def test_expectation_value() -> None:
c = Circuit(2)
c.H(0)
c.CX(0, 1)
op = QubitPauliOperator({
QubitPauliString({
Qubit(0): Pauli.Z,
Qubit(1): Pauli.Z
}):
1.0,
QubitPauliString({
Qubit(0): Pauli.X,
Qubit(1): Pauli.X
}):
0.3,
QubitPauliString({
Qubit(0): Pauli.Z,
Qubit(1): Pauli.Y
}):
0.8j,
QubitPauliString({Qubit(0): Pauli.Y}):
-0.4j,
})
b = MyBackend()
b.compile_circuit(c)
assert get_operator_expectation_value(c, op, b) == pytest.approx(1.3)
def test_sampler_expectation_value() -> None:
c = Circuit(2)
c.H(0)
c.CX(0, 1)
op = QubitPauliOperator({
QubitPauliString({
Qubit(0): Pauli.Z,
Qubit(1): Pauli.Z
}):
1.0,
QubitPauliString({
Qubit(0): Pauli.X,
Qubit(1): Pauli.X
}):
0.3,
QubitPauliString({
Qubit(0): Pauli.Z,
Qubit(1): Pauli.Y
}):
0.8j,
QubitPauliString({Qubit(0): Pauli.Y}):
-0.4j,
})
b = MySampler()
b.compile_circuit(c)
expectation = get_operator_expectation_value(c,
op,
b,
n_shots=2000,
seed=0)
assert (np.real(expectation), np.imag(expectation)) == pytest.approx(
(1.3, 0.0), abs=0.1)
def gen_pauli_measurement_circuits(state_circuit, compiler_pass, operator):
# compile main circuit once
state_cu = CompilationUnit(state_circuit)
compiler_pass.apply(state_cu)
compiled_state = state_cu.circuit
final_map = state_cu.final_map
# make a measurement circuit for each pauli
pauli_circuits = []
coeffs = []
energy = 0
for p, c in operator.terms.items():
if p == ():
# constant term
energy += c
else:
# make measurement circuits and compile them
pauli_circ = Circuit(state_circuit.n_qubits -
1) # ignore syndrome qubit
append_pauli_measurement(qps_from_openfermion(p), pauli_circ)
pauli_cu = CompilationUnit(pauli_circ)
compiler_pass.apply(pauli_cu)
pauli_circ = pauli_cu.circuit
init_map = pauli_cu.initial_map
# map measurements onto the placed qubits from the state
rename_map = {
i: final_map[o]
for o, i in init_map.items() if o in final_map
}
pauli_circ.rename_units(rename_map)
state_and_measure = compiled_state.copy()
state_and_measure.append(pauli_circ)
pauli_circuits.append(state_and_measure)
coeffs.append(c)
return pauli_circuits, coeffs, energy
def phi(qubits, alpha, n_qubits):
c = Circuit(n_qubits)
for i in range(len(qubits) - 1):
c.CX(qubits[i], qubits[i + 1])
c.Rz(qubits[-1], alpha)
for i in range(len(qubits) - 1):
c.CX(qubits[-i - 2], qubits[-i - 1])
return c
def test_bell() -> None:
c = Circuit(2)
c.H(0)
c.CX(0, 1)
b = MyBackend()
b.compile_circuit(c)
assert np.allclose(b.get_state(c),
np.asarray([1, 0, 0, 1]) * 1 / np.sqrt(2))
def ucc(params):
ansatz = Circuit(4)
ansatz.X(1).X(3)
add_excitation(ansatz, singles_a, params[0])
add_excitation(ansatz, singles_b, params[1])
add_excitation(ansatz, doubles, params[2])
DecomposeBoxes().apply(ansatz)
return ansatz
def test_basisorder() -> None:
c = Circuit(2)
c.X(1)
b = MyBackend()
b.compile_circuit(c)
assert np.allclose(b.get_state(c), np.asarray([0, 1, 0, 0]))
assert np.allclose(b.get_state(c, basis=BasisOrder.dlo),
np.asarray([0, 0, 1, 0]))
def sq(a, b, c):
circ = Circuit(1)
if c != 0:
circ.Rz(c, 0)
if b != 0:
circ.Rx(b, 0)
if a != 0:
circ.Rz(a, 0)
return circ
def hea(params):
ansatz = Circuit(4)
for i in range(4):
ansatz.Ry(params[i], i)
for i in range(3):
ansatz.CX(i, i + 1)
for i in range(4):
ansatz.Ry(params[4 + i], i)
return ansatz
def approximate_with_cnots(unitary):
n_qubits = int(math.log(unitary.shape[0], 2))
mnots = function_to_mnots(
matrix_to_function(suggest_cnot_unitary(unitary), n_qubits), n_qubits)
circs = mnot_templates(n_qubits)
c = Circuit(n_qubits)
for mnot in mnots:
n_controls = len(mnot[0])
c.add_circuit(circs[n_controls - 1], mnot[0] + [mnot[1]])
return c
def test_sampler_basisorder() -> None:
c = Circuit(2, 2)
c.X(1)
c.measure_all()
b = MySampler()
b.compile_circuit(c)
assert b.get_counts(c, n_shots=10, seed=0) == {(0, 1): 10}
assert b.get_counts(c, n_shots=10, seed=0, basis=BasisOrder.dlo) == {
(1, 0): 10
}
def cnot_unitary_to_circuit(u, n_qubits):
# desc = greedy(matrix_to_function(u, n_qubits), n_qubits) # Doesn't converge, don't run this
desc = decompose_unitary(u)
c = Circuit(n_qubits)
for d in desc:
if d[0] == "N":
c.X(int(d[2:]))
elif d[0] == "C":
c.CX(int(d.split(":")[1]), int(d.split(":")[2]))
return c
def U(s, n_qubits):
c = Circuit(n_qubits)
for i in range(len(s)):
if s[i] == "X":
c.H(i)
elif s[i] == "Y":
c.Rx(i, 0.5)
elif s[i] == "Z":
pass
return c
def to_tket(self):
if self.out_par != mat22partition(Mat2.id(self.n_qubits)):
print(self.out_par)
raise NotImplementedError("The linear transformation part of the phase polynomial cannot yet be transformed into a tket circuit")
circuit = TketCircuit(self.n_qubits)
for parity, phase in self.zphases.items():
qubits = [i for i,s in enumerate(parity) if s == '1']
circuit.add_pauliexpbox(PauliExpBox([Pauli.Z]*len(qubits), phase), qubits)
# TODO add the linear combination part with CNOTs
Transform.DecomposeBoxes().apply(circuit)
return circuit
def add_params_to_template(template: Circuit, params: np.ndarray):
params = params.reshape((template.n_gates * 2 + template.n_qubits, 3))
c = Circuit(template.n_qubits)
for i in range(template.n_gates):
cnot_gate = template.get_commands()[i]
c.add_operation(OpType.U3, params[2 * i], [cnot_gate.qubits[0]])
c.add_operation(OpType.U3, params[2 * i + 1], [cnot_gate.qubits[1]])
c.CX(cnot_gate.qubits[0], cnot_gate.qubits[1])
for q in range(template.n_qubits):
c.add_operation(OpType.U3, params[template.n_gates * 2 + q], [q])
return c
def measure_x(quantum_circuit: Circuit, qubit: int, bit: int) -> None:
"""Measures a specified qubit in the Hadamard (X) basis.
Args:
quantum_circuit (pytket.circuit.Circuit): The quantum circuit to apply the measurement to.
qubit (int): The qubit to measure.
bit (int): The bit to save the qubit measurement result into.
"""
# Apply Hadamard gate to the qubit.
# Measure the qubit into the bit.
quantum_circuit.H(qubit)
quantum_circuit.Measure(qubit, bit)
def ucc(params):
ansatz = Circuit(4)
# Set initial reference state
ansatz.X(1).X(3)
# Evolve by excitations
for term, coeff in singles_a.items():
add_operator_term(ansatz, term, coeff * params[0])
for term, coeff in singles_b.items():
add_operator_term(ansatz, term, coeff * params[1])
for term, coeff in doubles.items():
add_operator_term(ansatz, term, coeff * params[2])
return ansatz
def get_pauli_expectation(c: Circuit,
initial_circuit: Circuit,
pauli_string: str,
backend,
shots: int = 100) -> float:
n_qubits = len(pauli_string)
circuit = initial_circuit.copy()
circuit.add_circuit(c.copy(), list(range(n_qubits)))
black_box_exp = backend.get_pauli_expectation_value(
circuit, [(i, pauli_string[i])
for i in range(len(pauli_string)) if pauli_string[i] != "I"],
shots=shots).real
return black_box_exp
def test_sampler_compilation_pass() -> None:
b = MySampler()
for opt_level in range(3):
c = Circuit(2)
c.CX(0, 1)
u = np.asarray([[0, 1], [-1j, 0]])
c.add_unitary1qbox(Unitary1qBox(u), 1)
c.CX(0, 1)
c.add_gate(OpType.CRz, 0.35, [1, 0])
c.measure_all()
assert not (b.valid_circuit(c))
b.compile_circuit(c, optimisation_level=opt_level)
assert b.valid_circuit(c)
def instructions_to_circuit(instructions):
c = Circuit(n_qubit)
for inst in instructions:
t = inst.op.get_type()
c.add_operation(t, inst.op.get_params(), inst.qubits)
return c
# circ = instructions_to_circuit(circ.get_commands()[:24])
# print(circ.n_gates)
# print(dag_to_circuit(tk_to_dagcircuit(circ)))
def test_transpile_grid_circuit(self):
c = Circuit()
qb = pytket._tket.circuit.Qubit("grid", 0, 0)
c.add_qubit(qb)
c.Rz(1, qb)
qsc = jaqal_circuit_from_tket_circuit(c)
jcirc = CircuitBuilder()
reg = jcirc.register("baseregister", 1)
reg2 = jcirc.map("grid0_0", reg, slice(0, 1, 1))
block = jcirc.block()
block.gate("prepare_all")
block.gate("Rz", reg2[0], pi)
block.gate("measure_all")
self.assertEqual(generate_jaqal_program(jcirc.build()),
generate_jaqal_program(qsc))
def approximate_via_cnots(unitary):
dim = unitary.shape[0]
n_qubits = int(math.log(dim, 2))
converter.n_qubits = n_qubits
r = RewriteTket(Circuit(n_qubits), [], [])
r.set_target_unitary(unitary)
# cnot_circuit = approximate_with_cnots(unitary)
cnot_circuit = qft3
# print(dag_to_circuit(tk_to_dagcircuit(cnot_circuit)))
n_params = (cnot_circuit.n_gates * 2 + n_qubits) * 3
def distance(params):
f = r.fidelity(add_params_to_template(cnot_circuit, params))
return (1 - max(0, f)**0.5)**0.5
best_distance = 1
best_circ = None
for _ in range(10):
initial = np.random.random((n_params, ))
res = scipy.optimize.minimize(distance,
initial,
method='SLSQP',
options={'ftol': 0.01},
callback=lambda x: print(distance(x)))
if res.success and res.fun < best_distance:
best_distance = res.fun
best_circ = add_params_to_template(cnot_circuit, res.x)
if res.fun < 0.01:
return best_circ, best_distance
return best_circ, best_distance
def simulated_annealing(unitary, n_layers):
dim = unitary.shape[0]
n_qubits = int(math.log(dim, 2))
converter.n_qubits = n_qubits
n_cnots = (n_layers // 2) * (n_qubits // 2) + ((n_qubits - 1) // 2) * (
(n_layers + 1) // 2)
r = RewriteTket(Circuit(n_qubits), [], [])
r.set_target_unitary(unitary)
skips = []
circuit, distance = approximate_scipy(unitary, n_layers, r, [])
for k in range(10):
new_skip = random.choice([i for i in range(n_cnots) if i not in skips])
print("maybe I will skip", new_skip)
new_circ, new_d = approximate_scipy(unitary, n_layers, r,
skips + [new_skip])
if new_d <= distance: # or random.random() < (1 - new_d + distance) / (5 * k + 1):
print("skipping", new_skip)
circuit, distance, skips = new_circ, new_d, skips + [new_skip]
else:
print("not skipping", new_skip)
print(circuit.n_gates, circuit.n_gates_of_type(OpType.CX))
if distance < 0.01:
print("returning early", distance)
return circuit, r.fidelity(circuit)
print(distance)
return circuit, r.fidelity(circuit)
def measurement_circuits(hamiltonian, n_qubits):
all_circs = []
for pauli, _ in hamiltonian.terms.items():
if not pauli:
continue # Ignore the constant term
measure_circ = Circuit(n_qubits, n_qubits)
for qb, p in pauli:
if p == 'I':
continue # Discard I qubits
elif p == 'X':
measure_circ.H(qb)
elif p == 'Y':
measure_circ.Rx(0.5, qb)
measure_circ.Measure(qb, qb)
all_circs.append(measure_circ)
return all_circs
def ucc(params):
singles_params = {qps: params[0] * coeff for qps, coeff in singles.items()}
doubles_params = {qps: params[1] * coeff for qps, coeff in doubles.items()}
excitation_op = QubitPauliOperator({**singles_params, **doubles_params})
reference_circ = Circuit(4).X(1).X(3)
ansatz = gen_term_sequence_circuit(excitation_op, reference_circ)
GuidedPauliSimp().apply(ansatz)
FullPeepholeOptimise().apply(ansatz)
return ansatz
def generate_hf_wavefunction_circuit(n_qubit: int, n_alpha_electron: int, n_beta_electron: int):
"""
Args:
n_qubit (int):
n_alpha_electron (int):
n_beta_electron (int):
"""
circuit = Circuit(n_qubit)
for i in range(n_alpha_electron):
idx_alpha = 2 * i
circuit.X(idx_alpha)
for i in range(n_beta_electron):
idx_beta = 2 * i + 1
circuit.X(idx_beta)
return circuit
def measure_z(quantum_circuit: Circuit, qubit: int, bit: int) -> None:
"""Measures a specified qubit in the Computational (Z) basis.
Args:
quantum_circuit (pytket.circuit.Circuit): The quantum circuit to apply the measurement to.
qubit (int): The qubit to measure.
bit (int): The bit to save the qubit measurement result into.
"""
# Measure the qubit into the bit.
quantum_circuit.Measure(qubit, bit)
def circuit_from_string(op_string: Iterable[Any],
*,
n_qubits: int = 1,
key: Dict[Any, Callable] = None) -> Circuit:
if key is None:
key = ops
circuit = Circuit(n_qubits)
for s in op_string[::-1]:
key[s](circuit)
return circuit
def pauli_measurement(pauli_string:Iterable[Tuple[int,str]], circ:Circuit) :
"""Appends measurement instructions to a given circuit, measuring each qubit in a given basis
:param pauli_string: The pauli operator to measure, as tuples of pauli name and qubit.
:type pauli_string: Iterable[Tuple[int,str]]
:param circ: Circuit to add measurement to.
:type circ: Circuit
"""
measured_qbs = []
for qb_idx, p in pauli_string:
measured_qbs.append(qb_idx)
if p=='X':
circ.H(qb_idx)
elif p=='Y':
circ.Sdg(qb_idx)
circ.H(qb_idx)
for idx in measured_qbs:
circ.Measure(idx, idx)
def run_multiple(n_qubits, n_iter):
c = Circuit(n_qubits)
rewriter = RewriteTket(c, single_noise, cnot_noise, verbose=False)
rewriter.verbose = True
for _ in range(n_iter):
circuit = random_pauli_gadget(n_qubits)
Transform.OptimisePauliGadgets().apply(circuit)
rewriter.set_circuit_and_target(circuit)
rewriter.reduce()
print(rewriter.instructions)
print("\n\n")
