Python quil.Program方法代碼示例

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def _defgate(self, program, gate_name, gate_matrix):
        """Defines a gate named gate_name with matrix gate_matrix in program. In addition, updates
         self.defined_gates to track what has been defined.

        :param Program program: Pyquil Program to add the defgate and gate to.
        :param str gate_name: The name of the gate to add to program.
        :param numpy.ndarray gate_matrix: The array corresponding to the gate to define.
        :return: the modified Program.
        :retype: Program
        """
        new_program = pq.Program()
        new_program += program
        if gate_name not in self.defined_gates:
            new_program.defgate(gate_name, gate_matrix)
            self.defined_gates.add(gate_name)
        return new_program 

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def state_tomography_programs(state_prep, qubits=None,
                              rotation_generator=tomography.default_rotations):
    """
    Yield tomographic sequences that prepare a state with Quil program `state_prep` and then append
    tomographic rotations on the specified `qubits`. If `qubits is None`, it assumes all qubits in
    the program should be tomographically rotated.

    :param Program state_prep: The program to prepare the state to be tomographed.
    :param list|NoneType qubits: A list of Qubits or Numbers, to perform the tomography on. If
    `None`, performs it on all in state_prep.
    :param generator rotation_generator: A generator that yields tomography rotations to perform.
    :return: Program for state tomography.
    :rtype: Program
    """
    if qubits is None:
        qubits = state_prep.get_qubits()
    for tomography_program in rotation_generator(*qubits):
        state_tomography_program = Program(Pragma("PRESERVE_BLOCK"))
        state_tomography_program.inst(state_prep)
        state_tomography_program.inst(tomography_program)
        state_tomography_program.inst(Pragma("END_PRESERVE_BLOCK"))
        yield state_tomography_program 

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def do_state_tomography(preparation_program, nsamples, cxn, qubits=None, use_run=False):
    """
    Method to perform both a QPU and QVM state tomography, and use the latter as
    as reference to calculate the fidelity of the former.

    :param Program preparation_program: Program to execute.
    :param int nsamples: Number of samples to take for the program.
    :param QVMConnection|QPUConnection cxn: Connection on which to run the program.
    :param list qubits: List of qubits for the program.
    to use in the tomography analysis.
    :param bool use_run: If ``True``, use append measurements on all qubits and use ``cxn.run``
        instead of ``cxn.run_and_measure``.
    :return: The state tomogram.
    :rtype: StateTomography
    """
    return tomography._do_tomography(preparation_program, nsamples, cxn, qubits,
                                     tomography.MAX_QUBITS_STATE_TOMO,
                                     StateTomography, state_tomography_programs,
                                     DEFAULT_STATE_TOMO_SETTINGS, use_run=use_run) 

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def basis_state_preps(*qubits):
    """
    Generate a sequence of programs that prepares the measurement
    basis states of some set of qubits in the order such that the qubit
    with highest index is iterated over the most quickly:
    E.g., for ``qubits=(0, 1)``, it returns the circuits::

        I_0 I_1
        I_0 X_1
        X_0 I_1
        X_0 X_1

    :param list qubits: Each qubit to include in the basis state preparation.
    :return: Yields programs for each basis state preparation.
    :rtype: Program
    """
    for prep in cartesian_product([I, X], repeat=len(qubits)):
        basis_prep = Program(Pragma("PRESERVE_BLOCK"))
        for gate, qubit in zip(prep, qubits):
            basis_prep.inst(gate(qubit))
        basis_prep.inst(Pragma("END_PRESERVE_BLOCK"))
        yield basis_prep 

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def density(self, pyquil_program):
        """
        Run program and compute the density matrix

        Loads and checks program if all gates are within the stabilizer set.
        Then executes program and returns the final density matrix for the
        stabilizer

        :param Program pyquil_program: a pyquil Program containing only
                                       CNOT-H-S-MEASUREMENT operations
        :return:
        """
        self.load_program(pyquil_program)
        self.tableau = self._n_qubit_tableau(self.num_qubits)
        self.kernel()
        stabilizers = binary_stabilizer_to_pauli_stabilizer(self.stabilizer_tableau())
        pauli_ops = reduce(lambda x, y: x * y, [0.5 * (sI(0) + b) for b in stabilizers])
        return tensor_up(pauli_ops, self.num_qubits) 

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def get_compiled_prog(theta):
    return Program([
        RZgate(-pi/2, 0),
        RXgate(-pi/2, 0),
        RZgate(-pi/2, 1),
        RXgate( pi/2, 1),
        CZgate(1, 0),
        RZgate(-pi/2, 1),
        RXgate(-pi/2, 1),
        RZgate(theta, 1),
        RXgate( pi/2, 1),
        CZgate(1, 0),
        RXgate( pi/2, 0),
        RZgate( pi/2, 0),
        RZgate(-pi/2, 1),
        RXgate( pi/2, 1),
        RZgate(-pi/2, 1),
    ]) 

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def test_exp_circuit(qvm):
    true_wf = np.array([0.54030231-0.84147098j,
                        0.00000000+0.j,
                        0.00000000+0.j,
                        0.00000000+0.j,
                        0.00000000+0.j,
                        0.00000000+0.j,
                        0.00000000+0.j,
                        0.00000000+0.j])

    create2kill1 = PauliTerm("X", 1, -0.25)*PauliTerm("Y", 2)
    create2kill1 += PauliTerm("Y", 1, 0.25)*PauliTerm("Y", 2)
    create2kill1 += PauliTerm("Y", 1, 0.25)*PauliTerm("X", 2)
    create2kill1 += PauliTerm("X", 1, 0.25)*PauliTerm("X", 2)
    create2kill1 += PauliTerm("I", 0, 1.0)
    prog = Program()
    for term in create2kill1.terms:
        single_exp_prog = exponentiate(term)
        prog += single_exp_prog

    wf, _ = qvm.wavefunction(prog)
    wf = np.reshape(wf.amplitudes, -1)
    assert np.allclose(wf.dot(np.conj(wf).T), true_wf.dot(np.conj(true_wf).T)) 

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def test_errors(qvm):
    # NOP unsupported
    prog = Program(NOP)
    with pytest.raises(TypeError):
        qvm.wavefunction(prog)
    with pytest.raises(TypeError):
        qvm.run(prog)
    with pytest.raises(TypeError):
        qvm.run_and_measure(prog)

    # WAIT unsupported
    prog = Program(WAIT)
    with pytest.raises(TypeError):
        qvm.wavefunction(prog)
    with pytest.raises(TypeError):
        qvm.run(prog)
    with pytest.raises(TypeError):
        qvm.run_and_measure(prog) 

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def tests_against_cloud(qvm):

    # simple program
    p = Program(H(0))
    cloud_results = [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]]
    local_results = qvm.run(p, classical_addresses=[0], trials=10)
    assert len(cloud_results) == len(local_results)
    
    cloud_wf = dc.HADAMARD_WF
    local_wf, _ = qvm.wavefunction(p)
    assert np.allclose(cloud_wf, local_wf.amplitudes)

    # complex program
    p = Program(dc.QFT_8_INSTRUCTIONS)
    cloud_wf = dc.QFT_8_WF_PROBS
    local_wf, _ = qvm.wavefunction(p)
    assert np.allclose(cloud_wf, local_wf.amplitudes) 

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def test_random_gates(qvm_unitary):
    p = Program().inst([H(0), H(1), H(0)])
    test_unitary = qvm_unitary.unitary(p)
    actual_unitary = np.kron(gate_matrix['H'], np.eye(2 ** 1))
    assert np.allclose(test_unitary, actual_unitary)

    p = Program().inst([H(0), X(1), Y(2), Z(3)])
    test_unitary = qvm_unitary.unitary(p)
    actual_unitary = np.kron(gate_matrix['Z'],
                             np.kron(gate_matrix['Y'],
                                     np.kron(gate_matrix['X'],
                                             gate_matrix['H'])))
    assert np.allclose(test_unitary, actual_unitary)

    p = Program().inst([X(2), CNOT(2, 1), CNOT(1, 0)])
    test_unitary = qvm_unitary.unitary(p)
    # gates are multiplied in 'backwards' order
    actual_unitary = np.kron(np.eye(2 ** 1), gate_matrix['CNOT']).dot(
                     np.kron(gate_matrix['CNOT'], np.eye(2 ** 1))).dot(
                     np.kron(gate_matrix['X'], np.eye(2 ** 2)))
    assert np.allclose(test_unitary, actual_unitary) 

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def test_qaoa_unitary(qvm_unitary):
    wf_true = [0.00167784 + 1.00210180e-05*1j, 0.50000000 - 4.99997185e-01*1j,
               0.50000000 - 4.99997185e-01*1j, 0.00167784 + 1.00210180e-05*1j]

    prog = Program()
    prog.inst([RY(np.pi/2, 0), RX(np.pi, 0),
               RY(np.pi/2, 1), RX(np.pi, 1),
               CNOT(0, 1), RX(-np.pi/2, 1), RY(4.71572463191, 1),
               RX(np.pi/2, 1), CNOT(0, 1),
               RX(-2*2.74973750579, 0), RX(-2*2.74973750579, 1)])

    test_unitary = qvm_unitary.unitary(prog)
    wf_test = np.zeros(4)
    wf_test[0] = 1.0
    wf_test = test_unitary.dot(wf_test)
    assert np.allclose(wf_test, wf_true) 

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def test_tensor_gates_single_qubit():
    prog = Program().inst([Hgate(0)])
    test_unitary = tensor_gates(gate_matrix, {}, prog.instructions[0], 1).toarray()
    true_unitary = gate_matrix['H']
    assert np.allclose(test_unitary, true_unitary)

    prog = Program().inst([Hgate(0)])
    test_unitary = tensor_gates(gate_matrix, {}, prog.instructions[0], 5).toarray()
    true_unitary = np.kron(np.eye(2**4), gate_matrix['H'])
    assert np.allclose(test_unitary, true_unitary)

    prog = Program().inst([RXgate(0.2, 3)])
    test_unitary = tensor_gates(gate_matrix, {}, prog.instructions[0], 5).toarray()
    true_unitary = np.kron(np.eye(2**1), np.kron(gate_matrix['RX'](0.2),  np.eye(2**3)))
    assert np.allclose(test_unitary, true_unitary)

    prog = Program().inst([RXgate(0.5, 4)])
    test_unitary = tensor_gates(gate_matrix, {}, prog.instructions[0], 5).toarray()
    true_unitary = np.kron(np.eye(2**0), np.kron(gate_matrix['RX'](0.5),  np.eye(2**4)))
    assert np.allclose(test_unitary, true_unitary) 

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def test_simulation_phase():
    """
    Test if the Phase gate is applied correctly to the tableau

    S|0> = |0>

    S|+> = |R>
    """
    prog = Program().inst(S(0))
    qvmstab = QVM_Stabilizer(num_qubits=1)
    qvmstab._apply_phase(prog.instructions[0])
    true_stab = np.array([[1, 1, 0],
                          [0, 1, 0]])
    assert np.allclose(true_stab, qvmstab.tableau)

    prog = Program().inst([H(0), S(0)])
    qvmstab = QVM_Stabilizer(num_qubits=1)
    qvmstab._apply_hadamard(prog.instructions[0])
    qvmstab._apply_phase(prog.instructions[1])
    true_stab = np.array([[0, 1, 0],
                          [1, 1, 0]])
    assert np.allclose(true_stab, qvmstab.tableau) 

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def run(self, pyquil_program, classical_addresses=None, trials=1):
        """
        Run a pyQuil program multiple times, accumulating the values deposited
        in a list of classical addresses.

        This uses numpy's inverse sampling method to calculate bit string
        outcomes

        :param Program pyquil_program: A pyQuil program.
        :param list classical_addresses: A list of classical addresses.
                                         This is ignored but kept to have
                                         similar input as Forest QVM.
        :param int trials: Number of shots to collect.

        :return: A list of lists of bits. Each sublist corresponds to the
                 values in `classical_addresses`.
        :rtype: list
        """
        results = []
        for trial in range(trials):
            _ = self.density(pyquil_program)
            results.append(self.classical_memory[classical_addresses])
        return list(results) 

# 需要導入模塊: from pyquil import quil [as 別名]
# 或者: from pyquil.quil import Program [as 別名]
def run(self, pyquil_program, classical_addresses=None, trials=1):
        """
        Run a pyQuil program multiple times, accumulating the values deposited
        in a list of classical addresses.

        :param Program pyquil_program: A pyQuil program.
        :param list classical_addresses: A list of classical addresses.
        :param int trials: Number of shots to collect.

        :return: A list of lists of bits. Each sublist corresponds to the
                 values in `classical_addresses`.
        :rtype: list
        """
        results = []
        for trial in range(trials):
            _, classical_vals = self.wavefunction(pyquil_program,
                                                  classical_addresses=classical_addresses)
            results.append(classical_vals)

        return results