Establishing Baselines for Photonic Quantum Machine Learning: Insights from an Open, Collaborative Initiative

TL;DR

This paper presents a collaborative benchmark for photonic quantum machine learning using a simplified MNIST task, establishing performance baselines and emphasizing open, reproducible research to accelerate progress in the field.

Contribution

It introduces the Perceval Challenge, a reproducible benchmark for photonic quantum ML, and provides the first unified performance baseline from a large, international collaborative effort.

Findings

Photonic quantum circuits can learn and generalize from limited data.

Hybrid approaches show complementary strengths in photonic quantum ML.

Open benchmarking accelerates research and development in quantum-enhanced AI.

Abstract

The Perceval Challenge is an open, reproducible benchmark designed to assess the potential of photonic quantum computing for machine learning. Focusing on a reduced and hardware-feasible version of the MNIST digit classification task or near-term photonic processors, it offers a concrete framework to evaluate how photonic quantum circuits learn and generalize from limited data. Conducted over more than three months, the challenge attracted 64 teams worldwide in its first phase. After an initial selection, 11 finalist teams were granted access to GPU resources for large-scale simulation and photonic hardware execution through cloud service. The results establish the first unified baseline of photonic machine-learning performance, revealing complementary strengths between variational, hardware-native, and hybrid approaches. This challenge also underscores the importance of open, reproducible experimentation and interdisciplinary collaboration, highlighting how shared benchmarks can accelerate progress in quantum-enhanced learning. All implementations are publicly available in a single shared repository (https://github.com/Quandela/HybridAIQuantum-Challenge), supporting transparent benchmarking and cumulative research. Beyond this specific task, the Perceval Challenge illustrates how systematic, collaborative experimentation can map the current landscape of photonic quantum machine learning and pave the way toward hybrid, quantum-augmented AI workflows.