Studying the Impact of Quantum-Specific Hyperparameters on Hybrid Quantum-Classical Neural Networks

TL;DR

This paper investigates how quantum-specific hyperparameters affect the performance of hybrid quantum-classical neural networks in image classification, providing insights for designing more efficient quantum models.

Contribution

It systematically analyzes the impact of quantum hyperparameters on HQNN performance, offering foundational understanding for optimizing quantum neural network design.

Findings

Quantum hyperparameters significantly influence HQNN accuracy and training time.

Certain hyperparameter configurations lead to intuitive learning patterns.

The study guides future HQNN algorithm development and hyperparameter tuning.

Abstract

In current noisy intermediate-scale quantum devices, hybrid quantum-classical neural networks (HQNNs) represent a promising solution that combines the strengths of classical machine learning with quantum computing capabilities. Compared to classical deep neural networks (DNNs), HQNNs present an additional set of hyperparameters, which are specific to quantum circuits. These quantum-specific hyperparameters, such as quantum circuit depth, number of qubits, type of entanglement, number of shots, and measurement observables, can significantly impact the behavior of the HQNNs and their capabilities to learn the given task. In this paper, we investigate the impact of these variations on different HQNN models for image classification tasks, implemented on the PennyLane framework. We aim to uncover intuitive and counter-intuitive learning patterns of HQNN models within granular levels of controlled quantum perturbations, to form a sound basis for their correlation to accuracy and training time. The outcome of our study opens new avenues for designing efficient HQNN algorithms and builds a foundational base for comprehending and identifying tunable hyperparameters of HQNN models that can lead to useful design implementation and usage.