New cuQuantum Backend both on cuStateVec and cuTensorNet

limited gate support with cusv and cutn backend.

Updated basic CPU and GPU TN backend

cuTensorNet backend

beta version of cuTensorNet is available to run .run(backend="cuTN")

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.

bug fixed on qaoa

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)

Bug fixed

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)

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)

Circuit Drawing

• Circuit drawing function with networkx

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