def test_circuit_add_gate(self):
from qulacs import QuantumCircuit, QuantumState
from qulacs.gate import Identity, X, Y, Z, H, S, Sdag, T, Tdag, sqrtX, sqrtXdag, sqrtY, sqrtYdag
from qulacs.gate import P0, P1, U1, U2, U3, RX, RY, RZ, CNOT, CZ, SWAP, TOFFOLI, FREDKIN, Pauli, PauliRotation
from qulacs.gate import DenseMatrix, SparseMatrix, DiagonalMatrix, RandomUnitary, ReversibleBoolean, StateReflection
from qulacs.gate import BitFlipNoise, DephasingNoise, IndependentXZNoise, DepolarizingNoise, TwoQubitDepolarizingNoise, AmplitudeDampingNoise, Measurement
from qulacs.gate import merge, add, to_matrix_gate, Probabilistic, CPTP, Instrument, Adaptive
from scipy.sparse import lil_matrix
qc = QuantumCircuit(3)
qs = QuantumState(3)
ref = QuantumState(3)
sparse_mat = lil_matrix((4, 4))
sparse_mat[0, 0] = 1
sparse_mat[1, 1] = 1
def func(v, d):
return (v + 1) % d
def adap(v):
return True
gates = [
Identity(0), X(0), Y(0), Z(0), H(0), S(0), Sdag(0), T(0), Tdag(0), sqrtX(0), sqrtXdag(0), sqrtY(0), sqrtYdag(0),
Probabilistic([0.5, 0.5], [X(0), Y(0)]), CPTP([P0(0), P1(0)]), Instrument([P0(0), P1(0)], 1), Adaptive(X(0), adap),
CNOT(0, 1), CZ(0, 1), SWAP(0, 1), TOFFOLI(0, 1, 2), FREDKIN(0, 1, 2), Pauli([0, 1], [1, 2]), PauliRotation([0, 1], [1, 2], 0.1),
DenseMatrix(0, np.eye(2)), DenseMatrix([0, 1], np.eye(4)), SparseMatrix([0, 1], sparse_mat),
DiagonalMatrix([0, 1], np.ones(4)), RandomUnitary([0, 1]), ReversibleBoolean([0, 1], func), StateReflection(ref),
BitFlipNoise(0, 0.1), DephasingNoise(0, 0.1), IndependentXZNoise(0, 0.1), DepolarizingNoise(0, 0.1), TwoQubitDepolarizingNoise(0, 1, 0.1),
AmplitudeDampingNoise(0, 0.1), Measurement(0, 1), merge(X(0), Y(1)), add(X(0), Y(1)), to_matrix_gate(X(0)),
P0(0), P1(0), U1(0, 0.), U2(0, 0., 0.), U3(0, 0., 0., 0.), RX(0, 0.), RY(0, 0.), RZ(0, 0.),
]
gates.append(merge(gates[0], gates[1]))
gates.append(add(gates[0], gates[1]))
ref = None
for gate in gates:
qc.add_gate(gate)
for gate in gates:
qc.add_gate(gate)
qc.update_quantum_state(qs)
qc = None
qs = None
for gate in gates:
gate = None
gates = None
parametric_gates = None
def cost_phf_sample_oneshot(print_level, qulacs_hamiltonianZ, qulacs_s2Z,
qulacs_ancZ, coef0_H, coef0_S2, kappa_list):
"""Function:
Test function for sampling Hamiltonian and S** expectation values
with PHF just for once.
Author(s): Takashi Tsuchimochi
使われてない?
"""
t1 = time.time()
noa = Quket.noa
nob = Quket.nob
nva = Quket.nva
nvb = Quket.nvb
n_electrons = Quket.n_electrons
n_qubit_system = n_qubits
n_qubits = Quket.n_qubits + 1
anc = n_qubit_system
state = QuantumState(n_qubits)
circuit_rhf = set_circuit_rhfZ(n_qubits, n_electrons)
circuit_rhf.update_quantum_state(state)
circuit_uhf = set_circuit_uhfZ(n_qubits, noa, nob, nva, nvb, kappa_list)
circuit_uhf.update_quantum_state(state)
### Set post-measurement states ####
poststate0 = state.copy()
poststate1 = state.copy()
circuit0 = QuantumCircuit(n_qubits)
circuit1 = QuantumCircuit(n_qubits)
### Projection to anc = 0 or anc = 1 ###
circuit0.add_gate(P0(0))
circuit1.add_gate(P1(0))
circuit0.update_quantum_state(poststate0)
circuit1.update_quantum_state(poststate1)
### Renormalize each state ###
norm0 = poststate0.get_squared_norm()
norm1 = poststate1.get_squared_norm()
poststate0.normalize(norm0)
poststate1.normalize(norm1)
### grid loop ###
Ng = 4
beta = [-0.861136311594053, -0.339981043584856,
0.339981043584856, 0.861136311594053]
wg = [0.173927422568724, 0.326072577431273,
0.326072577431273, 0.173927422568724]
### a list to compute the probability to observe 0 in ancilla qubit
p0_list = np.full(n_qubits, 2)
p0_list[-1] = 0
### Array for <HUg>, <S2Ug>, <Ug>
samplelist = [5, 50, 500, 5000, 50000, 500000, 5000000]
Ng = 4
ncyc = 10
prints("", filepath="./log.txt", opentype="w")
for i_sample in samplelist:
sampleEn = []
sampleS2 = []
for icyc in range(ncyc):
prints(f"n_sample : {i_sample} ({icyc} / {ncyc})",
filepath="./log.txt")
HUg = []
S2Ug = []
Ug = []
Ephf = S2 = Norm = 0
for i in range(Ng):
### Copy quantum state of UHF (cannot be done in real device) ###
state_g = QuantumState(n_qubits)
circuit_rhf.update_quantum_state(state_g)
circuit_uhf.update_quantum_state(state_g)
### Construct Ug test
circuit_ug = QuantumCircuit(n_qubits)
### Hadamard on anc
circuit_ug.add_H_gate(anc)
controlled_Ug(circuit_ug, n_qubits, anc, np.arccos(beta[i]))
circuit_ug.add_H_gate(anc)
circuit_ug.update_quantum_state(state_g)
### Probabilities for getting 0 and 1 in ancilla qubit ###
p0 = state_g.get_marginal_probability(p0_list)
p1 = 1 - p0
### Compute expectation value <HUg> ###
HUg.append(sample_observable(state_g,
qulacs_hamiltonianZ,
i_sample).real)
### <S2Ug> ###
S2Ug.append(sample_observable(state_g,
qulacs_s2Z,
i_sample).real)
#S2Ug.append(qulacs_s2Z.get_expectation_value(state_g))
#Ug.append(p0 - p1)
Ug.append(sample_observable(state_g,
qulacs_ancZ,
i_sample).real)
### Norm accumulation ###
Norm += wg[i]*g[i]
sampleHUg[icyc, i] = HUg[i]
sampleS2Ug[icyc, i] = S2Ug[i]
sampleUg[icyc, i] = Ug[i]
#print(f"{p0=} {p1=} {p0-p1=}")
sampleHUg1.append(HUg[0])
sampleHUg2.append(HUg[1])
sampleHUg3.append(HUg[2])
sampleHUg4.append(HUg[3])
sampleS2Ug1.append(S2Ug[0])
sampleS2Ug2.append(S2Ug[1])
sampleS2Ug3.append(S2Ug[2])
SAMpleS2Ug4.append(S2Ug[3])
sampleUg1.append(Ug[0])
sampleUg2.append(Ug[1])
sampleUg3.append(Ug[2])
sampleUg4.append(Ug[3])
### Energy calculation <HP>/<P> and <S**2P>/<P> ###
Ephf = 0
for i in range(Ng):
Ephf += wg[i]*HUg[i]/Norm
S2 += wg[i]*S2Ug[i]/Norm
#print(f" E[PHF] = {Ephf} <S**2> = {S2} (Nsample = {i_sample})")
Ephf += coef0_H
S2 += coef0_S2
sampleEn[icyc, 0] = Ephf
sampleS2[icyc, 0] = S2
#print(f"(n_sample = {i_sample}): sample HUg1\n", sampleHUg1)
#print(f"(n_sample = {i_sample}): sample HUg2\n", sampleHUg2)
#print(f"(n_sample = {i_sample}): sample HUg3\n", sampleHUg3)
#print(f"(n_sample = {i_sample}): sample HUg4\n", sampleHUg4)
#print(f"(n_sample = {i_sample}): sample S2Ug1\n", sampleS2Ug1)
#print(f"(n_sample = {i_sample}): sample S2Ug2\n", sampleS2Ug2)
#print(f"(n_sample = {i_sample}): sample S2Ug3\n", sampleS2Ug3)
#print(f"(n_sample = {i_sample}): sample S2Ug4\n", sampleS2Ug4)
#print(f"(n_sample = {i_sample}): sample Ug1\n", sampleUg1)
#print(f"(n_sample = {i_sample}): sample Ug2\n", sampleUg2)
#print(f"(n_sample = {i_sample}): sample Ug3\n", sampleUg3)
#print(f"(n_sample = {i_sample}): sample Ug4\n", sampleUg4)
#print(f"(n_sample = {i_sample}): sample HUg1\n", sampleHUg1)
#print(f"(n_sample = {i_sample}): sample HUg2\n", sampleHUg2)
#print(f"(n_sample = {i_sample}): sample HUg3\n", sampleHUg3)
#print(f"(n_sample = {i_sample}): sample HUg4\n", sampleHUg4)
#print(f"(n_sample = {i_sample}): sample En\n", sampleEn)
#print(f"(n_sample = {i_sample}): sample S2\n", sampleS2)
with open(f"./HUg_{i_sample}.csv", "w") as fHUg:
writer = csv.writer(fHUg)
writer.writerows(sampleHUg)
with open(f"./S2Ug_{i_sample}.csv", "w") as fS2Ug:
writer = csv.writer(fS2Ug)
writer.writerows(sampleS2Ug)
with open(f"./Ug_{i_sample}.csv", "w") as fUg:
writer = csv.writer(fUg)
writer.writerows(sampleUg)
with open(f"./En_{i_sample}.csv", "w") as fEn:
writer = csv.writer(fEn)
writer.writerows(sampleEn)
with open(f"./S2_{i_sample}.csv", "w") as fS2:
writer = csv.writer(fS2)
writer.writerows(sampleS2)
return Ephf, S2
def cost_phf_sample(Quket, print_level,
qulacs_hamiltonian, qulacs_hamiltonianZ, qulacs_s2Z,
qulacs_ancZ, coef0_H, coef0_S2, ref, theta_list,
samplelist):
"""Function:
Sample Hamiltonian and S**2 expectation values with PHF and PUCCSD.
Write out the statistics in csv files.
Author(s): Takashi Tsuchimochi
"""
t1 = time.time()
noa = Quket.noa
nob = Quket.nob
nva = Quket.nva
nvb = Quket.nvb
n_electrons = Quket.n_electrons
n_qubit_system = n_qubits
n_qubits = Quket.n_qubits + 1
anc = n_qubit_system
ndim1 = Quket.ndim1
state = QuantumState(n_qubits)
circuit_rhf = set_circuit_rhfZ(n_qubits, n_electrons)
circuit_rhf.update_quantum_state(state)
if ref == "phf":
circuit_uhf = set_circuit_uhfZ(n_qubits, noa, nob, nva, nvb, theta_list)
circuit_uhf.update_quantum_state(state)
print("pHF")
elif ref == "puccsd":
circuit = set_circuit_uccsd(n_qubits, noa, nob, nva, nvb, theta_list,
ndim1)
for i in range(rho):
circuit.update_quantum_state(state)
print("UCCSD")
if print_level > -1:
print("State before projection")
utils.print_state(state, n_qubit_system)
#### Set post-measurement states ####
#poststate0 = state.copy()
#poststate1 = state.copy()
#circuit0 = QuantumCircuit(n_qubits)
#circuit1 = QuantumCircuit(n_qubits)
#### Projection to anc = 0 or anc = 1 ###
#circuit0.add_gate(P0(0))
#circuit1.add_gate(P1(0))
#circuit0.update_quantum_state(poststate0)
#circuit1.update_quantum_state(poststate1)
#### Renormalize each state ###
#norm0 = poststate0.get_squared_norm()
#norm1 = poststate1.get_squared_norm()
#poststate0.normalize(norm0)
#poststate1.normalize(norm1)
### grid loop ###
Ng = 4
beta = [-0.861136311594053, -0.339981043584856,
0.339981043584856, 0.861136311594053]
wg = [0.173927422568724, 0.326072577431273,
0.326072577431273, 0.173927422568724]
Ng = 2
beta = [0.577350269189626, -0.577350269189626]
wg = [0.5, 0.5]
### a list to compute the probability to observe 0 in ancilla qubit
p0_list = np.full(n_qubits, 2)
p0_list[-1] = 0
### Array for <HUg>, <S2Ug>, <Ug>
# samplelist = [10,100,1000,10000,100000,1000000,10000000]
ncyc = 4
prints("", filepath="./log2.txt")
for i_sample in samplelist:
i_sample_x = i_sample
if i_sample == 10000000:
print("OK")
ncyc = ncyc*10
i_sample_x = 1000000
sampleHUg1 = []
sampleHUg2 = []
sampleHUg3 = []
sampleHUg4 = []
sampleS2Ug1 = []
sampleS2Ug2 = []
sampleS2Ug3 = []
sampleS2Ug4 = []
sampleUg1 = []
sampleUg2 = []
sampleUg3 = []
sampleUg4 = []
# sampleEn = []
# sampleS2 = []
sampleHUg = np.zeros((ncyc, Ng))
sampleS2Ug = np.zeros((ncyc, Ng))
sampleUg = np.zeros((ncyc, Ng))
sampleEn = np.zeros((ncyc, 1))
sampleS2 = np.zeros((ncyc, 1))
for icyc in range(ncyc):
prints(f"n_sample = {i_sample_x} ({icyc} / {ncyc})",
filepath="./log2.txt")
HUg = []
S2Ug = []
Ug = []
Ephf = S2 = Norm = 0
for i in range(Ng):
### Copy quantum state of UHF (cannot be done in real device) ###
state_g = QuantumState(n_qubits)
state_g.load(state)
### Construct Ug test
circuit_ug = QuantumCircuit(n_qubits)
### Hadamard on anc
circuit_ug.add_H_gate(anc)
controlled_Ug(circuit_ug, n_qubits, anc, np.arccos(beta[i]))
circuit_ug.add_H_gate(anc)
circuit_ug.update_quantum_state(state_g)
### Set post-measurement states ####
poststate0 = state_g.copy()
poststate1 = state_g.copy()
circuit0 = QuantumCircuit(n_qubits)
circuit1 = QuantumCircuit(n_qubits)
### Projection to anc = 0 or anc = 1 ###
circuit0.add_gate(P0(anc))
circuit1.add_gate(P1(anc))
circuit0.update_quantum_state(poststate0)
circuit1.update_quantum_state(poststate1)
### Renormalize each state ###
norm0 = poststate0.get_squared_norm()
norm1 = poststate1.get_squared_norm()
poststate0.normalize(norm0)
poststate1.normalize(norm1)
### Set ancilla qubit of poststate1 to zero (so that it won't be used) ###
circuit_anc = QuantumCircuit(n_qubits)
circuit_anc.add_X_gate(anc)
circuit_anc.update_quantum_state(poststate1)
print(
test_transition_observable(
state_g, qulacs_hamiltonianZ,
poststate0, poststate1, 100000))
# exit()
### Probabilities for getting 0 and 1 in ancilla qubit ###
p0 = state_g.get_marginal_probability(p0_list)
p1 = 1 - p0
### Compute expectation value <HUg> ###
HUg.append(sample_observable(state_g,
qulacs_hamiltonianZ,
i_sample_x).real)
#HUg.append(adaptive_sample_observable(state_g,
# qulacs_hamiltonianZ,
# i_sample_x).real)
### <S2Ug> ###
S2Ug.append(sample_observable(state_g,
qulacs_s2Z,
i_sample_x).real)
#S2Ug.append(adaptive_sample_observable(state_g,
# qulacs_s2Z,
# i_sample_x).real)
#S2Ug.append(qulacs_s2Z.get_expectation_value(state_g))
#HUg.append(0)
#S2Ug.append(0)
#Ug.append(p0 - p1)
n_term = qulacs_hamiltonianZ.get_term_count()
n_sample_total = i_sample_x * n_term
# in the worst-case scenario,
# Ug is measured as many times as n_sample_total
#(required to evaluate HUg)
Ug.append(sample_observable(state_g,
qulacs_ancZ,
i_sample_x*n_term).real)
#p0_sample = 0
#for j_sample in range(n_sample_total):
# if(p0 > np.random.rand()):
# p0_sample += 1
#Ug.append(2*p0_sample/n_sample_total - 1)
### Norm accumulation ###
Norm += wg[i]*Ug[i]
sampleHUg[icyc, i] = HUg[i]
sampleS2Ug[icyc, i] = S2Ug[i]
sampleUg[icyc, i] = Ug[i]
#print('p0 : ',p0,' p1 : ',p1, ' p0 - p1 : ',p0-p1)
sampleHUg1.append(HUg[0])
sampleHUg2.append(HUg[1])
#sampleHUg3.append(HUg[2])
#sampleHUg4.append(HUg[3])
sampleS2Ug1.append(S2Ug[0])
sampleS2Ug2.append(S2Ug[1])
#sampleS2Ug3.append(S2Ug[2])
#sampleS2Ug4.append(S2Ug[3])
sampleUg1.append(Ug[0])
sampleUg2.append(Ug[1])
#sampleUg3.append(Ug[2])
#sampleUg4.append(Ug[3])
### Energy calculation <HP>/<P> and <S**2P>/<P> ###
Ephf = 0
for i in range(Ng):
Ephf += wg[i]*HUg[i]/Norm
S2 += wg[i]*S2Ug[i]/Norm
# print(" <S**2> = ", S2, '\n')
Ephf += coef0_H
S2 += coef0_S2
sampleEn[icyc, 0] = Ephf
sampleS2[icyc, 0] = S2
# print(" <E[PHF]> (Nsample = ",i_sample,") = ", Ephf)
#print(f"(n_sample = {i_sample}): sample HUg1\n",sampleHUg1)
#print(f"(n_sample = {i_sample}): sample HUg2\n",sampleHUg2)
#print(f"(n_sample = {i_sample}): sample HUg3\n",sampleHUg3)
#print(f"(n_sample = {i_sample}): sample HUg4\n",sampleHUg4)
#print(f"(n_sample = {i_sample}): sample S2Ug1\n",sampleS2Ug1)
#print(f"(n_sample = {i_sample}): sample S2Ug2\n",sampleS2Ug2)
#print(f"(n_sample = {i_sample}): sample S2Ug3\n",sampleS2Ug3)
#print(f"(n_sample = {i_sample}): sample S2Ug4\n",sampleS2Ug4)
#print(f"(n_sample = {i_sample}): sample Ug1\n",sampleUg1)
#print(f"(n_sample = {i_sample}): sample Ug2\n",sampleUg2)
#print(f"(n_sample = {i_sample}): sample Ug3\n",sampleUg3)
#print(f"(n_sample = {i_sample}): sample Ug4\n",sampleUg4)
#print(f"(n_sample = {i_sample}): sample HUg1\n",sampleHUg1)
#print(f"(n_sample = {i_sample}): sample HUg2\n",sampleHUg2)
#print(f"(n_sample = {i_sample}): sample HUg3\n",sampleHUg3)
#print(f"(n_sample = {i_sample}): sample HUg4\n",sampleHUg4)
#print(f"(n_sample = {i_sample}): sample En\n",sampleEn)
#print(f"(n_sample = {i_sample}): sample S2\n",sampleS2)
with open(f"./Ug_{i_sample}.csv", "w") as fUg:
writer = csv.writer(fUg)
writer.writerows(sampleUg)
with open(f"./HUg_{i_sample}.csv", "w") as fHUg:
writer = csv.writer(fHUg)
writer.writerows(sampleHUg)
with open(f"./S2Ug_{i_sample}.csv", "w") as fS2Ug:
writer = csv.writer(fS2Ug)
writer.writerows(sampleS2Ug)
with open(f"./En_{i_sample}.csv", "w") as fEn:
writer = csv.writer(fEn)
writer.writerows(sampleEn)
with open(f"./S2_{i_smaple}.csv", "w") as fS2:
writer = csv.writer(fS2)
writer.writerows(sampleS2)
return Ephf, S2