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"metadata": {},
"source": [
"Fe4N2 - 3 - calculations on Quantinuum hardware \n",
"==============================================="
]
},
{
"cell_type": "markdown",
"id": "60c1176b-3c18-414b-a031-420dd2526699",
"metadata": {},
"source": [
"This part marks the conclusion of the three-part series tutorial, and the user will learn how to conduct both emulator and hardware calculations using Quantinuum resources on a system more complex than [H2](InQ_htut_qsys_H2.ipynb). It is important to note that one should run the [first part](InQ_tut_fe4n2_1.ipynb) and [second part](InQ_tut_fe4n2_2.ipynb) before proceeding to this tutorial. Additional details regarding the chemical systems, methodologies, and outcomes presented in this tutorial series are available in the associated [research paper](https://doi.org/10.1039/D3CP05167F). \n",
"\n",
"Let us move beyond a simple single-point energy calculation that is neither optimized nor variationally solved. Instead, we build upon the results obtained in the second part of this tutorial series, where the Adaptive Variational Quantum Eigensolver (ADAPT-VQE) algorithm was used to compute the total energy of the Fe$_3$N$_2$ molecule. This tutorial is also an extension of the initial part of the series, which provides detailed insights into the classical workflow necessary for defining the system.\n",
"\n",
"To carry out these calculations, access to the Quantinuum Systems is essential, which can be obtained by contacting Quantinuum support. Additionally, the user will require Hardware Quantum Credits (HQCs) to run jobs on Quantinuum Systems. Users with credentails and HQCs can access hardware and emulators through [pytket-quantinuum](https://docs.quantinuum.com/tket/extensions/pytket-quantinuum/) or via [Quantinuum Nexus](https://docs.quantinuum.com/nexus/trainings/notebooks/basics/getting_started.html).\n",
"\n",
"\n",
"Here are the steps outlined:\n",
"\n",
"* Import the parameters characterizing the ground-state wavefunction.\n",
"\n",
"* Conduct calculations on the Quantinuum emulator utilizing the PMSV error mitigation method.\n",
"\n",
"* Execute calculations on the Quantinuum hardware using the PMSV error mitigation method."
]
},
{
"cell_type": "markdown",
"id": "814ffcc8-65e2-4cc8-8714-290aac3c6431",
"metadata": {},
"source": [
"To begin with, let us start by importing the qubit Hamiltonian, the fermionic state and space from the first part, along with the ground state parameters and the list of fermionic pool operators from the [second part](InQ_tut_fe4n2_2.ipynb) of this tutorial."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c903f62b-6088-4747-ad06-ec1634f1b593",
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"\n",
"with open('InQ_tut_fe4n2_qubit_hamiltonian.pickle', 'rb') as handle:\n",
" qubit_hamiltonian = pickle.load(handle)\n",
"\n",
"with open('InQ_tut_fe4n2_state.pickle', 'rb') as handle:\n",
" state = pickle.load(handle) \n",
"\n",
"with open('InQ_tut_fe4n2_space.pickle', 'rb') as handle:\n",
" space = pickle.load(handle) \n",
"\n",
"with open('InQ_tut_fe4n2_gs_parameters.pickle', 'rb') as handle:\n",
" gs_parameters = pickle.load(handle) \n",
" \n",
"with open('InQ_tut_fe4n2_exponents_with_symbols.pickle', 'rb') as handle:\n",
" exponents_with_symbols = pickle.load(handle)\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"id": "96d66b6d-93ed-4fb5-b413-1ad51fa8a6f2",
"metadata": {},
"source": [
"InQuanto employs an efficient ansatz circuit compilation approach, provided by the `FermionSpaceStateExpChemicallyAware` class. This method is designed to minimize the computational resources required.\n",
"\n",
"Parameters used:\n",
"\n",
"- `fermion_operator_exponents` – Contains exponents and symbols. Assuming input exponents are ordered as single exponents first, followed by double exponents.\n",
"- `fermion_state` – Initial fermionic reference state."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "98608a33-1007-4a6f-a8ed-f9273b5be7e5",
"metadata": {},
"outputs": [],
"source": [
"from inquanto.ansatzes import FermionSpaceStateExpChemicallyAware\n",
"\n",
"ansatz=FermionSpaceStateExpChemicallyAware(fermion_operator_exponents=exponents_with_symbols, fermion_state=state)"
]
},
{
"cell_type": "markdown",
"id": "e7dc5130-db75-40ad-bf5a-3bcab2ba21fe",
"metadata": {},
"source": [
"The `state_circuit` attribute within InQuanto represents the symbolic state circuit, complete with a default compilation. In a Jupyter environment, one can visualize this symbolic state circuit by making use of the `render_circuit_jupyter` function provided by the pytket library."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "782eeefe-314d-45c9-a174-0164b9e21971",
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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"\n",
"\n",
"\n",
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\n",
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\n",
"\n"
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"metadata": {},
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],
"source": [
"from pytket.circuit.display import render_circuit_jupyter\n",
"\n",
"render_circuit_jupyter(ansatz.state_circuit)"
]
},
{
"cell_type": "markdown",
"id": "7f6a7b57-a941-4858-97c3-8ff1e8b8a750",
"metadata": {},
"source": [
"To obtain an overview of the quantum resource costs associated with creating the Ansatz, one can utilize the `generate_report` and `circuit_resources` methods of the Ansatz object. The report typically includes information such as the count of Ansatz parameters, and the number of required qubits, while the resources method holds gate metrics such as 2-qubit gate counts. In this circuit there are 6 qubits, 7 parameters, and 59 2-qubit gates. These metrics provide an overview of the complexity and requirements of the quantum circuit. \n",
"The circuit depth is the length of the longest path from the input (or from a preparation) to the output (or a measurement gate), moving forward in time along qubit wires.\n",
"\n",
"InQuanto offers the `generate_report()` and `circuit_resources()` methods for concise circuit analysis. However, for more in-depth diagnostics and analysis of the circuit, the user can examine the tket circuit object directly, which is usually accessed through `ansatz.state_circuit`. The [tket documentation](https://docs.quantinuum.com/tket/user-guide/examples/circuit_construction/circuit_analysis_example.html) provides comprehensive information and guidance on this topic."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5b01d814-562a-412c-a0a1-544e07d34ec4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ANSATZ REPORT:\n",
"{'n_parameters': 7, 'n_qubits': 6}\n",
"\n",
"\n",
"2-qubit GATES: 59\n"
]
}
],
"source": [
"from pytket.circuit import OpType\n",
"\n",
"print('ANSATZ REPORT:')\n",
"print(ansatz.generate_report())\n",
"print(ansatz.get_circuit())\n",
"print('\\n2-qubit GATES: {}'.format(ansatz.circuit_resources()['gates_2q']))"
]
},
{
"cell_type": "markdown",
"id": "f4a45a45-e4db-4c1f-a72a-23325a662f01",
"metadata": {},
"source": [
"Conducting emulator experiments prior to hardware experiments is a pivotal phase in the development and optimization of quantum algorithms and applications. Emulators offer a controlled setting with the possibility to refine algorithms, explore quantum error correction techniques, and gain valuable insights into the performance of quantum circuits, all without being restricted by the limitations of physical hardware.\n",
" \n",
"To simulate the specific noise profiles of machines, one can load and apply them to the simulations using the `QuantinuumBackend`, which retrieves information from the user's Quantinuum account. The `QuantinuumBackend` offers a range of available emulators, such as H1-1E and H1-2E. These emulators are designed for specific devices and they run remotely on a server.\n",
"\n",
"Parameters used:\n",
"\n",
"- `device_name` – Name of device, e.g. “H1-1”\n",
"- `label` – Job labels used if Circuits have no name, defaults to “job”\n",
"- `group` – String identifier of a collection of jobs, can be used for usage tracking."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8806527c-17ed-4ea9-b744-f0d57becee0e",
"metadata": {},
"outputs": [],
"source": [
"from pytket.extensions.quantinuum import QuantinuumBackend\n",
"\n",
"backend = QuantinuumBackend(device_name=\"\", label = \"\", group =\"\")#, label=\" \""
]
},
{
"cell_type": "markdown",
"id": "54b15d3e-a4f9-4950-9b51-8ca2daee1c32",
"metadata": {},
"source": [
"To reduce errors and inaccuracies caused by quantum noise and imperfections in the Quantinuum device, one can employ noise mitigation techniques. In this case, let us define the Qubit Operator symmetries within the system, which enables to utilize [PMSV](https://doi.org/10.1103/PhysRevResearch.4.033110) (Partition Measurement Symmetry Verification). PMSV is an efficient technique for symmetry-verified quantum calculations. It represents molecular symmetries using Pauli strings, including mirror planes (Z2) and electron-number conservation (U1). For systems with Abelian point group symmetry, qubit tapering methods can be applied. PMSV uses commutation between Pauli symmetries and Hamiltonian terms for symmetry verification. It groups them into sets of commuting Pauli strings. If each string in a set commutes with the symmetry operator, measurement circuits for that set can be verified for symmetry without additional quantum resources, discarding measurements violating system point group symmetries.\n",
"\n",
"Parameters used:\n",
"\n",
"`stabilizers` – List of state stabilzers as QubitOperators with only a single pauli strings in them.\n",
"\n",
"The InQuanto `symmetry_operators_z2_in_sector` function is employed to retrieve a list of symmetry operators applicable to the system under consideration. These symmetry operators are associated with the point group, spin parity, and particle number parity Z2 symmetries that uphold a specific symmetry sector. The users can find additional details in the linked [page](https://docs.quantinuum.com/inquanto/api/inquanto/spaces.html#inquanto.spaces.FermionSpace.symmetry_operators_z2_in_sector)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1323fbf1-0eb8-45dd-a212-2d98c0a1fc11",
"metadata": {},
"outputs": [],
"source": [
"from inquanto.protocols.averaging._mitigation import PMSV\n",
"from inquanto.mappings import QubitMappingJordanWigner\n",
"\n",
"stabilizers = QubitMappingJordanWigner().operator_map(\n",
" space.symmetry_operators_z2_in_sector(\n",
" state\n",
" )\n",
")\n",
"\n",
"mitms_pmsv = PMSV(stabilizers)"
]
},
{
"cell_type": "markdown",
"id": "5c7c11f6-3901-43cc-9fa7-f1f2d1c4c888",
"metadata": {},
"source": [
"Here, a technique known as \"batching\" is employed, wherein each experiment is iterated a certain number of times with a specific shot count as the target. For instance, if one aims for 10,000 shots, one conducts the experiment ten times, resulting in a total of 100,000 shots for that experiment. This limitation exists because each experiment can only accommodate a maximum of 10,000 shots and this implementation helps to avoid a single task from monopolizing the system's resources or a user accidentally using all their credits in a single instance.\n",
"\n",
"To compute the expectation value of a Hermitian operator through operator averaging on the system register, the `PauliAveraging` protocol is employed. This protocol effectively implements the procedure outlined in ['Hamiltonian Averaging'](https://arxiv.org/abs/1407.7863). To launch the circuits to the backend the `launch` function is used. This method processes all the circuits associated with the expectation value calculations and returns a list of `ResultHandle` objects representing the handles for the results. One can pickle these `ResultHandle` objects so that the results can be easily retrieved. The user can monitor the progress of the experiments on the [Quantinuum page](https://um.qapi.quantinuum.com/user) by using the same credentials used to execute the experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bc3f3e72-df8a-4313-b373-c507b9f0ecde",
"metadata": {},
"outputs": [],
"source": [
"from inquanto.protocols import PauliAveraging\n",
"from pytket.partition import PauliPartitionStrat\n",
"\n",
"\n",
"set_shots_10k = [10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000]\n",
"repeats = 10\n",
"\n",
"#build and compile the circuits once\n",
"protocol_template = PauliAveraging(\n",
" backend,\n",
" shots_per_circuit=10000,\n",
" pauli_partition_strategy=PauliPartitionStrat.CommutingSets\n",
") \n",
"protocol_template.build(gs_parameters, ansatz, qubit_hamiltonian, noise_mitigation=mitms_pmsv).compile_circuits()\n",
"protocol_pickle=protocol_template.dumps()\n",
"protocol_template.n_circuit\n",
"\n",
"# launch 10 repeats of these circuits \n",
"for i in range(repeats):\n",
" protocol= PauliAveraging.loads(protocol_pickle, backend)\n",
" handles = protocol.launch()\n",
" with open( \"handles_\" + str(i) + \".pickle\", 'wb') as handle:\n",
" pickle.dump(handles, handle, protocol=pickle.HIGHEST_PROTOCOL)"
]
},
{
"cell_type": "markdown",
"id": "73972a27-a123-47f9-8290-c0d1ef96f8c6",
"metadata": {},
"source": [
"After the experiments have finished, one can obtain the results by utilizing the `retrieve` function, which retrieves distributions from the backend for the specified source. The expectation value of a kernel for a specified quantum state is calculated by using the `evaluate_expectation_value` function. In addition, the `evaluate_expectation_uvalue` function can be used to calculate the expectation value of the Hermitian kernel while considering linear error propagation theory. The `std_dev` property returns the standard deviation which is used as the error associated with the calculation."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c9dae4d6-258b-45ed-ac14-4748bc4ff79a",
"metadata": {},
"outputs": [],
"source": [
"repeats = 10\n",
"emulator_energies_10k =[]\n",
"emulator_10k = []\n",
"\n",
"for i in range(repeats):\n",
" with open(\"handles_\" + str(i) + \".pickle\", 'rb') as handle:\n",
" handles = pickle.load(handle)\n",
" #only need 1 copy of protocol to eval sets of results\n",
" protocol.retrieve(handles) \n",
" energy_value = protocol.evaluate_expectation_value(ansatz, qubit_hamiltonian)\n",
" emulator_energies_10k.append(energy_value)\n",
" error= protocol.evaluate_expectation_uvalue(state, qubit_hamiltonian)\n",
" emulator_10k.append(error)\n",
" "
]
},
{
"cell_type": "markdown",
"id": "3e141002-e45f-4c96-9dd3-15b0f20e65d2",
"metadata": {},
"source": [
"Additionally, one can showcase circuit statistics after employing the `get_compiled_circuit` function to compile the sequence of circuits. In this case, both the circuit depth for each circuit measurement and the circuit depth associated with 2-qubit gates are visualized."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ebc63a2b-8e54-47bc-9c96-b634f7a9a301",
"metadata": {},
"outputs": [],
"source": [
"for meas_circ in protocol.get_circuits():\n",
" print(\"Circuit depth =\", backend.get_compiled_circuit(meas_circ).depth())\n",
" print(\"2qb gate depth =\", backend.get_compiled_circuit(meas_circ).depth_by_type({OpType.CX, OpType.CZ}))"
]
},
{
"cell_type": "markdown",
"id": "e93cf6e3-430e-421c-9a38-6d98994f3ff3",
"metadata": {},
"source": [
"After completing the 10 repetitions, the mean value is computed. The mean value is used to determine the energy values for shot counts such as 20k, 30k, and so forth. Meanwhile, the standard deviation is used to calculate the error bars."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "20f6e0d3-6017-4130-884e-1d25e0b723b4",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"emulator_10k_mean = [] \n",
"for i in range (1,11):\n",
" emulator_10k_mean.append(np.mean(emulator_energies_10k[:i]))\n",
"\n",
"emulator_10k_std= [] \n",
"for i in range (1,11):\n",
" emulator_10k_std.append(numpy.std(emulator_10k[:i]))"
]
},
{
"cell_type": "markdown",
"id": "c3f9a513-89c5-4c7a-aee5-02f5274a6c7e",
"metadata": {},
"source": [
"This procedure can be reiterated for various sets of shots to analyze how the number of shots impacts the energy and error estimations."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "15b387eb-9afe-4030-9b60-f8ab3e207dc2",
"metadata": {},
"outputs": [],
"source": [
"#target:5k\n",
"set_shots_5k = [5000, 10000, 15000, 20000, 25000, 30000, 35000, 40000, 45000, 50000]\n",
"\n",
"emulator_5k_mean = [-598.5233976942287, -598.528748007567, -598.5254365672856, -598.5266267836435,-598.5252272612659, \n",
" -598.5241590945266, -598.5255975795899, -598.525113940237,-598.5249076903503, -598.5239581110884]\n",
"\n",
"emulator_5k_std =[ 0,0.0016,0.0015,0.0014,0.0013,0.0012,0.0011, 0.0010,0.00095, 0.0009]\n",
"\n",
"\n",
"\n",
"#target:4k\n",
"set_shots_4k = [4000, 8000, 12000, 16000, 20000, 24000, 28000, 32000, 36000, 40000]\n",
"\n",
"\n",
"emulator_4k_mean = [-598.5261956279538, -598.5239748592571, -598.5250271719032, -598.5236355685819, -598.5232948710519, \n",
" -598.5243534836842, -598.5244650328887, -598.5251092284366, -598.524141170795, -598.5235882519817]\n",
"\n",
"emulator_4k_std =[0,0.00163,0.00154,0.0015,0.0014,0.0013,0.0012,0.00115,0.0011, 0.0010]\n",
" \n",
"\n",
"\n",
"#target:2.5k\n",
"set_shots_2_5k = [2500, 5000, 7500, 10000, 12500, 15000, 17500, 20000, 22500, 25000]\n",
"\n",
"emulator_2_5k_mean = [-598.5386462829877, -598.528495041739, -598.5223780085176, -598.5221927569415, -598.5220793440851,\n",
" -598.522950988168, -598.5245581694165, -598.5236303692766, -598.5243398619134, -598.5237327682132]\n",
"\n",
"emulator_2_5k_std =[0,0.0017,0.00165,0.0016,0.00155,0.0015,0.00145,0.0014,0.00135,0.0013]"
]
},
{
"cell_type": "markdown",
"id": "51081333-e8b2-41a0-b956-1e6def9d3e48",
"metadata": {},
"source": [
"Once collected all the results, one can proceed to visualize and analyze the data. By running the same set of instructions for different shot configurations, one can observe that the error bars notably decrease when a sufficiently large number of samples is used."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e434ac54-9fec-4aa4-9fb2-2d9614b7e684",
"metadata": {},
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"sns.set_style(\"whitegrid\")\n",
"\n",
"#plt.plot(set_shots_10k, emulator_10k_mean, label='emulator_10k',color='red')\n",
"plt.plot(set_shots_5k, emulator_5k_mean, label='emulator_5k',color='black')\n",
"plt.plot(set_shots_4k, emulator_4k_mean, label='emulator_4k',color='teal')\n",
"plt.plot(set_shots_2_5k, emulator_2_5k_mean, label='emulator_2.5k',color='orange')\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = (10,6)\n",
"plt.xticks(fontsize=15 )\n",
"plt.yticks(fontsize=15 )\n",
"\n",
"#y_error_10k = emulator_10k_std\n",
"y_error_5k = emulator_5k_std\n",
"y_error_4k = emulator_4k_std\n",
"y_error_2_5k = emulator_2_5k_std\n",
"\n",
"#plt.errorbar(set_shots_10k,emulator_10k_mean,\n",
"# yerr = y_error_10k, fmt ='s', color='red',\n",
"# elinewidth=1,capsize=2)\n",
"\n",
"plt.errorbar(set_shots_5k, emulator_5k_mean,\n",
" yerr = y_error_5k, fmt ='s', color='black',\n",
" elinewidth=1,capsize=2)\n",
"\n",
"plt.errorbar(set_shots_4k, emulator_4k_mean,\n",
" yerr = y_error_4k, fmt ='s', color='teal',\n",
" elinewidth=1,capsize=2)\n",
"\n",
"plt.errorbar(set_shots_2_5k, emulator_2_5k_mean,\n",
" yerr = y_error_2_5k, fmt ='s', color='orange',\n",
" elinewidth=1,capsize=2)\n",
"\n",
"plt.xlabel('Number of shots',fontsize=15)\n",
"plt.ylabel('$\\Delta E \\; [Ha]$',fontsize=15)\n",
"plt.title(\"Convergence behavior of the expectation value by varying the number of shots when running computations with the Quantinuum H1-1E noisy emulator backend.\")\n",
"plt.legend(fontsize=15)"
]
},
{
"cell_type": "markdown",
"id": "92dbea74-9a12-49b1-8231-395bfa411c84",
"metadata": {},
"source": [
"With InQuanto, it is extremely easy to go from hardware emulation to hardware experiment. This is because `pytket.extensions.quantinuum` requires the same details for emulating the hardware as for sending the experiment to the physical quantum circuit, the only difference being the substitution of the name of the quantum device. For example, from 'H1-1E' to 'H1-1'. \n",
"\n",
"Here, only one series of experiments was performed on the hardware device. To limit the effects of noise in estimating expectation values, the PMSV method was used.\n",
"\n",
"In this tutorial, three hardware experiments for three different structures of Fe$_4$N$_2$ cluster were performed in order to calculate the activation and dissociation energies of nitrogen on iron cluster.\n",
"More specifically, in this tutorial, the emulator calculations with 4000 shots were used. In addition, a hardware experiment was conducted simply by changing 'H1-1E' to 'H1-1', and these results served as the starting points in the figure below. \n",
"Subsequently, one can re-run the calculations described in this tutorial, altering only the initial geometry in the first part. Namely, one can use the geometry where the N-N bond is stretched, representing the transition state and denoted as 'ts' in the figure. Finally, one can repeat the three tutorials once again, using a geometry where the N-N bond breaks, denoted as the 'end' in the figure. Notice that in these six experiments (two for each geometry), the single experiments with both the emulator and hardware were used instead of the batching method described above.\n",
"The primary goal of this tutorial is to offer a deeper understanding of how quantum computers can contribute to the comprehension of dissociation processes.\n",
"\n",
"The electronic activation energy with respect to the kinetic constant of the process is calculated as $E_{Fe/N_2}^{tot}-E_{Fe/N_2^*}^{tot}$, while the activation energy is $E_{Fe/N_2}^{tot}-E_{Fe/2N}^{tot}$.\n",
" \n",
"The data points computed on the hardware are in excellent agreement with the emulation results."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e583f8eb-3ee0-49f2-b526-9e17fe80e4d2",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# these are example solved energies from hardware and emulator\n",
"\n",
"sns.set_style(\"whitegrid\")\n",
"\n",
"steps=[1,2,3]\n",
"emulator=[0,0.04681999999991149,-0.021029999999996107 ]\n",
"hardware=[0,0.06232999999997446,-0.018699999999967076]\n",
"\n",
"\n",
"plt.hlines(y=0, xmin=0, xmax=12, ls='--', colors='black')\n",
"\n",
"plt.scatter(x=steps, y=hardware, s=200, c=\"black\", label=\"Hardware\") \n",
"plt.plot(steps, hardware, c=\"black\")\n",
"\n",
"y_error_hard = [0.00196006200237305,0.00171393177265877,0.000768517902650537]\n",
"plt.errorbar(steps, hardware,\n",
" yerr = y_error_hard, fmt ='o', color='black',\n",
" elinewidth=10,capsize=10)\n",
"\n",
"\n",
"plt.scatter(x=steps, y=emulator, s=200, c=\"blue\", label=\"Emulator\") \n",
"plt.plot(steps, emulator, c=\"blue\")\n",
"\n",
"y_error_em = [0.00222116960181585,0.000835916404261285,0.000489635318758811]\n",
"\n",
"plt.errorbar(steps, emulator,\n",
" yerr = y_error_em, fmt ='o', color='blue',\n",
" elinewidth=10,capsize=10)\n",
"\n",
"\n",
"plt.ylabel('$Energy\\; [Ha]$', fontsize=15)\n",
"plt.xlabel('$NEB \\; Images\\;$', fontsize=15)\n",
"plt.xticks(fontsize=15 )\n",
"plt.yticks(fontsize=15 )\n",
"plt.xlim([0.5, 3.5])\n",
"plt.ylim([-0.04, 0.08])\n",
"x=[1,2,3]\n",
"my_xticks = ['start','ts','end']\n",
"plt.xticks(x, my_xticks)\n",
"plt.legend(fontsize=15, loc='lower left')\n",
"plt.title(\"Comparison of the activation and the dissociation energies by using ADAPT-VQE with Quantinuum H1-1 device and Quantinuum H1-1E noisy emulator backend.\")\n",
"plt.show()"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 5
}