Getting large-scale quantum neural networks ready for quantum hardware

TL;DR

This paper explores large-scale quantum neural networks trained with noisy measurements, demonstrating their ability to classify quantum states and their potential robustness to noise on current quantum hardware.

Contribution

It introduces physics-informed quantum neural network architectures capable of classifying quantum states using limited noisy data, suitable for NISQ devices.

Findings

Quantum neural networks can classify quantum states via order parameters.

The approach is compatible with current quantum hardware and noisy measurements.

The neural network dynamics relate to open many-body quantum systems, suggesting noise robustness.

Abstract

Quantum neural networks generalize classical artificial neural networks into the quantum domain. They are formulated as parameterized quantum circuits which are optimized by measuring and minimizing a suitably chosen loss function. The core challenge in understanding, implementing and ultimately using quantum neural networks is that they represent many-body systems with an exponentially large Hilbert space, in combination with a large parameter search space. Moreover, noise -- which is inherent to any quantum measurement -- sets practical limits for the estimation of training loss. Here, we study physics-informed large-scale quantum neural networks that are trained through a finite number of noisy loss function measurements. We show that this architecture permits the construction of nontrivial decision boundaries that enable the classification of quantum states through measuring an order parameter. Our approach can directly process quantum data that is output from quantum simulators and computers and is well suited for implementation on current hardware. Moreover, owed to a close link between the neural network dynamics and the evolution of Markovian open many-body quantum systems, one may expect a certain robustness to noise, which is ubiquitous in the current NISQ era.