Source code for qiskit.providers.aer.backends.aer_simulator
# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2019, 2021
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""
Qiskit Aer qasm simulator backend.
"""
import copy
import logging
from qiskit.providers.options import Options
from qiskit.providers.models import QasmBackendConfiguration
from ..version import __version__
from .aerbackend import AerBackend, AerError
from .backend_utils import (cpp_execute, available_methods,
available_devices,
MAX_QUBITS_STATEVECTOR)
# pylint: disable=import-error, no-name-in-module
from .controller_wrappers import aer_controller_execute
logger = logging.getLogger(__name__)
[docs]class AerSimulator(AerBackend):
"""
Noisy quantum circuit simulator backend.
**Configurable Options**
The `AerSimulator` supports multiple simulation methods and
configurable options for each simulation method. These may be set using the
appropriate kwargs during initialization. They can also be set of updated
using the :meth:`set_options` method.
Run-time options may also be specified as kwargs using the :meth:`run` method.
These will not be stored in the backend and will only apply to that execution.
They will also override any previously set options.
For example, to configure a density matrix simulator with a custom noise
model to use for every execution
.. code-block:: python
noise_model = NoiseModel.from_backend(backend)
backend = AerSimulator(method='density_matrix',
noise_model=noise_model)
**Simulating an IBMQ Backend**
The simulator can be automatically configured to mimic an IBMQ backend using
the :meth:`from_backend` method. This will configure the simulator to use the
basic device :class:`NoiseModel` for that backend, and the same basis gates
and coupling map.
.. code-block:: python
backend = AerSimulator.from_backend(backend)
**Returning the Final State**
The final state of the simulator can be saved to the returned
``Result`` object by appending the
:func:`~qiskit.providers.aer.library.save_state` instruction to a
quantum circuit. The format of the final state will depend on the
simulation method used. Additional simulation data may also be saved
using the other save instructions in :mod:`qiskit.provider.aer.library`.
**Simulation Method Option**
The simulation method is set using the ``method`` kwarg. A list supported
simulation methods can be returned using :meth:`available_methods`, these
are
* ``"automatic"``: Default simulation method. Select the simulation
method automatically based on the circuit and noise model.
* ``"statevector"``: A dense statevector simulation that can sample
measurement outcomes from *ideal* circuits with all measurements at
end of the circuit. For noisy simulations each shot samples a
randomly sampled noisy circuit from the noise model.
* ``"density_matrix"``: A dense density matrix simulation that may
sample measurement outcomes from *noisy* circuits with all
measurements at end of the circuit.
* ``"stabilizer"``: An efficient Clifford stabilizer state simulator
that can simulate noisy Clifford circuits if all errors in the noise
model are also Clifford errors.
* ``"extended_stabilizer"``: An approximate simulated for Clifford + T
circuits based on a state decomposition into ranked-stabilizer state.
The number of terms grows with the number of non-Clifford (T) gates.
* ``"matrix_product_state"``: A tensor-network statevector simulator that
uses a Matrix Product State (MPS) representation for the state. This
can be done either with or without truncation of the MPS bond dimensions
depending on the simulator options. The default behaviour is no
truncation.
* ``"unitary"``: A dense unitary matrix simulation of an ideal circuit.
This simulates the unitary matrix of the circuit itself rather than
the evolution of an initial quantum state. This method can only
simulate gates, it does not support measurement, reset, or noise.
* ``"superop"``: A dense superoperator matrix simulation of an ideal or
noisy circuit. This simulates the superoperator matrix of the circuit
itself rather than the evolution of an initial quantum state. This method
can simulate ideal and noisy gates, and reset, but does not support
measurement.
**GPU Simulation**
By default all simulation methods run on the CPU, however select methods
also support running on a GPU if qiskit-aer was installed with GPU support
on a compatible NVidia GPU and CUDA version.
+--------------------------+---------------+
| Method | GPU Supported |
+==========================+===============+
| ``automatic`` | Sometimes |
+--------------------------+---------------+
| ``statevector`` | Yes |
+--------------------------+---------------+
| ``density_matrix`` | Yes |
+--------------------------+---------------+
| ``stabilizer`` | No |
+--------------------------+---------------+
| `"matrix_product_state`` | No |
+--------------------------+---------------+
| ``extended_stabilizer`` | No |
+--------------------------+---------------+
| ``unitary`` | Yes |
+--------------------------+---------------+
| ``superop`` | No |
+--------------------------+---------------+
Running a GPU simulation is done using ``device="GPU"`` kwarg during
initialization or with :meth:`set_options`. The list of supported devices
for the current system can be returned using :meth:`available_devices`.
**Additional Backend Options**
The following simulator specific backend options are supported
* ``method`` (str): Set the simulation method (Default: ``"automatic"``).
* ``device`` (str): Set the simulation device (Default: ``"CPU"``).
* ``precision`` (str): Set the floating point precision for
certain simulation methods to either ``"single"`` or ``"double"``
precision (default: ``"double"``).
* ``zero_threshold`` (double): Sets the threshold for truncating
small values to zero in the result data (Default: 1e-10).
* ``validation_threshold`` (double): Sets the threshold for checking
if initial states are valid (Default: 1e-8).
* ``max_parallel_threads`` (int): Sets the maximum number of CPU
cores used by OpenMP for parallelization. If set to 0 the
maximum will be set to the number of CPU cores (Default: 0).
* ``max_parallel_experiments`` (int): Sets the maximum number of
qobj experiments that may be executed in parallel up to the
max_parallel_threads value. If set to 1 parallel circuit
execution will be disabled. If set to 0 the maximum will be
automatically set to max_parallel_threads (Default: 1).
* ``max_parallel_shots`` (int): Sets the maximum number of
shots that may be executed in parallel during each experiment
execution, up to the max_parallel_threads value. If set to 1
parallel shot execution will be disabled. If set to 0 the
maximum will be automatically set to max_parallel_threads.
Note that this cannot be enabled at the same time as parallel
experiment execution (Default: 0).
* ``max_memory_mb`` (int): Sets the maximum size of memory
to store a state vector. If a state vector needs more, an error
is thrown. In general, a state vector of n-qubits uses 2^n complex
values (16 Bytes). If set to 0, the maximum will be automatically
set to the system memory size (Default: 0).
* ``optimize_ideal_threshold`` (int): Sets the qubit threshold for
applying circuit optimization passes on ideal circuits.
Passes include gate fusion and truncation of unused qubits
(Default: 5).
* ``optimize_noise_threshold`` (int): Sets the qubit threshold for
applying circuit optimization passes on ideal circuits.
Passes include gate fusion and truncation of unused qubits
(Default: 12).
These backend options only apply when using the ``"statevector"``
simulation method:
* ``statevector_parallel_threshold`` (int): Sets the threshold that
the number of qubits must be greater than to enable OpenMP
parallelization for matrix multiplication during execution of
an experiment. If parallel circuit or shot execution is enabled
this will only use unallocated CPU cores up to
max_parallel_threads. Note that setting this too low can reduce
performance (Default: 14).
* ``statevector_sample_measure_opt`` (int): Sets the threshold that
the number of qubits must be greater than to enable a large
qubit optimized implementation of measurement sampling. Note
that setting this two low can reduce performance (Default: 10)
These backend options only apply when using the ``"stabilizer"``
simulation method:
* ``stabilizer_max_snapshot_probabilities`` (int): set the maximum
qubit number for the
`~qiskit.providers.aer.extensions.SnapshotProbabilities`
instruction (Default: 32).
These backend options only apply when using the ``"extended_stabilizer"``
simulation method:
* ``extended_stabilizer_sampling_methid`` (string): Choose how to simulate
measurements on qubits. The performance of the simulator depends
significantly on this choice. In the following, let n be the number of
qubits in the circuit, m the number of qubits measured, and S be the
number of shots. (Default: resampled_metropolis)
* ``"metropolis"``: Use a Monte-Carlo method to sample many output
strings from the simulator at once. To be accurate, this method
requires that all the possible output strings have a non-zero
probability. It will give inaccurate results on cases where
the circuit has many zero-probability outcomes.
This method has an overall runtime that scales as n^{2} + (S-1)n.
* ``"resampled_metropolis"``: A variant of the metropolis method,
where the Monte-Carlo method is reinitialised for every shot. This
gives better results for circuits where some outcomes have zero
probability, but will still fail if the output distribution
is sparse. The overall runtime scales as Sn^{2}.
* ``"norm_estimation"``: An alternative sampling method using
random state inner products to estimate outcome probabilites. This
method requires twice as much memory, and significantly longer
runtimes, but gives accurate results on circuits with sparse
output distributions. The overall runtime scales as Sn^{3}m^{3}.
* ``extended_stabilizer_metropolis_mixing_time`` (int): Set how long the
monte-carlo method runs before performing measurements. If the
output distribution is strongly peaked, this can be decreased
alongside setting extended_stabilizer_disable_measurement_opt
to True (Default: 5000).
* ``"extended_stabilizer_approximation_error"`` (double): Set the error
in the approximation for the extended_stabilizer method. A
smaller error needs more memory and computational time
(Default: 0.05).
* ``extended_stabilizer_norm_estimation_samples`` (int): The default number
of samples for the norm estimation sampler. The method will use the
default, or 4m^{2} samples where m is the number of qubits to be
measured, whichever is larger (Default: 100).
* ``extended_stabilizer_norm_estimation_repetitions`` (int): The number
of times to repeat the norm estimation. The median of these reptitions
is used to estimate and sample output strings (Default: 3).
* ``extended_stabilizer_parallel_threshold`` (int): Set the minimum
size of the extended stabilizer decomposition before we enable
OpenMP parallelization. If parallel circuit or shot execution
is enabled this will only use unallocated CPU cores up to
max_parallel_threads (Default: 100).
* ``extended_stabilizer_probabilities_snapshot_samples`` (int): If using
the metropolis or resampled_metropolis sampling method, set the number of
samples used to estimate probabilities in a probabilities snapshot
(Default: 3000).
These backend options only apply when using the ``"matrix_product_state"``
simulation method:
* ``matrix_product_state_max_bond_dimension`` (int): Sets a limit
on the number of Schmidt coefficients retained at the end of
the svd algorithm. Coefficients beyond this limit will be discarded.
(Default: None, i.e., no limit on the bond dimension).
* ``matrix_product_state_truncation_threshold`` (double):
Discard the smallest coefficients for which the sum of
their squares is smaller than this threshold.
(Default: 1e-16).
* ``mps_sample_measure_algorithm`` (str):
Choose which algorithm to use for ``"sample_measure"``. ``"mps_probabilities"``
means all state probabilities are computed and measurements are based on them.
It is more efficient for a large number of shots, small number of qubits and low
entanglement. ``"mps_apply_measure"`` creates a copy of the mps structure and
makes a measurement on it. It is more effients for a small number of shots, high
number of qubits, and low entanglement. If the user does not specify the algorithm,
a heuristic algorithm is used to select between the two algorithms.
(Default: "mps_heuristic").
These backend options apply in circuit optimization passes:
* ``fusion_enable`` (bool): Enable fusion optimization in circuit
optimization passes [Default: True]
* ``fusion_verbose`` (bool): Output gates generated in fusion optimization
into metadata [Default: False]
* ``fusion_max_qubit`` (int): Maximum number of qubits for a operation generated
in a fusion optimization [Default: 5]
* ``fusion_threshold`` (int): Threshold that number of qubits must be greater
than or equal to enable fusion optimization [Default: 14]
"""
# Supported basis gates for each simulation method
_BASIS_GATES = {
'statevector': sorted([
'u1', 'u2', 'u3', 'u', 'p', 'r', 'rx', 'ry', 'rz', 'id', 'x',
'y', 'z', 'h', 's', 'sdg', 'sx', 't', 'tdg', 'swap', 'cx',
'cy', 'cz', 'csx', 'cp', 'cu1', 'cu2', 'cu3', 'rxx', 'ryy',
'rzz', 'rzx', 'ccx', 'cswap', 'mcx', 'mcy', 'mcz', 'mcsx',
'mcphase', 'mcu1', 'mcu2', 'mcu3', 'mcrx', 'mcry', 'mcrz',
'mcr', 'mcswap', 'unitary', 'diagonal', 'multiplexer',
'initialize', 'delay', 'pauli', 'mcx_gray'
]),
'density_matrix': sorted([
'u1', 'u2', 'u3', 'u', 'p', 'r', 'rx', 'ry', 'rz', 'id', 'x',
'y', 'z', 'h', 's', 'sdg', 'sx', 't', 'tdg', 'swap', 'cx',
'cy', 'cz', 'cp', 'cu1', 'rxx', 'ryy', 'rzz', 'rzx', 'ccx',
'unitary', 'diagonal', 'delay', 'pauli',
]),
'matrix_product_state': sorted([
'u1', 'u2', 'u3', 'u', 'p', 'cp', 'cx', 'cy', 'cz', 'id', 'x', 'y', 'z', 'h', 's',
'sdg', 'sx', 't', 'tdg', 'swap', 'ccx', 'unitary', 'roerror', 'delay',
'r', 'rx', 'ry', 'rz', 'rxx', 'ryy', 'rzz', 'rzx', 'csx', 'cswap', 'diagonal',
'initialize'
]),
'stabilizer': sorted([
'id', 'x', 'y', 'z', 'h', 's', 'sdg', 'sx', 'cx', 'cy', 'cz',
'swap', 'delay',
]),
'extended_stabilizer': sorted([
'cx', 'cz', 'id', 'x', 'y', 'z', 'h', 's', 'sdg', 'sx',
'swap', 'u0', 't', 'tdg', 'u1', 'p', 'ccx', 'ccz', 'delay'
]),
'unitary': sorted([
'u1', 'u2', 'u3', 'u', 'p', 'r', 'rx', 'ry', 'rz', 'id', 'x',
'y', 'z', 'h', 's', 'sdg', 'sx', 't', 'tdg', 'swap', 'cx',
'cy', 'cz', 'csx', 'cp', 'cu1', 'cu2', 'cu3', 'rxx', 'ryy',
'rzz', 'rzx', 'ccx', 'cswap', 'mcx', 'mcy', 'mcz', 'mcsx',
'mcp', 'mcu1', 'mcu2', 'mcu3', 'mcrx', 'mcry', 'mcrz',
'mcr', 'mcswap', 'unitary', 'diagonal', 'multiplexer', 'delay', 'pauli',
]),
'superop': sorted([
'u1', 'u2', 'u3', 'u', 'p', 'r', 'rx', 'ry', 'rz', 'id', 'x',
'y', 'z', 'h', 's', 'sdg', 'sx', 't', 'tdg', 'swap', 'cx',
'cy', 'cz', 'cp', 'cu1', 'rxx', 'ryy',
'rzz', 'rzx', 'ccx', 'unitary', 'diagonal', 'delay',
])
}
# Automatic method basis gates are the union of statevector,
# density matrix, and stabilizer methods
_BASIS_GATES[None] = _BASIS_GATES['automatic'] = sorted(
set(_BASIS_GATES['statevector']).union(
_BASIS_GATES['stabilizer']).union(
_BASIS_GATES['density_matrix']).union(
_BASIS_GATES['matrix_product_state']).union(
_BASIS_GATES['unitary']).union(
_BASIS_GATES['superop']))
_CUSTOM_INSTR = {
'statevector': sorted([
'roerror', 'kraus', 'snapshot', 'save_expval', 'save_expval_var',
'save_probabilities', 'save_probabilities_dict',
'save_amplitudes', 'save_amplitudes_sq',
'save_density_matrix', 'save_state', 'save_statevector',
'save_statevector_dict', 'set_statevector'
]),
'density_matrix': sorted([
'roerror', 'kraus', 'superop', 'snapshot',
'save_state', 'save_expval', 'save_expval_var',
'save_probabilities', 'save_probabilities_dict',
'save_density_matrix', 'save_amplitudes_sq',
'set_density_matrix'
]),
'matrix_product_state': sorted([
'roerror', 'snapshot', 'kraus', 'save_expval', 'save_expval_var',
'save_probabilities', 'save_probabilities_dict',
'save_state', 'save_matrix_product_state', 'save_statevector',
'save_density_matrix', 'save_amplitudes', 'save_amplitudes_sq',
'set_matrix_product_state'
]),
'stabilizer': sorted([
'roerror', 'snapshot', 'save_expval', 'save_expval_var',
'save_probabilities', 'save_probabilities_dict',
'save_amplitudes_sq', 'save_state', 'save_stabilizer',
'set_stabilizer'
]),
'extended_stabilizer': sorted([
'roerror', 'snapshot', 'save_statevector'
]),
'unitary': sorted([
'snapshot', 'save_state', 'save_unitary', 'set_unitary'
]),
'superop': sorted([
'kraus', 'superop', 'save_state', 'save_superop', 'set_superop'
])
}
# Automatic method custom instructions are the union of statevector,
# density matrix, and stabilizer methods
_CUSTOM_INSTR[None] = _CUSTOM_INSTR['automatic'] = sorted(
set(_CUSTOM_INSTR['statevector']).union(
_CUSTOM_INSTR['stabilizer']).union(
_CUSTOM_INSTR['density_matrix']).union(
_CUSTOM_INSTR['matrix_product_state']).union(
_CUSTOM_INSTR['unitary']).union(
_CUSTOM_INSTR['superop']))
_DEFAULT_CONFIGURATION = {
'backend_name': 'aer_simulator',
'backend_version': __version__,
'n_qubits': MAX_QUBITS_STATEVECTOR,
'url': 'https://github.com/Qiskit/qiskit-aer',
'simulator': True,
'local': True,
'conditional': True,
'open_pulse': False,
'memory': True,
'max_shots': int(1e6),
'description': 'A C++ QasmQobj simulator with noise',
'coupling_map': None,
'basis_gates': _BASIS_GATES['automatic'],
'custom_instructions': _CUSTOM_INSTR['automatic'],
'gates': []
}
_SIMULATION_METHODS = [
'automatic', 'statevector', 'density_matrix',
'stabilizer', 'matrix_product_state', 'extended_stabilizer',
'unitary', 'superop'
]
_AVAILABLE_METHODS = None
_SIMULATION_DEVICES = ['CPU', 'GPU', 'Thrust']
_AVAILABLE_DEVICES = None
def __init__(self,
configuration=None,
properties=None,
provider=None,
**backend_options):
self._controller = aer_controller_execute()
# Update available methods and devices for class
if AerSimulator._AVAILABLE_METHODS is None:
AerSimulator._AVAILABLE_METHODS = available_methods(
self._controller, AerSimulator._SIMULATION_METHODS)
if AerSimulator._AVAILABLE_DEVICES is None:
AerSimulator._AVAILABLE_DEVICES = available_devices(
self._controller, AerSimulator._SIMULATION_DEVICES)
# Default configuration
if configuration is None:
configuration = QasmBackendConfiguration.from_dict(
AerSimulator._DEFAULT_CONFIGURATION)
# Cache basis gates since computing the intersection
# of noise model, method, and config gates is expensive.
self._cached_basis_gates = self._BASIS_GATES['automatic']
super().__init__(configuration,
properties=properties,
available_methods=AerSimulator._AVAILABLE_METHODS,
provider=provider,
backend_options=backend_options)
@classmethod
def _default_options(cls):
return Options(
# Global options
shots=1024,
method='automatic',
device='CPU',
precision="double",
zero_threshold=1e-10,
validation_threshold=None,
max_parallel_threads=None,
max_parallel_experiments=None,
max_parallel_shots=None,
max_memory_mb=None,
optimize_ideal_threshold=5,
optimize_noise_threshold=12,
fusion_enable=True,
fusion_verbose=False,
fusion_max_qubit=5,
fusion_threshold=14,
accept_distributed_results=None,
blocking_qubits=None,
blocking_enable=False,
memory=None,
noise_model=None,
seed_simulator=None,
# statevector options
statevector_parallel_threshold=14,
statevector_sample_measure_opt=10,
# stabilizer options
stabilizer_max_snapshot_probabilities=32,
# extended stabilizer options
extended_stabilizer_sampling_method='resampled_metropolis',
extended_stabilizer_metropolis_mixing_time=5000,
extended_stabilizer_approximation_error=0.05,
extended_stabilizer_norm_estimation_samples=100,
extended_stabilizer_norm_estimation_repitions=3,
extended_stabilizer_parallel_threshold=100,
extended_stabilizer_probabilities_snapshot_samples=3000,
# MPS options
matrix_product_state_truncation_threshold=1e-16,
matrix_product_state_max_bond_dimension=None,
mps_sample_measure_algorithm='mps_heuristic',
chop_threshold=1e-8,
mps_parallel_threshold=14,
mps_omp_threads=1)
def __repr__(self):
"""String representation of an AerSimulator."""
display = super().__repr__()
noise_model = getattr(self.options, 'noise_model', None)
if noise_model is None or noise_model.is_ideal():
return display
pad = ' ' * (len(self.__class__.__name__) + 1)
return '{}\n{}noise_model={})'.format(display[:-1], pad, repr(noise_model))
[docs] def name(self):
"""Format backend name string for simulator"""
name = self._configuration.backend_name
method = getattr(self.options, 'method', None)
if method not in [None, 'automatic']:
name += f'_{method}'
device = getattr(self.options, 'device', None)
if device not in [None, 'CPU']:
name += f'_{device}'.lower()
return name
[docs] @classmethod
def from_backend(cls, backend, **options):
"""Initialize simulator from backend."""
# pylint: disable=import-outside-toplevel
# Avoid cyclic import
from ..noise.noise_model import NoiseModel
# Get configuration and properties from backend
configuration = copy.copy(backend.configuration())
properties = copy.copy(backend.properties())
# Customize configuration name
name = configuration.backend_name
configuration.backend_name = 'aer_simulator({})'.format(name)
# Use automatic noise model if none is provided
if 'noise_model' not in options:
noise_model = NoiseModel.from_backend(backend)
if not noise_model.is_ideal():
options['noise_model'] = noise_model
# Initialize simulator
sim = cls(configuration=configuration,
properties=properties,
**options)
return sim
[docs] def available_devices(self):
"""Return the available simulation methods."""
return self._AVAILABLE_DEVICES
[docs] def configuration(self):
"""Return the simulator backend configuration.
Returns:
BackendConfiguration: the configuration for the backend.
"""
config = copy.copy(self._configuration)
for key, val in self._options_configuration.items():
setattr(config, key, val)
# Update basis gates based on custom options, config, method,
# and noise model
config.custom_instructions = self._CUSTOM_INSTR[
getattr(self.options, 'method', 'automatic')]
config.basis_gates = self._cached_basis_gates + config.custom_instructions
# Update simulator name
config.backend_name = self.name()
return config
def _execute(self, qobj):
"""Execute a qobj on the backend.
Args:
qobj (QasmQobj): simulator input.
Returns:
dict: return a dictionary of results.
"""
return cpp_execute(self._controller, qobj)
[docs] def set_options(self, **fields):
out_options = {}
update_basis_gates = False
for key, value in fields.items():
if key == 'method':
self._set_method_config(value)
update_basis_gates = True
out_options[key] = value
elif key in ['noise_model', 'basis_gates']:
update_basis_gates = True
out_options[key] = value
elif key == 'device':
if value is not None and value not in self._AVAILABLE_DEVICES:
raise AerError(
"Invalid simulation device {}. Available devices"
" are: {}".format(value, self._AVAILABLE_DEVICES))
out_options[key] = value
elif key == 'custom_instructions':
self._set_configuration_option(key, value)
else:
out_options[key] = value
super().set_options(**out_options)
if update_basis_gates:
self._cached_basis_gates = self._basis_gates()
def _validate(self, qobj):
"""Semantic validations of the qobj which cannot be done via schemas.
Warn if no measure or save instructions in run circuits.
"""
for experiment in qobj.experiments:
# If circuit does not contain measurement or save
# instructions raise a warning
no_data = True
for op in experiment.instructions:
if op.name == "measure" or op.name[:5] == "save_":
no_data = False
break
if no_data:
logger.warning(
'No measure or save instruction in circuit "%s": '
'results will be empty.',
experiment.header.name)
def _basis_gates(self):
"""Return simualtor basis gates.
This will be the option value of basis gates if it was set,
otherwise it will be the intersection of the configuration, noise model
and method supported basis gates.
"""
# Use option value for basis gates if set
if 'basis_gates' in self._options_configuration:
return self._options_configuration['basis_gates']
# Compute intersection with method basis gates
method = getattr(self._options, 'method', 'automatic')
method_gates = self._BASIS_GATES[method]
config_gates = self._configuration.basis_gates
if config_gates:
basis_gates = set(config_gates).intersection(
method_gates)
else:
basis_gates = method_gates
# Compute intersection with noise model basis gates
noise_model = getattr(self.options, 'noise_model', None)
if noise_model:
noise_gates = noise_model.basis_gates
basis_gates = basis_gates.intersection(noise_gates)
else:
noise_gates = None
if not basis_gates:
logger.warning(
"The intersection of configuration basis gates (%s), "
"simulation method basis gates (%s), and "
"noise model basis gates (%s) is empty",
config_gates, method_gates, noise_gates)
return sorted(basis_gates)
def _set_method_config(self, method=None):
"""Set non-basis gate options when setting method"""
super().set_options(method=method)
# Update configuration description and number of qubits
if method == 'statevector':
description = 'A C++ statevector simulator with noise'
n_qubits = MAX_QUBITS_STATEVECTOR
elif method == 'density_matrix':
description = 'A C++ density matrix simulator with noise'
n_qubits = MAX_QUBITS_STATEVECTOR // 2
elif method == 'unitary':
description = 'A C++ unitary matrix simulator'
n_qubits = MAX_QUBITS_STATEVECTOR // 2
elif method == 'superop':
description = 'A C++ superop matrix simulator with noise'
n_qubits = MAX_QUBITS_STATEVECTOR // 4
elif method == 'matrix_product_state':
description = 'A C++ matrix product state simulator with noise'
n_qubits = 63 # TODO: not sure what to put here?
elif method == 'stabilizer':
description = 'A C++ Clifford stabilizer simulator with noise'
n_qubits = 10000 # TODO: estimate from memory
elif method == 'extended_stabilizer':
description = 'A C++ Clifford+T extended stabilizer simulator with noise'
n_qubits = 63 # TODO: estimate from memory
else:
# Clear options to default
description = None
n_qubits = None
self._set_configuration_option('description', description)
self._set_configuration_option('n_qubits', n_qubits)