arXiv:2303.09491
Challenges and Opportunities in Quantum Machine Learning
원문 초록
At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and high-energy physics. Nevertheless, challenges remain regarding the trainability of QML models. Here we review current methods and applications for QML. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with QML.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
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
토론
이 논문과 연결된 포럼 글이 아직 없습니다.