Esempio n. 1
0
 def test_qregs(self):
     """Test getting quantum registers from circuit."""
     qr1 = QuantumRegister(10, "q")
     self.assertEqual(qr1.name, "q")
     self.assertEqual(qr1.size, 10)
     self.assertEqual(type(qr1), QuantumRegister)
 def test_append_opaque_wrong_dimension(self):
     """test appending opaque gate to wrong dimension wires."""
     qr = QuantumRegister(2)
     circ = QuantumCircuit(qr)
     opaque_gate = Gate(name="crz_2", num_qubits=2, params=[0.5])
     self.assertRaises(CircuitError, circ.append, opaque_gate, [qr[0]])
    def run(self, dag):
        """Run the ConsolidateBlocks pass on `dag`.

        Iterate over each block and replace it with an equivalent Unitary
        on the same wires.
        """

        if self.decomposer is None:
            return dag

        new_dag = DAGCircuit()
        for qreg in dag.qregs.values():
            new_dag.add_qreg(qreg)
        for creg in dag.cregs.values():
            new_dag.add_creg(creg)

        # compute ordered indices for the global circuit wires
        global_index_map = {wire: idx for idx, wire in enumerate(dag.qubits)}

        blocks = self.property_set['block_list']
        # just to make checking if a node is in any block easier
        all_block_nodes = {nd for bl in blocks for nd in bl}

        for node in dag.topological_op_nodes():
            if node not in all_block_nodes:
                # need to add this node to find out where in the list it goes
                preds = [
                    nd for nd in dag.predecessors(node) if nd.type == 'op'
                ]

                block_count = 0
                while preds:
                    if block_count < len(blocks):
                        block = blocks[block_count]

                        # if any of the predecessors are in the block, remove them
                        preds = [p for p in preds if p not in block]
                    else:
                        # should never occur as this would mean not all
                        # nodes before this one topologically had been added
                        # so not all predecessors were removed
                        raise TranspilerError(
                            "Not all predecessors removed due to error"
                            " in topological order")

                    block_count += 1

                # we have now seen all predecessors
                # so update the blocks list to include this block
                blocks = blocks[:block_count] + [[node]] + blocks[block_count:]

        # create the dag from the updated list of blocks
        basis_gate_name = self.decomposer.gate.name
        for block in blocks:
            if len(block) == 1 and (block[0].name != basis_gate_name
                                    or block[0].op.is_parameterized()):
                # an intermediate node that was added into the overall list
                new_dag.apply_operation_back(block[0].op, block[0].qargs,
                                             block[0].cargs)
            else:
                # find the qubits involved in this block
                block_qargs = set()
                block_cargs = set()
                for nd in block:
                    block_qargs |= set(nd.qargs)
                    if nd.condition:
                        block_cargs |= set(nd.condition[0])
                # convert block to a sub-circuit, then simulate unitary and add
                q = QuantumRegister(len(block_qargs))
                # if condition in node, add clbits to circuit
                if len(block_cargs) > 0:
                    c = ClassicalRegister(len(block_cargs))
                    subcirc = QuantumCircuit(q, c)
                else:
                    subcirc = QuantumCircuit(q)
                block_index_map = self._block_qargs_to_indices(
                    block_qargs, global_index_map)
                basis_count = 0
                for nd in block:
                    if nd.op.name == basis_gate_name:
                        basis_count += 1
                    subcirc.append(nd.op,
                                   [q[block_index_map[i]] for i in nd.qargs])
                unitary = UnitaryGate(
                    Operator(subcirc))  # simulates the circuit

                max_2q_depth = 20  # If depth > 20, there will be 1q gates to consolidate.
                if (self.force_consolidate or unitary.num_qubits > 2 or
                        self.decomposer.num_basis_gates(unitary) < basis_count
                        or len(subcirc) > max_2q_depth):
                    new_dag.apply_operation_back(
                        UnitaryGate(unitary),
                        sorted(block_qargs, key=lambda x: block_index_map[x]))
                else:
                    for nd in block:
                        new_dag.apply_operation_back(nd.op, nd.qargs, nd.cargs)

        return new_dag
    CCXGate,
    YGate,
    CYGate,
    RYYGate,
    ECRGate,
    ZGate,
    CZGate,
)

_sel = StandardEquivalenceLibrary = EquivalenceLibrary()

# Import existing gate definitions

# HGate

q = QuantumRegister(1, 'q')
def_h = QuantumCircuit(q)
def_h.append(U2Gate(0, pi), [q[0]], [])
_sel.add_equivalence(HGate(), def_h)

# CHGate

q = QuantumRegister(2, 'q')
def_ch = QuantumCircuit(q)
for inst, qargs, cargs in [(SGate(), [q[1]], []), (HGate(), [q[1]], []),
                           (TGate(), [q[1]], []), (CXGate(), [q[0], q[1]], []),
                           (TdgGate(), [q[1]], []), (HGate(), [q[1]], []),
                           (SdgGate(), [q[1]], [])]:
    def_ch.append(inst, qargs, cargs)
_sel.add_equivalence(CHGate(), def_ch)
Esempio n. 5
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 def test_dag_get_qubits(self):
     """get_qubits() method """
     dag = DAGCircuit()
     dag.add_qreg(QuantumRegister(1, 'qr1'))
     dag.add_qreg(QuantumRegister(1, 'qr10'))
     dag.add_qreg(QuantumRegister(1, 'qr0'))
     dag.add_qreg(QuantumRegister(1, 'qr3'))
     dag.add_qreg(QuantumRegister(1, 'qr4'))
     dag.add_qreg(QuantumRegister(1, 'qr6'))
     self.assertListEqual(dag.qubits(), [(QuantumRegister(1, 'qr1'), 0),
                                         (QuantumRegister(1, 'qr10'), 0),
                                         (QuantumRegister(1, 'qr0'), 0),
                                         (QuantumRegister(1, 'qr3'), 0),
                                         (QuantumRegister(1, 'qr4'), 0),
                                         (QuantumRegister(1, 'qr6'), 0)])
import os

from qiskit import execute
from qiskit.circuit import QuantumRegister, ClassicalRegister, QuantumCircuit

from quantuminspire.credentials import get_authentication
from quantuminspire.qiskit import QI

QI_URL = os.getenv('API_URL', 'https://api.quantum-inspire.com/')

project_name = 'Qiskit-entangle'
authentication = get_authentication()
QI.set_authentication(authentication, QI_URL, project_name=project_name)
qi_backend = QI.get_backend('QX single-node simulator')

q = QuantumRegister(2)
b = ClassicalRegister(2)
circuit = QuantumCircuit(q, b)

circuit.h(q[0])
circuit.cx(q[0], q[1])
circuit.measure(q, b)

qi_job = execute(circuit, backend=qi_backend, shots=256)
qi_result = qi_job.result()
histogram = qi_result.get_counts(circuit)
print('\nState\tCounts')
[
    print('{0}\t\t{1}'.format(state, counts))
    for state, counts in histogram.items()
]
Esempio n. 7
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    def __init__(self,
                 num_result_qubits: Optional[int] = None,
                 quadratic: Optional[Union[np.ndarray, List[List[Union[
                     float, ParameterExpression]]]]] = None,
                 linear: Optional[Union[np.ndarray, List[Union[
                     float, ParameterExpression]]]] = None,
                 offset: Optional[Union[float, ParameterExpression]] = None,
                 little_endian: bool = True) -> None:
        r"""
        Args:
            num_result_qubits: The number of qubits to encode the result. Called :math:`m` in
                the class documentation.
            quadratic: A matrix containing the quadratic coefficients, :math:`A`.
            linear: An array containing the linear coefficients, :math:`b`.
            offset: A constant offset, :math:`c`.
            little_endian: Encode the result in little endianness.

        Raises:
            ValueError: If ``linear`` and ``quadratic`` have mismatching sizes.
            ValueError: If ``num_result_qubits`` is unspecified but cannot be determined because
                some values of the quadratic form are parameterized.
        """
        # check inputs match
        if quadratic is not None and linear is not None:
            if len(quadratic) != len(linear):
                raise ValueError('Mismatching sizes of quadratic and linear.')

        # temporarily set quadratic and linear to [] instead of None so we can iterate over them
        if quadratic is None:
            quadratic = []

        if linear is None:
            linear = []

        if offset is None:
            offset = 0

        num_input_qubits = np.max([1, len(linear), len(quadratic)])

        # deduce number of result bits if not added
        if num_result_qubits is None:
            # check no value is parameterized
            if (any(
                    any(isinstance(q_ij, ParameterExpression) for q_ij in q_i)
                    for q_i in quadratic) or any(
                        isinstance(l_i, ParameterExpression) for l_i in linear)
                    or isinstance(offset, ParameterExpression)):
                raise ValueError(
                    'If the number of result qubits is not specified, the quadratic '
                    'form matrices/vectors/offset may not be parameterized.')
            num_result_qubits = self.required_result_qubits(
                quadratic, linear, offset)

        qr_input = QuantumRegister(num_input_qubits)
        qr_result = QuantumRegister(num_result_qubits)
        super().__init__(qr_input, qr_result, name='Q(x)')

        # set quadratic and linear again to None if they were None
        if len(quadratic) == 0:
            quadratic = None

        if len(linear) == 0:
            linear = None

        scaling = np.pi * 2**(1 - num_result_qubits)

        # initial QFT (just hadamards)
        self.h(qr_result)

        if little_endian:
            qr_result = qr_result[::-1]

        # constant coefficient
        if offset != 0:
            for i, q_i in enumerate(qr_result):
                self.u1(scaling * 2**i * offset, q_i)

        # the linear part consists of the vector and the diagonal of the
        # matrix, since x_i * x_i = x_i, as x_i is a binary variable
        for j in range(num_input_qubits):
            value = linear[j] if linear is not None else 0
            value += quadratic[j][j] if quadratic is not None else 0
            if value != 0:
                for i, q_i in enumerate(qr_result):
                    self.cu1(scaling * 2**i * value, qr_input[j], q_i)

        # the quadratic part adds A_ij and A_ji as x_i x_j == x_j x_i
        if quadratic is not None:
            for j in range(num_input_qubits):
                for k in range(j + 1, num_input_qubits):
                    value = quadratic[j][k] + quadratic[k][j]
                    if value != 0:
                        for i, q_i in enumerate(qr_result):
                            self.mcu1(scaling * 2**i * value,
                                      [qr_input[j], qr_input[k]], q_i)

        # add the inverse QFT
        iqft = QFT(num_result_qubits, do_swaps=False).inverse()
        self.append(iqft, qr_result)
Esempio n. 8
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 def _define(self):
     """The standard definition used the Gray code implementation."""
     q = QuantumRegister(self.num_qubits, name='q')
     self.definition = [(MCXGrayCode(self.num_ctrl_qubits), q[:], [])]
Esempio n. 9
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    def _define(self):
        """Define the MCX gate using a V-chain of CX gates."""
        q = QuantumRegister(self.num_qubits, name='q')
        q_controls = q[:self.num_ctrl_qubits]
        q_target = q[self.num_ctrl_qubits]
        q_ancillas = q[self.num_ctrl_qubits + 1:]

        definition = []

        if self._dirty_ancillas:
            i = self.num_ctrl_qubits - 3
            ancilla_pre_rule = [
                (U2Gate(0, numpy.pi), [q_target], []),
                (CXGate(), [q_target, q_ancillas[i]], []),
                (U1Gate(-numpy.pi / 4), [q_ancillas[i]], []),
                (CXGate(), [q_controls[-1], q_ancillas[i]], []),
                (U1Gate(numpy.pi / 4), [q_ancillas[i]], []),
                (CXGate(), [q_target, q_ancillas[i]], []),
                (U1Gate(-numpy.pi / 4), [q_ancillas[i]], []),
                (CXGate(), [q_controls[-1], q_ancillas[i]], []),
                (U1Gate(numpy.pi / 4), [q_ancillas[i]], []),
            ]
            for inst in ancilla_pre_rule:
                definition.append(inst)

            for j in reversed(range(2, self.num_ctrl_qubits - 1)):
                definition.append(
                    (RCCXGate(),
                     [q_controls[j], q_ancillas[i - 1], q_ancillas[i]], []))
                i -= 1

        definition.append(
            (RCCXGate(), [q_controls[0], q_controls[1], q_ancillas[0]], []))
        i = 0
        for j in range(2, self.num_ctrl_qubits - 1):
            definition.append(
                (RCCXGate(), [q_controls[j], q_ancillas[i],
                              q_ancillas[i + 1]], []))
            i += 1

        if self._dirty_ancillas:
            ancilla_post_rule = [
                (U1Gate(-numpy.pi / 4), [q_ancillas[i]], []),
                (CXGate(), [q_controls[-1], q_ancillas[i]], []),
                (U1Gate(numpy.pi / 4), [q_ancillas[i]], []),
                (CXGate(), [q_target, q_ancillas[i]], []),
                (U1Gate(-numpy.pi / 4), [q_ancillas[i]], []),
                (CXGate(), [q_controls[-1], q_ancillas[i]], []),
                (U1Gate(numpy.pi / 4), [q_ancillas[i]], []),
                (CXGate(), [q_target, q_ancillas[i]], []),
                (U2Gate(0, numpy.pi), [q_target], []),
            ]
            for inst in ancilla_post_rule:
                definition.append(inst)
        else:
            definition.append(
                (CCXGate(), [q_controls[-1], q_ancillas[i], q_target], []))

        for j in reversed(range(2, self.num_ctrl_qubits - 1)):
            definition.append(
                (RCCXGate(), [q_controls[j], q_ancillas[i - 1],
                              q_ancillas[i]], []))
            i -= 1
        definition.append(
            (RCCXGate(), [q_controls[0], q_controls[1], q_ancillas[i]], []))

        if self._dirty_ancillas:
            for i, j in enumerate(list(range(2, self.num_ctrl_qubits - 1))):
                definition.append(
                    (RCCXGate(),
                     [q_controls[j], q_ancillas[i], q_ancillas[i + 1]], []))

        self.definition = definition
Esempio n. 10
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 def test_qarg_noninteger_float(self):
     """Test attempt to pass non-integer float to QuantumRegister."""
     self.assertRaises(CircuitError, QuantumRegister, 2.2)
     # but an integer float should pass
     qr = QuantumRegister(2.0)
     self.assertEqual(qr.size, 2)
Esempio n. 11
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    def _multiplex(self, target_gate, list_of_angles):
        """
        Return a recursive implementation of a multiplexor circuit,
        where each instruction itself has a decomposition based on
        smaller multiplexors.

        The LSB is the multiplexor "data" and the other bits are multiplexor "select".

        Args:
            target_gate (Gate): Ry or Rz gate to apply to target qubit, multiplexed
                over all other "select" qubits
            list_of_angles (list[float]): list of rotation angles to apply Ry and Rz

        Returns:
            DAGCircuit: the circuit implementing the multiplexor's action
        """
        list_len = len(list_of_angles)
        local_num_qubits = int(math.log2(list_len)) + 1

        q = QuantumRegister(local_num_qubits)
        circuit = QuantumCircuit(q,
                                 name="multiplex" + local_num_qubits.__str__())

        lsb = q[0]
        msb = q[local_num_qubits - 1]

        # case of no multiplexing: base case for recursion
        if local_num_qubits == 1:
            circuit.append(target_gate(list_of_angles[0]), [q[0]])
            return circuit

        # calc angle weights, assuming recursion (that is the lower-level
        # requested angles have been correctly implemented by recursion
        angle_weight = scipy.kron([[0.5, 0.5], [0.5, -0.5]],
                                  np.identity(2**(local_num_qubits - 2)))

        # calc the combo angles
        list_of_angles = angle_weight.dot(np.array(list_of_angles)).tolist()

        # recursive step on half the angles fulfilling the above assumption
        multiplex_1 = self._multiplex(target_gate,
                                      list_of_angles[0:(list_len // 2)])
        circuit.append(multiplex_1.to_instruction(), q[0:-1])

        # attach CNOT as follows, thereby flipping the LSB qubit
        circuit.append(CnotGate(), [msb, lsb])

        # implement extra efficiency from the paper of cancelling adjacent
        # CNOTs (by leaving out last CNOT and reversing (NOT inverting) the
        # second lower-level multiplex)
        multiplex_2 = self._multiplex(target_gate,
                                      list_of_angles[(list_len // 2):])
        if list_len > 1:
            circuit.append(multiplex_2.to_instruction().mirror(), q[0:-1])
        else:
            circuit.append(multiplex_2.to_instruction(), q[0:-1])

        # attach a final CNOT
        circuit.append(CnotGate(), [msb, lsb])

        return circuit
Esempio n. 12
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 def test_qregs_eq_invalid_type(self):
     """Test getting quantum registers from circuit."""
     qr1 = QuantumRegister(10, "q")
     self.assertNotEqual(qr1, 3.14)
Esempio n. 13
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 def test_apply_cx_to_non_register(self):
     """test applying ccx to non-register raises"""
     qr = QuantumRegister(10)
     cr = ClassicalRegister(10)
     qc = QuantumCircuit(qr, cr)
     self.assertRaises(CircuitError, qc.cx, qc[0:2], qc[2:4])
Esempio n. 14
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 def test_apply_ccx_to_empty_slice(self):
     """test applying ccx to non-register raises"""
     qr = QuantumRegister(10)
     cr = ClassicalRegister(10)
     qc = QuantumCircuit(qr, cr)
     self.assertRaises(CircuitError, qc.ccx, qr[2:0], qr[4:2], qr[7:5])
Esempio n. 15
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    def convert(
        self,
        operator: CircuitStateFn,
        params: Union[ParameterExpression, ParameterVector,
                      List[ParameterExpression]],
    ) -> ListOp:
        r"""
        Args:
            operator: The operator corresponding to the quantum state :math:`|\psi(\omega)\rangle`
                for which we compute the QFI.
            params: The parameters :math:`\omega` with respect to which we are computing the QFI.

        Returns:
            A ``ListOp[ListOp]`` where the operator at position ``[k][l]`` corresponds to the matrix
            element :math:`k, l` of the QFI.

        Raises:
            TypeError: If ``operator`` is an unsupported type.
        """
        # QFI & phase fix observable
        qfi_observable = ~StateFn(4 * Z ^ (I ^ operator.num_qubits))
        phase_fix_observable = StateFn(
            (Z + 1j * Y) ^ (I ^ operator.num_qubits), is_measurement=True)
        # see https://arxiv.org/pdf/quant-ph/0108146.pdf

        # Check if the given operator corresponds to a quantum state given as a circuit.
        if not isinstance(operator, CircuitStateFn):
            raise TypeError(
                "LinCombFull is only compatible with states that are given as "
                f"CircuitStateFn, not {type(operator)}")

        # If a single parameter is given wrap it into a list.
        if isinstance(params, ParameterExpression):
            params = [params]
        elif isinstance(params, ParameterVector):
            params = params[:]  # unroll to list

        # First, the operators are computed which can compensate for a potential phase-mismatch
        # between target and trained state, i.e.〈ψ|∂lψ〉
        gradient_states = LinComb()._gradient_states(
            operator,
            meas_op=phase_fix_observable,
            target_params=params,
            open_ctrl=False,
            trim_after_grad_gate=True,
        )
        # if type(gradient_states) in [ListOp, SummedOp]:  # pylint: disable=unidiomatic-typecheck
        if type(gradient_states) == ListOp:
            phase_fix_states = gradient_states.oplist
        else:
            phase_fix_states = [gradient_states]

        # Get  4 * Re[〈∂kψ|∂lψ]
        qfi_operators = []
        # Add a working qubit
        qr_work = QuantumRegister(1, "work_qubit")
        state_qc = QuantumCircuit(*operator.primitive.qregs, qr_work)
        state_qc.h(qr_work)
        state_qc.compose(operator.primitive, inplace=True)

        # Get the circuits needed to compute〈∂iψ|∂jψ〉
        for i, param_i in enumerate(params):  # loop over parameters
            qfi_ops = []
            for j, param_j in enumerate(params[i:], i):
                # Get the gates of the quantum state which are parameterized by param_i
                qfi_op = []
                param_gates_i = state_qc._parameter_table[param_i]
                for gate_i, idx_i in param_gates_i:
                    grad_coeffs_i, grad_gates_i = LinComb._gate_gradient_dict(
                        gate_i)[idx_i]

                    # get the location of gate_i, used for trimming
                    location_i = None
                    for idx, (op, _, _) in enumerate(state_qc._data):
                        if op is gate_i:
                            location_i = idx
                            break

                    for grad_coeff_i, grad_gate_i in zip(
                            grad_coeffs_i, grad_gates_i):

                        # Get the gates of the quantum state which are parameterized by param_j
                        param_gates_j = state_qc._parameter_table[param_j]
                        for gate_j, idx_j in param_gates_j:
                            grad_coeffs_j, grad_gates_j = LinComb._gate_gradient_dict(
                                gate_j)[idx_j]

                            # get the location of gate_j, used for trimming
                            location_j = None
                            for idx, (op, _, _) in enumerate(state_qc._data):
                                if op is gate_j:
                                    location_j = idx
                                    break

                            for grad_coeff_j, grad_gate_j in zip(
                                    grad_coeffs_j, grad_gates_j):

                                grad_coeff_ij = np.conj(
                                    grad_coeff_i) * grad_coeff_j
                                qfi_circuit = LinComb.apply_grad_gate(
                                    state_qc,
                                    gate_i,
                                    idx_i,
                                    grad_gate_i,
                                    grad_coeff_ij,
                                    qr_work,
                                    open_ctrl=True,
                                    trim_after_grad_gate=(location_j <
                                                          location_i),
                                )

                                # create a copy of the original circuit with the same registers
                                qfi_circuit = LinComb.apply_grad_gate(
                                    qfi_circuit,
                                    gate_j,
                                    idx_j,
                                    grad_gate_j,
                                    1,
                                    qr_work,
                                    open_ctrl=False,
                                    trim_after_grad_gate=(location_j >=
                                                          location_i),
                                )

                                qfi_circuit.h(qr_work)
                                # Convert the quantum circuit into a CircuitStateFn and add the
                                # coefficients i, j and the original operator coefficient
                                coeff = operator.coeff
                                coeff *= np.sqrt(
                                    np.abs(grad_coeff_i) *
                                    np.abs(grad_coeff_j))
                                state = CircuitStateFn(qfi_circuit,
                                                       coeff=coeff)

                                param_grad = 1
                                for gate, idx, param in zip(
                                    [gate_i, gate_j], [idx_i, idx_j],
                                    [param_i, param_j]):
                                    param_expression = gate.params[idx]
                                    param_grad *= param_expression.gradient(
                                        param)
                                meas = param_grad * qfi_observable

                                term = meas @ state

                                qfi_op.append(term)

                # Compute −4 * Re(〈∂kψ|ψ〉〈ψ|∂lψ〉)
                def phase_fix_combo_fn(x):
                    return 4 * (-0.5) * (x[0] * np.conjugate(x[1]) +
                                         x[1] * np.conjugate(x[0]))

                phase_fix = ListOp([phase_fix_states[i], phase_fix_states[j]],
                                   combo_fn=phase_fix_combo_fn)
                # Add the phase fix quantities to the entries of the QFI
                # Get 4 * Re[〈∂kψ|∂lψ〉−〈∂kψ|ψ〉〈ψ|∂lψ〉]
                qfi_ops += [SummedOp(qfi_op) + phase_fix]

            qfi_operators.append(ListOp(qfi_ops))
        # Return the full QFI
        return ListOp(qfi_operators, combo_fn=triu_to_dense)
 def setUp(self):
     logger = getLogger()
     logger.setLevel('DEBUG')
     self.output = io.StringIO()
     logger.addHandler(StreamHandlerRaiseException(self.output))
     self.circuit = QuantumCircuit(QuantumRegister(1))
Esempio n. 17
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 def _define(self):
     """Calculate a subcircuit that implements this unitary."""
     q = QuantumRegister(self.num_qubits, 'q')
     qc = QuantumCircuit(q, name=self.name)
     qc.append(UnitaryGate(self.to_matrix()), qargs=q[:])
     self.definition = qc
Esempio n. 18
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    def run(self, dag):
        """Return a new circuit that has been optimized."""
        runs = dag.collect_runs(["u1", "u2", "u3"])
        runs = _split_runs_on_parameters(runs)
        for run in runs:
            right_name = "u1"
            right_parameters = (0, 0, 0)  # (theta, phi, lambda)

            for current_node in run:
                left_name = current_node.name
                if (current_node.condition is not None
                        or len(current_node.qargs) != 1
                        or left_name not in ["u1", "u2", "u3", "id"]):
                    raise TranspilerError("internal error")
                if left_name == "u1":
                    left_parameters = (0, 0, current_node.op.params[0])
                elif left_name == "u2":
                    left_parameters = (np.pi / 2, current_node.op.params[0],
                                       current_node.op.params[1])
                elif left_name == "u3":
                    left_parameters = tuple(current_node.op.params)
                else:
                    left_name = "u1"  # replace id with u1
                    left_parameters = (0, 0, 0)
                # If there are any sympy objects coming from the gate convert
                # to numpy.
                left_parameters = tuple([float(x) for x in left_parameters])
                # Compose gates
                name_tuple = (left_name, right_name)
                if name_tuple == ("u1", "u1"):
                    # u1(lambda1) * u1(lambda2) = u1(lambda1 + lambda2)
                    right_parameters = (0, 0, right_parameters[2] +
                                        left_parameters[2])
                elif name_tuple == ("u1", "u2"):
                    # u1(lambda1) * u2(phi2, lambda2) = u2(phi2 + lambda1, lambda2)
                    right_parameters = (np.pi / 2, right_parameters[1] +
                                        left_parameters[2],
                                        right_parameters[2])
                elif name_tuple == ("u2", "u1"):
                    # u2(phi1, lambda1) * u1(lambda2) = u2(phi1, lambda1 + lambda2)
                    right_name = "u2"
                    right_parameters = (np.pi / 2, left_parameters[1],
                                        right_parameters[2] +
                                        left_parameters[2])
                elif name_tuple == ("u1", "u3"):
                    # u1(lambda1) * u3(theta2, phi2, lambda2) =
                    #     u3(theta2, phi2 + lambda1, lambda2)
                    right_parameters = (right_parameters[0],
                                        right_parameters[1] +
                                        left_parameters[2],
                                        right_parameters[2])
                elif name_tuple == ("u3", "u1"):
                    # u3(theta1, phi1, lambda1) * u1(lambda2) =
                    #     u3(theta1, phi1, lambda1 + lambda2)
                    right_name = "u3"
                    right_parameters = (left_parameters[0], left_parameters[1],
                                        right_parameters[2] +
                                        left_parameters[2])
                elif name_tuple == ("u2", "u2"):
                    # Using Ry(pi/2).Rz(2*lambda).Ry(pi/2) =
                    #    Rz(pi/2).Ry(pi-2*lambda).Rz(pi/2),
                    # u2(phi1, lambda1) * u2(phi2, lambda2) =
                    #    u3(pi - lambda1 - phi2, phi1 + pi/2, lambda2 + pi/2)
                    right_name = "u3"
                    right_parameters = (np.pi - left_parameters[2] -
                                        right_parameters[1],
                                        left_parameters[1] + np.pi / 2,
                                        right_parameters[2] + np.pi / 2)
                elif name_tuple[1] == "nop":
                    right_name = left_name
                    right_parameters = left_parameters
                else:
                    # For composing u3's or u2's with u3's, use
                    # u2(phi, lambda) = u3(pi/2, phi, lambda)
                    # together with the qiskit.mapper.compose_u3 method.
                    right_name = "u3"
                    # Evaluate the symbolic expressions for efficiency
                    right_parameters = Optimize1qGates.compose_u3(
                        left_parameters[0], left_parameters[1],
                        left_parameters[2], right_parameters[0],
                        right_parameters[1], right_parameters[2])
                    # Why evalf()? This program:
                    #   OPENQASM 2.0;
                    #   include "qelib1.inc";
                    #   qreg q[2];
                    #   creg c[2];
                    #   u3(0.518016983430947*pi,1.37051598592907*pi,1.36816383603222*pi) q[0];
                    #   u3(1.69867232277986*pi,0.371448347747471*pi,0.461117217930936*pi) q[0];
                    #   u3(0.294319836336836*pi,0.450325871124225*pi,1.46804720442555*pi) q[0];
                    #   measure q -> c;
                    # took >630 seconds (did not complete) to optimize without
                    # calling evalf() at all, 19 seconds to optimize calling
                    # evalf() AFTER compose_u3, and 1 second to optimize
                    # calling evalf() BEFORE compose_u3.
                # 1. Here down, when we simplify, we add f(theta) to lambda to
                # correct the global phase when f(theta) is 2*pi. This isn't
                # necessary but the other steps preserve the global phase, so
                # we continue in that manner.
                # 2. The final step will remove Z rotations by 2*pi.
                # 3. Note that is_zero is true only if the expression is exactly
                # zero. If the input expressions have already been evaluated
                # then these final simplifications will not occur.
                # TODO After we refactor, we should have separate passes for
                # exact and approximate rewriting.

                # Y rotation is 0 mod 2*pi, so the gate is a u1
                if np.mod(right_parameters[0], (2 * np.pi)) == 0 \
                        and right_name != "u1":
                    right_name = "u1"
                    right_parameters = (0, 0, right_parameters[1] +
                                        right_parameters[2] +
                                        right_parameters[0])
                # Y rotation is pi/2 or -pi/2 mod 2*pi, so the gate is a u2
                if right_name == "u3":
                    # theta = pi/2 + 2*k*pi
                    if np.mod((right_parameters[0] - np.pi / 2),
                              (2 * np.pi)) == 0:
                        right_name = "u2"
                        right_parameters = (np.pi / 2, right_parameters[1],
                                            right_parameters[2] +
                                            (right_parameters[0] - np.pi / 2))
                    # theta = -pi/2 + 2*k*pi
                    if np.mod((right_parameters[0] + np.pi / 2),
                              (2 * np.pi)) == 0:
                        right_name = "u2"
                        right_parameters = (np.pi / 2,
                                            right_parameters[1] + np.pi,
                                            right_parameters[2] - np.pi +
                                            (right_parameters[0] + np.pi / 2))
                # u1 and lambda is 0 mod 2*pi so gate is nop (up to a global phase)
                if right_name == "u1" and np.mod(right_parameters[2],
                                                 (2 * np.pi)) == 0:
                    right_name = "nop"

            # Replace the the first node in the run with a dummy DAG which contains a dummy
            # qubit. The name is irrelevant, because substitute_node_with_dag will take care of
            # putting it in the right place.
            run_qarg = QuantumRegister(1, 'q')[0]
            new_op = Gate(name="", num_qubits=1, params=[])
            if right_name == "u1":
                new_op = U1Gate(right_parameters[2])
            if right_name == "u2":
                new_op = U2Gate(right_parameters[1], right_parameters[2])
            if right_name == "u3":
                new_op = U3Gate(*right_parameters)

            if right_name != 'nop':
                new_dag = DAGCircuit()
                new_dag.add_qreg(run_qarg.register)
                new_dag.apply_operation_back(new_op, [run_qarg], [])
                dag.substitute_node_with_dag(run[0], new_dag)

            # Delete the other nodes in the run
            for current_node in run[1:]:
                dag.remove_op_node(current_node)
            if right_name == "nop":
                dag.remove_op_node(run[0])

        return dag
Esempio n. 19
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def qs_decomposition(mat, opt_a1=True, decomposer_1q=None, decomposer_2q=None):
    """
    Decomposes unitary matrix into one and two qubit gates using Quantum Shannon Decomposition.

       ┌───┐               ┌───┐     ┌───┐     ┌───┐
      ─┤   ├─       ───────┤ Rz├─────┤ Ry├─────┤ Rz├─────
       │   │    ≃     ┌───┐└─┬─┘┌───┐└─┬─┘┌───┐└─┬─┘┌───┐
     /─┤   ├─       /─┤   ├──□──┤   ├──□──┤   ├──□──┤   ├
       └───┘          └───┘     └───┘     └───┘     └───┘

    The number of CX gates generated with the decomposition without optimizations is,

    .. math::

        \frac{9}{16} 4^n - frac{3}{2} 2^n

    If opt_a1=True, the CX count is further reduced by,

    .. math::

        \frac{1}{3} 4^{n - 2} - 1

    This decomposition is described in arXiv:quant-ph/0406176.

    Arguments:
       mat (ndarray): unitary matrix to decompose
       opt_a1 (bool): whether to try optimization A.1 from Shende. This should eliminate 1 cnot
          per call. If True CZ gates are left in the output. If desired these can be further decomposed
          to CX.
       decomposer_1q (None or Object): optional 1Q decomposer. If None, uses
          :class:`~qiskit.quantum_info.synthesis.one_qubit_decomposer.OneQubitEulerDecomser`
       decomposer_2q (None or Object): optional 2Q decomposer. If None, uses
          :class:`~qiskit.quantum_info.synthesis.two_qubit_decomposer.TwoQubitBasisDecomposer`
          with CXGate.

    Return:
       QuantumCircuit: Decomposed quantum circuit.
    """
    dim = mat.shape[0]
    nqubits = int(np.log2(dim))
    if np.allclose(np.identity(dim), mat):
        return QuantumCircuit(nqubits)
    if dim == 2:
        if decomposer_1q is None:
            decomposer_1q = one_qubit_decompose.OneQubitEulerDecomposer()
        circ = decomposer_1q(mat)
    elif dim == 4:
        if decomposer_2q is None:
            decomposer_2q = two_qubit_decompose.TwoQubitBasisDecomposer(CXGate())
        circ = decomposer_2q(mat)
    else:
        qr = QuantumRegister(nqubits)
        circ = QuantumCircuit(qr)
        dim_o2 = dim // 2
        # perform cosine-sine decomposition
        (u1, u2), vtheta, (v1h, v2h) = scipy.linalg.cossin(mat, separate=True, p=dim_o2, q=dim_o2)
        # left circ
        left_circ = _demultiplex(v1h, v2h, opt_a1=opt_a1)
        circ.append(left_circ.to_instruction(), qr)
        # middle circ
        if opt_a1:
            nangles = len(vtheta)
            half_size = nangles // 2
            # get UCG in terms of CZ
            circ_cz = _get_ucry_cz(nqubits, (2 * vtheta).tolist())
            circ.append(circ_cz.to_instruction(), range(nqubits))
            # merge final cz with right-side generic multiplexer
            u2[:, half_size:] = np.negative(u2[:, half_size:])
        else:
            circ.ucry((2 * vtheta).tolist(), qr[:-1], qr[-1])
        # right circ
        right_circ = _demultiplex(u1, u2, opt_a1=opt_a1)
        circ.append(right_circ.to_instruction(), qr)

    return circ
Esempio n. 20
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    def run(self, dag):
        """iterate over each block and replace it with an equivalent Unitary
        on the same wires.
        """
        new_dag = DAGCircuit()
        for qreg in dag.qregs.values():
            new_dag.add_qreg(qreg)
        for creg in dag.cregs.values():
            new_dag.add_creg(creg)

        # compute ordered indices for the global circuit wires
        global_index_map = {}
        for wire in dag.wires:
            if not isinstance(wire[0], QuantumRegister):
                continue
            global_qregs = list(dag.qregs.values())
            global_index_map[wire] = global_qregs.index(wire[0]) + wire[1]

        blocks = self.property_set['block_list']
        nodes_seen = set()

        for node in dag.topological_op_nodes():
            # skip already-visited nodes or input/output nodes
            if node in nodes_seen or node.type == 'in' or node.type == 'out':
                continue
            # check if the node belongs to the next block
            if blocks and node in blocks[0]:
                block = blocks[0]
                # find the qubits involved in this block
                block_qargs = set()
                for nd in block:
                    block_qargs |= set(nd.qargs)
                # convert block to a sub-circuit, then simulate unitary and add
                block_width = len(block_qargs)
                q = QuantumRegister(block_width)
                subcirc = QuantumCircuit(q)
                block_index_map = self._block_qargs_to_indices(
                    block_qargs, global_index_map)
                for nd in block:
                    nodes_seen.add(nd)
                    subcirc.append(nd.op,
                                   [q[block_index_map[i]] for i in nd.qargs])
                unitary = UnitaryGate(
                    Operator(subcirc))  # simulates the circuit
                new_dag.apply_operation_back(
                    unitary,
                    sorted(block_qargs, key=lambda x: block_index_map[x]))
                del blocks[0]
            else:
                # the node could belong to some future block, but in that case
                # we simply skip it. It is guaranteed that we will revisit that
                # future block, via its other nodes
                for block in blocks[1:]:
                    if node in block:
                        break
                # freestanding nodes can just be added
                else:
                    nodes_seen.add(node)
                    new_dag.apply_operation_back(node.op, node.qargs,
                                                 node.cargs)

        return new_dag
 def num_qubits(self, num_qubits):
     self._invalidate()
     self._num_qubits = num_qubits
     self.qregs = [QuantumRegister(self.num_qubits, name="q")]
Esempio n. 22
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    def _off_diag_circ(self, theta: float = 1) -> QuantumCircuit:
        """Circuit implementing the matrix consisting of entries in the off diagonals.

        Args:
            theta: Scale factor for the off diagonal entries (e.g. evolution_time/trotter_steps).

        Returns:
            The quantum circuit implementing the matrix consisting of entries in the off diagonals.
        """
        theta *= self.off_diag

        qr = QuantumRegister(self.num_state_qubits)
        if self.num_state_qubits > 1:
            qr_ancilla = AncillaRegister(max(1, self.num_state_qubits - 2))
            qc = QuantumCircuit(qr, qr_ancilla, name="off_diags")
        else:
            qc = QuantumCircuit(qr, name="off_diags")
            qr_ancilla = None

        qc.u(-2 * theta, 3 * np.pi / 2, np.pi / 2, qr[0])

        for i in range(0, self.num_state_qubits - 1):
            q_controls = []
            qc.cx(qr[i], qr[i + 1])
            q_controls.append(qr[i + 1])

            # Now we want controlled by 0
            qc.x(qr[i])
            for j in range(i, 0, -1):
                qc.cx(qr[i], qr[j - 1])
                q_controls.append(qr[j - 1])
            qc.x(qr[i])

            # Multicontrolled rotation
            if len(q_controls) > 1:
                ugate = UGate(-2 * theta, 3 * np.pi / 2, np.pi / 2)
                qc.append(
                    MCMTVChain(ugate, len(q_controls), 1),
                    q_controls[:] + [qr[i]] + qr_ancilla[:len(q_controls) - 1],
                )
            else:
                qc.cu(-2 * theta, 3 * np.pi / 2, np.pi / 2, 0, q_controls[0],
                      qr[i])

            # Uncompute
            qc.x(qr[i])
            for j in range(0, i):
                qc.cx(qr[i], qr[j])
            qc.x(qr[i])
            qc.cx(qr[i], qr[i + 1])

        # pylint: disable=unused-argument
        def control(num_ctrl_qubits=1, label=None, ctrl_state=None):
            qr_state = QuantumRegister(self.num_state_qubits + 1)
            if self.num_state_qubits > 1:
                qr_ancilla = AncillaRegister(max(1, self.num_state_qubits - 1))
                qc_control = QuantumCircuit(qr_state,
                                            qr_ancilla,
                                            name="off_diags")
            else:
                qc_control = QuantumCircuit(qr_state, name="off_diags")
                qr_ancilla = None
            # Control will be qr[0]
            q_control = qr_state[0]
            qr = qr_state[1:]
            qc_control.cu(-2 * theta, 3 * np.pi / 2, np.pi / 2, 0, q_control,
                          qr[0])

            for i in range(0, self.num_state_qubits - 1):
                q_controls = []
                q_controls.append(q_control)
                qc_control.cx(qr[i], qr[i + 1])
                q_controls.append(qr[i + 1])

                # Now we want controlled by 0
                qc_control.x(qr[i])
                for j in range(i, 0, -1):
                    qc_control.cx(qr[i], qr[j - 1])
                    q_controls.append(qr[j - 1])
                qc_control.x(qr[i])

                # Multicontrolled x rotation
                if len(q_controls) > 1:
                    ugate = UGate(-2 * theta, 3 * np.pi / 2, np.pi / 2)
                    qc_control.append(
                        MCMTVChain(ugate, len(q_controls), 1).to_gate(),
                        q_controls[:] + [qr[i]] +
                        qr_ancilla[:len(q_controls) - 1],
                    )
                else:
                    qc_control.cu(-2 * theta, 3 * np.pi / 2, np.pi / 2, 0,
                                  q_controls[0], qr[i])

                # Uncompute
                qc_control.x(qr[i])
                for j in range(0, i):
                    qc_control.cx(qr[i], qr[j])
                qc_control.x(qr[i])
                qc_control.cx(qr[i], qr[i + 1])
            return qc_control

        qc.control = control
        return qc
Esempio n. 23
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def _compose_transforms(basis_transforms, source_basis, source_dag):
    """Compose a set of basis transforms into a set of replacements.

    Args:
        basis_transforms (List[Tuple[gate_name, params, equiv]]): List of
            transforms to compose.
        source_basis (Set[Tuple[gate_name: str, gate_num_qubits: int]]): Names
            of gates which need to be translated.
        source_dag (DAGCircuit): DAG with example gates from source_basis.
            (Used to determine num_params for gate in source_basis.)

    Returns:
        Dict[gate_name, Tuple(params, dag)]: Dictionary mapping between each gate
            in source_basis and a DAGCircuit instance to replace it. Gates in
            source_basis but not affected by basis_transforms will be included
            as a key mapping to itself.
    """

    example_gates = {(node.op.name, node.op.num_qubits): node.op for node in source_dag.op_nodes()}
    mapped_instrs = {}

    for gate_name, gate_num_qubits in source_basis:
        # Need to grab a gate instance to find num_qubits and num_params.
        # Can be removed following https://github.com/Qiskit/qiskit-terra/pull/3947 .
        example_gate = example_gates[gate_name, gate_num_qubits]
        num_params = len(example_gate.params)

        placeholder_params = ParameterVector(gate_name, num_params)
        placeholder_gate = Gate(gate_name, gate_num_qubits, list(placeholder_params))
        placeholder_gate.params = list(placeholder_params)

        dag = DAGCircuit()
        qr = QuantumRegister(gate_num_qubits)
        dag.add_qreg(qr)
        dag.apply_operation_back(placeholder_gate, qr[:], [])
        mapped_instrs[gate_name, gate_num_qubits] = placeholder_params, dag

    for gate_name, gate_num_qubits, equiv_params, equiv in basis_transforms:
        logger.debug(
            "Composing transform step: %s/%s %s =>\n%s",
            gate_name,
            gate_num_qubits,
            equiv_params,
            equiv,
        )

        for mapped_instr_name, (dag_params, dag) in mapped_instrs.items():
            doomed_nodes = [
                node
                for node in dag.op_nodes()
                if (node.op.name, node.op.num_qubits) == (gate_name, gate_num_qubits)
            ]

            if doomed_nodes and logger.isEnabledFor(logging.DEBUG):
                from qiskit.converters import dag_to_circuit

                logger.debug(
                    "Updating transform for mapped instr %s %s from \n%s",
                    mapped_instr_name,
                    dag_params,
                    dag_to_circuit(dag),
                )

            for node in doomed_nodes:
                from qiskit.converters import circuit_to_dag

                replacement = equiv.assign_parameters(
                    dict(zip_longest(equiv_params, node.op.params))
                )

                replacement_dag = circuit_to_dag(replacement)

                dag.substitute_node_with_dag(node, replacement_dag)

            if doomed_nodes and logger.isEnabledFor(logging.DEBUG):
                from qiskit.converters import dag_to_circuit

                logger.debug(
                    "Updated transform for mapped instr %s %s to\n%s",
                    mapped_instr_name,
                    dag_params,
                    dag_to_circuit(dag),
                )

    return mapped_instrs
Esempio n. 24
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def random_circuit(n_qubits,
                   depth,
                   coupling_map=None,
                   basis_gates=None,
                   prob_one_q_op=0.5,
                   reset=False,
                   seed=None):
    """Generate RQC faithfully according to the given coupling_map and basis_gates.

    Args:
        n_qubits (int):                     the number of qubits, must > largest qubit label in coupling_map
        depth (int):                        the number of the layers of operations
        coupling_map (CouplingMap or list): coupling map specifies allowed CNOTs
            default:                        fully connected graph coupling n_qubits
        basis_gates (list):                 the list of labels of basis gates used in construction
            default:                        all available gates in gate_set
        prob_one_q_op (float):              the probability of selecting a one-qubit operation when two_q_op is allowed
            default:                        equal probability 0.5
        reset (bool):                       if True, insert middle resets
        seed (int):                         sets random seed (optional)

    Returns:
        QuantumCircuit: constructed circuit

    Raises:
        CircuitError: when invalid options given
    """
    max_operands = 2
    assert max_operands == 2

    if isinstance(coupling_map, list):
        coupling_map = CouplingMap(coupling_map)
    if coupling_map != None and n_qubits < max(
            coupling_map.physical_qubits) + 1:
        raise CircuitError("n_qubits is not enough to accomodate CouplingMap")

    if basis_gates == None:
        basis_gates = gate_set

    one_q_ops = [
        label2gate[name] for name in one_q_ops_label & set(basis_gates)
    ]
    two_q_ops = [
        label2gate[name] for name in two_q_ops_label & set(basis_gates)
    ]
    one_param = [
        label2gate[name] for name in one_param_label & set(basis_gates)
    ]
    two_param = [
        label2gate[name] for name in two_param_label & set(basis_gates)
    ]
    three_param = [
        label2gate[name] for name in three_param_label & set(basis_gates)
    ]

    if len(one_q_ops) == 0:
        raise CircuitError("no available one-qubit gate")
    if len(two_q_ops) == 0:
        raise CircuitError("CNOT is not available")

    qreg = QuantumRegister(n_qubits, 'q')
    qc = QuantumCircuit(n_qubits)
    # default coupling_map is fully connected
    if coupling_map == None:
        coupling_map = CouplingMap.from_full(n_qubits)

    if reset:
        one_q_ops += [Reset]

    if seed is None:
        seed = np.random.randint(0, np.iinfo(np.int32).max)
    rng = np.random.RandomState(seed)

    for _ in range(depth):
        remaining_qubits = coupling_map.physical_qubits
        remaining_edges = coupling_map.get_edges()
        if remaining_edges:
            allow_two_q_op = True
        while remaining_qubits:
            if allow_two_q_op:
                max_possible_operands = min(len(remaining_qubits),
                                            max_operands)
            else:
                max_possible_operands = 1
            if max_possible_operands == 1:
                possible_operands_set = [1]
                num_operands = 1
            else:
                possible_operands_set = [1, 2]
                num_operands = (not (rng.uniform() < prob_one_q_op)) + 1
            if num_operands == 1:
                operation = rng.choice(one_q_ops)
                operands = rng.choice(remaining_qubits)
                register_operands = [qreg[int(operands)]]
                operands = [operands]
            elif num_operands == 2:
                operation = rng.choice(two_q_ops)
                operands = remaining_edges[rng.choice(
                    range(len(remaining_edges)))]
                register_operands = [qreg[i] for i in operands]
            remaining_qubits = [
                q for q in remaining_qubits if q not in operands
            ]
            if remaining_edges:
                remaining_edges = [
                    pair for pair in remaining_edges
                    if pair[0] not in operands and pair[1] not in operands
                ]
            if allow_two_q_op and not remaining_edges:
                allow_two_q_op = False
            if operation in one_param:
                num_angles = 1
            elif operation in two_param:
                num_angles = 2
            elif operation in three_param:
                num_angles = 3
            else:
                num_angles = 0
            angles = [rng.uniform(0, 2 * np.pi) for x in range(num_angles)]

            op = operation(*angles)

            qc.append(op, register_operands)

    return qc
Esempio n. 25
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 def test_add_reg_duplicate(self):
     """add_qreg with the same register twice is not allowed."""
     dag = DAGCircuit()
     qr = QuantumRegister(2)
     dag.add_qreg(qr)
     self.assertRaises(DAGCircuitError, dag.add_qreg, qr)
Esempio n. 26
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qtest_data_ft3=qtest_data['petal width (cm)']
qtest_data_ft4=[]
qtest_data_ft4=qtest_data['petal width (cm)']


# In[66]:


from qiskit.quantum_info import state_fidelity


# In[67]:


device=Aer.get_backend('qasm_simulator')
q=QuantumRegister(5)
c=ClassicalRegister(5)
cir1=QuantumCircuit(q,c)
cir1.u3(qtrain_data_ft1[2],qtrain_data_ft2[2],0,q[1]) #quantum state training data ft1&ft2
cir1.u3(qtrain_data_ft3[2],qtrain_data_ft4[2],0,q[2]) #quantum state training data ft3&ft4
cir1.u3(qtest_data_ft1[3],qtest_data_ft2[3],0,q[3]) #quantum state testing data ft1&ft2
cir1.u3(qtest_data_ft3[3],qtest_data_ft4[3],0,q[4]) #quantum state testing data ft3&ft4
cir1.barrier()
cir1.h(q[0])
cir1.cswap(q[0],q[1],q[3])
cir1.cswap(q[0],q[2],q[4])
cir1.h(q[0])
cir1.measure(q[0],c[0])
cir1.draw(output='mpl')

Esempio n. 27
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    def _process_node(self, node):
        """Carry out the action associated with a node."""
        if node.type == "program":
            self._process_children(node)

        elif node.type == "qreg":
            qreg = QuantumRegister(node.index, node.name)
            self.dag.add_qreg(qreg)

        elif node.type == "creg":
            creg = ClassicalRegister(node.index, node.name)
            self.dag.add_creg(creg)

        elif node.type == "id":
            raise QiskitError("internal error: _process_node on id")

        elif node.type == "int":
            raise QiskitError("internal error: _process_node on int")

        elif node.type == "real":
            raise QiskitError("internal error: _process_node on real")

        elif node.type == "indexed_id":
            raise QiskitError("internal error: _process_node on indexed_id")

        elif node.type == "id_list":
            # We process id_list nodes when they are leaves of barriers.
            return [self._process_bit_id(node_children)
                    for node_children in node.children]

        elif node.type == "primary_list":
            # We should only be called for a barrier.
            return [self._process_bit_id(m) for m in node.children]

        elif node.type == "gate":
            self._process_gate(node)

        elif node.type == "custom_unitary":
            self._process_custom_unitary(node)

        elif node.type == "universal_unitary":
            args = self._process_node(node.children[0])
            qid = self._process_bit_id(node.children[1])
            for element in qid:
                u3_gate = U3Gate(*args, element)
                u3_gate.condition = self.condition
                self.dag.apply_operation_back(u3_gate)

        elif node.type == "cnot":
            self._process_cnot(node)

        elif node.type == "expression_list":
            return node.children

        elif node.type == "binop":
            raise QiskitError("internal error: _process_node on binop")

        elif node.type == "prefix":
            raise QiskitError("internal error: _process_node on prefix")

        elif node.type == "measure":
            self._process_measure(node)

        elif node.type == "format":
            self.version = node.version()

        elif node.type == "barrier":
            ids = self._process_node(node.children[0])
            qubits = []
            for qubit in ids:
                for j, _ in enumerate(qubit):
                    qubits.append(qubit[j])
            self.dag.apply_operation_back(Barrier(len(qubits)), qubits, [])

        elif node.type == "reset":
            id0 = self._process_bit_id(node.children[0])
            for i, _ in enumerate(id0):
                reset = Reset()
                reset.condition = self.condition
                self.dag.apply_operation_back(reset, [id0[i]], [])

        elif node.type == "if":
            self._process_if(node)

        elif node.type == "opaque":
            self._process_gate(node, opaque=True)

        elif node.type == "external":
            raise QiskitError("internal error: _process_node on external")

        else:
            raise QiskitError("internal error: undefined node type",
                              node.type, "line=%s" % node.line,
                              "file=%s" % node.file)
        return None
Esempio n. 28
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    def construct_circuit(
            self, matrix: Union[np.ndarray, QuantumCircuit],
            vector: Union[np.ndarray, QuantumCircuit]) -> QuantumCircuit:
        """Construct the HHL circuit.

        Args:
            matrix: The matrix specifying the system, i.e. A in Ax=b.
            vector: The vector specifying the right hand side of the equation in Ax=b.

        Returns:
            The HHL circuit.

        Raises:
            ValueError: If the input is not in the correct format.
            ValueError: If the type of the input matrix is not supported.
        """
        # State preparation circuit - default is qiskit
        if isinstance(vector, QuantumCircuit):
            nb = vector.num_qubits
            vector_circuit = vector
        elif isinstance(vector, np.ndarray):
            nb = int(np.log2(len(vector)))
            vector_circuit = QuantumCircuit(nb)
            vector_circuit.isometry(vector / np.linalg.norm(vector),
                                    list(range(nb)), None)

        # If state preparation is probabilistic the number of qubit flags should increase
        nf = 1

        # Hamiltonian simulation circuit - default is Trotterization
        if isinstance(matrix, QuantumCircuit):
            matrix_circuit = matrix
        elif isinstance(matrix, (list, np.ndarray)):
            if isinstance(matrix, list):
                matrix = np.array(matrix)

            if matrix.shape[0] != matrix.shape[1]:
                raise ValueError("Input matrix must be square!")
            if np.log2(matrix.shape[0]) % 1 != 0:
                raise ValueError("Input matrix dimension must be 2^n!")
            if not np.allclose(matrix, matrix.conj().T):
                raise ValueError("Input matrix must be hermitian!")
            if matrix.shape[0] != 2**vector_circuit.num_qubits:
                raise ValueError("Input vector dimension does not match input "
                                 "matrix dimension! Vector dimension: " +
                                 str(vector_circuit.num_qubits) +
                                 ". Matrix dimension: " + str(matrix.shape[0]))
            matrix_circuit = NumPyMatrix(matrix, evolution_time=2 * np.pi)
        else:
            raise ValueError(f'Invalid type for matrix: {type(matrix)}.')

        # Set the tolerance for the matrix approximation
        if hasattr(matrix_circuit, "tolerance"):
            matrix_circuit.tolerance = self._epsilon_a

        # check if the matrix can calculate the condition number and store the upper bound
        if hasattr(matrix_circuit, "condition_bounds") and matrix_circuit.condition_bounds() is not\
                None:
            kappa = matrix_circuit.condition_bounds()[1]
        else:
            kappa = 1
        # Update the number of qubits required to represent the eigenvalues
        nl = max(nb + 1, int(np.log2(kappa)) + 1)

        # check if the matrix can calculate bounds for the eigenvalues
        if hasattr(matrix_circuit,
                   "eigs_bounds") and matrix_circuit.eigs_bounds() is not None:
            lambda_min, lambda_max = matrix_circuit.eigs_bounds()
            # Constant so that the minimum eigenvalue is represented exactly, since it contributes
            # the most to the solution of the system
            delta = self._get_delta(nl, lambda_min, lambda_max)
            # Update evolution time
            matrix_circuit.evolution_time = 2 * np.pi * delta / lambda_min
            # Update the scaling of the solution
            self.scaling = lambda_min
        else:
            delta = 1 / (2**nl)
            print("The solution will be calculated up to a scaling factor.")

        if self._exact_reciprocal:
            reciprocal_circuit = ExactReciprocal(nl, delta)
            # Update number of ancilla qubits
            na = matrix_circuit.num_ancillas
        else:
            # Calculate breakpoints for the reciprocal approximation
            num_values = 2**nl
            constant = delta
            a = int(round(num_values**(2 / 3)))  # pylint: disable=invalid-name

            # Calculate the degree of the polynomial and the number of intervals
            r = 2 * constant / a + np.sqrt(np.abs(1 - (2 * constant / a)**2))
            degree = min(
                nb,
                int(
                    np.log(1 +
                           (16.23 * np.sqrt(np.log(r)**2 +
                                            (np.pi / 2)**2) * kappa *
                            (2 * kappa - self._epsilon_r)) / self._epsilon_r)))
            num_intervals = int(
                np.ceil(np.log((num_values - 1) / a) / np.log(5)))

            # Calculate breakpoints and polynomials
            breakpoints = []
            for i in range(0, num_intervals):
                # Add the breakpoint to the list
                breakpoints.append(a * (5**i))

                # Define the right breakpoint of the interval
                if i == num_intervals - 1:
                    breakpoints.append(num_values - 1)

            reciprocal_circuit = PiecewiseChebyshev(
                lambda x: np.arcsin(constant / x), degree, breakpoints, nl)
            na = max(matrix_circuit.num_ancillas,
                     reciprocal_circuit.num_ancillas)

        # Initialise the quantum registers
        qb = QuantumRegister(nb)  # right hand side and solution
        ql = QuantumRegister(nl)  # eigenvalue evaluation qubits
        if na > 0:
            qa = AncillaRegister(na)  # ancilla qubits
        qf = QuantumRegister(nf)  # flag qubits

        if na > 0:
            qc = QuantumCircuit(qb, ql, qa, qf)
        else:
            qc = QuantumCircuit(qb, ql, qf)

        # State preparation
        qc.append(vector_circuit, qb[:])
        # QPE
        phase_estimation = PhaseEstimation(nl, matrix_circuit)
        if na > 0:
            qc.append(phase_estimation,
                      ql[:] + qb[:] + qa[:matrix_circuit.num_ancillas])
        else:
            qc.append(phase_estimation, ql[:] + qb[:])
        # Conditioned rotation
        if self._exact_reciprocal:
            qc.append(reciprocal_circuit, ql[::-1] + [qf[0]])
        else:
            qc.append(reciprocal_circuit.to_instruction(),
                      ql[:] + [qf[0]] + qa[:reciprocal_circuit.num_ancillas])
        # QPE inverse
        if na > 0:
            qc.append(phase_estimation.inverse(),
                      ql[:] + qb[:] + qa[:matrix_circuit.num_ancillas])
        else:
            qc.append(phase_estimation.inverse(), ql[:] + qb[:])
        return qc
Esempio n. 29
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def random_circuit(n_qubits,
                   depth,
                   max_operands=3,
                   measure=False,
                   conditional=False,
                   reset=False,
                   seed=None):
    """Generate random circuit of arbitrary size and form.

    Args:
        n_qubits (int): number of quantum wires
        depth (int): layers of operations (i.e. critical path length)
        max_operands (int): maximum operands of each gate (between 1 and 3)
        measure (bool): if True, measure all qubits at the end
        conditional (bool): if True, insert middle measurements and conditionals
        reset (bool): if True, insert middle resets
        seed (int): sets random seed (optional)

    Returns:
        QuantumCircuit: constructed circuit

    Raises:
        CircuitError: when invalid options given
    """
    if max_operands < 1 or max_operands > 3:
        raise CircuitError("max_operands must be between 1 and 3")

    one_q_ops = [
        IGate, U1Gate, U2Gate, U3Gate, XGate, YGate, ZGate, HGate, SGate,
        SdgGate, TGate, TdgGate, RXGate, RYGate, RZGate
    ]
    one_param = [U1Gate, RXGate, RYGate, RZGate, RZZGate, CU1Gate, CRZGate]
    two_param = [U2Gate]
    three_param = [U3Gate, CU3Gate]
    two_q_ops = [
        CXGate, CYGate, CZGate, CHGate, CRZGate, CU1Gate, CU3Gate, SwapGate,
        RZZGate
    ]
    three_q_ops = [CCXGate, CSwapGate]

    qr = QuantumRegister(n_qubits, 'q')
    qc = QuantumCircuit(n_qubits)

    if measure or conditional:
        cr = ClassicalRegister(n_qubits, 'c')
        qc.add_register(cr)

    if reset:
        one_q_ops += [Reset]

    if seed is None:
        seed = np.random.randint(0, np.iinfo(np.int32).max)
    rng = np.random.RandomState(seed)

    # apply arbitrary random operations at every depth
    for _ in range(depth):
        # choose either 1, 2, or 3 qubits for the operation
        remaining_qubits = list(range(n_qubits))
        while remaining_qubits:
            max_possible_operands = min(len(remaining_qubits), max_operands)
            num_operands = rng.choice(range(max_possible_operands)) + 1
            rng.shuffle(remaining_qubits)
            operands = remaining_qubits[:num_operands]
            remaining_qubits = [
                q for q in remaining_qubits if q not in operands
            ]
            if num_operands == 1:
                operation = rng.choice(one_q_ops)
            elif num_operands == 2:
                operation = rng.choice(two_q_ops)
            elif num_operands == 3:
                operation = rng.choice(three_q_ops)
            if operation in one_param:
                num_angles = 1
            elif operation in two_param:
                num_angles = 2
            elif operation in three_param:
                num_angles = 3
            else:
                num_angles = 0
            angles = [rng.uniform(0, 2 * np.pi) for x in range(num_angles)]
            register_operands = [qr[i] for i in operands]
            op = operation(*angles)

            # with some low probability, condition on classical bit values
            if conditional and rng.choice(range(10)) == 0:
                value = rng.randint(0, np.power(2, n_qubits))
                op.condition = (cr, value)

            qc.append(op, register_operands)

    if measure:
        qc.measure(qr, cr)

    return qc
Esempio n. 30
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 def test_basic_slice(self):
     """simple slice test"""
     qr = QuantumRegister(5)
     cr = ClassicalRegister(5)
     self.assertEqual(len(qr[0:3]), 3)
     self.assertEqual(len(cr[0:3]), 3)