qml.devices.qubit.simulate

simulate(circuit, debugger=None, state_cache=None, **execution_kwargs)

Simulate a single quantum script.

This is an internal function that is used by``default.qubit``.

Parameters

• circuit (QuantumTape) – The single circuit to simulate

• debugger (_Debugger) – The debugger to use

• state_cache=None (Optional[dict]) – A dictionary mapping the hash of a circuit to the pre-rotated state. Used to pass the state between forward passes and vjp calculations.

• rng (Optional[numpy.random._generator.Generator]) – A NumPy random number generator.

• prng_key (Optional[jax.random.PRNGKey]) – An optional jax.random.PRNGKey. This is the key to the JAX pseudo random number generator. If None, a random key will be generated. Only for simulation using JAX.

• interface (str) – The machine learning interface to create the initial state with

• postselect_mode (str) – Configuration for handling shots with mid-circuit measurement postselection. Use "hw-like" to discard invalid shots and "fill-shots" to keep the same number of shots. Default is None.

• mcm_method (str) – Strategy to use when executing circuits with mid-circuit measurements. "deferred" is ignored. If mid-circuit measurements are found in the circuit, the device will use "tree-traversal" if specified and the "one-shot" method otherwise. For usage details, please refer to the dynamic quantum circuits page.

Returns

The results of the simulation

Return type

tuple(TensorLike)

Note that this function can return measurements for non-commuting observables simultaneously.

This function assumes that all operations provide matrices.

>>> qs = qml.tape.QuantumScript([qml.RX(1.2, wires=0)], [qml.expval(qml.Z(0)), qml.probs(wires=(0,1))])
>>> simulate(qs)
(0.36235775447667357,
tensor([0.68117888, 0.        , 0.31882112, 0.        ], requires_grad=True))

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