Quantum machine learning over infinite dimensions

Abstract

Machine learning is a fascinating and exciting field within computer science. Recently, this excitement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the finite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practical, infinite-dimensional systems. We present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that achieve an exponential speedup compared to their equivalent classical counterparts. Finally, we also map out an experimental implementation which can be used as a blueprint for future photonic demonstrations.

9 pages, 1 figure

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Cited by in corpus (50)

• Quantum Machine Learning
• Continuous-variable quantum neural networks
• Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
• Entangling Bosonic Modes via an Engineered Exchange Interaction
• Quantum singular value decomposition of non-sparse low-rank matrices
• Machine learning method for state preparation and gate synthesis on photonic quantum computers
• Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices
• Quantum algorithm for non-homogeneous linear partial differential equations
• Analytic continuation via domain-knowledge free machine learning
• Production of photonic universal quantum gates enhanced by machine learning
• Hybrid quantum computation gate with trapped ion system
• Building a large-scale quantum computer with continuous-variable optical technologies
• A quantum walk assisted approximate algorithm for bounded NP optimisation problems
• Quantum Imaginary Time Evolution Algorithm for Quantum Field Theories with Continuous Variables
• Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions
• Machine learning \& artificial intelligence in the quantum domain
• Quantum computation and simulation with vibrational modes of trapped ions
• Machine learning based localization and classification with atomic magnetometers
• Exact gate decompositions for photonic quantum computing
• Continuous-variable quantum Gaussian process regression and quantum singular value decomposition of non-sparse low rank matrices
• Selected topics of quantum computing for nuclear physics
• Exact and approximate continuous-variable gate decompositions
• Complexity measures and uncertainty relations of the high-dimensional harmonic and hydrogenic systems
• Continuous-variable assisted thermal quantum simulation
• Experimental realization of a quantum image classifier via tensor-network-based machine learning
• Simple implementation of high fidelity controlled-$i$SWAP gates and quantum circuit exponentiation of non-Hermitian gates
• Protocol for implementing quantum nonparametric learning with trapped ions
• Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer
• Quantum-enhanced least-square support vector machine: simplified quantum algorithm and sparse solutions
• Realizing quantum linear regression with auxiliary qumodes
• Quantum Computation of Scattering Amplitudes in Scalar Quantum Electrodynamics
• Quantum Gaussian filter for exploring ground-state properties
• Quantum kernels with squeezed-state encoding for machine learning
• Characterizing the performance of continuous-variable Gaussian quantum gates
• Experimental demonstration of quantum learning speed-up with classical input data
• Client-friendly continuous-variable blind and verifiable quantum computing
• Universal quantum computing with thermal state bosonic systems
• Ancilla-Error-Transparent Controlled Beam Splitter Gate
• Continuous-variable quantum kernel method on a programmable photonic quantum processor
• Quantum Machine Learning and its Supremacy in High Energy Physics
• The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning
• Qumode transfer between continuous and discrete variable devices
• Inverse iteration quantum eigensolvers assisted with a continuous variable
• Universal compiling and (No-)Free-Lunch theorems for continuous variable quantum learning
• Experimental semi-autonomous eigensolver using reinforcement learning
• Entangled N-photon states for fair and optimal social decision making
• Quantum Recurrence Plot Algorithm Based on Quantum Principal Component Analysis
• Towards Continuous-variable Quantum Neural Networks for Biomedical Imaging
• Noisy dynamics of Gaussian entanglement: a transient bound entangled phase before separability
• Variational quantum simulation of the quantum critical regime