396,7 +439,9 @@
"cell_type": "code",
"execution_count": 11,
"id": "18e2234d",
"metadata": {},
"metadata": {
"id": "68ad7583b8e7"
},
"outputs": [
{
"data": {
@@ -426,7 +471,9 @@
{
"cell_type": "markdown",
"id": "6d44c004",
"metadata": {},
"metadata": {
"id": "57f7723b3a54"
},
"source": [
"Finally, we are interested in the mean, variance, skewness, and kurtosis of this distribution. These are stored in `res_amplitudes.mean`, `res_amplitudes.variance`, `res_amplitudes.skewness`, and `res_amplitudes.kurtosis`, and similarly for `res_bitstrs`.\n",
"\n",
@@ -437,7 +484,9 @@
"cell_type": "code",
"execution_count": 12,
"id": "30507cb5",
"metadata": {},
"metadata": {
"id": "245cad3944b5"
},
"outputs": [
{
"name": "stdout",
@@ -460,7 +509,9 @@
{
"cell_type": "markdown",
"id": "d6c84778",
"metadata": {},
"metadata": {
"id": "a1644a042bd2"
},
"source": [
"For the paper, we ran exact density matrix simulations up to cycle 8, which give, for this choice of $t=2$, $\\mu=0.5$, and $(\\theta, \\phi) = (0.4\\pi, 0.8\\pi)$,\n",
"\n",
@@ -479,7 +530,9 @@
"cell_type": "code",
"execution_count": 13,
"id": "e572c635",
"metadata": {},
"metadata": {
"id": "b45b27be9ee3"
},
"outputs": [
{
"name": "stdout",
@@ -636,7 +689,9 @@
{
"cell_type": "markdown",
"id": "decdc752",
"metadata": {},
"metadata": {
"id": "e096bf94b21b"
},
"source": [
"Note that runtime is increasing exponentially $t$. The runtime, beyond $t=13$ or so, can be improved using GPUs if you have access to them, either using [qsim's built-in GPU backend](https://quantumai.google/reference/python/qsimcirq/QSimOptions), or [NVIDIA's cuQuantum](https://docs.nvidia.com/cuda/cuquantum/latest/appliance/cirq.html), the latter of which allows you to pool the resources of multiple GPUs. For instructions on picking hardware, see [this](https://quantumai.google/qsim/choose_hw) page, and for a tutorial on setting up the GPU backends, see [this](https://quantumai.google/qsim/tutorials/gcp_gpu) page. For the paper, we perform simulations to cycle 18 using 8 NVIDIA A100 GPUs.\n",
"\n",
@@ -647,7 +702,9 @@
"cell_type": "code",
"execution_count": 14,
"id": "46514834",
"metadata": {},
"metadata": {
"id": "e01de6de7b3b"
},
"outputs": [
{
"data": {
@@ -700,51 +757,18 @@
"\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b51f1408",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "0cdcec41",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "9dd7aa82",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"name": "kpz.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"display_name": "Python 3",
"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": "3.10.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
"nbformat_minor": 0
}
75 changes: 47 additions & 28 deletions docs/qcqmc/experimental_wavefunctions.ipynb
View file
Original file line number Diff line number Diff line change
@@ -2,7 +2,9 @@
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "38c97d59911f"
},
"source": [
"# Analyzing the Experimentally Reconstructed Wavefunctions\n",
"\n",
@@ -13,7 +15,9 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "d6b7b1b4dab4"
},
"source": [
"# Setup\n",
"\n",
@@ -23,7 +27,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "0b0bebea3b3b"
},
"outputs": [],
"source": [
"try:\n",
@@ -35,7 +41,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "74a0376d4994"
},
"outputs": [],
"source": [
"import urllib.request\n",
@@ -50,7 +58,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "076bfcaf2d2b"
},
"outputs": [],
"source": [
"# Look at the README from the downloaded data\n",
@@ -60,7 +70,9 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "7c5357bacca0"
},
"source": [
"# Loading the wavefunctions\n",
"\n",
@@ -91,7 +103,9 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "ab2170f48d78"
},
"source": [
"We can reproduce this table from the data by using [FQE](https://quantumai.google/openfermion/fqe) to read the wavefunctions and Hamiltonian and recompute the variational energy of the shadow trial wavefunction.\n",
"\n",
@@ -101,7 +115,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "ddd470a99b23"
},
"outputs": [],
"source": [
"import fqe\n",
@@ -133,15 +149,19 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "f9b796592be8"
},
"source": [
"Now we can glob all of the wavefunctions from the zenodo repo and recompute the variational energy:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "74e1bb9f9fd2"
},
"outputs": [],
"source": [
"import glob\n",
@@ -162,15 +182,19 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "d3af2312462a"
},
"source": [
"Given these inputs we can also re-run AFQMC using the shadow wavefunctions as a trial wavefuncktion. To do so, we first need to parse the wavefunction and hamiltonian which is provided in ipie format in the zenodo repo: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "3a4fb113b330"
},
"outputs": [],
"source": [
"from ipie.qmc.afqmc import AFQMC\n",
@@ -186,15 +210,19 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "04a5e9acad51"
},
"source": [
"Finally we can analyze the result and compare it to the literature value which we agree with within error bars."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "d7fc200484dc"
},
"outputs": [],
"source": [
"from ipie.analysis.extraction import extract_observable\n",
@@ -219,24 +247,15 @@
}
],
"metadata": {
"colab": {
"name": "experimental_wavefunctions.ipynb",
"toc_visible": true
},
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 0
}
140 changes: 94 additions & 46 deletions docs/qcqmc/full_workflow.ipynb
View file
Original file line number Diff line number Diff line change
@@ -3,7 +3,9 @@
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "8fcd86dea77a"
},
"source": [
"# End-to-End Example\n",
"\n",
@@ -12,7 +14,9 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "d6b7b1b4dab4"
},
"source": [
"# Setup\n",
"\n",
@@ -22,7 +26,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "0b0bebea3b3b"
},
"outputs": [],
"source": [
"try:\n",
@@ -34,7 +40,9 @@
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "b3b7d7eca9a7"
},
"source": [
"## Define an SCF Job\n",
"\n",
@@ -44,7 +52,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "4a1faf7a8ab8"
},
"outputs": [],
"source": [
"from recirq.qcqmc.hamiltonian import PyscfHamiltonianParams, HamiltonianData\n",
@@ -67,7 +77,9 @@
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "6b18f3db0743"
},
"source": [
"## Build Perfect Pairing Wavefunction\n",
"\n",
@@ -77,7 +89,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "180a012e0579"
},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -105,15 +119,19 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "0fe3452d66a6"
},
"source": [
"We can comare the wavefunction of the optimized ansatz to the exact ground state energy:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "f50a14acd6af"
},
"outputs": [],
"source": [
"print(f\"Trial wavefunction energy: {trial_wf.ansatz_energy}\")\n",
@@ -122,15 +140,19 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "06ad291fd2e1"
},
"source": [
"Next, we build an `experiment` which will combine the trial wavefunction circuit with that required for shadow tomography. In this case the experiment will be simulated using a statevector simulator."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "2301d967e68d"
},
"outputs": [],
"source": [
"from recirq.qcqmc.blueprint import BlueprintParamsTrialWf, BlueprintData\n",
@@ -161,15 +183,19 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "55aa2e8b0050"
},
"source": [
"The experimental output is post-processed to extract the reconstructed trial wavefunction:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "6d22cbe86339"
},
"outputs": [],
"source": [
"from typing import Dict\n",
@@ -192,15 +218,19 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "040bde1bfe54"
},
"source": [
"Finally, we save the wavefunction in a format that is acceptable for ipie."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "7cecbbc8052a"
},
"outputs": [],
"source": [
"from recirq.qcqmc.convert_to_ipie import save_wavefunction_for_ipie\n",
@@ -215,7 +245,9 @@
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "bf3fed265e17"
},
"source": [
"## Run AFQMC \n",
"\n",
@@ -231,7 +263,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "927c01d9cf98"
},
"outputs": [],
"source": [
"from ipie.systems.generic import Generic\n",
@@ -248,15 +282,19 @@
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "261e70d57dfa"
},
"source": [
"Next, we need to build a trial wavefunction from quantum wavefunction. In practice, the quantum trial is expanded as a linear combination of (orthogonal) Slater Determinants. Within ipie, this style of trial wavefunction is defined as a `ParticleHole` trial wavefunction. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "fd9d66faea78"
},
"outputs": [],
"source": [
"import h5py\n",
@@ -273,7 +311,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "2b9c637242f5"
},
"outputs": [],
"source": [
"wfn.calculate_energy(system, ham)\n",
@@ -284,7 +324,9 @@
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "c16d33d36551"
},
"source": [
"Note that the variational energy might differ slightly from the result compute from the previous section. This is because the cholesky factorization uses a threshold of $1\\times10^{-5}$ a stopping criteria for convergence. Reducing the parameter `chol_cut` in `generate_hamiltonian_from_chk` will yield better agreement.\n",
"\n",
@@ -294,7 +336,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "5cce31ab8e71"
},
"outputs": [],
"source": [
"from ipie.qmc.afqmc import AFQMC\n",
@@ -305,7 +349,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "7cae84bd5b10"
},
"outputs": [],
"source": [
"from recirq.qcqmc.config import OUTDIRS\n",
@@ -319,15 +365,19 @@
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "4eb32a627148"
},
"source": [
"Next, we can visualize the data which is by default saved to `estimates.0.h5`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "7f2dbce4d733"
},
"outputs": [],
"source": [
"from ipie.analysis.extraction import extract_observable\n",
@@ -343,15 +393,19 @@
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "e72a3c54bc56"
},
"source": [
"Through visual inspection, we can determine when the calculation has equilibrated, after which we should be sampling the (approximate) ground state. We can use samples from this point on to estimate the AFQMC energy and error bar. The function `reblock_minimal` will perform the necessary error analysis taking into account the serial temporal correlation in the AFQMC data. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"id": "81fe96e4438d"
},
"outputs": [],
"source": [
"from ipie.analysis.blocking import reblock_minimal \n",
@@ -360,14 +414,18 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "428c61015a05"
},
"source": [
"Note that the number of walkers and the number of blocks is probably too low to obtain statistically significant results and we would advise increasing both of these. For larger scale simulations, we can use MPI to distribute the work among many MPI processes. See [ipie](https://github.com/JoonhoLee-Group/ipie/) for further details."
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"id": "88925e9e87b9"
},
"source": [
"# Next steps\n",
"\n",
@@ -380,25 +438,15 @@
}
],
"metadata": {
"colab": {
"name": "full_workflow.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "qcqmc",
"language": "python",
"display_name": "Python 3",
"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": "3.12.3"
},
"orig_nbformat": 4
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 0
}
99 changes: 64 additions & 35 deletions docs/qcqmc/high_level.ipynb
View file
Original file line number Diff line number Diff line change
@@ -3,7 +3,9 @@
{
"cell_type": "markdown",
"id": "679c5fd1",
"metadata": {},
"metadata": {
"id": "1d8c49d4c18a"
},
"source": [
"# QCQMC\n",
"\n",
@@ -15,7 +17,9 @@
{
"cell_type": "markdown",
"id": "5463daa8",
"metadata": {},
"metadata": {
"id": "d6b7b1b4dab4"
},
"source": [
"# Setup\n",
"\n",
@@ -26,7 +30,9 @@
"cell_type": "code",
"execution_count": null,
"id": "2c790a14",
"metadata": {},
"metadata": {
"id": "0b0bebea3b3b"
},
"outputs": [],
"source": [
"try:\n",
@@ -39,7 +45,9 @@
"cell_type": "code",
"execution_count": null,
"id": "16e16876",
"metadata": {},
"metadata": {
"id": "203da17822fa"
},
"outputs": [],
"source": [
"import attrs\n",
@@ -53,7 +61,9 @@
{
"cell_type": "markdown",
"id": "00d220ee",
"metadata": {},
"metadata": {
"id": "3f7ff58f5c13"
},
"source": [
"## Hamiltonian\n",
"\n",
@@ -66,7 +76,9 @@
"cell_type": "code",
"execution_count": null,
"id": "f003aaa5",
"metadata": {},
"metadata": {
"id": "3ca1f4ed0638"
},
"outputs": [],
"source": [
"from recirq.qcqmc.hamiltonian import HamiltonianFileParams\n",
@@ -83,7 +95,9 @@
"cell_type": "code",
"execution_count": null,
"id": "3d0fc1f3",
"metadata": {},
"metadata": {
"id": "1817d5e35fa6"
},
"outputs": [],
"source": [
"from recirq.qcqmc.hamiltonian import HamiltonianData\n",
@@ -94,7 +108,9 @@
{
"cell_type": "markdown",
"id": "74230882",
"metadata": {},
"metadata": {
"id": "05d01a884e76"
},
"source": [
"## Trial Wavefunction\n",
"\n",
@@ -107,7 +123,9 @@
"cell_type": "code",
"execution_count": null,
"id": "ef084640",
"metadata": {},
"metadata": {
"id": "80201c367c0c"
},
"outputs": [],
"source": [
"from recirq.qcqmc.trial_wf import PerfectPairingPlusTrialWavefunctionParams\n",
@@ -123,7 +141,9 @@
"cell_type": "code",
"execution_count": null,
"id": "a942ca71",
"metadata": {},
"metadata": {
"id": "f785eb581ae6"
},
"outputs": [],
"source": [
"from recirq.qcqmc.trial_wf import TrialWavefunctionData \n",
@@ -140,7 +160,9 @@
{
"cell_type": "markdown",
"id": "f536a0d8",
"metadata": {},
"metadata": {
"id": "0804e5e224ac"
},
"source": [
"## Blueprint\n",
"\n",
@@ -153,7 +175,9 @@
"cell_type": "code",
"execution_count": null,
"id": "f43b9a12",
"metadata": {},
"metadata": {
"id": "97149a59d7a5"
},
"outputs": [],
"source": [
"qubits = trial_wf_params.qubits_linearly_connected"
@@ -163,7 +187,9 @@
"cell_type": "code",
"execution_count": null,
"id": "b45591cc",
"metadata": {},
"metadata": {
"id": "0a9b4a4d80da"
},
"outputs": [],
"source": [
"from recirq.qcqmc.blueprint import BlueprintParamsTrialWf\n",
@@ -181,7 +207,9 @@
"cell_type": "code",
"execution_count": null,
"id": "d00ea516",
"metadata": {},
"metadata": {
"id": "572f8884dd4e"
},
"outputs": [],
"source": [
"from recirq.qcqmc.blueprint import BlueprintData \n",
@@ -196,7 +224,9 @@
{
"cell_type": "markdown",
"id": "d34b627e",
"metadata": {},
"metadata": {
"id": "58c04ebedb9e"
},
"source": [
"## Experiment\n",
"\n",
@@ -209,7 +239,9 @@
"cell_type": "code",
"execution_count": null,
"id": "86ef4223",
"metadata": {},
"metadata": {
"id": "1bd91060926b"
},
"outputs": [],
"source": [
"from recirq.qcqmc.experiment import SimulatedExperimentParams\n",
@@ -227,7 +259,7 @@
"execution_count": null,
"id": "a2e1a569",
"metadata": {
"scrolled": true
"id": "1ee1e12d878f"
},
"outputs": [],
"source": [
@@ -243,7 +275,9 @@
{
"cell_type": "markdown",
"id": "6d8278e5",
"metadata": {},
"metadata": {
"id": "1b3566cb37e3"
},
"source": [
"## Overlap Analysis\n",
"\n",
@@ -256,7 +290,9 @@
"cell_type": "code",
"execution_count": null,
"id": "a938f2ef",
"metadata": {},
"metadata": {
"id": "84a79c4e9255"
},
"outputs": [],
"source": [
"from recirq.qcqmc.analysis import OverlapAnalysisParams\n",
@@ -272,7 +308,9 @@
"cell_type": "code",
"execution_count": null,
"id": "db222311",
"metadata": {},
"metadata": {
"id": "946e61658ab9"
},
"outputs": [],
"source": [
"from recirq.qcqmc.analysis import OverlapAnalysisData\n",
@@ -286,24 +324,15 @@
}
],
"metadata": {
"colab": {
"name": "high_level.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"display_name": "Python 3",
"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": "3.10.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
"nbformat_minor": 0
}