Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning
원문 초록
Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. It seeks to revolutionize machine learning by harnessing the unique capabilities of quantum mechanics and employs machine learning techniques to advance quantum computing research. This paper introduces quantum computing for the machine learning paradigm, where variational quantum circuits (VQC) are used to develop QML architectures on noisy intermediate-scale quantum (NISQ) devices. We discuss machine learning for the quantum computing paradigm, showcasing our recent theoretical and empirical findings. In particular, we delve into future directions for studying QML, exploring the potential industrial impacts of QML research.
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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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