def test_CU_Y0Y1(self):
n_qubits = 3
a_idx = 2
theta = np.pi/4
state = QuantumState(n_qubits)
input_states_bin = [0b001, 0b010, 0b101, 0b110]
input_states = []
output_states = []
circuit = QuantumCircuit(n_qubits)
# change basis from Z to Y
circuit.add_S_gate(0)
circuit.add_S_gate(1)
circuit.add_H_gate(0)
circuit.add_H_gate(1)
circuit.add_CNOT_gate(1, 0)
# RZ
circuit.add_RZ_gate(0, -0.5*theta)
circuit.add_CNOT_gate(a_idx, 0)
circuit.add_RZ_gate(0, 0.5*theta)
circuit.add_CNOT_gate(a_idx, 0)
circuit.add_CNOT_gate(1, 0)
# change basis from Z to Y
circuit.add_H_gate(0)
circuit.add_H_gate(1)
circuit.add_Sdag_gate(0)
circuit.add_Sdag_gate(1)
for b in input_states_bin:
psi = state.copy()
psi.set_computational_basis(b)
input_states += [psi]
psi_out = psi.copy()
circuit.update_quantum_state(psi_out)
output_states += [psi_out]
p_list = []
for in_state in input_states:
for out_state in output_states:
prod = inner_product(in_state, out_state)
p_list += [prod]
# |001>
exp_list = [1.0, 0.0, 0.0, 0.0]
# |010>
exp_list += [0.0, 1.0, 0.0, 0.0]
# |101>
exp_list += [0.0, 0.0, np.cos(theta/2), complex(0, -np.sin(theta/2))]
# |110>
exp_list += [0.0, 0.0, complex(0, -np.sin(theta/2)), np.cos(theta/2)]
for result, expected in zip(p_list, exp_list):
self.assertAlmostEqual(result, expected, places=6)
def set_input_state(self, x_list):
"""入力状態のリストを作成"""
x_list_normalized = min_max_scaling(x_list) # xを[-1, 1]の範囲にスケール
st_list = []
for x in x_list_normalized:
st = QuantumState(self.nqubit)
input_gate = self.create_input_gate(x)
input_gate.update_quantum_state(st)
st_list.append(st.copy())
self.input_state_list = st_list
def set_input_state(self, x_list):
"""List of input state"""
x_list_normalized = min_max_scaling(x_list) # x within [-1, 1]
st_list = []
for x in x_list_normalized:
st = QuantumState(self.nqubit)
input_gate = self.create_input_gate(x)
input_gate.update_quantum_state(st)
st_list.append(st.copy())
self.input_state_list = st_list
def set_input_state(self, x_list, uin_type):
"""List of input state"""
x_list_normalized = min_max_scaling(x_list) # x within [-1, 1]
#x_list_normalized_0to2pi = min_max_scaling_0to2pi(x_list) # x within [0, 2pi]
#print('x_list_normalized_0to2pi=',x_list_normalized_0to2pi)
st_list = []
for x in x_list_normalized:
#for x in x_list_normalized_0to2pi:
st = QuantumState(self.nqubit)
input_gate = self.create_input_gate(x, uin_type)
input_gate.update_quantum_state(st)
st_list.append(st.copy())
self.input_state_list = st_list
def test_CU_Z0(self):
n_qubits = 2
a_idx = 1
theta = np.pi/8
state = QuantumState(n_qubits)
input_states_bin = [0b00, 0b10]
input_states = []
output_states = []
circuit_H = QuantumCircuit(n_qubits)
circuit_H.add_H_gate(0)
# |0>|+> and |1>|+>
for b in input_states_bin:
psi = state.copy()
psi.set_computational_basis(b)
input_states += [psi]
circuit_H.update_quantum_state(psi)
circuit = QuantumCircuit(n_qubits)
circuit.add_RZ_gate(0, -0.5*theta)
circuit.add_CNOT_gate(a_idx, 0)
circuit.add_RZ_gate(0, 0.5*theta)
circuit.add_CNOT_gate(a_idx, 0)
for in_state in input_states:
psi = in_state.copy()
circuit.update_quantum_state(psi)
output_states += [psi]
p_list = []
for in_state in input_states:
for out_state in output_states:
p_list += [inner_product(in_state, out_state)]
# <0|<+|0>|+> = 1
# <0|<+|1>|H> = 0
# <1|<+|0>|+> = 0
# <1|<+|1>|H> = cos(pi/16)
exp_list = [1.0, 0.0, 0.0, np.cos(theta/2)]
for result, expected in zip(p_list, exp_list):
self.assertAlmostEqual(result, expected, places=6)
def test_iterative_phase_estimation(self):
theta = 5*np.pi/16
# theta = np.pi/3 # -0.5235987755982988
# print(-theta/2)
n_itter = 6
n_qubits = 2
a_idx = 1
state = QuantumState(n_qubits) # |ancilla>|logical>
kickback_phase = 0.0
for k in reversed(range(1, n_itter)):
psi = state.copy()
phi = kickback_phase/2
# print('k={}, phi={} mod (np.pi)'.format(k, phi))
circuit = QuantumCircuit(n_qubits)
# Apply H to ancilla bit to get |+> state
circuit.add_H_gate(a_idx)
# Apply kickback phase rotation to ancilla bit
circuit.add_RZ_gate(a_idx, -np.pi * phi)
# Apply C-U(Z0)
theta_k = 2 ** (k-1) * theta
# print('phase:{} mod (np.pi)'.format(theta_k/np.pi))
circuit.add_RZ_gate(0, -theta_k)
circuit.add_CNOT_gate(a_idx, 0)
circuit.add_RZ_gate(0, theta_k)
circuit.add_CNOT_gate(a_idx, 0)
# Apply H to ancilla bit to get |+> state
circuit.add_H_gate(a_idx)
# run circuit
circuit.update_quantum_state(psi)
# print(psi.get_vector())
# partial trace
p0 = psi.get_marginal_probability([2, 0])
p1 = psi.get_marginal_probability([2, 1])
# print(p0, p1)
# update kickback phase
kth_digit = 1 if (p0 < p1) else 0
kickback_phase = kickback_phase/2 + kth_digit
# print(kickback_phase)
# print(-np.pi * kickback_phase/2)
self.assertAlmostEqual(-np.pi * kickback_phase/2, -theta/2)
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
def qite_anti(Quket, id_set, size):
### Parameter setting
ansatz = Quket.ansatz
n = Quket.n_qubits
db = Quket.dt
qbit = Quket.det
ntime = Quket.maxiter
observable = Quket.qulacs.Hamiltonian
S2_observable = Quket.qulacs.S2
threshold = Quket.ftol
S2 = 0
prints(f"QITE: Pauli operator group size = {size}")
if ansatz != "cite":
sigma_list, sigma_ij_index, sigma_ij_coef = qite_s_operators(id_set, n)
len_list = len(sigma_list)
prints(f" Unique sigma list = {len_list}")
index = np.arange(n)
delta = QuantumState(n)
first_state = QuantumState(n)
first_state.set_computational_basis(qbit)
energy = []
psi_dash = first_state.copy()
t1 = time.time()
cf.t_old = t1
En = observable.get_expectation_value(psi_dash)
energy.append(En)
if S2_observable is not None:
S2 = S2_observable.get_expectation_value(psi_dash)
dE = 100
for t in range(ntime):
t2 = time.time()
cput = t2 - cf.t_old
cf.t_old = t2
if cf.debug:
print_state(psi_dash)
prints(f"{t*db:6.2f}: E = {En:.12f} <S**2> = {S2:17.15f} "
f"CPU Time = {cput: 5.2f}")
if abs(dE) < threshold:
break
if t == 0:
xv = np.zeros(size)
T0 = time.time()
delta = calc_delta(psi_dash, observable, n, db)
T1 = time.time()
if ansatz == "cite":
delta.add_state(psi_dash)
psi_dash = delta.copy()
else:
#for i in range(size):
# pauli_id = id_set[i]
# circuit_i = make_gate(n, index, pauli_id)
# state_i = psi_dash.copy()
# circuit_i.update_quantum_state(state_i)
# print(i)
# for j in range(i+1):
# pauli_id = id_set[j]
# circuit_j = make_gate(n, index, pauli_id)
# state_j = psi_dash.copy()
# circuit_j.update_quantum_state(state_j)
# s = inner_product(state_j, state_i)
# S[i][j] = s
# S[j][i] = s
### Compute Sij as expectation values of sigma_list
Sij_list = np.zeros(len_list)
Sij_my_list = np.zeros(len_list)
ipos, my_ndim = mpi.myrange(len_list)
T2 = time.time()
for iope in range(ipos, ipos + my_ndim):
val = sigma_list[iope].get_expectation_value(psi_dash)
Sij_my_list[iope] = val
T3 = time.time()
mpi.comm.Allreduce(Sij_my_list, Sij_list, mpi.MPI.SUM)
T4 = time.time()
### Distribute Sij
ij = 0
sizeT = size * (size - 1) // 2
ipos, my_ndim = mpi.myrange(sizeT)
S = np.zeros((size, size), dtype=complex)
my_S = np.zeros((size, size), dtype=complex)
for i in range(size):
for j in range(i):
if ij in range(ipos, ipos + my_ndim):
ind = sigma_ij_index[ij]
coef = sigma_ij_coef[ij]
my_S[i, j] = coef * Sij_list[ind]
my_S[j, i] = my_S[i, j].conjugate()
ij += 1
mpi.comm.Allreduce(my_S, S, mpi.MPI.SUM)
for i in range(size):
S[i, i] = 1
T5 = time.time()
sigma = []
for i in range(size):
pauli_id = id_set[i]
circuit_i = make_gate(n, index, pauli_id)
state_i = psi_dash.copy()
circuit_i.update_quantum_state(state_i)
sigma.append(state_i)
T6 = time.time()
b_l = np.empty(size)
for i in range(size):
b_i = inner_product(sigma[i], delta)
b_i = -2 * b_i.imag
b_l[i] = b_i
Amat = 2 * np.real(S)
T7 = time.time()
zct = b_l @ Amat
def cost_fun(vct):
return LA.norm(Amat @ vct - b_l)**2
def J_cost_fun(vct):
wct = Amat.T @ Amat @ vct
return 2.0 * (wct - zct)
#x = sp.optimize.minimize(cost_fun, x0=xv, method='Newton-CG',
# jac=J_cost_fun, tol=1e-8).x
#xv = x.copy()
#x = sp.optimize.least_squares(cost_fun, x0=xv, ftol=1e-8).x
#xv = x.copy()
x, res, rnk, s = lstsq(Amat, b_l, cond=1e-8)
a = x.copy()
### Just in case, broadcast a...
mpi.comm.Bcast(a, root=0)
T8 = time.time()
psi_dash = calc_psi_lessH(psi_dash, n, index, a, id_set)
T9 = time.time()
if cf.debug:
prints(f"T0 -> T1 {T1-T0}")
prints(f"T1 -> T2 {T2-T1}")
prints(f"T2 -> T3 {T3-T2}")
prints(f"T3 -> T4 {T4-T3}")
prints(f"T4 -> T5 {T5-T4}")
prints(f"T5 -> T6 {T6-T5}")
prints(f"T6 -> T7 {T7-T6}")
prints(f"T7 -> T8 {T8-T7}")
prints(f"T8 -> T9 {T9-T8}")
En = observable.get_expectation_value(psi_dash)
if S2_observable is not None:
S2 = S2_observable.get_expectation_value(psi_dash)
energy.append(En)
dE = energy[t + 1] - energy[t]
print_state(psi_dash, name="QITE")
class QulacsDevice(QubitDevice):
"""Qulacs device"""
name = "Qulacs device"
short_name = "qulacs.simulator"
pennylane_requires = ">=0.11.0"
version = __version__
author = "Steven Oud and Xanadu"
gpu_supported = GPU_SUPPORTED
_capabilities = {
"model": "qubit",
"tensor_observables": True,
"inverse_operations": True
}
_operation_map = {
"QubitStateVector": None,
"BasisState": None,
"QubitUnitary": None,
"Toffoli": gate.TOFFOLI,
"CSWAP": gate.FREDKIN,
"CRZ": crz,
"SWAP": gate.SWAP,
"CNOT": gate.CNOT,
"CZ": gate.CZ,
"S": gate.S,
"T": gate.T,
"RX": gate.RX,
"RY": gate.RY,
"RZ": gate.RZ,
"PauliX": gate.X,
"PauliY": gate.Y,
"PauliZ": gate.Z,
"Hadamard": gate.H,
"PhaseShift": phase_shift,
}
_observable_map = {
"PauliX": "X",
"PauliY": "Y",
"PauliZ": "Z",
"Identity": "I",
"Hadamard": None,
"Hermitian": None,
}
operations = _operation_map.keys()
observables = _observable_map.keys()
# Add inverse gates to _operation_map
_operation_map.update({k + ".inv": v for k, v in _operation_map.items()})
def __init__(self, wires, shots=1000, analytic=True, gpu=False, **kwargs):
super().__init__(wires=wires, shots=shots, analytic=analytic)
if gpu:
if not QulacsDevice.gpu_supported:
raise DeviceError(
"GPU not supported with installed version of qulacs. "
"Please install 'qulacs-gpu' to use GPU simulation.")
self._state = QuantumStateGpu(self.num_wires)
else:
self._state = QuantumState(self.num_wires)
self._circuit = QuantumCircuit(self.num_wires)
self._pre_rotated_state = self._state.copy()
def apply(self, operations, **kwargs):
rotations = kwargs.get("rotations", [])
self.apply_operations(operations)
self._pre_rotated_state = self._state.copy()
# Rotating the state for measurement in the computational basis
if rotations:
self.apply_operations(rotations)
def apply_operations(self, operations):
"""Apply the circuit operations to the state.
This method serves as an auxiliary method to :meth:`~.QulacsDevice.apply`.
Args:
operations (List[pennylane.Operation]): operations to be applied
"""
for i, op in enumerate(operations):
if i > 0 and isinstance(op, (QubitStateVector, BasisState)):
raise DeviceError(
"Operation {} cannot be used after other Operations have already been applied "
"on a {} device.".format(op.name, self.short_name))
if isinstance(op, QubitStateVector):
self._apply_qubit_state_vector(op)
elif isinstance(op, BasisState):
self._apply_basis_state(op)
elif isinstance(op, QubitUnitary):
self._apply_qubit_unitary(op)
elif isinstance(op, (CRZ, PhaseShift)):
self._apply_matrix(op)
else:
self._apply_gate(op)
def _apply_qubit_state_vector(self, op):
"""Initialize state with a state vector"""
wires = op.wires
input_state = op.parameters[0]
if len(input_state) != 2**len(wires):
raise ValueError("State vector must be of length 2**wires.")
if input_state.ndim != 1 or len(input_state) != 2**len(wires):
raise ValueError("State vector must be of length 2**wires.")
if not np.isclose(np.linalg.norm(input_state, 2), 1.0, atol=tolerance):
raise ValueError("Sum of amplitudes-squared does not equal one.")
input_state = _reverse_state(input_state)
# call qulacs' state initialization
self._state.load(input_state)
def _apply_basis_state(self, op):
"""Initialize a basis state"""
wires = op.wires
par = op.parameters
# translate from PennyLane to Qulacs wire order
bits = par[0][::-1]
n_basis_state = len(bits)
if not set(bits).issubset({0, 1}):
raise ValueError(
"BasisState parameter must consist of 0 or 1 integers.")
if n_basis_state != len(wires):
raise ValueError(
"BasisState parameter and wires must be of equal length.")
basis_state = 0
for bit in bits:
basis_state = (basis_state << 1) | bit
# call qulacs' basis state initialization
self._state.set_computational_basis(basis_state)
def _apply_qubit_unitary(self, op):
"""Apply unitary to state"""
# translate op wire labels to consecutive wire labels used by the device
device_wires = self.map_wires(op.wires)
par = op.parameters
if len(par[0]) != 2**len(device_wires):
raise ValueError(
"Unitary matrix must be of shape (2**wires, 2**wires).")
if op.inverse:
par[0] = par[0].conj().T
# reverse wires (could also change par[0])
reverse_wire_labels = device_wires.tolist()[::-1]
unitary_gate = gate.DenseMatrix(reverse_wire_labels, par[0])
self._circuit.add_gate(unitary_gate)
unitary_gate.update_quantum_state(self._state)
def _apply_matrix(self, op):
"""Apply predefined gate-matrix to state (must follow qulacs convention)"""
# translate op wire labels to consecutive wire labels used by the device
device_wires = self.map_wires(op.wires)
par = op.parameters
mapped_operation = self._operation_map[op.name]
if op.inverse:
mapped_operation = self._get_inverse_operation(
mapped_operation, device_wires, par)
if callable(mapped_operation):
gate_matrix = mapped_operation(*par)
else:
gate_matrix = mapped_operation
# gate_matrix is already in correct order => no wire-reversal needed
dense_gate = gate.DenseMatrix(device_wires.labels, gate_matrix)
self._circuit.add_gate(dense_gate)
gate.DenseMatrix(device_wires.labels,
gate_matrix).update_quantum_state(self._state)
def _apply_gate(self, op):
"""Apply native qulacs gate"""
# translate op wire labels to consecutive wire labels used by the device
device_wires = self.map_wires(op.wires)
par = op.parameters
mapped_operation = self._operation_map[op.name]
if op.inverse:
mapped_operation = self._get_inverse_operation(
mapped_operation, device_wires, par)
# Negating the parameters such that it adheres to qulacs
par = np.negative(par)
# mapped_operation is already in correct order => no wire-reversal needed
self._circuit.add_gate(mapped_operation(*device_wires.labels, *par))
mapped_operation(*device_wires.labels,
*par).update_quantum_state(self._state)
@staticmethod
def _get_inverse_operation(mapped_operation, device_wires, par):
"""Return the inverse of an operation"""
if mapped_operation is None:
return mapped_operation
# if an inverse variant of the operation exists
try:
inverse_operation = getattr(gate,
mapped_operation.get_name() + "dag")
except AttributeError:
# if the operation is hard-coded
try:
if callable(mapped_operation):
inverse_operation = np.conj(mapped_operation(*par)).T
else:
inverse_operation = np.conj(mapped_operation).T
# if mapped_operation is a qulacs.gate and np.conj is applied on it
except TypeError:
# else, redefine the operation as the inverse matrix
def inverse_operation(*p):
# embed the gate in a unitary matrix with shape (2**wires, 2**wires)
g = mapped_operation(*p).get_matrix()
mat = reduce(np.kron, [np.eye(2)] *
len(device_wires)).astype(complex)
mat[-len(g):, -len(g):] = g
# mat follows PL convention => reverse wire-order
reverse_wire_labels = device_wires.tolist()[::-1]
gate_mat = gate.DenseMatrix(reverse_wire_labels,
np.conj(mat).T)
return gate_mat
return inverse_operation
def analytic_probability(self, wires=None):
"""Return the (marginal) analytic probability of each computational basis state."""
if self._state is None:
return None
all_probs = self._abs(self.state)**2
prob = self.marginal_prob(all_probs, wires)
return prob
def expval(self, observable):
if self.analytic:
qulacs_observable = Observable(self.num_wires)
if isinstance(observable.name, list):
observables = [
self._observable_map[obs] for obs in observable.name
]
else:
observables = [self._observable_map[observable.name]]
if None not in observables:
applied_wires = self.map_wires(observable.wires).tolist()
opp = " ".join([
f"{obs} {applied_wires[i]}"
for i, obs in enumerate(observables)
])
qulacs_observable.add_operator(1.0, opp)
return qulacs_observable.get_expectation_value(
self._pre_rotated_state)
# exact expectation value
eigvals = self._asarray(observable.eigvals, dtype=self.R_DTYPE)
prob = self.probability(wires=observable.wires)
return self._dot(eigvals, prob)
# estimate the ev
return np.mean(self.sample(observable))
@property
def state(self):
# returns the state after all operations are applied
return _reverse_state(self._state.get_vector())
def reset(self):
self._state.set_zero_state()
self._pre_rotated_state = self._state.copy()
self._circuit = QuantumCircuit(self.num_wires)
class QulacsDevice(Device):
"""Qulacs device"""
name = 'Qulacs device'
short_name = 'qulacs.simulator'
pennylane_requires = '>=0.5.0'
version = __version__
author = 'Steven Oud'
_capabilities = {'model': 'qubit', 'tensor_observables': True}
_operations_map = {
'QubitStateVector': None,
'BasisState': None,
'QubitUnitary': None,
'Toffoli': toffoli,
'CSWAP': CSWAP,
'CRZ': crz,
'Rot': None,
'SWAP': gate.SWAP,
'CNOT': gate.CNOT,
'CZ': gate.CZ,
'S': gate.S,
'Sdg': gate.Sdag,
'T': gate.T,
'Tdg': gate.Tdag,
'RX': gate.RX,
'RY': gate.RY,
'RZ': gate.RZ,
'PauliX': gate.X,
'PauliY': gate.Y,
'PauliZ': gate.Z,
'Hadamard': gate.H
}
_observable_map = {
'PauliX': X,
'PauliY': Y,
'PauliZ': Z,
'Hadamard': H,
'Identity': I,
'Hermitian': hermitian
}
operations = _operations_map.keys()
observables = _observable_map.keys()
def __init__(self, wires, gpu=False, **kwargs):
super().__init__(wires=wires)
if gpu:
if not GPU_SUPPORTED:
raise DeviceError(
'GPU not supported with installed version of qulacs. '
'Please install "qulacs-gpu" to use GPU simulation.')
self._state = QuantumStateGpu(wires)
else:
self._state = QuantumState(wires)
self._circuit = QuantumCircuit(wires)
self._first_operation = True
def apply(self, operation, wires, par):
par = np.negative(par)
if operation == 'BasisState' and not self._first_operation:
raise DeviceError(
'Operation {} cannot be used after other Operations have already been applied '
'on a {} device.'.format(operation, self.short_name))
self._first_operation = False
if operation == 'QubitStateVector':
if len(par[0]) != 2**len(wires):
raise ValueError('State vector must be of length 2**wires.')
self._state.load(par[0])
elif operation == 'BasisState':
if len(par[0]) != len(wires):
raise ValueError('Basis state must prepare all qubits.')
basis_state = 0
for bit in reversed(par[0]):
basis_state = (basis_state << 1) | bit
self._state.set_computational_basis(basis_state)
elif operation == 'QubitUnitary':
if len(par[0]) != 2**len(wires):
raise ValueError(
'Unitary matrix must be of shape (2**wires, 2**wires).')
unitary_gate = gate.DenseMatrix(wires, par[0])
self._circuit.add_gate(unitary_gate)
elif operation == 'Rot':
self._circuit.add_gate(
gate.merge([
gate.RZ(wires[0], par[0]),
gate.RY(wires[0], par[1]),
gate.RZ(wires[0], par[2])
]))
elif operation in ('CRZ', 'Toffoli', 'CSWAP'):
mapped_operation = self._operations_map[operation]
if callable(mapped_operation):
gate_matrix = mapped_operation(*par)
else:
gate_matrix = mapped_operation
dense_gate = gate.DenseMatrix(wires, gate_matrix)
self._circuit.add_gate(dense_gate)
else:
mapped_operation = self._operations_map[operation]
self._circuit.add_gate(mapped_operation(*wires, *par))
@property
def state(self):
return self._state.get_vector()
def pre_measure(self):
self._circuit.update_quantum_state(self._state)
def expval(self, observable, wires, par):
bra = self._state.copy()
if isinstance(observable, list):
A = self._get_tensor_operator_matrix(observable, par)
wires = [item for sublist in wires for item in sublist]
else:
A = self._get_operator_matrix(observable, par)
dense_gate = gate.DenseMatrix(wires, A)
dense_gate.update_quantum_state(self._state)
expectation = inner_product(bra, self._state)
return expectation.real
def probabilities(self):
states = itertools.product(range(2), repeat=self.num_wires)
probs = np.abs(self.state)**2
return OrderedDict(zip(states, probs))
def reset(self):
self._state.set_zero_state()
self._circuit = QuantumCircuit(self.num_wires)
def _get_operator_matrix(self, operation, par):
A = self._observable_map[operation]
if not callable(A):
return A
return A(*par)
def _get_tensor_operator_matrix(self, obs, par):
ops = [self._get_operator_matrix(o, p) for o, p in zip(obs, par)]
return functools.reduce(np.kron, ops)
def qite_exact(Quket):
nspin = Quket.n_qubits
db = Quket.dt
ntime = Quket.maxiter
qbit = Quket.det
observable = Quket.qulacs.Hamiltonian
threshold = Quket.ftol
active_qubit = [x for x in range(nspin)]
n = nspin
size = 4**nspin
index = np.arange(n)
delta = QuantumState(n)
first_state = QuantumState(n)
first_state.set_computational_basis(qbit)
prints(f"Exact QITE: Pauli operator group size = {size}")
energy = []
psi_dash = first_state.copy()
value = observable.get_expectation_value(psi_dash)
energy.append(value)
t1 = time.time()
cf.t_old = t1
dE = 100
for t in range(ntime):
t2 = time.time()
cput = t2 - cf.t_old
cf.t_old = t2
if cf.debug:
print_state(psi_dash)
prints(f"{t*db:6.2f}: E = {value:.12f} CPU Time = {cput:5.2f}")
if abs(dE) < threshold:
break
#if t == 0:
# xv = np.zeros(size)
psi_dash_copy = psi_dash.copy()
#mpi.comm.bcast(size, root=0)
#S_part = np.zeros((size, size), dtype=complex)
#S = np.zeros((size, size), dtype=complex)
#sizeT = size*(size+1)//2
#nblock = sizeT//mpi.nprocs
#ij = mpi.rank*nblock
#start = int(np.sqrt(2*ij + 1/4) - 1/2)
#end = int(np.sqrt(2*(ij+nblock) + 1/4) - 1/2)
#for i in range(start, end):
# for j in range(i+1):
# S_part[i, j] = calc_inner1(i, j, n, active_qubit,
# index, psi_dash)
# ij += 1
# S[:i, i] = S[i, :i]
#mpi.comm.Allreduce(S_part, S, mpi.MPI.SUM)
S_part = np.zeros((size, size), dtype=complex)
S = np.zeros((size, size), dtype=complex)
sizeT = size * (size - 1) // 2
ipos, my_ndim = mpi.myrange(sizeT)
ij = 0
for i in range(size):
for j in range(i):
if ij in range(ipos, ipos + my_ndim):
S_part[i, j] = calc_inner1(i, j, n, active_qubit, index,
psi_dash)
S_part[j, i] = S_part[i, j].conjugate()
ij += 1
mpi.comm.Allreduce(S_part, S, mpi.MPI.SUM)
for i in range(size):
S[i, i] = 1
sigma = []
for i in range(size):
state_i = make_state1(i, n, active_qubit, index, psi_dash)
sigma.append(state_i)
delta = calc_delta(psi_dash, observable, n, db)
b_l = []
b_l = np.empty(size)
for i in range(size):
b_i = inner_product(sigma[i], delta)
b_i = -2 * b_i.imag
b_l[i] = b_i
Amat = 2 * np.real(S)
zct = b_l @ Amat
#def cost_fun(vct):
# return LA.norm(Amat@vct - b_l)**2
#def J_cost_fun(vct):
# wct = Amat.T@Amat@vct
# return 2.0*(wct-zct)
#x = sp.optimize.minimize(cost_fun, x0=xv, method="Newton-CG",
# jac=J_cost_fun, tol=1e-8).x
#xv = x.copy()
x, res, rnk, s = lstsq(Amat, b_l, cond=1.0e-8)
a = x.copy()
### Just in case, broadcast a...
mpi.comm.Bcast(a, root=0)
psi_dash = calc_psi(psi_dash_copy, n, index, a, active_qubit)
value = observable.get_expectation_value(psi_dash)
energy.append(value)
dE = energy[t + 1] - energy[t]
def __run_all(qcirc=None,
shots=1,
cid=None,
backend=None,
proc='CPU',
out_state=False):
if qcirc is None:
raise ValueError("quantum circuit must be specified.")
qubit_num = qcirc.qubit_num
cmem_num = qcirc.cmem_num
if cid is None:
cid = list(range(cmem_num))
if cmem_num < len(cid):
raise ValueError(
"length of cid must be less than classical resister size of qcirc")
#
# initialize
#
if proc == 'CPU':
qstate = QuantumState(qubit_num)
else:
from qulacs import QuantumStateGpu
qstate = QuantumStateGpu(qubit_num)
cmem = [0] * cmem_num
#
# before measurement gate
#
while True:
kind = qcirc.kind_first()
if kind is None or kind is cfg.MEASURE or kind is cfg.RESET:
break
(kind, qid, para, c, ctrl) = qcirc.pop_gate()
if ctrl is None or (ctrl is not None and cmem[ctrl] == 1):
__qulacs_operate_qgate(qstate,
qubit_num,
kind=kind,
qid=qid,
phase=para[0],
phase1=para[1],
phase2=para[2])
if kind is None:
result = Result()
result.qubit_num = qubit_num
result.cmem_num = cmem_num
result.cid = cid
result.shots = shots
result.frequency = None
result.backend = backend
if out_state is True:
result.qstate = __transform_qlazy_qstate(qstate)
result.cmem = __transform_qlazy_cmem(cmem)
return result
#
# after measurement gate
#
if set(qcirc.kind_list()) == {cfg.MEASURE}:
q_list = []
while True:
kind = qcirc.kind_first()
if kind is None:
break
(kind, qid, para, c, ctrl) = qcirc.pop_gate()
q_list.append(qid[0])
frequency, qstate = __qulacs_measure_shots(qstate, q_list, shots)
result = Result()
result.qubit_num = qubit_num
result.cmem_num = cmem_num
result.cid = cid
result.shots = shots
result.frequency = frequency
result.backend = backend
if out_state is True:
result.qstate = __transform_qlazy_qstate(qstate)
result.cmem = __transform_qlazy_cmem(cmem)
return result
frequency = Counter()
qstate_tmp = None
for _ in range(shots):
qstate_tmp = qstate.copy()
qcirc_tmp = qcirc.clone()
while True:
kind = qcirc_tmp.kind_first()
if kind is None:
break
if kind == cfg.MEASURE:
(kind, qid, para, c, ctrl) = qcirc_tmp.pop_gate()
mval = __qulacs_measure(qstate_tmp, qubit_num, qid[0])
if c is not None:
cmem[c] = mval
elif kind == cfg.RESET:
(kind, qid, para, c, ctrl) = qcirc_tmp.pop_gate()
__qulacs_reset(qstate_tmp, qubit_num, qid[0])
else:
(kind, qid, para, c, ctrl) = qcirc_tmp.pop_gate()
if (ctrl is None or (ctrl is not None and cmem[ctrl] == 1)):
__qulacs_operate_qgate(qstate_tmp,
qubit_num,
kind=kind,
qid=qid,
phase=para[0],
phase1=para[1],
phase2=para[2])
if len(cmem) > 0:
mval = ''.join(map(str, [cmem[i] for i in cid]))
frequency[mval] += 1
if qstate_tmp is not None:
qstate.load(qstate_tmp.get_vector())
if len(frequency) == 0:
frequency = None
result = Result()
result.qubit_num = qubit_num
result.cmem_num = cmem_num
result.cid = cid
result.shots = shots
result.frequency = frequency
result.backend = backend
if out_state is True:
result.qstate = __transform_qlazy_qstate(qstate)
result.cmem = __transform_qlazy_cmem(cmem)
return result
