diff --git a/examples/qmatchatea_intro/qmatchatea_introduction.ipynb b/examples/qmatchatea_intro/qmatchatea_introduction.ipynb index 198e982..3702fad 100644 --- a/examples/qmatchatea_intro/qmatchatea_introduction.ipynb +++ b/examples/qmatchatea_intro/qmatchatea_introduction.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "id": "6722d94e-e311-48f9-b6df-c6d829bf67fb", "metadata": {}, "outputs": [], @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 15, "id": "64162116-1555-4a68-811c-01593739d622", "metadata": {}, "outputs": [], @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "id": "4a22a172-f50d-411d-afa3-fa61937c7b3a", "metadata": {}, "outputs": [], @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "id": "76f23c57-6d08-496b-9a27-52fb63bbfcb1", "metadata": {}, "outputs": [ @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "id": "07b2c097-cea2-42ec-8f1d-b4bbb5b71d98", "metadata": {}, "outputs": [], @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "id": "34452bfd-2287-4b38-8099-e072239eab74", "metadata": {}, "outputs": [], @@ -157,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 21, "id": "221ef886-5578-4200-a019-dcafa51aada3", "metadata": {}, "outputs": [ @@ -169,7 +169,8 @@ "\u001b[0;34m\u001b[0m \u001b[0mcircuit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0minitial_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mnshots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mprob_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'U'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mprob_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mreturn_array\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mprob_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", @@ -180,18 +181,18 @@ " - Frequencies (if the number of shots is specified).\n", " - Probabilities computed using various methods.\n", "\n", - "The following probability computation methods are available, as implemented \n", + "The following probability computation methods are available, as implemented\n", "in Quantum Matcha Tea:\n", - " - **\"E\" (Even):** Probabilities are computed by evenly descending the probability tree, \n", + " - **\"E\" (Even):** Probabilities are computed by evenly descending the probability tree,\n", " pruning branches (states) with probabilities below a threshold.\n", - " - **\"G\" (Greedy):** Probabilities are computed by following the most probable states \n", + " - **\"G\" (Greedy):** Probabilities are computed by following the most probable states\n", " in descending order until reaching a given coverage (sum of probabilities).\n", - " - **\"U\" (Unbiased):** An optimal probability measure that is unbiased and designed \n", + " - **\"U\" (Unbiased):** An optimal probability measure that is unbiased and designed\n", " for best performance. See https://arxiv.org/abs/2401.10330 for details.\n", "\n", "Args:\n", " circuit: A Qibo circuit to execute.\n", - " initial_state: The initial state of the system (default is the vacuum state \n", + " initial_state: The initial state of the system (default is the vacuum state\n", " for tensor network simulations).\n", " nshots: The number of shots for shot-noise simulation (optional).\n", " prob_type: The probability computation method. Must be one of:\n", @@ -203,7 +204,7 @@ " - For \"E\" and \"G\", requires ``prob_threshold``.\n", "\n", "Returns:\n", - " TensorNetworkResult: An object with methods to reconstruct the state, \n", + " TensorNetworkResult: An object with methods to reconstruct the state,\n", " compute probabilities, and generate frequencies.\n", "\u001b[0;31mFile:\u001b[0m ~/Documents/PhD/qibotn/src/qibotn/backends/qmatchatea.py\n", "\u001b[0;31mType:\u001b[0m method\n" @@ -219,7 +220,31 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 22, + "id": "f271d3f3-c7f2-49b3-94e2-d6e843a169d0", + "metadata": {}, + "outputs": [], + "source": [ + "circuit = build_circuit(nqubits=4, nlayers=2)\n", + "\n", + "# Setting random parameters\n", + "circuit.set_parameters(\n", + " parameters=np.random.uniform(-np.pi, np.pi, len(circuit.get_parameters())),\n", + ")\n", + "\n", + "# We first define the useful objects \n", + "convergence_parameters = QCConvergenceParameters(cut_ratio=1e-12, max_bond_dimension=1100)\n", + "\n", + "# And then call the dedicate configuration function\n", + "qmatcha_backend.configure_tn_simulation(\n", + " convergence_params=convergence_parameters,\n", + " ansatz=\"MPS\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, "id": "35a244c3-adba-4b8b-b28c-0ab592b0f7cf", "metadata": {}, "outputs": [ @@ -229,43 +254,43 @@ "{'nqubits': 4,\n", " 'backend': QMatchaTeaBackend(),\n", " 'measures': None,\n", - " 'measured_probabilities': {'U': {'0000': (0.0, 0.08390937969317301),\n", - " '0001': (0.08390937969317301, 0.08858639088838134),\n", - " '0010': (0.08858639088838131, 0.1832549957082757),\n", - " '0011': (0.1832549957082757, 0.25896776804349736),\n", - " '0100': (0.2589677680434974, 0.33039716334036867),\n", - " '0101': (0.33039716334036867, 0.386620221067355),\n", - " '0110': (0.3866202210673549, 0.4380808691410473),\n", - " '0111': (0.4380808691410473, 0.47837271988834),\n", - " '1000': (0.47837271988834, 0.5916815553716759),\n", - " '1001': (0.5916815553716759, 0.5972581739037379),\n", - " '1010': (0.5972581739037378, 0.6359857590550054),\n", - " '1011': (0.6359857590550054, 0.6894851559808782),\n", - " '1100': (0.6894851559808783, 0.7030911408535467),\n", - " '1101': (0.7030911408535467, 0.8264027395524797),\n", - " '1110': (0.8264027395524797, 0.8981519382820797),\n", - " '1111': (0.8981519382820797, 0.9999999999999998)},\n", + " 'measured_probabilities': {'U': {'0000': (0.0, 0.06042361322153613),\n", + " '0001': (0.06042361322153613, 0.16103484648184754),\n", + " '0010': (0.1610348464818476, 0.3885436985884956),\n", + " '0011': (0.3885436985884956, 0.4596882048691001),\n", + " '0100': (0.4596882048691, 0.46449216980829905),\n", + " '0101': (0.46449216980829905, 0.47672801247815266),\n", + " '0110': (0.47672801247815266, 0.5016979158974251),\n", + " '0111': (0.5016979158974251, 0.5398303779135739),\n", + " '1000': (0.539830377913574, 0.6539396910265846),\n", + " '1001': (0.6539396910265846, 0.7584098289087688),\n", + " '1010': (0.7584098289087688, 0.7781986778747929),\n", + " '1011': (0.7781986778747929, 0.8661307878495997),\n", + " '1100': (0.8661307878495996, 0.8908084103632258),\n", + " '1101': (0.8908084103632258, 0.9107085647613623),\n", + " '1110': (0.9107085647613623, 0.9481703292728131),\n", + " '1111': (0.9481703292728131, 0.9999999999999999)},\n", " 'E': [None],\n", " 'G': [None]},\n", " 'prob_type': 'U',\n", - " 'statevector': array([ 0.08809627-0.27595005j, 0.24859731-0.22695421j,\n", - " 0.18807826+0.18988408j, 0.09444097+0.06846085j,\n", - " 0.00470148+0.30764671j, 0.17371395-0.09247188j,\n", - " -0.18900305+0.12545316j, -0.17359753+0.20399288j,\n", - " -0.0517478 +0.04471215j, -0.0411739 -0.06230031j,\n", - " 0.22377064+0.07842041j, -0.21784975-0.27541439j,\n", - " -0.27208941+0.04098933j, -0.22748127+0.04185292j,\n", - " 0.17105258-0.10503745j, -0.01729753-0.31866731j])}" + " 'statevector': array([-0.13931487-0.20252155j, -0.21974897+0.25655351j,\n", + " 0.02884164-0.06302479j, -0.15237615+0.03819857j,\n", + " -0.3172761 -0.35615268j, -0.10121749+0.09769272j,\n", + " 0.14826026-0.0546699j , -0.1814861 -0.06726486j,\n", + " -0.10907416+0.29784906j, 0.30276076+0.11316387j,\n", + " 0.07771728+0.07871383j, -0.12379363+0.0676409j ,\n", + " -0.21860099+0.15283362j, 0.06862868+0.28848261j,\n", + " -0.04640848-0.18968056j, -0.22262942-0.04760056j])}" ] }, - "execution_count": 8, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Simple execution (defaults)\n", - "outcome = qmatcha_backend.execute_circuit(circuit=circuit)\n", + "outcome = qmatcha_backend.execute_circuit(circuit=circuit, return_array=True)\n", "\n", "# Print outcome\n", "vars(outcome)" @@ -273,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 26, "id": "60501c3d-2a44-421f-b434-4a508714b132", "metadata": {}, "outputs": [ @@ -284,30 +309,25 @@ " 'backend': QMatchaTeaBackend(),\n", " 'measures': None,\n", " 'measured_probabilities': {'U': [None],\n", - " 'E': {'0000': 0.08390937969317301,\n", - " '0010': 0.09466860481989439,\n", - " '0011': 0.07571277233522165,\n", - " '0100': 0.07142939529687124,\n", - " '0101': 0.05622305772698632,\n", - " '0110': 0.05146064807369245,\n", - " '1000': 0.11330883548333581,\n", - " '1011': 0.053499396925872765,\n", - " '1101': 0.12331159869893296,\n", - " '1110': 0.07174919872960005,\n", - " '1111': 0.10184806171792007},\n", - " 'G': [None]},\n", - " 'prob_type': 'E',\n", - " 'statevector': array([ 0.08809627-0.27595005j, 0.24859731-0.22695421j,\n", - " 0.18807826+0.18988408j, 0.09444097+0.06846085j,\n", - " 0.00470148+0.30764671j, 0.17371395-0.09247188j,\n", - " -0.18900305+0.12545316j, -0.17359753+0.20399288j,\n", - " -0.0517478 +0.04471215j, -0.0411739 -0.06230031j,\n", - " 0.22377064+0.07842041j, -0.21784975-0.27541439j,\n", - " -0.27208941+0.04098933j, -0.22748127+0.04185292j,\n", - " 0.17105258-0.10503745j, -0.01729753-0.31866731j])}" + " 'E': [None],\n", + " 'G': {'0010': 0.227508852106648,\n", + " '0011': 0.07114450628060452,\n", + " '1000': 0.227508852106648,\n", + " '1001': 0.07114450628060452,\n", + " '0000': 0.06042361322153613,\n", + " '0001': 0.1006112332603114}},\n", + " 'prob_type': 'G',\n", + " 'statevector': array([-0.13931487-0.20252155j, -0.21974897+0.25655351j,\n", + " 0.02884164-0.06302479j, -0.15237615+0.03819857j,\n", + " -0.3172761 -0.35615268j, -0.10121749+0.09769272j,\n", + " 0.14826026-0.0546699j , -0.1814861 -0.06726486j,\n", + " -0.10907416+0.29784906j, 0.30276076+0.11316387j,\n", + " 0.07771728+0.07871383j, -0.12379363+0.0676409j ,\n", + " -0.21860099+0.15283362j, 0.06862868+0.28848261j,\n", + " -0.04640848-0.18968056j, -0.22262942-0.04760056j])}" ] }, - "execution_count": 9, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -317,8 +337,9 @@ "# We use here \"E\", which is cutting some of the components if under a threshold\n", "outcome = qmatcha_backend.execute_circuit(\n", " circuit=circuit,\n", - " prob_type=\"E\",\n", - " prob_threshold=0.05,\n", + " prob_type=\"G\",\n", + " prob_threshold=0.9,\n", + " return_array=True,\n", ")\n", "\n", "# Print outcome\n", @@ -337,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 27, "id": "c0443efc-21ef-4ed5-9cf4-785d204a1881", "metadata": {}, "outputs": [ @@ -346,15 +367,15 @@ "output_type": "stream", "text": [ "Probabilities:\n", - " {'0000': 0.08390937969317301, '0010': 0.09466860481989439, '0011': 0.07571277233522165, '0100': 0.07142939529687124, '0101': 0.05622305772698632, '0110': 0.05146064807369245, '1000': 0.11330883548333581, '1011': 0.053499396925872765, '1101': 0.12331159869893296, '1110': 0.07174919872960005, '1111': 0.10184806171792007}\n", + " [0.22750885 0.07114451 0.22750885 0.07114451 0.06042361 0.10061123]\n", "\n", "State:\n", - " [ 0.08809627-0.27595005j 0.24859731-0.22695421j 0.18807826+0.18988408j\n", - " 0.09444097+0.06846085j 0.00470148+0.30764671j 0.17371395-0.09247188j\n", - " -0.18900305+0.12545316j -0.17359753+0.20399288j -0.0517478 +0.04471215j\n", - " -0.0411739 -0.06230031j 0.22377064+0.07842041j -0.21784975-0.27541439j\n", - " -0.27208941+0.04098933j -0.22748127+0.04185292j 0.17105258-0.10503745j\n", - " -0.01729753-0.31866731j]\n", + " [-0.13931487-0.20252155j -0.21974897+0.25655351j 0.02884164-0.06302479j\n", + " -0.15237615+0.03819857j -0.3172761 -0.35615268j -0.10121749+0.09769272j\n", + " 0.14826026-0.0546699j -0.1814861 -0.06726486j -0.10907416+0.29784906j\n", + " 0.30276076+0.11316387j 0.07771728+0.07871383j -0.12379363+0.0676409j\n", + " -0.21860099+0.15283362j 0.06862868+0.28848261j -0.04640848-0.18968056j\n", + " -0.22262942-0.04760056j]\n", "\n" ] } @@ -376,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 28, "id": "8a868f3e-8383-47ee-a93a-27c5f85e48c5", "metadata": {}, "outputs": [], @@ -396,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 29, "id": "68122cd3-662f-4fd1-bb9c-d33b6f5448dd", "metadata": {}, "outputs": [ @@ -405,47 +426,37 @@ "text/plain": [ "{'nqubits': 4,\n", " 'backend': QMatchaTeaBackend(),\n", - " 'measures': {'0000': 88,\n", - " '0001': 7,\n", - " '0010': 90,\n", - " '0011': 78,\n", - " '0100': 72,\n", - " '0101': 56,\n", - " '0110': 47,\n", - " '0111': 41,\n", - " '1000': 120,\n", - " '1001': 7,\n", - " '1010': 41,\n", - " '1011': 53,\n", - " '1100': 20,\n", - " '1101': 129,\n", - " '1110': 73,\n", - " '1111': 102},\n", + " 'measures': {'0000': 75,\n", + " '0001': 113,\n", + " '0010': 202,\n", + " '0011': 59,\n", + " '0100': 9,\n", + " '0101': 14,\n", + " '0110': 30,\n", + " '0111': 37,\n", + " '1000': 130,\n", + " '1001': 113,\n", + " '1010': 19,\n", + " '1011': 95,\n", + " '1100': 19,\n", + " '1101': 26,\n", + " '1110': 39,\n", + " '1111': 44},\n", " 'measured_probabilities': {'U': [None],\n", - " 'E': {'0000': 0.08390937969317301,\n", - " '0010': 0.09466860481989439,\n", - " '0011': 0.07571277233522165,\n", - " '0100': 0.07142939529687124,\n", - " '0101': 0.05622305772698632,\n", - " '0110': 0.05146064807369245,\n", - " '1000': 0.11330883548333581,\n", - " '1011': 0.053499396925872765,\n", - " '1101': 0.12331159869893296,\n", - " '1110': 0.07174919872960005,\n", - " '1111': 0.10184806171792007},\n", + " 'E': {'0000': 0.06042361322153613,\n", + " '0001': 0.1006112332603114,\n", + " '0010': 0.227508852106648,\n", + " '0011': 0.07114450628060452,\n", + " '1000': 0.11410931311301056,\n", + " '1001': 0.10447013788218423,\n", + " '1011': 0.08793210997480687,\n", + " '1111': 0.05182967072718676},\n", " 'G': [None]},\n", " 'prob_type': 'E',\n", - " 'statevector': array([ 0.08809627-0.27595005j, 0.24859731-0.22695421j,\n", - " 0.18807826+0.18988408j, 0.09444097+0.06846085j,\n", - " 0.00470148+0.30764671j, 0.17371395-0.09247188j,\n", - " -0.18900305+0.12545316j, -0.17359753+0.20399288j,\n", - " -0.0517478 +0.04471215j, -0.0411739 -0.06230031j,\n", - " 0.22377064+0.07842041j, -0.21784975-0.27541439j,\n", - " -0.27208941+0.04098933j, -0.22748127+0.04185292j,\n", - " 0.17105258-0.10503745j, -0.01729753-0.31866731j])}" + " 'statevector': None}" ] }, - "execution_count": 12, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -466,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 30, "id": "ef0e9591-ccca-4cdd-a81b-2bfb3caaf3d0", "metadata": {}, "outputs": [ @@ -475,7 +486,7 @@ "output_type": "stream", "text": [ "Frequencies:\n", - " {'0000': 88, '0001': 7, '0010': 90, '0011': 78, '0100': 72, '0101': 56, '0110': 47, '0111': 41, '1000': 120, '1001': 7, '1010': 41, '1011': 53, '1100': 20, '1101': 129, '1110': 73, '1111': 102}\n", + " {'0000': 75, '0001': 113, '0010': 202, '0011': 59, '0100': 9, '0101': 14, '0110': 30, '0111': 37, '1000': 130, '1001': 113, '1010': 19, '1011': 95, '1100': 19, '1101': 26, '1110': 39, '1111': 44}\n", "\n" ] } @@ -495,7 +506,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 31, "id": "9e9632d5-28dd-4846-8457-c579d0cb9453", "metadata": {}, "outputs": [], @@ -563,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 32, "id": "5620f476-1bc7-4cf8-9868-b127c715b6ec", "metadata": {}, "outputs": [], @@ -582,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 33, "id": "32d605e9-672a-40b6-8a1a-eb17bbc3c45e", "metadata": {}, "outputs": [ @@ -621,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 34, "id": "0b46e315-7786-4247-bd2a-83ea1c5842eb", "metadata": {}, "outputs": [], @@ -631,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 35, "id": "37385485-e8a3-4ab0-ad44-bcc4e9da24ca", "metadata": {}, "outputs": [ @@ -639,10 +650,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "0: ─RY─RZ─o─────X─RY─RZ─o─────X─RY─RZ─o─────X─M─\n", - "1: ─RY─RZ─X─o───|─RY─RZ─X─o───|─RY─RZ─X─o───|─M─\n", - "2: ─RY─RZ───X─o─|─RY─RZ───X─o─|─RY─RZ───X─o─|─M─\n", - "3: ─RY─RZ─────X─o─RY─RZ─────X─o─RY─RZ─────X─o─M─\n" + "0: ─RY─RZ─o─────X─RY─RZ─o─────X─M─\n", + "1: ─RY─RZ─X─o───|─RY─RZ─X─o───|─M─\n", + "2: ─RY─RZ───X─o─|─RY─RZ───X─o─|─M─\n", + "3: ─RY─RZ─────X─o─RY─RZ─────X─o─M─\n" ] } ], @@ -658,33 +669,19 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 36, "id": "ddecc910-7804-4199-8577-a7db38a16db8", "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle - 1.5 X_{0} Z_{2} + 0.5 Z_{0} Z_{1} + Z_{3}$" - ], - "text/plain": [ - "-1.5*X0*Z2 + 0.5*Z0*Z1 + Z3" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# We can create an Hamiltonian form\n", "form = 0.5 * Z(0) * Z(1) +- 1.5 * X(0) * Z(2) + Z(3)\n", - "form" + "hamiltonian = hamiltonians.SymbolicHamiltonian(form)" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 37, "id": "a6599df3-989e-4131-8664-3451d0cc4372", "metadata": {}, "outputs": [ @@ -693,24 +690,26 @@ "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mqmatcha_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpectation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcircuit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobservable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m\n", - "Compute the expectation value of a Qibo-friendly ``observable``\n", - "on the Tensor Network constructed from a Qibo ``circuit``.\n", + "Compute the expectation value of a Qibo-friendly ``observable`` on\n", + "the Tensor Network constructed from a Qibo ``circuit``.\n", "\n", - "This method takes a Qibo-style symbolic Hamiltonian (e.g., `X(0)*Z(1) + 2.0*Y(2)*Z(0)`) \n", + "This method takes a Qibo-style symbolic Hamiltonian (e.g., `X(0)*Z(1) + 2.0*Y(2)*Z(0)`)\n", "as the observable, converts it into a Quantum Matcha Tea (qmatchatea) observable\n", - "(using `TNObsTensorProduct` and `TNObsWeightedSum`), and computes its expectation \n", + "(using `TNObsTensorProduct` and `TNObsWeightedSum`), and computes its expectation\n", "value using the provided circuit.\n", "\n", "Args:\n", " circuit: A Qibo quantum circuit object on which the expectation value\n", " is computed. The circuit should be compatible with the qmatchatea\n", " Tensor Network backend.\n", - " observable: The observable whose expectation value we want to compute. \n", + " observable: The observable whose expectation value we want to compute.\n", " This must be provided in the symbolic Hamiltonian form supported by Qibo\n", " (e.g., `X(0)*Y(1)` or `Z(0)*Z(1) + 1.5*Y(2)`).\n", "\n", "Returns:\n", - " qmatchatea.SimulationResult [TEMPORARY]\n", + " qibotn.TensorNetworkResult class, providing methods to retrieve\n", + " probabilities, frequencies and state always according to the chosen\n", + " simulation setup.\n", "\u001b[0;31mFile:\u001b[0m ~/Documents/PhD/qibotn/src/qibotn/backends/qmatchatea.py\n", "\u001b[0;31mType:\u001b[0m method\n" ] @@ -725,17 +724,17 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 38, "id": "163b70a3-814a-4a62-a98a-2ffca933a544", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.41329220819521995" + "-0.27540575238693377" ] }, - "execution_count": 22, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -743,30 +742,23 @@ "source": [ "qmatcha_backend.expectation(\n", " circuit=circuit,\n", - " observable=form,\n", + " observable=hamiltonian,\n", ")" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 39, "id": "2d8c4a9c-eca3-49d0-bdbf-ab054172c4e5", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.16|INFO|2025-01-28 22:13:05]: Using qibojit (numba) backend on /CPU:0\n" - ] - }, { "data": { "text/plain": [ - "0.4132922081952206" + "-0.275405752386936" ] }, - "execution_count": 23, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" }