Sub state vector

cirq-core/cirq/linalg/transformations.py

@@ -25,14 +25,6 @@
 import numpy as np
 
 from cirq import protocols
-from cirq.linalg import predicates
-
-# This is a special indicator value used by the `sub_state_vector` method to
-# determine whether or not the caller provided a 'default' argument. It must be
-# of type np.ndarray to ensure the method has the correct type signature in that
-# case. It is checked for using `is`, so it won't have a false positive if the
-# user provides a different np.array([]) value.
-RaiseValueErrorIfNotProvided: np.ndarray = np.array([])
 
 
 def reflection_matrix_pow(reflection_matrix: np.ndarray, exponent: float) -> np.ndarray:
@@ -479,7 +471,7 @@ def sub_state_vector(
     state_vector: np.ndarray,
     keep_indices: list[int],
     *,
-    default: np.ndarray = RaiseValueErrorIfNotProvided,
+    default: np.ndarray | None = None,
     atol: float = 1e-6,
 ) -> np.ndarray:
     r"""Attempts to factor a state vector into two parts and return one of them.
@@ -503,16 +495,16 @@ def sub_state_vector(
 
     If the provided `state_vector` cannot be factored into a pure state over
     `keep_indices`, the method will fall back to return `default`. If `default`
-    is not provided, the method will fail and raise `ValueError`.
+    is not provided, the method will fail and raise EntangledStateError.
 
     Args:
         state_vector: The target state_vector.
         keep_indices: Which indices to attempt to get the separable part of the
             `state_vector` on.
         default: Determines the fallback behavior when `state_vector` doesn't
             have a pure state factorization. If the factored state is not pure
-            and `default` is not set, a ValueError is raised. If default is set
-            to a value, that value is returned.
+            and `default` is not set, an EntangledStateError is raised. If
+            default is set to a value, that value is returned.
         atol: The minimum tolerance for comparing the output state's coherence
             measure to 1.
 
@@ -540,36 +532,42 @@ def sub_state_vector(
     ret_shape: tuple[int] | tuple[int, ...]
     if state_vector.shape == (state_vector.size,):
         ret_shape = (keep_dims,)
-        state_vector = state_vector.reshape((2,) * n_qubits)
     elif state_vector.shape == (2,) * n_qubits:
+        state_vector = state_vector.reshape(-1)
         ret_shape = tuple(2 for _ in range(len(keep_indices)))
     else:
         raise ValueError("Input state_vector must be shaped like (2 ** n,) or (2,) * n")
 
-    keep_dims = 1 << len(keep_indices)
     if not np.isclose(np.linalg.norm(state_vector), 1):
         raise ValueError("Input state must be normalized.")
     if len(set(keep_indices)) != len(keep_indices):
         raise ValueError(f"keep_indices were {keep_indices} but must be unique.")
     if any(ind >= n_qubits for ind in keep_indices):
         raise ValueError("keep_indices {} are an invalid subset of the input state vector.")
 
-    other_qubits = sorted(set(range(n_qubits)) - set(keep_indices))
-    candidates = [
-        state_vector[predicates.slice_for_qubits_equal_to(other_qubits, k)].reshape(keep_dims)
-        for k in range(1 << len(other_qubits))
-    ]
-    # The coherence measure is computed using unnormalized candidates.
-    best_candidate = max(candidates, key=lambda c: float(np.linalg.norm(c, 2)))
-    best_candidate = best_candidate / np.linalg.norm(best_candidate)
-    left = np.conj(best_candidate.reshape((keep_dims,))).T
-    coherence_measure = sum(abs(np.dot(left, c.reshape((keep_dims,)))) ** 2 for c in candidates)
-
-    if protocols.approx_eq(coherence_measure, 1, atol=atol):
-        return np.exp(2j * np.pi * np.random.random()) * best_candidate.reshape(ret_shape)
+    # The permutation moves the specified qubits to the start of the qubit order.
+    keeps = frozenset(keep_indices)
+    remainder = np.array([i for i in range(n_qubits) if i not in keeps], dtype=np.int64)
+    permutation = np.concatenate([keep_indices, remainder])
+
+    # Permute qubits and construct the pure-state density matrix.
+    raveled = state_vector.reshape([2] * n_qubits)
+    raveled = np.transpose(raveled, permutation)
+    num_qubits_out = len(keep_indices)
+    c_psi = raveled.reshape([2**num_qubits_out, -1])
+    rho = c_psi @ c_psi.conj().T
+
+    # Return the eigenvector with eigenvalue 1.
+    evals, evec = np.linalg.eigh(rho)
+    if np.isclose(evals, 1, atol=atol).any():
+        factor_index = np.argmax(evals)
+        factored = evec[:, factor_index]
+        # Prevent accidental reliance on global phase.
+        random_phase = np.exp(2j * np.pi * np.random.random())
+        return random_phase * factored.reshape(ret_shape)
 
     # Method did not yield a pure state. Fall back to `default` argument.
-    if default is not RaiseValueErrorIfNotProvided:
+    if default is not None:
         return default
 
     raise EntangledStateError(

cirq-core/cirq/linalg/transformations_test.py

@@ -385,23 +385,31 @@ def test_sub_state_vector() -> None:
         cirq.sub_state_vector(reshaped_state, [5, 6, 7, 8], atol=1e-15), c
     )
 
+    # Make an imperfect product state to probe tolerances.
+    rng = np.random.default_rng(0)
+    noise = rng.uniform(-1, 1, size=state.size) + 1j * rng.uniform(-1, 1, state.size)
+    imperfect_state = state + 1e-2 * noise.reshape((2,) * 9)
+    imperfect_state /= np.linalg.norm(imperfect_state)
+
     # Reject factoring for very tight tolerance.
     assert (
-        cirq.sub_state_vector(state, [0, 1], default=_DEFAULT_ARRAY, atol=1e-16) is _DEFAULT_ARRAY
+        cirq.sub_state_vector(imperfect_state, [0, 1], default=_DEFAULT_ARRAY, atol=1e-16)
+        is _DEFAULT_ARRAY
     )
     assert (
-        cirq.sub_state_vector(state, [2, 3, 4], default=_DEFAULT_ARRAY, atol=1e-16)
+        cirq.sub_state_vector(imperfect_state, [2, 3, 4], default=_DEFAULT_ARRAY, atol=1e-16)
         is _DEFAULT_ARRAY
     )
     assert (
-        cirq.sub_state_vector(state, [5, 6, 7, 8], default=_DEFAULT_ARRAY, atol=1e-16)
+        cirq.sub_state_vector(imperfect_state, [5, 6, 7, 8], default=_DEFAULT_ARRAY, atol=1e-16)
         is _DEFAULT_ARRAY
     )
 
     # Permit invalid factoring for loose tolerance.
     for q1 in range(9):
         assert (
-            cirq.sub_state_vector(state, [q1], default=_DEFAULT_ARRAY, atol=1) is not _DEFAULT_ARRAY
+            cirq.sub_state_vector(imperfect_state, [q1], default=_DEFAULT_ARRAY, atol=1)
+            is not _DEFAULT_ARRAY
         )