Merge branch 'master' into master

.github/workflows/bazeltest.yml

@@ -60,6 +60,10 @@ jobs:
       - name: Install requirements
         run: |
           python3 -m pip install -r requirements.txt
+      - name: Upgrade libc 
+      # An LLVM update broke this test, fix per is https://bugs.llvm.org/show_bug.cgi?id=27310.
+        run: |
+          sudo apt-get upgrade libc-bin
       - name: Run C++ tests
         run: |
           bazel test --config=avx --config=openmp \

.github/workflows/release_wheels.yml

@@ -45,6 +45,8 @@ jobs:
 
       # Used to host cibuildwheel
       - uses: actions/setup-python@v2
+        with:
+          python-version: '3.11'
 
       - name: Install cibuildwheel and twine
         run: python -m pip install cibuildwheel==2.12.3

.github/workflows/testing_wheels.yml

@@ -50,6 +50,8 @@ jobs:
 
       # Used to host cibuildwheel
       - uses: actions/setup-python@v2
+        with:
+          python-version: '3.11'
 
       - name: Install cibuildwheel and twine
         run: python -m pip install cibuildwheel==2.12.3

docs/choose_hw.md

@@ -258,7 +258,7 @@ are “embarrassingly parallelizable”, there is an automated workflow for
 distributing these trajectories over multiple nodes. A simulation of many
 noiseless circuits can also be distributed over multiple compute nodes.
 
-For mor information about running a mulitnode simulation, see [Multinode quantum
+For more information about running a mulitnode simulation, see [Multinode quantum
 simulation using HTCondor on Google Cloud](/qsim/tutorials/multinode).
 
 ## Runtime estimates

install/tests/Dockerfile

@@ -3,9 +3,15 @@ FROM debian
 
 # Install requirements
 RUN apt-get update
-RUN apt-get install -y python3-dev python3-pip
+RUN apt-get install -y python3-dev python3-pip python3-venv
 RUN apt-get install -y cmake git
 
+# Create venv to avoid collision between system packages (e.g. numpy) and Cirq's deps.
+RUN python3 -m venv test_env
+
+# Activate venv.
+ENV PATH="test_env/bin:$PATH"
+
 COPY ./ /qsim/
 RUN pip3 install /qsim/
 

pybind_interface/Dockerfile

@@ -2,11 +2,16 @@
 FROM qsim
 
 # Install additional requirements
-RUN apt-get install -y python3-dev python3-pybind11 python3-pytest python3-pip
+RUN apt-get install -y python3-dev python3-pybind11 python3-pip python3-venv
 
-# The --force flag is used mainly so that the old numpy installation from pybind
-# gets replaced with the one cirq requires
-RUN pip3 install --prefer-binary cirq-core --force
+# Create venv to avoid collision between system packages (e.g. numpy) and Cirq's deps.
+RUN python3 -m venv test_env
+
+# Activate venv.
+ENV PATH="test_env/bin:$PATH"
+
+# break-system-packages flag to override system packages (e.g. numpy) with Cirq's deps.
+RUN pip3 install --prefer-binary cirq-core
 
 # Copy relevant files
 COPY ./pybind_interface/ /qsim/pybind_interface/
@@ -18,5 +23,8 @@ WORKDIR /qsim/
 # Build pybind code early to cache the results
 RUN make -C /qsim/ pybind
 
+# Install pytest
+RUN pip3 install pytest
+
 # Compile and run qsim tests
 ENTRYPOINT make -C /qsim/ run-py-tests

qsimcirq/_version.py

@@ -1,3 +1,3 @@
 """The version number defined here is read automatically in setup.py."""
 
-__version__ = "0.16.3"
+__version__ = "0.17.0"

qsimcirq/qsim_simulator.py

@@ -435,42 +435,13 @@ def compute_amplitudes_sweep_iter(
             options["s"] = self.get_seed()
             yield simulator_fn(options)
 
-    def simulate_sweep_iter(
+    def _simulate_impl(
         self,
         program: cirq.Circuit,
         params: cirq.Sweepable,
         qubit_order: cirq.QubitOrderOrList = cirq.QubitOrder.DEFAULT,
         initial_state: Optional[Union[int, np.ndarray]] = None,
-    ) -> Iterator[cirq.StateVectorTrialResult]:
-        """Simulates the supplied Circuit.
-
-        This method returns a result which allows access to the entire
-        wave function. In contrast to simulate, this allows for sweeping
-        over different parameter values.
-
-        Avoid using this method with `use_gpu=True` in the simulator options;
-        when used with GPU this method must copy state from device to host memory
-        multiple times, which can be very slow. This issue is not present in
-        `simulate_expectation_values_sweep`.
-
-        Args:
-            program: The circuit to simulate.
-            params: Parameters to run with the program.
-            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. This can either
-              be an integer representing a pure state (e.g. 11010) or a numpy
-              array containing the full state vector. If none is provided, this
-              is assumed to be the all-zeros state.
-
-        Returns:
-            List of SimulationTrialResults for this run, one for each
-            possible parameter resolver.
-
-        Raises:
-            TypeError: if an invalid initial_state is provided.
-        """
+    ) -> Iterator[Tuple[cirq.ParamResolver, np.ndarray, Sequence[int]]]:
         if initial_state is None:
             initial_state = 0
         if not isinstance(initial_state, (int, np.ndarray)):
@@ -526,8 +497,68 @@ def simulate_sweep_iter(
             assert qsim_state.dtype == np.float32
             assert qsim_state.ndim == 1
 
+            yield prs, qsim_state.view(np.complex64), cirq_order
+
+    def simulate_into_1d_array(
+        self,
+        program: cirq.AbstractCircuit,
+        param_resolver: cirq.ParamResolverOrSimilarType = None,
+        qubit_order: cirq.QubitOrderOrList = cirq.ops.QubitOrder.DEFAULT,
+        initial_state: Any = None,
+    ) -> Tuple[cirq.ParamResolver, np.ndarray, Sequence[int]]:
+        """Same as simulate() but returns raw simulation result without wrapping it.
+
+            The returned result is not wrapped in a StateVectorTrialResult but can be used
+            to create a StateVectorTrialResult.
+
+        Returns:
+            Tuple of (param resolver, final state, qubit order)
+        """
+        params = cirq.study.ParamResolver(param_resolver)
+        return next(self._simulate_impl(program, params, qubit_order, initial_state))
+
+    def simulate_sweep_iter(
+        self,
+        program: cirq.Circuit,
+        params: cirq.Sweepable,
+        qubit_order: cirq.QubitOrderOrList = cirq.QubitOrder.DEFAULT,
+        initial_state: Optional[Union[int, np.ndarray]] = None,
+    ) -> Iterator[cirq.StateVectorTrialResult]:
+        """Simulates the supplied Circuit.
+
+        This method returns a result which allows access to the entire
+        wave function. In contrast to simulate, this allows for sweeping
+        over different parameter values.
+
+        Avoid using this method with `use_gpu=True` in the simulator options;
+        when used with GPU this method must copy state from device to host memory
+        multiple times, which can be very slow. This issue is not present in
+        `simulate_expectation_values_sweep`.
+
+        Args:
+            program: The circuit to simulate.
+            params: Parameters to run with the program.
+            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. This can either
+              be an integer representing a pure state (e.g. 11010) or a numpy
+              array containing the full state vector. If none is provided, this
+              is assumed to be the all-zeros state.
+
+        Returns:
+            Iterator over SimulationTrialResults for this run, one for each
+            possible parameter resolver.
+
+        Raises:
+            TypeError: if an invalid initial_state is provided.
+        """
+
+        for prs, state_vector, cirq_order in self._simulate_impl(
+            program, params, qubit_order, initial_state
+        ):
             final_state = cirq.StateVectorSimulationState(
-                initial_state=qsim_state.view(np.complex64), qubits=cirq_order
+                initial_state=state_vector, qubits=cirq_order
             )
             # create result for this parameter
             # TODO: We need to support measurements.

qsimcirq_tests/qsimcirq_test.py

@@ -2058,3 +2058,13 @@ def test_cirq_global_phase_gate():
     assert cirq.approx_eq(
         qsim_result.state_vector(), cirq_result.state_vector(), atol=1e-6
     )
+
+
+def test_1d_representation():
+    qsim_sim = qsimcirq.QSimSimulator()
+    qs = cirq.LineQubit.range(2)
+    c = cirq.Circuit(cirq.H.on_each(qs), cirq.X(qs[0]), cirq.Y(qs[1]))
+
+    want = np.array([0.0 - 0.5j, 0.0 + 0.5j, 0.0 - 0.5j, 0.0 + 0.5j])
+    _, res, _ = qsim_sim.simulate_into_1d_array(c)
+    np.testing.assert_allclose(res, np.array(want, dtype=np.complex64))