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‎Meta-VQE.ipynb

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"plt.legend()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Approximate running time\n",
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"meta-VQE (test and training): 23.09759492079417 min.\n",
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"Standard VQE (training points): 2.973057436943054 min.\n",
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"GA-VQE (test and training): 8.279220434029897 min.\n",
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"opt-meta-VQE (training points): 7.258111691474914 min.\n",
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"opt-GA-VQE(training points): 1.9057298064231873 min.\n"
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]
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}
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],
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"source": [
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"print(\"Approximate running time\")\n",
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"print(\"meta-VQE (test and training): \", ((t1_metaVQE-t0_metaVQE)+(t3_metaVQE-t2_metaVQE)+(t5_metaVQE-t4_metaVQE))/60, \" min.\")\n",
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"print(\"Standard VQE (training points): \", (t1_VQE - t0_VQE)/60, \" min.\")\n",
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"print(\"GA-VQE (test and training): \", (t1_GAVQE - t0_GAVQE)/60, \" min.\")\n",
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"print(\"opt-meta-VQE (training points): \", (t1_optmetaVQE - t0_optmetaVQE)/60, \" min.\")\n",
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"print(\"opt-GA-VQE(training points): \", (t1_optGAVQE - t0_optGAVQE)/60, \" min.\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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"metadata": {
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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}

‎README.md

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* `data` folder: contains all data presented in the main article.
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* `img` folder: contains the plots of the main article and other plots generated with the available data.
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* `Meta-VQE` demo: jupyter notebook with the source code used to run the simulations.
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* `Molecular-Meta-VQE` demo: An explcit illustration of a non-linear meta-VQE for molecules as described in the article
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* `Molecular-Meta-VQE` demo: An explicit illustration of a non-linear meta-VQE for molecules as described in the article.
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* `transmon`: main code to obtain the results for the single-transmon simulation.
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__Dependencies:__
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‎data/transmon/nqub_4/nlay_[1, 1]/VQE_seed_matterlab.txt

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‎data/transmon/nqub_4/nlay_[1, 1]/VQE_smart_seed_matterlab.txt

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‎data/transmon/nqub_4/nlay_[1, 1]/VQE_smart_test_seed_matterlab.txt

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‎data/transmon/nqub_4/nlay_[1, 1]/VQE_test_seed_matterlab.txt

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‎data/transmon/nqub_4/nlay_[1, 1]/basic_data_seed_matterlab.txt

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qubits = 4
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layers [encoding, variational circuit] = [1, 1]
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training points = 10
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test points = 50
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equispaced train and test = True , True
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Optimization options: BFGS , 2-point , qulacs , lr = 0.01 , other: {'finite_diff_rel_step': 0.0001, 'maxiter': 200}

‎data/transmon/nqub_4/nlay_[1, 1]/metaVQE_seed_matterlab.txt

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‎data/transmon/nqub_4/nlay_[1, 1]/metaVQE_test_seed_matterlab.txt

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‎data/transmon/nqub_4/nlay_[1, 1]/metaVQE_train_seed_matterlab.txt

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