An MPS (approximate) simulator

cirq/__init__.py

@@ -363,6 +363,10 @@
     final_density_matrix,
     final_state_vector,
     final_wavefunction,
+    MPSSimulator,
+    MPSSimulatorStepResult,
+    MPSState,
+    MPSTrialResult,
     sample,
     sample_density_matrix,
     sample_state_vector,

cirq/protocols/json_serialization_test.py

@@ -298,6 +298,10 @@ def test_mutually_exclusive_blacklist():
     'LinearCombinationOfOperations',
     'Linspace',
     'ListSweep',
+    'MPSSimulator',
+    'MPSSimulatorStepResult',
+    'MPSState',
+    'MPSTrialResult',
     'NeutralAtomDevice',
     'PauliInteractionGate',
     'PauliStringPhasor',

cirq/sim/__init__.py

@@ -32,6 +32,13 @@
     DensityMatrixTrialResult,
 )
 
+from cirq.sim.mps_simulator import (
+    MPSSimulator,
+    MPSSimulatorStepResult,
+    MPSState,
+    MPSTrialResult,
+)
+
 from cirq.sim.mux import (
     CIRCUIT_LIKE,
     final_density_matrix,

cirq/sim/mps_simulator.py

@@ -0,0 +1,350 @@
+# Copyright 2019 The Cirq Developers
+#
+# 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
+#
+#     https://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.
+"""An MPS simulator.
+
+This is based on this paper:
+https://arxiv.org/abs/2002.07730
+
+TODO(tonybruguier): Currently, only linear circuits are handled, while the paper
+handles more general topologies.
+
+TODO(tonybruguier): Currently, numpy is used for tensor computations. For speed
+switch to QIM for speed.
+"""
+
+import collections
+import math
+from typing import Any, Dict, List, Iterator, Sequence
+
+import numpy as np
+
+import cirq
+from cirq import circuits, study, ops, protocols, value
+from cirq.sim import simulator
+
+
+class MPSSimulator(simulator.SimulatesSamples, simulator.SimulatesIntermediateState):
+    """An efficient simulator for MPS circuits."""
+
+    def __init__(self, seed: 'cirq.RANDOM_STATE_OR_SEED_LIKE' = None):
+        """Creates instance of `MPSSimulator`.
+
+        Args:
+            seed: The random seed to use for this simulator.
+        """
+        self.init = True
+        self._prng = value.parse_random_state(seed)
+
+    def _base_iterator(
+        self, circuit: circuits.Circuit, qubit_order: ops.QubitOrderOrList, initial_state: int
+    ) -> Iterator['cirq.MPSSimulatorStepResult']:
+        """Iterator over MPSSimulatorStepResult from Moments of a Circuit
+
+        Args:
+            circuit: The circuit to simulate.
+            qubit_order: Determines the canonical ordering of the qubits. This
+                is often used in specifying the initial state, i.e. the
+                ordering of the computational basis states.
+            initial_state: The initial state for the simulation in the
+                computational basis. Represented as a big endian int.
+
+
+        Yields:
+            MPSStepResult from simulating a Moment of the Circuit.
+        """
+        qubits = ops.QubitOrder.as_qubit_order(qubit_order).order_for(circuit.all_qubits())
+
+        qubit_map = {q: i for i, q in enumerate(qubits)}
+
+        if len(circuit) == 0:
+            yield MPSSimulatorStepResult(
+                measurements={}, state=MPSState(qubit_map, initial_state=initial_state)
+            )
+            return
+
+        state = MPSState(qubit_map, initial_state=initial_state)
+
+        for moment in circuit:
+            measurements: Dict[str, List[int]] = collections.defaultdict(list)
+
+            for op in moment:
+                if isinstance(op.gate, ops.MeasurementGate):
+                    key = str(protocols.measurement_key(op))
+                    measurements[key].extend(state.perform_measurement(op.qubits, self._prng))
+                elif protocols.has_unitary(op):
+                    state.apply_unitary(op)
+                else:
+                    raise NotImplementedError(f"Unrecognized operation: {op!r}")
+
+            yield MPSSimulatorStepResult(measurements=measurements, state=state)
+
+    def _simulator_iterator(
+        self,
+        circuit: circuits.Circuit,
+        param_resolver: study.ParamResolver,
+        qubit_order: ops.QubitOrderOrList,
+        initial_state: int,
+    ) -> Iterator:
+        """See definition in `cirq.SimulatesIntermediateState`.
+
+        Args:
+            inital_state: An integer specifying the inital
+            state in the computational basis.
+        """
+        param_resolver = param_resolver or study.ParamResolver({})
+        resolved_circuit = protocols.resolve_parameters(circuit, param_resolver)
+        self._check_all_resolved(resolved_circuit)
+        actual_initial_state = 0 if initial_state is None else initial_state
+
+        return self._base_iterator(resolved_circuit, qubit_order, actual_initial_state)
+
+    def _create_simulator_trial_result(
+        self,
+        params: study.ParamResolver,
+        measurements: Dict[str, np.ndarray],
+        final_simulator_state,
+    ):
+
+        return MPSTrialResult(
+            params=params, measurements=measurements, final_simulator_state=final_simulator_state
+        )
+
+    def _run(
+        self, circuit: circuits.Circuit, param_resolver: study.ParamResolver, repetitions: int
+    ) -> Dict[str, List[np.ndarray]]:
+
+        param_resolver = param_resolver or study.ParamResolver({})
+        resolved_circuit = protocols.resolve_parameters(circuit, param_resolver)
+        self._check_all_resolved(resolved_circuit)
+
+        measurements = {}  # type: Dict[str, List[np.ndarray]]
+        if repetitions == 0:
+            for _, op, _ in resolved_circuit.findall_operations_with_gate_type(ops.MeasurementGate):
+                measurements[protocols.measurement_key(op)] = np.empty([0, 1])
+
+        for _ in range(repetitions):
+            all_step_results = self._base_iterator(
+                resolved_circuit, qubit_order=ops.QubitOrder.DEFAULT, initial_state=0
+            )
+
+            for step_result in all_step_results:
+                for k, v in step_result.measurements.items():
+                    if not k in measurements:
+                        measurements[k] = []
+                    measurements[k].append(np.array(v, dtype=int))
+
+        return {k: np.array(v) for k, v in measurements.items()}
+
+    def _check_all_resolved(self, circuit):
+        """Raises if the circuit contains unresolved symbols."""
+        if protocols.is_parameterized(circuit):
+            unresolved = [
+                op for moment in circuit for op in moment if protocols.is_parameterized(op)
+            ]
+            raise ValueError(
+                'Circuit contains ops whose symbols were not specified in '
+                'parameter sweep. Ops: {}'.format(unresolved)
+            )
+
+
+class MPSTrialResult(simulator.SimulationTrialResult):
+    def __init__(
+        self,
+        params: study.ParamResolver,
+        measurements: Dict[str, np.ndarray],
+        final_simulator_state: 'MPSState',
+    ) -> None:
+        super().__init__(
+            params=params, measurements=measurements, final_simulator_state=final_simulator_state
+        )
+
+        self.final_state = final_simulator_state
+
+    def __str__(self) -> str:
+        samples = super().__str__()
+        final = self._final_simulator_state
+        return f'measurements: {samples}\noutput state: {final}'
+
+
+class MPSSimulatorStepResult(simulator.StepResult):
+    """A `StepResult` that includes `StateVectorMixin` methods."""
+
+    def __init__(self, state, measurements):
+        """Results of a step of the simulator.
+        Attributes:
+            state: A MPSState
+            measurements: A dictionary from measurement gate key to measurement
+                results, ordered by the qubits that the measurement operates on.
+            qubit_map: A map from the Qubits in the Circuit to the the index
+                of this qubit for a canonical ordering. This canonical ordering
+                is used to define the state vector (see the state_vector()
+                method).
+        """
+        self.measurements = measurements
+        self.state = state.copy()
+
+    def __str__(self) -> str:
+        def bitstring(vals):
+            return ','.join(str(v) for v in vals)
+
+        results = sorted([(key, bitstring(val)) for key, val in self.measurements.items()])
+
+        if len(results) == 0:
+            measurements = ''
+        else:
+            measurements = ' '.join([f'{key}={val}' for key, val in results]) + '\n'
+
+        final = self.state
+
+        return f'{measurements}{final}'
+
+    def _simulator_state(self):
+        return self.state
+
+    def sample(
+        self,
+        qubits: List[ops.Qid],
+        repetitions: int = 1,
+        seed: 'cirq.RANDOM_STATE_OR_SEED_LIKE' = None,
+    ) -> np.ndarray:
+
+        measurements: List[int] = []
+
+        for _ in range(repetitions):
+            measurements.append(
+                self.state.perform_measurement(
+                    qubits, value.parse_random_state(seed), collapse_state_vector=False
+                )
+            )
+
+        return np.array(measurements, dtype=int)
+
+
+@value.value_equality
+class MPSState:
+    """A state of the MPS simulation."""
+
+    def __init__(self, qubit_map, initial_state=0):
+        self.qubit_map = qubit_map
+        self.M = []
+        for qubit in qubit_map.keys():
+            d = qubit.dimension
+            x = np.zeros(
+                (
+                    1,
+                    1,
+                    d,
+                )
+            )
+            x[0, 0, (initial_state % d)] = 1.0
+            self.M.append(x)
+            initial_state = initial_state // d
+        self.M = self.M[::-1]
+        self.threshold = 1e-3
+
+    def __str__(self) -> str:
+        return str(self.M)
+
+    def _value_equality_values_(self) -> Any:
+        return self.qubit_map, self.M, self.threshold
+
+    def copy(self) -> 'MPSState':
+        state = MPSState(self.qubit_map)
+        state.M = [x.copy() for x in self.M]
+        state.threshold = self.threshold
+        return state
+
+    def state_vector(self):
+        M = np.ones((1, 1))
+        for i in range(len(self.M)):
+            M = np.einsum('ni,npj->pij', M, self.M[i])
+            M = M.reshape(M.shape[0], -1)
+        assert M.shape[0] == 1
+        return M[0, :]
+
+    def to_numpy(self) -> np.ndarray:
+        return self.state_vector()
+
+    def apply_unitary(self, op: 'cirq.Operation'):
+        idx = [self.qubit_map[qubit] for qubit in op.qubits]
+        U = protocols.unitary(op).reshape([qubit.dimension for qubit in op.qubits] * 2)
+
+        if len(idx) == 1:
+            n = idx[0]
+            self.M[n] = np.einsum('ij,mnj->mni', U, self.M[n])
+        elif len(idx) == 2:
+            n = idx[0]
+            p = idx[1]
+            if abs(n - p) != 1:
+                raise ValueError('Can only handle continguous qubits')
+            T = np.einsum('klij,mni,npj->mkpl', U, self.M[n], self.M[p])
+            X, S, Y = np.linalg.svd(T.reshape([T.shape[0] * T.shape[1], T.shape[2] * T.shape[3]]))
+            X = X.reshape([T.shape[0], T.shape[1], -1])
+            Y = Y.reshape([-1, T.shape[2], T.shape[3]])
+
+            S = np.asarray([math.sqrt(x) for x in S])
+
+            nkeep = 0
+            for i in range(S.shape[0]):
+                if S[i] >= S[0] * self.threshold:
+                    nkeep = i + 1
+
+            X = X[:, :, :nkeep]
+            S = np.diag(S[:nkeep])
+            Y = Y[:nkeep, :, :]
+
+            self.M[n] = np.einsum('mis,sn->mni', X, S)
+            self.M[p] = np.einsum('ns,spj->npj', S, Y)
+        else:
+            raise ValueError('Can only handle 1 and 2 qubit operations')
+
+    def perform_measurement(
+        self, qubits: Sequence[ops.Qid], prng: np.random.RandomState, collapse_state_vector=True
+    ) -> List[int]:
+        results: List[int] = []
+
+        if collapse_state_vector:
+            state = self
+        else:
+            state = self.copy()
+
+        for qubit in qubits:
+            n = state.qubit_map[qubit]
+
+            M = np.ones((1, 1))
+            for i in range(len(state.M)):
+                if i == n:
+                    M = np.einsum('ni,npj->pij', M, state.M[i])
+                else:
+                    M = np.einsum('ni,npj->pi', M, state.M[i])
+                M = M.reshape(M.shape[0], -1)
+            assert M.shape[0] == 1
+            M = M.reshape(-1)
+            probs = [abs(x) ** 2 for x in M]
+
+            # Because the computation is approximate, the probabilities do not
+            # necessarily add up to 1.0, and thus we re-normalize them.
+            norm_probs = [x / sum(probs) for x in probs]
+
+            d = qubit.dimension
+            result: int = int(prng.choice(d, p=norm_probs))
+
+            renormalizer = np.zeros((d, d))
+            renormalizer[result][result] = 1.0 / math.sqrt(probs[result])
+
+            state.M[n] = np.einsum('ij,mnj->mni', renormalizer, state.M[n])
+
+            results.append(result)
+
+        return results