Variational Quantum Circuit simulator in Julia, under GPLv3, developed with <3 by Supremacy Future Technologies.

VQC is an open source framework that can simulate variational quantum circuits and used for quantum machine learning tasks.

• Simple but powerful. VQC supports any single-qubit, two-qubit, three-qubit gate operations, as well as measurements. The same quantum circuit can be used as variational quantum circuits almost for free.

• Everything is differentiable. Not only the quantum circuit, the quantum state itself is also differentiable, almost without any changing of code. In most of the cases, user can write a very complex expression built on top of the quantum circuit and the quantum state, and the whole expression will be differentiable.

• Flexiable operations on quantum gates and quantum circuits. Quantum circuit and quantum gates suport operations such as adjoint, transpose, conjugate, shift to make life easier when building very complex circuits.

• Zygote as backend for auto differentiation. VQC use Zygote as backend for auto differentiation.

Now at version 0.1.0!

VQC is a Julia language package. To install VQC, please open Julia's interactive session (known as REPL) and type

pkg> import Pkg; Pkg.add("VQC")

# Using functions from VQC
julia> using VQC

# Create a two qubit quantum state |00>
julia> state = qstate([0,0])
StateVector{Complex{Float64}}(Complex{Float64}[1.0 + 0.0im, 0.0 + 0.0im, 0.0 + 0.0im, 0.0 + 0.0im])

# Create and empty quantum circuit
julia> circuit = QCircuit()
QCircuit(VQC.AbstractQuantumOperation[])

# pushing gate operations into the quantum circuit
julia> push!(circuit, HGate(1))
1-element Array{VQC.AbstractQuantumOperation,1}:
 OneBodyGate{Array{Float64,2}}(1, [0.7071067811865475 0.7071067811865475; 0.7071067811865475 -0.7071067811865475])

# pushing measure operation into the quantum circuit
julia> push!(circuit, QMeasure(1)))
2-element Array{VQC.AbstractQuantumOperation,1}:
OneBodyGate{Array{Float64,2}}(1, [0.7071067811865475 0.7071067811865475; 0.7071067811865475 -0.7071067811865475])
QMeasure(1, true, true, [1.0 0.0; 0.0 1.0]) 

# apply quantum circuit to quantum state
julia> results = apply!(circuit, state)
2-element Observables:
 ("Q:Z1", 1)                    
 ("C:Z1->1", 0.4999999999999999)

# In "Q:Z1", Q means quantum observable, Z refer to the basis, 1 is the qubit label
# Similarly, in "C:Z1->1", C means classical observables, 0.4999.. means the probability
# Obtain all the measurement outcomes.
julia> qvalues(results)
1-element Array{Int64,1}:
 1

# Obtain all the measurement probabilities
julia> cvalues(results)
1-element Array{Float64,1}:
 0.4999999999999999

1. Tutorial 1: Basic operations

You are welcome to leave your comment or suggestions as an issues. For commercial purpose, please email us at support [at] supremacyfuture.com

Please cite the following paper when using VQC:

@article{liu2019hybrid,
  title={Hybrid Quantum-Classical Convolutional Neural Networks},
  author={Liu, Junhua and Lim, Kwan Hui and Wood, Kristin L and Huang, Wei and Guo, Chu and Huang, He-Liang},
  journal={arXiv preprint arXiv:1911.02998},
  year={2019}
}

VQC is published under GPLv3

Copyright (C) 2019 Supremacy Future Technologies

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see here.