arXiv:2309.15259
공식 코드 · Papers with Code
SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers
원문 초록
Exploration into quantum machine learning has grown tremendously in recent years due to the ability of quantum computers to speed up classical programs. However, these efforts have yet to solve unsupervised similarity detection tasks due to the challenge of porting them to run on quantum computers. To overcome this challenge, we propose SLIQ, the first open-sourced work for resource-efficient quantum similarity detection networks, built with practical and effective quantum learning and variance-reducing algorithms.
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The interplay of robustness and generalization in quantum machine learning
Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators
QuXAI: Explainers for Hybrid Quantum Machine Learning Models
Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing
Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders
Fine-Tuning Large Language Models on Quantum Optimization Problems for Circuit Generation
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