Experimental demonstration of quantum learning speedup with classical input data

Abstract

We consider quantum-classical hybrid machine learning in which large-scale input channels remain classical and small-scale working channels process quantum operations conditioned on classical input data. This does not require the conversion of classical (big) data to a quantum superposed state, in contrast to recently developed approaches for quantum machine learning. We performed optical experiments to illustrate a single-bit universal machine, which can be extended to a large-bit circuit for binary classification task. Our experimental machine exhibits quantum learning speed-up of approximately 36%, as compared to the fully classical machine. In addition, it features strong robustness against dephasing noise.

Quantum secure learning with classical samples

“…Such an oracle architecture indeed differs from other hybrid schemes. It has been argued that such hybridization can offer the advantage of being NISQ implementable and of achieving speedups [16,17].…”

“…Therefore, such condition cannot be defined in any classical regime. Our paper also leads to an intriguing classical-quantum interplay, namely, in which the (large) input data remain classical while the useful quantum properties are explored for a small quantum system [16,17]. Such architecture helps avoid the use of a largely superposed sample and is well suited to noisy intermediate-scale quantum (NISQ) technologies [18].…”

Efficient Learning for Deep Quantum Neural Networks

“…With the first small quantum computers available, many people have studied how quantum machine learning (and quantum-assisted ML) proposals can be implemented on near-term quantum computing devices [6,38,39].…”