Hybrid boson sampling-neural network architecture for enhanced classification

arXiv:2510.13332 · quant-ph · Submitted 2025-10-15 · Read on arXiv

quant-ph

Submitted: 2025-10-15

Updated: 2026-10-16

Comments: 14 pages, 6 figures

Journal ref: npj Quantum Information 12 (2026)

DOI: 10.1038/s41534-026-01321-z

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adaptability — 1× in this paper · explained in 1 episode
• Hilbert Space — 1× in this paper · explained in 2 episodes

Sources

• A fast quantum mechanical algorithm for database search
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• Quantum Computing in the NISQ era and beyond
• Noisy intermediate-scale quantum (NISQ) algorithms
• The Computational Complexity of Linear Optics
• Quantum computing and the entanglement frontier
• Computational advantage of quantum random sampling
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Strong quantum computational advantage using a superconducting quantum processor
• Phase transition in Random Circuit Sampling
• Establishing a New Benchmark in Quantum Computational Advantage with 105-qubit Zuchongzhi 3.0 Processor
• Quantum computational advantage using photons
• Phase-Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light
• Robust quantum computational advantage with programmable 3050-photon Gaussian boson sampling
• Boson Sampling on a Photonic Chip
• Boson sampling with 20 input photons in 60-mode interferometers at $10^{14}$ state spaces
• Quantum Machine Learning
• A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
• Challenges and Opportunities in Quantum Machine Learning
• From basic science to technological development: the case for two avenues

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