Quantum neural network

This is a library for creating and training quantum neural networks with Qiskit. It has been used in the following works:

Quantum Self-Supervised Learning

Training with entirely quantum networks

Images can be loaded into a quantum circuit using the data handlers in quantum-neural-network/input. The parameters can then be trained by an external optimiser.

A working example of a single forward pass for a random input vector can be run:

python run_simple_network.py

This uses a vector_data_handler in which the data is inputted as single qubit rotations on a product state.

These networks can run by themselves or can be integrated as layers into a larger classical neural network.

Embedding into classical neural networks

For running on current quantum computers, it may be beneficial to embed a QNN within a classical network. Rather than inputting a whole image into the quantum circuit, the feature vector from the previous classical layer is passed in.

A working example of how to integrate our QNet class with PyTorch can be found in mnist_examples/pytorch_with_qnet.py. The general structure is:

from qnet import QNet
import torch.nn as nn


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()

        # CLASSICAL PYTORCH LAYERS

        self.qnet = QNet(n_qubits=2, encoding='vector', ansatz_type='farhi', layers=1,
                         activation_function_type='partial_measurement_1')

    def forward(self, x):
        # CLASSICAL PYTORCH LAYERS

        x = self.qnet(x)
        return x

where QNet returns a feature vector, the length of which is determiend by the input dimension and ansatz type.

Config options

Ansatzes

• abbas (https://arxiv.org/abs/2011.00027)
• alternating_layer_tdcnot (https://arxiv.org/abs/2002.04612)
• farhi (https://arxiv.org/abs/1802.06002)
• sim_circ_13, sim_circ_13_half, sim_circ_14, sim_circ_14_half, sim_circ_15, sim_circ_19 (https://arxiv.org/abs/1905.10876)

Quantum activation functions

• parial_meas_x where x is the number of qubits to be measured between each layer. x can also be 'half'.

Data handlers

• frqi (https://link.springer.com/article/10.1007/s11128-010-0177-y)
• havlicek (https://arxiv.org/abs/1804.11326)
• neqr (https://link.springer.com/article/10.1007/s11128-013-0567-z)

Usage and citation

This repository was developed in conjunction with the following work, which we kindly ask any publication, whitepaper or project using this code to cite:

Jaderberg, B., Anderson, L.W., Xie, W., Albanie, S., Kiffner, M. and Jaksch, D., 2021. Quantum Self-Supervised Learning. arXiv preprint arXiv:2103.14653.