Training quantum neural networks using the Quantum Information Bottleneck method

TL;DR

This paper introduces a method for training quantum neural networks by maximizing the quantum information bottleneck, providing algorithms for computing and optimizing this quantity efficiently in fully quantum settings.

Contribution

It presents a concrete, operationally grounded approach for training quantum autoencoders using the quantum information bottleneck, including algorithms for its computation and gradient estimation.

Findings

Efficient algorithms for computing the quantum information bottleneck within specified error bounds.

Polynomial-time methods for estimating derivatives of the QIB function.

Quantum neural networks can be trained effectively using the QIB measure.

Abstract

We provide in this paper a concrete method for training a quantum neural network to maximize the relevant information about a property that is transmitted through the network. This is significant because it gives an operationally well founded quantity to optimize when training autoencoders for problems where the inputs and outputs are fully quantum. We provide a rigorous algorithm for computing the value of the quantum information bottleneck quantity within error $\epsilon$ that requires $O(\log^2(1/\epsilon) + 1/\delta^2)$ queries to a purification of the input density operator if its spectrum is supported on $\{0\}~\bigcup ~[\delta,1-\delta]$ for $\delta>0$ and the kernels of the relevant density matrices are disjoint. We further provide algorithms for estimating the derivatives of the QIB function, showing that quantum neural networks can be trained efficiently using the QIB quantity given that the number of gradient steps required is polynomial.