Quantum-enhanced data classification with a variational entangled sensor network

Abstract

Variational quantum circuits (VQCs) built upon noisy intermediate-scale quantum (NISQ) hardware, in conjunction with classical processing, constitute a promising architecture for quantum simulations, classical optimization, and machine learning. However, the required VQC depth to demonstrate a quantum advantage over classical schemes is beyond the reach of available NISQ devices. Supervised learning assisted by an entangled sensor network (SLAEN) is a distinct paradigm that harnesses VQCs trained by classical machine-learning algorithms to tailor multipartite entanglement shared by sensors for solving practically useful data-processing problems. Here, we report the first experimental demonstration of SLAEN and show an entanglement-enabled reduction in the error probability for classification of multidimensional radio-frequency signals. Our work paves a new route for quantum-enhanced data processing and its applications in the NISQ era.

19 pages, 15 figures

References in corpus (8)

• A Quantum Approximate Optimization Algorithm
• Entanglement-Enhanced Sensing in a Lossy and Noisy Environment
• Unsupervised Machine Learning on a Hybrid Quantum Computer
• Optimum mixed-state discrimination for noisy entanglement-enhanced sensing
• Distributed quantum phase estimation with entangled photons
• A variational toolbox for quantum multi-parameter estimation
• Quantum Algorithms for Fixed Qubit Architectures
• Entanglement-Assisted Communication Surpassing the Ultimate Classical Capacity

Cited by in corpus (25)

• Entanglement-Based Quantum Information Technology
• Demonstration of algorithmic quantum speedup
• Generative quantum machine learning via denoising diffusion probabilistic models
• Generalized quantum circuit differentiation rules
• Entangled multiplets and unusual spreading of quantum correlations in a continuously monitored tight-binding chain
• Robust in Practice: Adversarial Attacks on Quantum Machine Learning
• Quantum Imaginary Time Evolution Algorithm for Quantum Field Theories with Continuous Variables
• Quantum reservoir computation utilising scale-free networks
• Quantum Metrological Power of Continuous-Variable Quantum Networks
• Heisenberg-limited estimation robust to detector inefficiency in a multi-parameter Mach-Zehnder network with squeezed light
• Energy-dependent barren plateau in bosonic variational quantum circuits
• Evaluating the quantum Ziv-Zakai bound in noisy environments
• Optimizing one-axis twists for variational Bayesian quantum metrology
• Demonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem
• Enhancing distributed sensing with imperfect error correction
• Single-shot Quantum Signal Processing Interferometry
• Non-Classical Kernels in Continuous Variable Systems
• Analytical Bounds for Dynamic Multi-Channel Discrimination
• Quantum Technology for Military Applications
• Quantum Cluster Algorithm for Data classification
• Quantum-secure multiparty deep learning
• Quantum-enhanced learning with a controllable bosonic variational sensor network
• Cat-state-like non-Gaussian entanglement in magnon systems
• Impact of the Unruh effect on the estimation precision of Gaussian channel parameters
• Physical-Layer Machine Learning with Multimode Interferometric Photon Counting