Quantum Kernel Evaluation via Hong-Ou-Mandel Interference

Abstract

One of the fastest growing areas of interest in quantum computing is its use within machine learning methods, in particular through the application of quantum kernels. Despite this large interest, there exist very few proposals for relevant physical platforms to evaluate quantum kernels. In this article, we propose and simulate a protocol capable of evaluating quantum kernels using Hong-Ou-Mandel (HOM) interference, an experimental technique that is widely accessible to optics researchers. Our proposal utilises the orthogonal temporal modes of a single photon, allowing one to encode multi-dimensional feature vectors. As a result, interfering two photons and using the detected coincidence counts, we can perform a direct measurement and binary classification. This physical platform confers an exponential quantum advantage also described theoretically in other works. We present a complete description of this method and perform a numerical experiment to demonstrate a sample application for binary classification of classical data.

arXiv comment7 pages, 4 figures, comments are welcome

References in corpus (36)

• Quantum Machine Learning
• Variational Quantum Algorithms
• Supervised learning with quantum enhanced feature spaces
• Machine learning and the physical sciences
• Quantum support vector machine for big data classification
• Quantum machine learning in feature Hilbert spaces
• Kernel methods in machine learning
• Quantum principal component analysis
• Electromagnetic Angular Momentum
• Electromagnetic Force and Momentum
• Circuit-centric quantum classifiers
• Power of data in quantum machine learning
• Photonic quantum information processing: a review
• Photonic quantum information processing: a concise review
• Twisted Photons: New Quantum Perspectives in High Dimensions
• Quantum machine learning: a classical perspective
• Continuous-variable quantum neural networks
• Quantum Boltzmann Machine
• Photon number resolution using a time-multiplexed single-photon detector
• Differentiable Learning of Quantum Circuit Born Machine
• Entanglement-Based Machine Learning on a Quantum Computer
• Two-photon interference: the Hong-Ou-Mandel effect
• Supervised quantum machine learning models are kernel methods
• The Born Supremacy: Quantum Advantage and Training of an Ising Born Machine
• A 12-mode Universal Photonic Processor for Quantum Information Processing
• Experimental quantum kernel machine learning with nuclear spins in a solid
• Evaluating Performance of Photon-Number-Resolving Detectors
• Benchmarking photon number resolving detectors
• Fock State-enhanced Expressivity of Quantum Machine Learning Models
• Quantum machine learning with adaptive linear optics
• Noisy Quantum Kernel Machines
• Nearly deterministic Bell measurement with multiphoton entanglement for efficient quantum information processing
• Quantum Mean Embedding of Probability Distributions
• Photonic kernel machine learning for ultrafast spectral analysis
• A Physical Quantum Agent
• Non-Classical Kernels in Continuous Variable Systems

Cited by in corpus (6)

• Quantum Kernel Machine Learning With Continuous Variables
• Experimental benchmarking of quantum state overlap estimation strategies with photonic systems
• Quantum optical classifier with superexponential speedup
• Modeling and benchmarking quantum optical neurons for efficient neural computation
• Quantum optical shallow networks
• Quantum Algorithms for Causal Estimands