blueqat 2.0.4 - 2023-04-02

New cuQuantum Backend both on cuStateVec and cuTensorNet

limited gate support with cusv and cutn backend.

blueqat 2.0.0 - 2022-05-22

New default backend based on Tensor Network

The back end has been changed to the tensor network. The previous backend environment can still be used with .run(backend="numpy").

New QAOA function

QAOA function is changed to the tensornetwork based.

blueqat 1.0.4 - 2022-05-14

bug fixed on qaoa

blueqat 1.0.3 - 2022-05-14

QAOA with Tensornetwork

from blueqat.utils import qaoa

from blueqat import Circuit
from blueqat.pauli import qubo_bit as q
from blueqat.pauli import X,Y,Z,I

hamiltonian = (15-(1+2*q(0)+4*q(1))*(1+2*q(2)))**2
step = 1
init = Circuit().h[0].cx[0,1].x[0]
mixer = (X[0]*X[1]+Y[0]*Y[1])*0.5

result = qaoa(hamiltonian, step, init, mixer)

result.circuit.run(backend="quimb", shots=1000)

blueqat 1.0.2 - 2022-05-14

Bug fixed

blueqat 1.0.0 - 2022-05-06

Added backend for tensor network

The backend of blueqat will be changed to tensor network in the near future. Now try specifying the back end as "quimb".

from blueqat import Circuit
Circuit(50).h[:].run(backend="quimb")

Get the single amplitude

Circuit(4).h[:].run(backend="quimb", amplitude="0101")

Get the sample

Circuit(4).h[:].run(backend="quimb", shots=100)

Get the expectation value of hamiltonian

from blueqat.pauli import Z
hamiltonian = 1*Z[0]+1*Z[1]
Circuit(4).x[:].run(backend="quimb", hamiltonian=hamiltonian)

blueqat 0.6.0 - 2022-04-18

Photonics Continuous Variable Programming Integrated

from blueqat import photonqat as pq
import numpy as np
import matplotlib.pyplot as plt

# mode number = 2, cutoff dimension = 15
F = pq.Fock(2, cutoff = 15)

alpha = (1 + 1j)
r = -0.5

F.D(0, alpha) # Displacement to mode 0
F.S(1, r) # Squeezeng to mode 1

F.run()

# Plot Wigner fucntion for mode 0 using matplotlib
(x, p, W) = F.Wigner(0, plot = 'y', xrange = 5.0, prange = 5.0)

blueqat 0.5.0 - 2022-04-17

Circuit Drawing

• Circuit drawing function with networkx

Circuit.h[0].cx[0,1].run(backend="draw")

blueqat 0.4.9 - 2022-02-08

Debug

• Correct TGate.matrix(), TDagGate.matrix(), IGate.matrix()

Experimental Feature

• New sampling feature is available in the numpy backend. This feature may effective for mid-circuit measurement.

# Specify `m(key="keyname")` in the measurement and `run(shots=..., returns="samples").
Circuit().x[1].m(key="key1")[0, 1].x[0].m(key="key2")[0, 1].run(shots=5, returns="samples")

# It returns
[{'key1': [0, 1], 'key2': [1, 1]},
 {'key1': [0, 1], 'key2': [1, 1]},
 {'key1': [0, 1], 'key2': [1, 1]},
 {'key1': [0, 1], 'key2': [1, 1]},
 {'key1': [0, 1], 'key2': [1, 1]}]
# You get 5 samples for each measured keys.

# When run(returns="sample"), measured result without the key is omitted.

# If the key is duplicated, another option is needed, "replace" or "append".

# "replace" pattern
Circuit().x[0].m(key="a")[:].x[0].m(key="a", duplicated="replace")[0].run(shots=2, returns="samples")

# It returns
[{'a': [0]}, {'a': [0]}]
# So, the first measured result of key "a" is omitted and replaced to second one.

# "append" pattern
Circuit().x[0].m(key="a")[:].x[0].m(key="a", duplicated="append")[0].run(shots=2, returns="samples")

# It returns
[{'a': [1, 0]}, {'a': [1, 0]}]
# So, the second measured result is appended to first one.

Changes

• Circuit JSON scheme is updated due to experimental features.

Internal Changes

Implementation of gates are changed

blueqat 0.4.8 - 2021-10-15

New feature

• Builtin-macros
• mcx_with_ancilla(ctrl: list[int], target: int, ancilla: int) -> Circuit
• mcz_with_ancilla(ctrl: list[int], target: int, ancilla: int) -> Circuit

Major changes

• Remove wq module
  • We recommend using dwave-ocean-sdk library for annealing task.
  • wq.pauli function (It translates QUBO to pauli matrix for QAOA) is moved to pauli.to_qubo function.

Minor changes

• Internal implementation of Circuit
  • It cheats type-checker likes pyright for suppressing errors.