diff --git a/docs/index.md b/docs/index.md index 6042210c..f93609df 100644 --- a/docs/index.md +++ b/docs/index.md @@ -170,6 +170,13 @@ mitex.get_task_graph() ## Contents +```{toctree} +:caption: Overview: +:maxdepth: 2 + +tutorial.md +``` + ```{toctree} :hidden: diff --git a/docs/tutorial.md b/docs/tutorial.md new file mode 100644 index 00000000..00b5ebc0 --- /dev/null +++ b/docs/tutorial.md @@ -0,0 +1,207 @@ +--- +file_format: mystnb +kernelspec: + name: python3 +--- + +# Tutorial + +```{code-cell} ipython3 +from pytket import Circuit +from pytket.circuit.display import render_circuit_jupyter + +circ = Circuit(2,2).H(0).CX(0,1).measure_all() +render_circuit_jupyter(circ) +``` + +```{code-cell} ipython3 +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt + +def plot_state_probs(state): + state_dict = {'State':[i for i in range(len(result_state))], 'Probability':abs(state)**2} + state_df = pd.DataFrame(state_dict) + sns.catplot(x='State', y='Probability', kind='bar', data=state_df, aspect = 3, height=2) + plt.show() + +def plot_counts(counts): + counts_record = [{"State":str(state), "Count":count} for state, count in counts.items()] + count_df = pd.DataFrame().from_records(counts_record) + sns.catplot(x='State', y='Count', kind='bar', data=count_df, aspect = 3, height=2) + plt.show() +``` + +```{code-cell} ipython3 +import qiskit_aer.noise as noise + +def depolarizing_noise_model(n_qubits, prob_1, prob_2, prob_ro): + + noise_model = noise.NoiseModel() + + error_2 = noise.depolarizing_error(prob_2, 2) + for edge in [[i,j] for i in range(n_qubits) for j in range(i)]: + noise_model.add_quantum_error(error_2, ['cx'], [edge[0], edge[1]]) + noise_model.add_quantum_error(error_2, ['cx'], [edge[1], edge[0]]) + + error_1 = noise.depolarizing_error(prob_1, 1) + for node in range(n_qubits): + noise_model.add_quantum_error(error_1, ['h', 'rx', 'rz', 'u'], [node]) + + probabilities = [[1-prob_ro, prob_ro],[prob_ro, 1-prob_ro]] + error_ro = noise.ReadoutError(probabilities) + for i in range(n_qubits): + noise_model.add_readout_error(error_ro, [i]) + + return noise_model +``` + +```{code-cell} ipython3 +from qermit.taskgraph.mitex import gen_compiled_MitRes +from pytket.extensions.qiskit import AerBackend +from qermit import CircuitShots + +n_shots = 100000 +noisy_backend = AerBackend( + depolarizing_noise_model(5, 0.001, 0.01, 0.05) +) +noisy_mitres = gen_compiled_MitRes(noisy_backend, optimisation_level=0) + +circ_shots_list = [CircuitShots(circ, n_shots)] + +noisy_result_list = noisy_mitres.run(circ_shots_list) +noisy_result_counts = noisy_result_list[0].get_counts() +plot_counts(noisy_result_counts) +``` + +```{code-cell} ipython3 +noisy_mitres.get_task_graph() +``` + +```{code-cell} ipython3 +from qermit.spam import gen_UnCorrelated_SPAM_MitRes + +spam_mr = gen_UnCorrelated_SPAM_MitRes(noisy_backend, n_shots) +spam_result_list = spam_mr.run(circ_shots_list) +spam_result_counts = spam_result_list[0].get_counts() +plot_counts(spam_result_counts) +``` + +```{code-cell} ipython3 +spam_mr.get_task_graph() +``` + +```{code-cell} ipython3 +import numpy as np +from scipy.stats import unitary_group +from pytket.circuit import Unitary2qBox + +def random_circ(n_qubits: int, depth: int, seed:int = None) -> Circuit: + + np.random.seed(seed) + + c = Circuit(n_qubits) + + for _ in range(depth): + + qubits = np.random.permutation([i for i in range(n_qubits)]) + qubit_pairs = [[qubits[i], qubits[i + 1]] for i in range(0, n_qubits - 1, 2)] + + for pair in qubit_pairs: + + # Generate random 4x4 unitary matrix. + SU4 = unitary_group.rvs(4) # random unitary in SU4 + SU4 = SU4 / (np.linalg.det(SU4) ** 0.25) + SU4 = np.matrix(SU4) + + # Add gate corresponding to unitary. + c.add_unitary2qbox(Unitary2qBox(SU4), *pair) + + return c +``` + +```{code-cell} ipython3 +n_qubits = 4 +rand_circ = random_circ(n_qubits,n_qubits,seed=23126) +render_circuit_jupyter(rand_circ) +``` + +```{code-cell} ipython3 +from pytket.utils import QubitPauliOperator +from qermit import ( + AnsatzCircuit, SymbolsDict, + ObservableExperiment, ObservableTracker, + MitEx +) +from pytket.pauli import Pauli, QubitPauliString +from pytket import Qubit + +ideal_backend = AerBackend() +ideal_mitex = MitEx(ideal_backend) + +qps = QubitPauliString( + [Qubit(i) for i in range(n_qubits)], + [Pauli.Z for i in range(n_qubits)] +) + +obs_exp = ObservableExperiment( + AnsatzCircuit(rand_circ, n_shots, SymbolsDict()), + ObservableTracker(QubitPauliOperator({qps:1})) +) +obs_exp_list = [obs_exp] + +ideal_expectation = ideal_mitex.run(obs_exp_list) +print(f"Ideal expectation: {ideal_expectation[0]}") +``` + +```{code-cell} ipython3 +noisy_mitex = MitEx(noisy_backend) + +noisy_expectation = noisy_mitex.run(obs_exp_list) +print(f"Noisy expectation: {noisy_expectation[0]}") +``` + +```{code-cell} ipython3 +from qermit.zero_noise_extrapolation import ( + gen_ZNE_MitEx, Fit, Folding +) + +zne_me = gen_ZNE_MitEx( + backend=noisy_backend, + noise_scaling_list=[9,7,5,3,1], + fit_type=Fit.exponential, + folding_type=Folding.circuit, + show_fit=True, +) +``` + +```{code-cell} ipython3 +import seaborn as sns +sns.set_style("whitegrid") +``` + +```{code-cell} ipython3 +zne_me.get_task_graph() +``` + +```{code-cell} ipython3 +zne_me.run(obs_exp_list) +``` + +```{code-cell} ipython3 +zne_spam_me = gen_ZNE_MitEx( + backend=noisy_backend, + noise_scaling_list=[9,7,5,3,1], + fit_type=Fit.exponential, + show_fit=True, + experiment_mitres=spam_mr, +) +``` + +```{code-cell} ipython3 +zne_spam_me.run(obs_exp_list) +``` + +```{code-cell} ipython3 +zne_spam_me.get_task_graph() +``` \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 96931e56..9e580e06 100644 --- a/poetry.lock +++ b/poetry.lock @@ -35,21 +35,18 @@ files = [ [[package]] name = "asttokens" -version = "2.4.1" +version = "3.0.0" description = "Annotate AST trees with source code positions" optional = true -python-versions = "*" +python-versions = ">=3.8" 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