transmon added

Meta-VQE.ipynb

@@ -659,40 +659,6 @@
     "plt.legend()\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Approximate running time\n",
-      "meta-VQE (test and training):  23.09759492079417  min.\n",
-      "Standard VQE (training points):  2.973057436943054  min.\n",
-      "GA-VQE (test and training):  8.279220434029897  min.\n",
-      "opt-meta-VQE (training points):  7.258111691474914  min.\n",
-      "opt-GA-VQE(training points):  1.9057298064231873  min.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Approximate running time\")\n",
-    "print(\"meta-VQE (test and training): \", ((t1_metaVQE-t0_metaVQE)+(t3_metaVQE-t2_metaVQE)+(t5_metaVQE-t4_metaVQE))/60, \" min.\")\n",
-    "print(\"Standard VQE (training points): \", (t1_VQE - t0_VQE)/60, \" min.\")\n",
-    "print(\"GA-VQE (test and training): \", (t1_GAVQE - t0_GAVQE)/60, \" min.\")\n",
-    "print(\"opt-meta-VQE (training points): \", (t1_optmetaVQE - t0_optmetaVQE)/60, \" min.\")\n",
-    "print(\"opt-GA-VQE(training points): \", (t1_optGAVQE - t0_optGAVQE)/60, \" min.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -716,4 +682,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

README.md

@@ -7,7 +7,8 @@ __Content:__
 * `data` folder: contains all data presented in the main article.
 * `img` folder: contains the plots of the main article and other plots generated with the available data.
 * `Meta-VQE` demo: jupyter notebook with the source code used to run the simulations. 
-* `Molecular-Meta-VQE` demo: An explcit illustration of a non-linear meta-VQE for molecules as described in the article
+* `Molecular-Meta-VQE` demo: An explicit illustration of a non-linear meta-VQE for molecules as described in the article.
+* `transmon`: main code to obtain the results for the single-transmon simulation.
 
 __Dependencies:__
 

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

@@ -0,0 +1,6 @@
+qubits =  4
+layers [encoding, variational circuit] =  [1, 1]
+training points =  10
+test points =  50
+equispaced train and test =  True ,  True
+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_test_seed_matterlab.txt

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

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

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

@@ -0,0 +1,4 @@
+meta VQE (test+training):  7.3641571799914045  min.
+VQE (training points):  3.0173523823420205  min.
+VQE with encoding (training points):  3.07886639436086  min.
+opt-meta-VQE (training points):  3.3124189615249633  min.

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

@@ -0,0 +1,6 @@
+Variable counting: number of optimization variables
+Meta-VQE training:  38
+Meta-VQE test:  38
+Standard VQE:  30
+Standard VQE smart:  38
+opt-meta-VQE:  38