Demonstration of a bosonic quantum classifier with data re-uploading

Abstract

In a single qubit system, a universal quantum classifier can be realised using the data-reuploading technique. In this study, we propose a new quantum classifier applying this technique to bosonic systems and successfully demonstrated it using silicon optical integrated quantum circuits. We established a theory of quantum machine learning algorithm applicable to bosonic systems and implemented a programmable optical circuit combined with an interferometer. Learning and classification using part of the implemented optical quantum circuit with uncorrelated two-photons resulted in a classification with a reproduction rate of approximately 94\% in the proof of principle experiment. As this method can be applied to arbitrary two-mod N-photon system, further development of optical quantum classifiers, such as extensions to quantum entangled and multi-photon states, is expected in the future.

arXiv comment9 pages, 4 figures

References in corpus (29)

• Quantum Machine Learning
• Variational Quantum Algorithms
• Deep Learning with Coherent Nanophotonic Circuits
• Quantum Simulation
• Supervised learning with quantum enhanced feature spaces
• Quantum Circuit Learning
• Quantum Convolutional Neural Networks
• Integrated Photonic Quantum Technologies
• On the volume of the set of mixed entangled states
• Universal Linear Optics
• Circuit-centric quantum classifiers
• The effect of data encoding on the expressive power of variational quantum machine learning models
• Experimental Boson Sampling
• Data re-uploading for a universal quantum classifier
• Large-scale silicon quantum photonics implementing arbitrary two-qubit processing
• Quantum transport simulations in a programmable nanophotonic processor
• Generation and sampling of quantum states of light in a silicon chip
• Entanglement-Based Machine Learning on a Quantum Computer
• Stochastic gradient descent for hybrid quantum-classical optimization
• Quantum speedup for active learning agents
• Sequential minimal optimization for quantum-classical hybrid algorithms
• Quantum Hopfield neural network
• An On-chip Homodyne Detector for Measuring Quantum States and Generating Random Numbers
• 60dB high-extinction auto-configured Mach--Zehnder interferometer
• 9~GHz measurement of squeezed light by interfacing silicon photonics and integrated electronics
• On the Quantum versus Classical Learnability of Discrete Distributions
• An end-to-end trainable hybrid classical-quantum classifier
• Reconstruction of a Photonic Qubit State with Reinforcement Learning
• Single-qubit universal classifier implemented on an ion-trap quantum device

Cited by in corpus (3)

• Information processing at the speed of light
• Neural quantum kernels: training quantum kernels with quantum neural networks
• Demonstration of sequential processors with quantum advantage and analysis of classical performance limits