Implementing arbitrary quantum operations via quantum walks on a cycle graph

TL;DR

This paper introduces a quantum neural network based on discrete-time quantum walks on a cycle graph, capable of implementing arbitrary unitary operations and measurements without decomposition, with efficient training and noise robustness.

Contribution

It presents a universal DTQW-based neural network model for quantum operations, enabling direct implementation and training of arbitrary unitaries and POVMs.

Findings

Can realize any unitary operation via appropriate coin operators

Capable of approximating arbitrary unitaries through training

Demonstrates implementation of arbitrary 2-outcome POVMs

Abstract

The quantum circuit model is the most commonly used model for implementing quantum computers and quantum neural networks whose essential tasks are to realize certain unitary operations. Here we propose an alternative approach; we use a simple discrete-time quantum walk (DTQW) on a cycle graph to model an arbitrary unitary operation $U(N)$ without the need to decompose it into a sequence of gates of smaller sizes. Our model is essentially a quantum neural network based on DTQW. Firstly, it is universal as we show that any unitary operation $U(N)$ can be realized via an appropriate choice of coin operators. Secondly, our DTQW-based neural network can be updated efficiently via a learning algorithm, i.e., a modified stochastic gradient descent algorithm adapted to our network. By training this network, one can promisingly find approximations to arbitrary desired unitary operations. With an additional measurement on the output, the DTQW-based neural network can also implement general measurements described by positive-operator-valued measures (POVMs). We show its capacity in implementing arbitrary 2-outcome POVM measurements via numeric simulation. We further demonstrate that the network can be simplified and can overcome device noises during the training so that it becomes more friendly for laboratory implementations. Our work shows the capability of the DTQW-based neural network in quantum computation and its potential in laboratory implementations.