Variational quantum classifiers via a programmable photonic microprocessor

TL;DR

This paper demonstrates a silicon-based photonic microprocessor implementing a variational quantum classifier that effectively handles complex nonlinear classification tasks, showing promising results on synthetic and real-world datasets.

Contribution

It introduces a novel photonic quantum classifier platform using a programmable microprocessor and a gradient-free training algorithm, advancing practical quantum machine learning applications.

Findings

Achieved high classification accuracy on synthetic tasks (87.5%, 92.5%, 85%)

Achieved 98.8% accuracy on Iris dataset

Demonstrated effectiveness of photonic VQC in complex data patterns

Abstract

Quantum computing holds promise across various fields, particularly with the advent of Noisy Intermediate-Scale Quantum (NISQ) devices, which can outperform classical supercomputers in specific tasks. However, challenges such as noise and limited qubit capabilities hinder its practical applications. Variational Quantum Algorithms (VQAs) offer a viable strategy to achieve quantum advantage by combining quantum and classical computing. Leveraging on VQAs, the performance of Variational Quantum Classifiers (VQCs) is competitive with many classical classifiers. This work implements a VQC using a silicon-based quantum photonic microprocessor and a classical computer, demonstrating its effectiveness in nonlinear binary and multi-classification tasks. An efficient gradient free genetic algorithm is employed for training. The VQC's performance was evaluated on three synthetic binary classification tasks with square-, circular-, and sine-shape decision boundaries and a real-world multiclass Iris dataset. The accuracies on the three binary classification tasks were 87.5%, 92.5%, and 85.0%, respectively, and 98.8% on the real world Iris dataset, highlighting the platform's potential to handle complex data patterns.