def pea_program(ancillary_start, ancillary_num, time, H2_distance,
trotter_order):
"""
Creates a pyquil Program for the phase estimation algorithm (PEA)
:param ancillary_start: index of the first ancillary qubit.
:param ancillary_num: how many ancillaries to use corresponds
to precision in PEA algorithm
:param time: t in exp(-iHt), where H is the Hamiltonian
:param H2_distance: the distance between to H atoms in H2 molecule
:return: a pyquil Program for PEA algorithm
"""
phase_estimation_program = Program()
unitary = exp_hamiltoniantrot_H2(time, H2_distance, trotter_order)
phase_estimation_program.inst(create_CRX(), create_CRZ(), create_CH())
Hadamard_ancilaries = Program()
for index in range(0, ancillary_num):
Hadamard_ancilaries.inst(H(ancillary_start + index))
phase_estimation_program += Hadamard_ancilaries
for index in range(0, ancillary_num):
cont_first_order = Program()
control_program(unitary, cont_first_order, ancillary_start + index)
control_unitary = repeat_program(cont_first_order, 2**index)
phase_estimation_program += control_unitary
ancilary_list = list(
range(ancillary_start, ancillary_start + ancillary_num))
inv_qft_prog = inverse_qft(ancilary_list)
phase_estimation_program += inv_qft_prog
return phase_estimation_program
def test_qc_calibration_2q(forest):
# noise model with 95% symmetrized readout fidelity per qubit
noise_model = asymmetric_ro_model([0, 1], 0.945, 0.955)
qc = get_qc("2q-qvm")
qc.qam.noise_model = noise_model
# bell state program (doesn't matter)
p = Program()
p += RESET()
p += H(0)
p += CNOT(0, 1)
p.wrap_in_numshots_loop(10000)
# ZZ experiment
sz = ExperimentSetting(in_state=sZ(0) * sZ(1), out_operator=sZ(0) * sZ(1))
e = Experiment(settings=[sz], program=p)
results = qc.calibrate(e)
# ZZ expectation should just be (1 - 2 * readout_error_q0) * (1 - 2 * readout_error_q1)
np.isclose(results[0].expectation, 0.81, atol=0.01)
assert results[0].total_counts == 40000
def test_qc_calibration_1q(client_configuration: QCSClientConfiguration):
# noise model with 95% symmetrized readout fidelity per qubit
noise_model = asymmetric_ro_model([0], 0.945, 0.955)
qc = get_qc("1q-qvm", client_configuration=client_configuration)
qc.qam.noise_model = noise_model
# bell state program (doesn't matter)
p = Program()
p += RESET()
p += H(0)
p += CNOT(0, 1)
p.wrap_in_numshots_loop(10000)
# Z experiment
sz = ExperimentSetting(in_state=_pauli_to_product_state(sZ(0)), out_operator=sZ(0))
e = Experiment(settings=[sz], program=p)
results = qc.calibrate(e)
# Z expectation value should just be 1 - 2 * readout_error
np.isclose(results[0].expectation, 0.9, atol=0.01)
assert results[0].total_counts == 20000
def phase_estimation(U: np.ndarray,
accuracy: int,
reg_offset: int = 0) -> Program:
"""
Generate a circuit for quantum phase estimation.
:param U: A unitary matrix.
:param accuracy: Number of bits of accuracy desired.
:param reg_offset: Where to start writing measurements (default 0).
:return: A Quil program to perform phase estimation.
"""
assert isinstance(accuracy, int)
rows, cols = U.shape
m = int(log2(rows))
output_qubits = range(0, accuracy)
U_qubits = range(accuracy, accuracy + m)
p = Program()
ro = p.declare('ro', 'BIT', len(output_qubits))
# Hadamard initialization
for i in output_qubits:
p.inst(H(i))
# Controlled unitaries
for i in output_qubits:
if i > 0:
U = np.dot(U, U)
cU = controlled(U)
name = "CONTROLLED-U{0}".format(2**i)
# define the gate
p.defgate(name, cU)
# apply it
p.inst((name, i) + tuple(U_qubits))
# Compute the QFT
p = p + inverse_qft(output_qubits)
# Perform the measurements
for i in output_qubits:
p.measure(i, ro[reg_offset + i])
return p
def test_run(client_configuration: QCSClientConfiguration):
quantum_processor = NxQuantumProcessor(nx.complete_graph(3))
qc = QuantumComputer(
name="testy!",
qam=QVM(client_configuration=client_configuration, gate_noise=(0.01, 0.01, 0.01)),
compiler=DummyCompiler(quantum_processor=quantum_processor, client_configuration=client_configuration),
)
bitstrings = qc.run(
Program(
Declare("ro", "BIT", 3),
H(0),
CNOT(0, 1),
CNOT(1, 2),
MEASURE(0, MemoryReference("ro", 0)),
MEASURE(1, MemoryReference("ro", 1)),
MEASURE(2, MemoryReference("ro", 2)),
).wrap_in_numshots_loop(1000)
)
assert bitstrings.shape == (1000, 3)
parity = np.sum(bitstrings, axis=1) % 3
assert 0 < np.mean(parity) < 0.15
def test_expectation():
"""expectation() routine can take a PauliSum operator on a matrix. Check
this functionality and the return of the correct scalar value"""
X = np.array([[0, 1], [1, 0]])
def RX_gate(phi):
return expm(-1j * phi * X)
def rotation_wavefunction(phi):
state = np.array([[1], [0]])
return RX_gate(phi).dot(state)
prog = Program([RX(-2.5)(0)])
hamiltonian = PauliTerm("Z", 0, 1.0)
minimizer = MagicMock()
fake_result = Mock()
fake_result.fun = 1.0
minimizer.return_value = fake_result
fake_qvm = Mock(spec=['wavefunction', 'expectation', 'run'])
fake_qvm.wavefunction.return_value = (Wavefunction(
rotation_wavefunction(-2.5)))
fake_qvm.expectation.return_value = [0.28366219]
# for testing expectation
fake_qvm.run.return_value = [[0], [0]]
inst = VQE(minimizer)
energy = inst.expectation(prog, PauliSum([hamiltonian]), None, fake_qvm)
assert np.isclose(energy, 0.28366219)
hamiltonian = np.array([[1, 0], [0, -1]])
energy = inst.expectation(prog, hamiltonian, None, fake_qvm)
assert np.isclose(energy, 0.28366219)
prog = Program(H(0))
hamiltonian = PauliSum([PauliTerm('X', 0)])
energy = inst.expectation(prog, hamiltonian, 2, fake_qvm)
assert np.isclose(energy, 1.0)
def teleport(start_index, end_index, ancilla_index):
"""Teleport a qubit from start to end using an ancilla qubit
"""
program = make_bell_pair(end_index, ancilla_index)
ro = program.declare("ro", memory_size=3)
# do the teleportation
program.inst(CNOT(start_index, ancilla_index))
program.inst(H(start_index))
# measure the results and store them in classical registers [0] and [1]
program.measure(start_index, ro[0])
program.measure(ancilla_index, ro[1])
program.if_then(ro[1], X(2))
program.if_then(ro[0], Z(2))
program.measure(end_index, ro[2])
print(program)
return program
def test_run(forest):
device = NxDevice(nx.complete_graph(3))
qc = QuantumComputer(
name="testy!",
qam=QVM(connection=forest, gate_noise=[0.01] * 3),
device=device,
compiler=DummyCompiler(),
)
bitstrings = qc.run(
Program(
Declare("ro", "BIT", 3),
H(0),
CNOT(0, 1),
CNOT(1, 2),
MEASURE(0, MemoryReference("ro", 0)),
MEASURE(1, MemoryReference("ro", 1)),
MEASURE(2, MemoryReference("ro", 2)),
).wrap_in_numshots_loop(1000))
assert bitstrings.shape == (1000, 3)
parity = np.sum(bitstrings, axis=1) % 3
assert 0 < np.mean(parity) < 0.15
def prepare_bitstring(bitstring: Sequence[int], register: Sequence[int], in_x_basis: bool = False):
"""
Creates a program to prepare the input bitstring on the qubits given by the corresponding
label in the register.
:param bitstring:
:param register: a list of qubits on which to prepare the bitstring. The first
:param in_x_basis: if true, prepare the bitstring-representation of the numbers in the x basis.
:returns: state_prep_prog - program
"""
state_prep_prog = Program()
for bit, qubit_label in zip(bitstring, register):
if bit == 1:
state_prep_prog += X(qubit_label)
# if we are doing logic in X basis, follow each bit preparation with a Hadamard
# H |0> = |+> and H |1> = |-> where + and - label the X basis vectors.
if in_x_basis:
state_prep_prog += H(qubit_label)
return state_prep_prog
def run_bell_low_level(n_shots=1000):
# Step 1. Get some device components
qc = get_qc('9q-generic-qvm')
compiler = qc.compiler
qam = qc.qam
del qc
q = [4, 5] # qubits
# Step 2. Construct your program
program = Program()
program += H(q[0])
program += CNOT(q[0], q[1])
# Step 2.1. Manage read-out memory
ro = program.declare('ro', memory_type='BIT', memory_size='2')
program += MEASURE(q[0], ro[0])
program += MEASURE(q[1], ro[1])
# Step 2.2. Run the program in a loop
program = program.wrap_in_numshots_loop(n_shots)
# Step 3. Compile and run
nq_program = compiler.quil_to_native_quil(program)
executable = compiler.native_quil_to_executable(nq_program)
bitstrings = qam.load(executable) \
.run() \
.wait() \
.read_memory(region_name="ro")
# Bincount bitstrings
basis = np.array([2**i for i in range(len(q))])
ints = np.sum(bitstrings * basis, axis=1)
print('bincounts', np.bincount(ints))
# Check parity
parities = np.sum(bitstrings, axis=1) % 2
print('avg parity', np.mean(parities))
def test_control_flows():
outer_loop = Program()
classical_flag_register = outer_loop.declare('classical_flag_register', 'BIT')
outer_loop += MOVE(classical_flag_register, 1) # initialize
inner_loop = Program()
inner_loop += Program(X(0), H(0))
inner_loop += MEASURE(0, classical_flag_register)
# run inner_loop in a loop until classical_flag_register is 0
outer_loop.while_do(classical_flag_register, inner_loop)
assert outer_loop.out() == '\n'.join([
"DECLARE classical_flag_register BIT[1]",
"MOVE classical_flag_register 1",
"LABEL @START1",
"JUMP-UNLESS @END2 classical_flag_register",
"X 0",
"H 0",
"MEASURE 0 classical_flag_register",
"JUMP @START1",
"LABEL @END2",
""
])
def test_variance_bootstrap():
qubits = [0, 1]
qc = get_test_qc(n_qubits=len(qubits))
state_prep = Program([H(q) for q in qubits])
state_prep.inst(CZ(qubits[0], qubits[1]))
tomo_expt = generate_state_tomography_experiment(state_prep, qubits)
results = list(measure_observables(qc=qc, tomo_experiment=tomo_expt, n_shots=4000))
estimate, status = iterative_mle_state_estimate(results=results, qubits=qubits,
dilution=0.5)
rho_est = estimate.estimate.state_point_est
purity = np.trace(rho_est @ rho_est)
purity = np.real_if_close(purity)
assert purity.imag == 0.0
def my_mle_estimator(_r, _q):
return iterative_mle_state_estimate(results=_r, qubits=_q,
dilution=0.5, entropy_penalty=0.0, beta=0.0)[0]
boot_purity, boot_var = estimate_variance(results=results, qubits=qubits,
tomo_estimator=my_mle_estimator, functional=dm.purity,
n_resamples=5, project_to_physical=False)
np.testing.assert_allclose(purity, boot_purity, atol=2 * np.sqrt(boot_var), rtol=0.01)
def test_expectation(forest: ForestConnection):
# The forest fixture (argument) to this test is to ensure this is
# skipped when a forest web api key is unavailable. You could also
# pass it to the constructor of WavefunctionSimulator() but it is not
# necessary.
wfnsim = WavefunctionSimulator()
bell = Program(
H(0),
CNOT(0, 1),
)
expects = wfnsim.expectation(bell, [
sZ(0) * sZ(1),
sZ(0),
sZ(1),
sX(0) * sX(1),
])
assert expects.size == 4
np.testing.assert_allclose(expects, [1, 0, 0, 1])
pauli_sum = PauliSum([sZ(0) * sZ(1)])
expects = wfnsim.expectation(bell, pauli_sum)
assert expects.size == 1
np.testing.assert_allclose(expects, [1])
def test_qc_expectation(forest):
device = NxDevice(nx.complete_graph(2))
qc = QuantumComputer(name='testy!',
qam=QVM(connection=forest),
device=device,
compiler=DummyCompiler())
# bell state program
p = Program()
p += RESET()
p += H(0)
p += CNOT(0, 1)
p.wrap_in_numshots_loop(10)
# XX, YY, ZZ experiment
sx = ExperimentSetting(in_state=sZ(0) * sZ(1), out_operator=sX(0) * sX(1))
sy = ExperimentSetting(in_state=sZ(0) * sZ(1), out_operator=sY(0) * sY(1))
sz = ExperimentSetting(in_state=sZ(0) * sZ(1), out_operator=sZ(0) * sZ(1))
e = TomographyExperiment(settings=[sx, sy, sz], program=p)
results = qc.experiment(e)
# XX expectation value for bell state |00> + |11> is 1
assert np.isclose(results[0].expectation, 1)
assert np.isclose(results[0].std_err, 0)
assert results[0].total_counts == 40
# YY expectation value for bell state |00> + |11> is -1
assert np.isclose(results[1].expectation, -1)
assert np.isclose(results[1].std_err, 0)
assert results[1].total_counts == 40
# ZZ expectation value for bell state |00> + |11> is 1
assert np.isclose(results[2].expectation, 1)
assert np.isclose(results[2].std_err, 0)
assert results[2].total_counts == 40
def amplification_circuit(algorithm,
oracle,
qubits,
num_iter,
decompose_diffusion=False):
"""
Returns a program that does ``num_iter`` rounds of amplification, given a measurement-less
algorithm, an oracle, and a list of qubits to operate on.
:param Program algorithm: A program representing a measurement-less algorithm run on qubits.
:param Program oracle: An oracle maps any basis vector ``|psi>`` to either ``+|psi>`` or
``-|psi>`` depending on whether ``|psi>`` is in the desirable subspace or the undesirable
subspace.
:param Sequence qubits: the qubits to operate on
:param int num_iter: number of iterations of amplifications to run
:param bool decompose_diffusion: If True, decompose the Grover diffusion gate into two qubit
gates. If False, use a defgate to define the gate.
:return: The amplified algorithm.
:rtype: Program
"""
program = pq.Program()
uniform_superimposer = pq.Program().inst([H(qubit) for qubit in qubits])
program += uniform_superimposer
if decompose_diffusion:
diffusion = decomposed_diffusion_program(qubits)
else:
diffusion = diffusion_program(qubits)
# To avoid redefining gates, we collect them before building our program.
defined_gates = oracle.defined_gates + algorithm.defined_gates + diffusion.defined_gates
for _ in range(num_iter):
program += (oracle.instructions + algorithm.dagger().instructions +
diffusion.instructions + algorithm.instructions)
# We redefine the gates in the new program.
for gate in defined_gates:
program.defgate(gate.name, gate.matrix)
return program
def test_get_expectation_values_for_circuitset(self, backend):
# Given
num_circuits = 10
circuitset = [
Circuit(Program(H(0), CNOT(0, 1), CNOT(1, 2))) for _ in range(num_circuits)
]
operator = IsingOperator("[]")
target_expectation_values = np.array([1])
# When
backend.n_samples = 1
expectation_values_set = backend.get_expectation_values_for_circuitset(
circuitset, operator
)
# Then
assert len(expectation_values_set) == num_circuits
for expectation_values in expectation_values_set:
assert isinstance(expectation_values, ExpectationValues)
assert isinstance(expectation_values.values, np.ndarray)
assert expectation_values.values == pytest.approx(
target_expectation_values, abs=1e-15
)
def grover_run(self, bit):
self._run_init(bit)
oracle = Program()
oracle_name = "grover_oracle"
oracle.defgate(oracle_name, self._grover_oracle_matrix(bit))
oracle.inst(tuple([oracle_name] + self.qubits))
diffusion = self._grover_diffusion_op()
p = Program()
Hm = Program().inst([H(qubit) for qubit in grover.qubits])
p += Hm
## Repeating part of the algorithm
for _ in range(self.num_iter):
p += oracle
p += Hm
p += diffusion
p += Hm
# run the program on a QVM
qc = get_qc('9q-square-qvm')
result = qc.run_and_measure(p, trials=10)
pprint.pprint(result)
def get_test_program(measure: bool = False) -> Program:
PI = float(pi.evalf())
p = Program()
p += X(0)
p += Y(1)
p += Z(2)
p += H(3)
p += S(0)
p += T(1)
p += RX(PI / 2, 2)
p += RY(PI / 2, 3)
p += RZ(PI / 2, 0)
p += CZ(0, 1)
p += CNOT(2, 3)
p += CCNOT(0, 1, 2)
p += CPHASE(PI / 4, 2, 1)
p += SWAP(0, 3)
if measure:
ro = p.declare("ro", "BIT", 4)
p += MEASURE(0, ro[0])
p += MEASURE(3, ro[1])
p += MEASURE(2, ro[2])
p += MEASURE(1, ro[3])
return p
def amplification_circuit(algorithm, oracle, qubits, num_iter):
"""
Returns a program that does n rounds of amplification, given a measurement-less algorithm,
an oracle, and a list of qubits to operate on.
:param Program algorithm: A program representing a measurement-less algorithm run on qubits.
:param Program oracle: An oracle maps any basis vector to either |0> or |1>.
:param Sequence qubits: the qubits to operate on
:param int num_iter: number of iterations of amplifications to run
:return: The amplified algorithm.
:rtype: Program
"""
if num_iter <= 0:
raise ValueError("num_iter must be greater than zero")
prog = pq.Program()
uniform_superimposer = pq.Program().inst([H(qubit) for qubit in qubits])
prog += uniform_superimposer
for _ in range(num_iter):
prog += oracle + algorithm.dagger() + diffusion_program(
qubits) + algorithm
return prog
def test_get_expectation_values_for_circuitset(self):
# Given
num_circuits = 10
circuitset = [
Circuit(Program(H(0), CNOT(0, 1), CNOT(1, 2)))
for _ in range(num_circuits)
]
operator = IsingOperator('[]')
target_expectation_values = np.array([1])
# When
for backend in self.backends:
backend.n_samples = 1
expectation_values_set = backend.get_expectation_values_for_circuitset(
circuitset, operator)
# Then
self.assertEqual(len(expectation_values_set), num_circuits)
for expectation_values in expectation_values_set:
self.assertIsInstance(expectation_values, ExpectationValues)
np.testing.assert_array_almost_equal(expectation_values.values,
target_expectation_values,
decimal=15)
def get_qaoa_program(qubits, driver, reward, betas, gammas) -> Program:
"""
Return the pyQuil program to construct the QAOA state for the problem given beta
and gamma angles
:param driver: The driver PauliSum. Usually X_i on all qubits.
:param problem_ham: The PauliSum representing the problem reward
:param betas: Beta angles for parameterizing the driver unitary.
:param gammas: Gamma angles for parameterizing the problem hamiltonian unitary.
:return: The program
"""
assert len(betas) == len(gammas)
prob_progs = [exponentiate_reward(reward, gamma) for gamma in gammas]
driver_progs = [exponentiate_driver(driver, beta) for beta in betas]
interleaved_progs = [
prob_prog + driver_prog
for prob_prog, driver_prog in zip(prob_progs, driver_progs)
]
prog = Program([H(q) for q in qubits])
for iprog in interleaved_progs:
prog += iprog
return prog
def test_qc_expectation_larger_lattice(client_configuration: QCSClientConfiguration, dummy_compiler: DummyCompiler):
qc = QuantumComputer(name="testy!", qam=QVM(client_configuration=client_configuration), compiler=dummy_compiler)
q0 = 2
q1 = 3
# bell state program
p = Program()
p += RESET()
p += H(q0)
p += CNOT(q0, q1)
p.wrap_in_numshots_loop(10)
# XX, YY, ZZ experiment
sx = ExperimentSetting(in_state=_pauli_to_product_state(sZ(q0) * sZ(q1)), out_operator=sX(q0) * sX(q1))
sy = ExperimentSetting(in_state=_pauli_to_product_state(sZ(q0) * sZ(q1)), out_operator=sY(q0) * sY(q1))
sz = ExperimentSetting(in_state=_pauli_to_product_state(sZ(q0) * sZ(q1)), out_operator=sZ(q0) * sZ(q1))
e = Experiment(settings=[sx, sy, sz], program=p)
results = qc.experiment(e)
# XX expectation value for bell state |00> + |11> is 1
assert np.isclose(results[0].expectation, 1)
assert np.isclose(results[0].std_err, 0)
assert results[0].total_counts == 40
# YY expectation value for bell state |00> + |11> is -1
assert np.isclose(results[1].expectation, -1)
assert np.isclose(results[1].std_err, 0)
assert results[1].total_counts == 40
# ZZ expectation value for bell state |00> + |11> is 1
assert np.isclose(results[2].expectation, 1)
assert np.isclose(results[2].std_err, 0)
assert results[2].total_counts == 40
def rotate_density(rho, pauli_term, debug=False):
"""
Rotate the density so I can read off in the computational basis
"""
# rotate operator into computational basis
rot_prog = Program()
n_qubits = int(np.log2(rho.shape[0]))
marked_qubits = []
for key, value in pauli_term._ops.iteritems():
marked_qubits.append(key)
if value == "X":
rot_prog.inst(H(key))
elif value == "Y":
rot_prog.inst(RX(np.pi / 2)(key))
rot_prog.inst(I(n_qubits - 1))
qvm_unitary = QVMConnection(type_trans='unitary')
if debug:
ham_op = tensor_up(PauliSum([pauli_term]), n_qubits)
e_true = np.trace(ham_op.dot(rho))
unitary = qvm_unitary.unitary(rot_prog)
rho = unitary.dot(rho.dot(np.conj(unitary).T))
if debug:
z_term = PauliTerm("I", 0)
for idx in marked_qubits:
z_term = z_term * PauliTerm("Z", idx)
ham_op_2 = tensor_up(pauli_term.coefficient * PauliSum([z_term]),
n_qubits)
test_expect = np.trace(np.dot(ham_op_2, rho))
assert np.isclose(test_expect, e_true)
return rho, marked_qubits
def maxcut_qaoa_program(gamma: float) -> Program:
"""
Generates a 2Q MAXCUT QAOA circuit with beta = pi/8 and with the provided
gamma.
Args:
gamma: One of the two variational parameters (the other is fixed).
Returns:
A 2Q MAXCUT QAOA circuit with fixed beta and gamma.
"""
q0, q1 = (0, 1)
p = Program()
p += H(q0)
p += H(q1)
p += CNOT(q0, q1)
p += RZ(2 * gamma, q1)
p += CNOT(q0, q1)
p += H(q0)
p += H(q1)
p += RZ(np.pi / 4, q0)
p += RZ(np.pi / 4, q1)
p += H(q0)
p += H(q1)
return p
def test_append_measure_register():
q0 = QubitPlaceholder()
p = Program(H(q0), RX(np.pi/2, 0))
p = append_measure_register(p)
assert str(p[-1]) == "MEASURE 0 ro[1]"
def test_run_and_measure_qubits(client_configuration: QCSClientConfiguration):
wfnsim = WavefunctionSimulator(client_configuration=client_configuration)
bell = Program(H(0), CNOT(0, 1))
bitstrings = wfnsim.run_and_measure(bell, qubits=[0, 100], trials=1000)
assert np.all(bitstrings[:, 1] == 0)
assert 0.4 < np.mean(bitstrings[:, 0]) < 0.6
def test_run_and_measure(client_configuration: QCSClientConfiguration):
wfnsim = WavefunctionSimulator(client_configuration=client_configuration)
bell = Program(H(0), CNOT(0, 1))
bitstrings = wfnsim.run_and_measure(bell, trials=1000)
parity = np.sum(bitstrings, axis=1) % 2
assert np.all(parity == 0)
def test_trotterize():
term_one = PauliTerm("X", 0, 1.0)
term_two = PauliTerm("Z", 0, 1.0)
with pytest.raises(ValueError):
trotterize(term_one, term_two, trotter_order=0)
with pytest.raises(ValueError):
trotterize(term_one, term_two, trotter_order=5)
prog, _ = trotterize(term_one, term_one)
result_prog = Program().inst(
[H(0), RZ(2.0)(0), H(0),
H(0), RZ(2.0)(0), H(0)])
compare_progs(prog, result_prog)
# trotter_order 1 steps 1
prog, _ = trotterize(term_one, term_two, trotter_steps=1)
result_prog = Program().inst([H(0), RZ(2.0)(0), H(0), RZ(2.0)(0)])
compare_progs(prog, result_prog)
# trotter_order 1 steps 2
prog, _ = trotterize(term_one, term_two, trotter_steps=2)
result_prog = Program().inst([
H(0),
RZ(1.0)(0),
H(0),
RZ(1.0)(0),
H(0),
RZ(1.0)(0),
H(0),
RZ(1.0)(0)
])
compare_progs(prog, result_prog)
# trotter_order 2 steps 1
prog, _ = trotterize(term_one, term_two, trotter_order=2)
result_prog = Program().inst(
[H(0), RZ(1.0)(0),
H(0), RZ(2.0)(0),
H(0), RZ(1.0)(0),
H(0)])
compare_progs(prog, result_prog)
# trotter_order 2 steps 2
prog, _ = trotterize(term_one, term_two, trotter_order=2, trotter_steps=2)
result_prog = Program().inst([
H(0),
RZ(0.5)(0),
H(0),
RZ(1.0)(0),
H(0),
RZ(0.5)(0),
H(0),
H(0),
RZ(0.5)(0),
H(0),
RZ(1.0)(0),
H(0),
RZ(0.5)(0),
H(0)
])
compare_progs(prog, result_prog)
# trotter_order 3 steps 1
prog, _ = trotterize(term_one, term_two, trotter_order=3, trotter_steps=1)
result_prog = Program().inst([
H(0),
RZ(14.0 / 24)(0),
H(0),
RZ(4.0 / 3.0)(0),
H(0),
RZ(1.5)(0),
H(0),
RZ(-4.0 / 3.0)(0),
H(0),
RZ(-2.0 / 24)(0),
H(0),
RZ(2.0)(0)
])
compare_progs(prog, result_prog)
def test_exponentiate():
# test rotation of single qubit
generator = PauliTerm("Z", 0, 1.0)
para_prog = exponential_map(generator)
prog = para_prog(1)
result_prog = Program().inst(RZ(2.0)(0))
compare_progs(prog, result_prog)
# testing general 2-circuit
generator = PauliTerm("Z", 1, 1.0) * PauliTerm("Z", 0, 1.0)
para_prog = exponential_map(generator)
prog = para_prog(1)
result_prog = Program().inst(CNOT(0, 1)).inst(RZ(2.0)(1)).inst(CNOT(0, 1))
compare_progs(prog, result_prog)
# testing change of basis position 0
generator = PauliTerm("Z", 1, 1.0) * PauliTerm("X", 0, 1.0)
param_prog = exponential_map(generator)
prog = param_prog(1)
result_prog = Program().inst(
[H(0), CNOT(0, 1), RZ(2.0)(1),
CNOT(0, 1), H(0)])
compare_progs(prog, result_prog)
# testing change of basis position 1
generator = PauliTerm("X", 1, 1.0) * PauliTerm("Z", 0, 1.0)
para_prog = exponential_map(generator)
prog = para_prog(1)
result_prog = Program().inst(
[H(1), CNOT(0, 1), RZ(2.0)(1),
CNOT(0, 1), H(1)])
compare_progs(prog, result_prog)
# testing change of basis position 0
generator = PauliTerm("Z", 1, 1.0) * PauliTerm("Y", 0, 1.0)
para_prog = exponential_map(generator)
prog = para_prog(1)
result_prog = Program().inst([
RX(math.pi / 2.0)(0),
CNOT(0, 1),
RZ(2.0)(1),
CNOT(0, 1),
RX(-math.pi / 2)(0)
])
compare_progs(prog, result_prog)
# testing change of basis position 1
generator = PauliTerm("Y", 1, 1.0) * PauliTerm("Z", 0, 1.0)
para_prog = exponential_map(generator)
prog = para_prog(1)
result_prog = Program().inst([
RX(math.pi / 2.0)(1),
CNOT(0, 1),
RZ(2.0)(1),
CNOT(0, 1),
RX(-math.pi / 2.0)(1)
])
compare_progs(prog, result_prog)
# testing circuit for 3-terms with change of basis
generator = PauliTerm("X", 2, 1.0) * PauliTerm("Y", 1, 1.0) * PauliTerm(
"Z", 0, 1.0)
para_prog = exponential_map(generator)
prog = para_prog(1)
result_prog = Program().inst([
RX(math.pi / 2.0)(1),
H(2),
CNOT(0, 1),
CNOT(1, 2),
RZ(2.0)(2),
CNOT(1, 2),
CNOT(0, 1),
RX(-math.pi / 2.0)(1),
H(2)
])
compare_progs(prog, result_prog)
# testing circuit for 3-terms non-sequential
generator = PauliTerm("Y", 3, 1.0) * PauliTerm("Y", 2, 1.0) * PauliTerm(
"I", 1, 1.0) * PauliTerm("Y", 0, 1.0)
para_prog = exponential_map(generator)
prog = para_prog(1)
result_prog = Program().inst([
RX(math.pi / 2.0)(0),
RX(math.pi / 2.0)(2),
RX(math.pi / 2.0)(3),
CNOT(0, 2),
CNOT(2, 3),
RZ(2.0)(3),
CNOT(2, 3),
CNOT(0, 2),
RX(-math.pi / 2.0)(0),
RX(-math.pi / 2.0)(2),
RX(-math.pi / 2.0)(3)
])
compare_progs(prog, result_prog)
def implementGate(device, gate, qubit, script, frac=0):
# *This function contains SDK specific code.*
#
# Input:
# * *device* - String specifying the device on which the game is played.
# Details about the device will be obtained using getLayout.
# * *gate* - String that specifies gate type.
# * *qubit* - Qubit, list of two qubits or qubit register on which the gate is applied.
# * *script* -
# * *frac* -
#
# Process:
# * For gates of type 'X', 'Z' and 'XX', the gate $U = \exp(-i \,\times\, gate \,\times\, frac )$ is implemented on the qubit or pair of qubits in *qubit*.
# * *gate='Finish'* implements the measurement command on the qubit register required for ProjectQ to not complain.
#
# Output:
# * None are returned, but modifications are made to the classes that contain the quantum program.
num, area, entangleType, pairs, pos, example, sdk, runs = getLayout(device)
if sdk in ["QISKit", "ManualQISKit"]:
if gate == 'X':
script.u3(frac * math.pi, -math.pi / 2, math.pi / 2, qubit)
elif gate == 'Z': # actually a Y axis rotation
script.u3(frac * math.pi, 0, 0, qubit)
elif gate == 'XX':
if entangleType == 'CX':
script.cx(qubit[0], qubit[1])
script.u3(frac * math.pi, -math.pi / 2, math.pi / 2, qubit[0])
script.cx(qubit[0], qubit[1])
elif entangleType == 'CZ':
script.h(qubit[1])
script.cz(qubit[0], qubit[1])
script.u3(frac * math.pi, -math.pi / 2, math.pi / 2, qubit[0])
script.cz(qubit[0], qubit[1])
script.h(qubit[1])
else:
print("Support for this is yet to be added")
elif sdk == "ProjectQ":
if gate == 'X':
Rx(frac * math.pi) | qubit
elif gate == 'Z': # actually a Y axis rotation
Ry(frac * math.pi) | qubit
elif gate == 'XX':
if entangleType == 'CX':
CNOT | (qubit[0], qubit[1])
Rx(frac * math.pi) | qubit[0]
CNOT | (qubit[0], qubit[1])
elif entangleType == 'CZ':
H | qubit[1]
C(Z) | (qubit[0], qubit[1])
Rx(frac * math.pi) | qubit[0]
C(Z) | (qubit[0], qubit[1])
H | qubit[1]
else:
print("Support for this is yet to be added")
elif gate == 'finish':
Measure | qubit
elif sdk == "Forest":
if gate == 'X':
if qubit in pos.keys(): # only if qubit is active
script.inst(RX(frac * math.pi, qubit))
elif gate == 'Z': # actually a Y axis rotation
if qubit in pos.keys(): # only if qubit is active
script.inst(RY(frac * math.pi, qubit))
elif gate == 'XX':
if entangleType == 'CX':
script.inst(CNOT(qubit[0], qubit[1]))
script.inst(RX(frac * math.pi, qubit[0]))
script.inst(CNOT(qubit[0], qubit[1]))
elif entangleType == 'CZ':
script.inst(H(qubit[1]))
script.inst(CZ(qubit[0], qubit[1]))
script.inst(RX(frac * math.pi, qubit[0]))
script.inst(CZ(qubit[0], qubit[1]))
script.inst(H(qubit[1]))
elif entangleType == 'none':
script.inst(RX(frac * math.pi, qubit[0]))
script.inst(RX(frac * math.pi, qubit[1]))
else:
print("Support for this is yet to be added")
