Dequantization and Quantum Advantage in Learning from Experiments - IPAM at UCLA

Explore the potential of quantum technology in revolutionizing data acquisition and processing for learning about the physical world in this 32-minute conference talk by Jarrod McClean from Google. Delve into the advantages of experimental setups that transduce data from physical systems to stable quantum memory and process it using quantum computers. Discover how quantum machines can learn from exponentially fewer experiments compared to conventional methods in tasks such as predicting physical system properties, performing quantum principal component analysis on noisy states, and learning approximate models of physical dynamics. Examine recent dequantization results in the context of quantum linear algebra and discuss the nuances related to dequantizing quantum algorithms. Gain insights into quantum machine learning, data-assisted problems, quantum memory, and quantum-enhanced experiments. Learn about Bell measurements as a feature in learning, experimental demonstrations of advantage, and unsupervised discovery and classification of processes. Understand the concepts of SWAPs and virtual distillation in quantum PCA, and explore the implications for future research in this field.