##############################################################################
# Copyright 2018 Rigetti Computing
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################
import logging
from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Sequence, Type, Union
import numpy as np
from numpy.random.mtrand import RandomState
from pyquil.api import QAM, QuantumExecutable, QAMExecutionResult
from pyquil.paulis import PauliTerm, PauliSum
from pyquil.quil import Program
from pyquil.quilatom import Label, LabelPlaceholder, MemoryReference
from pyquil.quilbase import (
Gate,
Measurement,
ResetQubit,
DefGate,
JumpTarget,
JumpConditional,
JumpWhen,
JumpUnless,
Halt,
Wait,
Reset,
Nop,
UnaryClassicalInstruction,
ClassicalNeg,
ClassicalNot,
LogicalBinaryOp,
ClassicalAnd,
ClassicalInclusiveOr,
ClassicalExclusiveOr,
ArithmeticBinaryOp,
ClassicalAdd,
ClassicalSub,
ClassicalMul,
ClassicalDiv,
ClassicalMove,
ClassicalExchange,
Jump,
Pragma,
Declare,
RawInstr,
DefGateByPaulis,
DefPermutationGate,
)
log = logging.getLogger(__name__)
QUIL_TO_NUMPY_DTYPE = {"INT": np.int_, "REAL": np.float_, "BIT": np.int8, "OCTET": np.uint8}
[docs]class AbstractQuantumSimulator(ABC):
@abstractmethod
def __init__(self, n_qubits: int, rs: RandomState):
"""
Initialize.
:param n_qubits: Number of qubits to simulate.
:param rs: a RandomState (shared with the owning :py:class:`PyQVM`) for
doing anything stochastic.
"""
[docs] @abstractmethod
def do_gate(self, gate: Gate) -> "AbstractQuantumSimulator":
"""
Perform a gate.
:return: ``self`` to support method chaining.
"""
[docs] @abstractmethod
def do_gate_matrix(self, matrix: np.ndarray, qubits: Sequence[int]) -> "AbstractQuantumSimulator":
"""
Apply an arbitrary unitary; not necessarily a named gate.
:param matrix: The unitary matrix to apply. No checks are done
:param qubits: A list of qubits to apply the unitary to.
:return: ``self`` to support method chaining.
"""
[docs] def do_program(self, program: Program) -> "AbstractQuantumSimulator":
"""
Perform a sequence of gates contained within a program.
:param program: The program
:return: self
"""
for gate in program:
if not isinstance(gate, Gate):
raise ValueError("Can only compute the simulate a program composed of `Gate`s")
self.do_gate(gate)
return self
[docs] @abstractmethod
def do_measurement(self, qubit: int) -> int:
"""
Measure a qubit and collapse the wavefunction
:return: The measurement result. A 1 or a 0.
"""
[docs] @abstractmethod
def expectation(self, operator: Union[PauliTerm, PauliSum]) -> complex:
"""
Compute the expectation of an operator.
:param operator: The operator
:return: The operator's expectation value
"""
[docs] @abstractmethod
def reset(self) -> "AbstractQuantumSimulator":
"""
Reset the wavefunction to the ``|000...00>`` state.
:return: ``self`` to support method chaining.
"""
[docs] @abstractmethod
def sample_bitstrings(self, n_samples: int) -> np.ndarray:
"""
Sample bitstrings from the current state.
:param n_samples: The number of bitstrings to sample
:return: A numpy array of shape (n_samples, n_qubits)
"""
[docs] @abstractmethod
def do_post_gate_noise(self, noise_type: str, noise_prob: float, qubits: List[int]) -> "AbstractQuantumSimulator":
"""
Apply noise that happens after each gate application.
WARNING! This is experimental and the signature of this interface will likely change.
:param noise_type: The name of the noise type
:param noise_prob: The probability of that noise happening
:param qubits: Apply noise to these qubits.
:return: ``self`` to support method chaining
"""
[docs]class PyQVM(QAM["PyQVM"]):
def __init__(
self,
n_qubits: int,
quantum_simulator_type: Optional[Type[AbstractQuantumSimulator]] = None,
seed: Optional[int] = None,
post_gate_noise_probabilities: Optional[Dict[str, float]] = None,
):
"""
PyQuil's built-in Quil virtual machine.
This class implements common control flow and plumbing and dispatches the "actual" work to
quantum simulators like ReferenceWavefunctionSimulator, ReferenceDensitySimulator,
and NumpyWavefunctionSimulator
:param n_qubits: The number of qubits. Typically this results in the allocation of a large
ndarray, so be judicious.
:param quantum_simulator_type: A class that can be instantiated to handle the quantum
aspects of this QVM. If not specified, the default will be either
NumpyWavefunctionSimulator (no noise) or ReferenceDensitySimulator (noise)
:param post_gate_noise_probabilities: A specification of noise model given by
probabilities of certain types of noise. The dictionary keys are from "relaxation",
"dephasing", "depolarizing", "phase_flip", "bit_flip", and "bitphase_flip".
WARNING: experimental. This interface will likely change.
:param seed: An optional random seed for performing stochastic aspects of the QVM.
"""
if quantum_simulator_type is None:
if post_gate_noise_probabilities is None:
from pyquil.simulation._numpy import NumpyWavefunctionSimulator
quantum_simulator_type = NumpyWavefunctionSimulator
else:
from pyquil.simulation._reference import ReferenceDensitySimulator
log.info("Using ReferenceDensitySimulator as the backend for PyQVM")
quantum_simulator_type = ReferenceDensitySimulator
self.n_qubits = n_qubits
self.ram: Dict[str, np.ndarray] = {}
if post_gate_noise_probabilities is None:
post_gate_noise_probabilities = {}
self.post_gate_noise_probabilities = post_gate_noise_probabilities
self.program: Optional[Program] = None
self.program_counter: int = 0
self.defined_gates: Dict[str, np.ndarray] = dict()
# private implementation details
self._qubit_to_ram: Optional[Dict[int, int]] = None
self._ro_size: Optional[int] = None
self._memory_results = {} # type: ignore
self.rs = np.random.RandomState(seed=seed)
self.wf_simulator = quantum_simulator_type(n_qubits=n_qubits, rs=self.rs)
self._last_measure_program_loc = None
def _extract_defined_gates(self) -> None:
self.defined_gates = dict()
assert self.program is not None
for dg in self.program.defined_gates:
if dg.parameters is not None and len(dg.parameters) > 0:
raise NotImplementedError("PyQVM does not support parameterized DEFGATEs")
if isinstance(dg, DefPermutationGate) or isinstance(dg, DefGateByPaulis):
raise NotImplementedError("PyQVM does not support DEFGATE ... AS MATRIX | PAULI-SUM.")
self.defined_gates[dg.name] = dg.matrix
[docs] def write_memory(self, *, region_name: str, offset: int = 0, value: int = 0) -> "PyQVM":
assert region_name != "ro"
self.ram[region_name][offset] = value
return self
[docs] def execute(self, executable: QuantumExecutable) -> "PyQVM":
"""
Execute a program on the PyQVM. Note that the state of the instance is reset on each
call to ``execute``.
:return: ``self`` to support method chaining.
"""
if not isinstance(executable, Program):
raise TypeError("`executable` argument must be a `Program`")
self.program = executable
self._memory_results = {}
self.ram = {}
self.wf_simulator.reset()
# grab the gate definitions for future use
self._extract_defined_gates()
self._memory_results = {}
for _ in range(self.program.num_shots):
self.wf_simulator.reset()
self._execute_program()
for name in self.ram.keys():
self._memory_results.setdefault(name, list())
self._memory_results[name].append(self.ram[name])
self._memory_results = {k: np.asarray(v) for k, v in self._memory_results.items()}
self._bitstrings = self._memory_results.get("ro")
return self
[docs] def get_result(self, execute_response: "PyQVM") -> QAMExecutionResult:
"""
Return results from the PyQVM according to the common QAM API. Note that while the
``execute_response`` is not used, it's accepted in order to conform to that API; it's
unused because the PyQVM, unlike other QAM's, is itself stateful.
"""
assert self.program is not None
return QAMExecutionResult(
executable=self.program.copy(), readout_data={k: v for k, v in self._memory_results.items()}
)
[docs] def read_memory(self, *, region_name: str) -> np.ndarray:
assert self._memory_results is not None
return np.asarray(self._memory_results[region_name])
[docs] def find_label(self, label: Union[Label, LabelPlaceholder]) -> int:
"""
Helper function that iterates over the program and looks for a JumpTarget that has a
Label matching the input label.
:param label: Label object to search for in program
:return: Program index where ``label`` is found
"""
assert self.program is not None
for index, action in enumerate(self.program):
if isinstance(action, JumpTarget):
if label == action.label:
return index
raise RuntimeError("Improper program - Jump Target not found in the input program!")
[docs] def transition(self) -> bool:
"""
Implements a QAM-like transition.
This function assumes ``program`` and ``program_counter`` instance variables are set
appropriately, and that the wavefunction simulator and classical memory ``ram`` instance
variables are in the desired QAM input state.
:return: whether the QAM should halt after this transition.
"""
assert self.program is not None
instruction = self.program[self.program_counter]
if isinstance(instruction, Gate):
if instruction.name in self.defined_gates:
self.wf_simulator.do_gate_matrix(
matrix=self.defined_gates[instruction.name],
qubits=[q.index for q in instruction.qubits],
)
else:
self.wf_simulator.do_gate(gate=instruction)
for noise_type, noise_prob in self.post_gate_noise_probabilities.items():
self.wf_simulator.do_post_gate_noise(
noise_type, noise_prob, qubits=[q.index for q in instruction.qubits]
)
self.program_counter += 1
elif isinstance(instruction, Measurement):
measured_val = self.wf_simulator.do_measurement(qubit=instruction.qubit.index)
meas_reg: Optional[MemoryReference] = instruction.classical_reg
assert meas_reg is not None
self.ram[meas_reg.name][meas_reg.offset] = measured_val
self.program_counter += 1
elif isinstance(instruction, Declare):
if instruction.shared_region is not None:
raise NotImplementedError("SHARING is not (yet) implemented.")
self.ram[instruction.name] = np.zeros(
instruction.memory_size, dtype=QUIL_TO_NUMPY_DTYPE[instruction.memory_type]
)
self.program_counter += 1
elif isinstance(instruction, Pragma):
# TODO: more stringent checks for what's being pragma'd and warnings
self.program_counter += 1
elif isinstance(instruction, Jump):
# unconditional Jump; go directly to Label
self.program_counter = self.find_label(instruction.target)
elif isinstance(instruction, JumpTarget):
# Label; pass straight over
self.program_counter += 1
elif isinstance(instruction, JumpConditional):
# JumpConditional; check classical reg
jump_reg: Optional[MemoryReference] = instruction.condition
assert jump_reg is not None
cond = self.ram[jump_reg.name][jump_reg.offset]
if not isinstance(cond, (bool, np.bool_, np.int8)):
raise ValueError("{} requires a data type of BIT; not {}".format(instruction.op, type(cond)))
dest_index = self.find_label(instruction.target)
if isinstance(instruction, JumpWhen):
jump_if_cond = True
elif isinstance(instruction, JumpUnless):
jump_if_cond = False
else:
raise TypeError("Invalid JumpConditional")
if not (cond ^ jump_if_cond):
# jumping: set prog counter to JumpTarget
self.program_counter = dest_index
else:
# not jumping: hop over this JumpConditional
self.program_counter += 1
elif isinstance(instruction, UnaryClassicalInstruction):
# UnaryClassicalInstruction; set classical reg
target = instruction.target
old = self.ram[target.name][target.offset]
if isinstance(instruction, ClassicalNeg):
if not isinstance(old, (int, float, np.int_, np.float_)):
raise ValueError("NEG requires a data type of REAL or INTEGER; not {}".format(type(old)))
self.ram[target.name][target.offset] *= -1
elif isinstance(instruction, ClassicalNot):
if not isinstance(old, (bool, np.bool_)):
raise ValueError("NOT requires a data type of BIT; not {}".format(type(old)))
self.ram[target.name][target.offset] = not old
else:
raise TypeError("Invalid UnaryClassicalInstruction")
self.program_counter += 1
elif isinstance(instruction, (LogicalBinaryOp, ArithmeticBinaryOp, ClassicalMove)):
left_ind = instruction.left
left_val = self.ram[left_ind.name][left_ind.offset]
if isinstance(instruction.right, MemoryReference):
right_ind = instruction.right
right_val = self.ram[right_ind.name][right_ind.offset]
else:
right_val = instruction.right
if isinstance(instruction, ClassicalAnd):
assert isinstance(left_val, int) and isinstance(right_val, int)
new_val: Union[int, float] = left_val & right_val
elif isinstance(instruction, ClassicalInclusiveOr):
assert isinstance(left_val, int) and isinstance(right_val, int)
new_val = left_val | right_val
elif isinstance(instruction, ClassicalExclusiveOr):
assert isinstance(left_val, int) and isinstance(right_val, int)
new_val = left_val ^ right_val
elif isinstance(instruction, ClassicalAdd):
new_val = left_val + right_val
elif isinstance(instruction, ClassicalSub):
new_val = left_val - right_val
elif isinstance(instruction, ClassicalMul):
new_val = left_val * right_val
elif isinstance(instruction, ClassicalDiv):
new_val = left_val / right_val
elif isinstance(instruction, ClassicalMove):
new_val = right_val
else:
raise ValueError("Unknown BinaryOp {}".format(type(instruction)))
self.ram[left_ind.name][left_ind.offset] = new_val
self.program_counter += 1
elif isinstance(instruction, ClassicalExchange):
left_ind_ex = instruction.left
right_ind_ex = instruction.right
tmp = self.ram[left_ind_ex.name][left_ind_ex.offset]
self.ram[left_ind_ex.name][left_ind_ex.offset] = self.ram[right_ind_ex.name][right_ind_ex.offset]
self.ram[right_ind_ex.name][right_ind_ex.offset] = tmp
self.program_counter += 1
elif isinstance(instruction, Reset):
self.wf_simulator.reset()
self.program_counter += 1
elif isinstance(instruction, ResetQubit):
raise NotImplementedError("Need to implement in wf simulator")
elif isinstance(instruction, Wait):
self.program_counter += 1
elif isinstance(instruction, Nop):
# well that was easy
self.program_counter += 1
elif isinstance(instruction, DefGate):
if instruction.parameters is not None and len(instruction.parameters) > 0:
raise NotImplementedError("PyQVM does not support parameterized DEFGATEs")
self.defined_gates[instruction.name] = instruction.matrix
self.program_counter += 1
elif isinstance(instruction, RawInstr):
raise NotImplementedError("PyQVM does not support raw instructions. Parse your program")
elif isinstance(instruction, Halt):
return True
else:
raise ValueError("Unsupported instruction type: {}".format(instruction))
# return HALTED (i.e. program_counter is end of program)
assert self.program is not None
return self.program_counter == len(self.program)
def _execute_program(self) -> "PyQVM":
self.program_counter = 0
assert self.program is not None
halted = len(self.program) == 0
while not halted:
halted = self.transition()
return self
[docs] def execute_once(self, program: Program) -> "PyQVM":
"""
Execute one outer loop of a program on the PyQVM without re-initializing its state.
Note that the PyQVM is stateful. Subsequent calls to :py:func:`execute_once` will not
automatically reset the wavefunction or the classical RAM. If this is desired,
consider starting your program with ``RESET``.
:return: ``self`` to support method chaining.
"""
self.program = program
self._extract_defined_gates()
return self._execute_program()