[Waves in AI 2024] Advances in Hybrid Quantum-classical Machine Learning

Advances in Hybrid Quantum-classical Machine Learning
結合量子與傳統機器學習的進展
Samuel Yen-Chi Chen (陳彥奇)
Abstract: The evolution of machine learning (ML) and quantum computing (QC) hardware has sparked significant interest in the development of quantum machine learning (QML) applications. This presentation aims to offer a comprehensive insight into the hybrid quantum-classical machine learning approach, encompassing pivotal aspects such as quantum gradient calculation. Furthermore, it will delve into recent strides made in QML across diverse domains, including distributed or federated learning, natural language processing, reinforcement learning, sequential learning, and classification. The presentation will also address the potential advantages, scalability, and application scenarios of QML in the NISQ era.
摘要：機器學習（ML）和量子運算（QC）硬體的演進造成量子機器學習（QML）應用開發受到許多關注。本次演講旨在全面探討混合量子-經典機器學習方法，涵蓋量子梯度計算等關鍵方面。此外，演講還將深入探討QML在分佈式或聯邦式（distributed or federated learning）學習、自然語言處理、強化學習、時序學習（sequential learning）和分類等不同領域取得的最新進展。演講還將討論QML在NISQ時代的潛在優勢、可擴展性和應用情境。
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Waves in AI 2024:
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