Пример #1
0
def print_gate(t):
    print('t =', t)
    matrix = U00(t)
    gates = qd.matrix_to_gates(matrix)
    circuit = qd.matrix_to_cirq_circuit(matrix)
    print(circuit)
    print()
Пример #2
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def matrix_to_sycamore_operations(
    target_qubits: List[cirq.GridQubit],
    matrix: np.ndarray,
) -> Tuple[cirq.OP_TREE, List[cirq.GridQubit]]:
    """Handle cases for 1 (and 2) qubit(s) separately.
    
    A method to convert a unitary matrix to a list of Sycamore operations.

    This method will return a list of `cirq.Operation`s using the qubits and (optionally) ancilla
    qubits to implement the unitary matrix `matrix` on the target qubits `qubits`.
    The operations are also supported by `cirq.google.gate_sets.SYC_GATESET`.
    
    Args:
        target_qubits: list of qubits the returned operations will act on. The qubit order defined by the list
            is assumed to be used by the operations to implement `matrix`.
        matrix: a matrix that is guaranteed to be unitary and of size (2**len(qs), 2**len(qs)).
    Returns:
        A tuple of operations and ancilla qubits allocated.
            Operations: In case the matrix is supported, a list of operations `ops` is returned.
                `ops` acts on `qs` qubits and for which `cirq.unitary(ops)` is equal to `matrix` up
                 to certain tolerance. In case the matrix is not supported, it might return NotImplemented to
                 reduce the noise in the judge output.
            Ancilla qubits: In case ancilla qubits are allocated a list of ancilla qubits. Otherwise
                an empty list.
    """
    unitaries = decompose_to_two_level_unitaries(target_qubits.count(), matrix)

    # ops = decompose_unitaries_to_ops(target_qubits, unitaries)

    # Validate ops with cirq.google.gate_sets.SYC_GATESET and return accordingly

    gates = qd.matrix_to_cirq_circuit(matrix)
    # print(gates)
    return NotImplemented, []
Пример #3
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def random_matrix(
    target_qubits: List[cirq.GridQubit],
    matrix: np.ndarray,
    swap=True,
) -> Tuple[cirq.OP_TREE, List[cirq.GridQubit]]:

    circuit = quantum_decomp.matrix_to_cirq_circuit(matrix)
    old_qubits = [(str(x), x) for x in circuit.all_qubits()]
    old_qubits = sorted(old_qubits)
    old_qubits = [x[1] for x in old_qubits]
    mapping = dict(zip(old_qubits, target_qubits))

    print(mapping)

    decomp_ops = []
    ops = circuit.all_operations()

    for op in ops:
        if type(op) == cirq.ops.controlled_operation.ControlledOperation:
            gate = op

            controls = gate.controls
            target = gate.sub_operation.qubits[0]
            matrix = cirq.unitary(gate.sub_operation.gate)

            decomp_ops.extend([
                x.transform_qubits(mapping)
                for x in cirq.optimizers.decompose_multi_controlled_rotation(
                    matrix, list(controls), target)
            ])
        else:
            decomp_ops.append(op.transform_qubits(mapping))

    swapped_ops = []
    for op in decomp_ops:
        if len(op.qubits) == 2:
            q0 = op.qubits[0]
            q1 = op.qubits[1]

            if swap:
                oplist, target = do_swap(q0, q1)
                adjacentop = op.transform_qubits({q0: target, q1: q1})
                revlist = swap_to(target, q0)

                swapped_ops.extend(oplist)
                swapped_ops.append(adjacentop)
                swapped_ops.extend(revlist)
            else:
                swapped_ops.append(op)
        else:
            swapped_ops.append(op)

    SycamoreGates = cirq.google.optimized_for_sycamore(
        cirq.Circuit(swapped_ops),
        optimizer_type='sycamore',
    )

    return SycamoreGates, []
Пример #4
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                    [0, 0, 0, np.exp(-1j * t)]])
    U11 = np.array([[np.exp(1j * t), 0, 0, 0],
                    [0, (np.exp(1j * t) + 1) / 2,
                     np.expm1(1j * t) / 2, 0],
                    [0, np.expm1(1j * t) / 2, (np.exp(1j * t) + 1) / 2, 0],
                    [0, 0, 0, 1]])
    Up = np.array([[np.exp(1j * t), 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0],
                   [0, 0, 0, np.exp(-1j * t)]])
    Um = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])
    return np.kron(o0000, U00) + np.kron(o1111, U11) + np.kron(
        opp, Up) + np.kron(omm, Um)


# for i,t in enumerate(np.arange(0, 2*π+0.1, π/8)):
#     print('t =', str(i) + 'π/8')
#     print(qd.matrix_to_cirq_circuit(U0_matrix(t), optimize=True))

# for matrix in qd.two_level_decompose(U0_matrix(π/8)):
#     print(matrix)


def keep_fuction(oper: cirq.Operation) -> bool:
    print(oper, oper.gate.num_qubits())
    return oper.gate.num_qubits() <= 2


print(qd.matrix_to_cirq_circuit(U0_matrix(π / 8)))
decomp_circuit = cirq.decompose(qd.matrix_to_cirq_circuit(U0_matrix(π / 8)),
                                keep=keep_fuction)
print(cirq.Circuit(decomp_circuit))
Пример #5
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 def _check(A):
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
         assert_all_close(A, qd.matrix_to_cirq_circuit(A).unitary())