blueqat

A quantum computing SDK

Version

Build info

Tutorial

https://github.com/Blueqat/Blueqat-tutorials

Install

git clone https://github.com/Blueqat/Blueqat
cd Blueqat
pip3 install -e .

or

pip3 install blueqat

Circuit

from blueqat import Circuit
import math

#number of qubit is not specified
c = Circuit()

#if you want to specified the number of qubit
c = Circuit(3) #3qubits

Method Chain

# write as chain
Circuit().h[0].x[0].z[0]

# write in separately
c = Circuit().h[0]
c.x[0].z[0]

Slice

Circuit().z[1:3] # Zgate on 1,2
Circuit().x[:3] # Xgate on (0, 1, 2)
Circuit().h[:] # Hgate on all qubits
Circuit().x[1, 2] # 1qubit gate with comma

Rotation Gate

Circuit().rz(math.pi / 4)[0]

Measurement

Circuit().m[0]

Run()

Circuit().h[0].cx[0,1].run()

Run(shots=n)

c = Circuit().h[0].cx[0,1].m[:]
c.run(shots=100) # => Counter({'00': 48, '11': 52}) (random value.)

Hamiltonian

from blueqat.pauli import *

hamiltonian1 = (1.23 * Z[0] + 4.56 * X[1] * Z[2]) ** 2
hamiltonian2 = (2.46 * Y[0] + 5.55 * Z[1] * X[2] * X[1]) ** 2
hamiltonian = hamiltonian1 + hamiltonian2
print(hamiltonian)

simplify the hamiltonian

hamiltonian = hamiltonian.simplify()
print(hamiltonian)

VQE

from blueqat import vqe
from blueqat.pauli import qubo_bit as q

hamiltonian = -3*q(0)-3*q(1)-3*q(2)-3*q(3)-3*q(4)+2*q(0)*q(1)+2*q(0)*q(2)+2*q(0)*q(3)+2*q(0)*q(4)+2*q(1)*q(2)+2*q(1)*q(3)+2*q(1)*q(4)+2*q(2)*q(3)+2*q(2)*q(4)+2*q(3)*q(4)
step = 2

result = vqe.Vqe(vqe.QaoaAnsatz(hamiltonian, step)).run()
print(result.most_common(12))

If you want to create an ising model hamiltonian use Z(x) instead of q(x) in the equation

hamiltonian = Z(0)-3*Z(1)+2*Z(0)*Z(1)+2*Z(0)*Z(2)

Blueqat to Qiskit

qiskit.register(APItoken)
sampler = blueqat.vqe.get_qiskit_sampler(backend="backend name")
result = blueqat.vqe.Vqe(QaoaAnsatz(...), sampler=sampler).run(verbose=True)

Blueqat to QASM

Circuit.to_qasm()

#OPENQASM 2.0;
#include "qelib1.inc";
#qreg q[1];
#creg c[1];
#h q[0];

Example

2-qubit Grover

from blueqat import Circuit
c = Circuit().h[:2].cz[0,1].h[:].x[:].cz[0,1].x[:].h[:].m[:]
print(c.run(shots=1))

Maxcut QAOA

from blueqat import vqe, pauli
edges = [(0, 1), (1, 2), (2, 3), (3, 0), (1, 3), (0, 2), (4, 0), (4, 3)]
ansatz = vqe.QaoaAnsatz(sum([pauli.Z(i) * pauli.Z(j) for i, j in edges]), 1)
result = vqe.Vqe(ansatz).run()
print(
"""   {4}
  / \\
 {0}---{3}
 | x |
 {1}---{2}""".format(*result.most_common()[0][0]))

Optimization

from blueqat.opt import Opt
c = Opt().add([[1,1],[1,1]]).add("(q0+q1)^2")

#qaoa
print(c.qaoa().most_common(5))
#=>(((0, 0), 0.7639901896866), ((1, 0), 0.10321404014639714), ((0, 1), 0.10321404014639707), ((1, 1), 0.029581730020605202))

#annealing
print(c.run())
[0, 0]

SA Annealing

import blueqat.opt as wq
a = wq.opt()
a.qubo = wq.sel(3,1) #creating QUBO matrix
result = a.sa(shots=100,sampler="fast")
wq.counter(result)

Counter({'010': 29, '100': 34, '001': 37})

SA Parameters

Some parameters for simualtion is adjustable

#for sa
a.Ts  = 10    #default 5
a.R   = 0.99  #default 0.95
a.ite = 10000 #default 1000

SA Energy Function

Energy function of the calculation is stored in attribute E as an array.

print(a.E[-1]) #=>[0.0]

#if you want to check the time evolution
a.plot()

SA Sampling

Sampling and counter function with number of shots.

result = a.sa(shots=100,sampler="fast")

print(result)

[[0, 1, 0],
 [0, 0, 1],
 [0, 1, 0],
 [0, 0, 1],
 [0, 1, 0],

 counter(result) # => Counter({'001': 37, '010': 25, '100': 38})

Connection to D-Wave cloud

Direct connection to D-Wave machine with apitoken https://github.com/dwavesystems/dwave-cloud-client is required

from blueqat.opt import Opt
a = Opt()
a.dwavetoken = "your token here"
a.qubo = [[0,0,0,0,-4],[0,2,0,0,-4],[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,4]]
a.dw()

# => [1,1,-1,1,1,0,0,0,0,0,0]

QUBO Functions

sel(N,K,array) Automatically create QUBO which select K qubits from N qubits

print(wq.sel(5,2))
#=>
[[-3  2  2  2  2]
 [ 0 -3  2  2  2]
 [ 0  0 -3  2  2]
 [ 0  0  0 -3  2]
 [ 0  0  0  0 -3]]

if you set array on the 3rd params, the result likely to choose the nth qubit in the array

print(wq.sel(5,2,[0,2]))
#=>
[[-3.5  2.   2.   2.   2. ]
 [ 0.  -3.   2.   2.   2. ]
 [ 0.   0.  -3.5  2.   2. ]
 [ 0.   0.   0.  -3.   2. ]
 [ 0.   0.   0.   0.  -3. ]]

net(arr,N) Automatically create QUBO which has value 1 for all connectivity defined by array of edges and graph size N

print(wq.net([[0,1],[1,2]],4))
#=>
[[0. 1. 0. 0.]
 [0. 0. 1. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

this create 4*4 QUBO and put value 1 on connection between 0th and 1st qubit, 1st and 2nd qubit

zeros(N) Create QUBO with all element value as 0

print(wq.zeros(3))
#=>
[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]

diag(list) Create QUBO with diag from list

print(wq.diag([1,2,1]))
#=>
[[1 0 0]
 [0 2 0]
 [0 0 1]]

rands(N) Create QUBO with random number

print(wq.rands(2))
#=>
[[0.89903411 0.68839641]
 [0.         0.28554602]]

Document

https://blueqat.readthedocs.io/en/latest/

Author

Takumi Kato (MDR), Yuichiro Minato (MDR), Yuma Murata (D Slit Technologies), Satoshi Takezawa (TerraSky)

Disclaimer

Copyright 2019 The Blueqat Developers.