qcmet.benchmarks.HamiltonianSimulation#

class qcmet.benchmarks.HamiltonianSimulation(simulation_name, qubits, evolution_circuit=None, init_circuit=None, n_steps=1, save_path=None)[source]#

Benchmark class for simulating quantum dynamics of a Hamiltonian with Trotter evolution.

This class implements a general Trotterized Hamiltonian simulation bencharking workflow, including estimating the final populations under a Trotterized evolution for an initial state a parameterized ansatz and evaluating the the normalized fidelity as the final metric.

Concrete HamiltonianSimulation instances should either inherit from this base class and overwrite the _trotter_step function, or pass in the a single Trotter layer (as qiskit circuit) into the constructor.

__init__(simulation_name, qubits, evolution_circuit=None, init_circuit=None, n_steps=1, save_path=None)[source]#

Initialize the HamiltonianSimulation benchmark instance with configuration and circuits.

Stores the evolution circuit (a single Trotter step), and initial circuit, if these are not passed in, the respective properties/functions should be overwritten in inheriting classes. Sets the number of steps for the application of the Trotter evolution ansatz.

Parameters:

  • simulation_name (str) – Name of the Hamiltonian simulation.

  • qubits (list[int]) – List of qubit indices used in the simulation.

  • evolution_circuit (QuantumCircuit, optional) – Circuit representing one step of the system’s evolution.

  • init_circuit (QuantumCircuit, optional) – Circuit for preparing the initial quantum state.

  • n_steps (int, optional) – Number of Trotter steps to apply. Defaults to 1.

  • save_path (str | Path | FileManager | None, optional) – Directory path to save results. Defaults to None.

Methods

__init__(simulation_name, qubits[, ...])

Initialize the HamiltonianSimulation benchmark instance with configuration and circuits.

analyze()

Analyze measurements to return benchmark results.

generate_circuits()

Generate benchmark circuits, user facing.

has_plotting()

Check if _plot function is implemented in benchmark.

load_circuit_measurements(circuit_measurements)

Load measurement counts into the experiment_data DataFrame.

measurements_to_probabilities()

Convert raw measurement counts to normalized probabilities.

plot([axes])

Plot benchmark result, user facing.

run([device, num_shots, max_circs_per_job])

Run benchmark.

save()

Save benchmark current state.

set_save_path(save_path)

Set benchmark save path if not set in class constructor.

Attributes

circuits

Gets all benchmark circuits.

evolution_circuit

Return the composed quantum circuit with initial state, Trotter steps, and measurements.

experiment_data

Getter for experiment_data dataframe.

initial_state

Return the initial quantum circuit for state preparation.

num_qubits

Number of qubits in this benchmark.
property evolution_circuit#

Return the composed quantum circuit with initial state, Trotter steps, and measurements.

Returns:

The composed quantum circuit with initial state preparation, Trotter evolution, and measurement.

Return type:

QuantumCircuit

property initial_state#

Return the initial quantum circuit for state preparation.

Returns:

The initial state circuit or a default empty circuit.

Return type:

QuantumCircuit