add test case for mqmatrix method

ccsrc/lib/simulator/densitymatrix/detail/cpu_common/cpu_densitymatrix_core_policy.cpp

@@ -252,12 +252,11 @@ auto CPUDensityMatrixPolicyBase<derived_, calc_type_>::GetPartialTrace(const qs_
     return res;
 }
 
-
 template <typename derived_, typename calc_type_>
 auto CPUDensityMatrixPolicyBase<derived_, calc_type_>::PureStateVector(const qs_data_p_t& qs, index_t dim)
     -> py_qs_datas_t {
     auto p = Purity(qs, dim);
-    if (std::abs(p - 1) > 1e-8) {
+    if (1 - p > 1e-6) {
         throw(std::runtime_error("PureStateVector(): Cannot transform mixed density matrix to vector."));
     }
     if (qs == nullptr) {

mindquantum/core/gates/basicgate.py

@@ -70,6 +70,7 @@ class UnivMathGate(NoneParamNonHermMat):
 
     def __init__(self, name, matrix_value):
         """Initialize a UnivMathGate object."""
+        _check_input_type('matrix_value', np.ndarray, matrix_value)
         if len(matrix_value.shape) != 2:
             raise ValueError(f"matrix_value require shape of 2, but get shape of {matrix_value.shape}")
         if matrix_value.shape[0] != matrix_value.shape[1]:

mindquantum/core/gates/channel.py

@@ -621,7 +621,7 @@ def __init__(self, name: str, kraus_op, **kwargs):
         sum_of_mat = np.zeros((2, 2), 'complex128')
         for mat in kraus_op:
             sum_of_mat += np.dot(mat.T.conj(), mat)
-        if not np.allclose(sum_of_mat, [[1, 0], [0, 1]]):
+        if not np.allclose(sum_of_mat, [[1, 0], [0, 1]], atol=1e-6):
             raise ValueError(f"kraus_op need to satisfy the completeness condition, but get {sum_of_mat}")
         kwargs['name'] = name
         kwargs['n_qubits'] = 1

mindquantum/simulator/mqsim.py

@@ -471,7 +471,7 @@ def set_qs(self, quantum_state: np.ndarray):
                     raise ValueError("Wrong quantum state.")
                 self.sim.set_qs(quantum_state / norm_factor)
             elif len(quantum_state.shape) == 2:
-                if not np.allclose(quantum_state, quantum_state.T.conj()):
+                if not np.allclose(quantum_state, quantum_state.T.conj(), atol=1e-6):
                     raise ValueError("density matrix must be hermitian.")
                 if (quantum_state.diagonal() < 0).any():
                     raise ValueError("the diagonal terms in density matrix cannot be negative.")
@@ -528,6 +528,6 @@ def get_pure_state_vector(self) -> np.ndarray:
         """Get the state vector from a pure density matrix."""
         if self.name.startswith('mqvector'):
             return self.get_qs()
-        if 1 - self.purity() > 1e-8:
+        if 1 - self.purity() > 1e-6:
             raise ValueError("Cannot transform mixed density matrix to vector.")
         return np.array(self.sim.pure_state_vector())

mindquantum/simulator/simulator.py

@@ -679,21 +679,21 @@ def fidelity(rho: np.ndarray, sigma: np.ndarray):
         if np.log2(qs.shape[0]) % 1 != 0:
             raise ValueError(f"quantum state size {qs.shape[0]} is not power of 2")
         if qs.ndim == 1:
-            if not np.allclose(np.sum(np.abs(qs) ** 2), 1):
+            if not np.allclose(np.sum(np.abs(qs) ** 2), 1, atol=1e-6):
                 raise ValueError("state vector must be normalized.")
         elif qs.ndim == 2:
             if qs.shape[0] != qs.shape[1]:
                 raise ValueError("the row of matrix is not equal to column.")
-            if not np.allclose(qs, qs.T.conj()):
+            if not np.allclose(qs, qs.T.conj(), atol=1e-6):
                 raise ValueError("density matrix must be hermitian.")
             if (qs.diagonal() < 0).any():
                 raise ValueError("the diagonal terms in density matrix cannot be negative.")
-            if not np.allclose(np.real(np.trace(qs)), 1):
+            if not np.allclose(np.real(np.trace(qs)), 1, atol=1e-6):
                 raise ValueError("the trace of density matrix must equal to 1.")
         else:
             raise ValueError(f"input quantum state requires a vector or matrix, but get shape {rho.shape}.")
     if rho.ndim == 1 and sigma.ndim == 1:
-        return np.abs(np.inner(rho, sigma)) ** 2
+        return np.abs(np.inner(rho.conj().T, sigma)) ** 2
     if rho.ndim == 1 and sigma.ndim == 2:
         return np.real(rho.conj().T @ sigma @ rho)
     if rho.ndim == 2 and sigma.ndim == 1: