UChicago PME Special Quantum Seminar- Fangjun Hu

With the rapid advancement of quantum technology in recent years, applying quantum computing to execute machine learning algorithms has obtained increasing attention in the noisy intermediate-scale quantum (NISQ) era. The practical implementation of many quantum machine learning (QML) algorithms known today is limited by the quantum shot noise and coherence time of the executing quantum hardware. In this talk, I will introduce a theoretical tool, Eigentask Analysis, which quantifies and mitigates quantum shot noise in QML (Hu et al., PRX, 2023). Those tools pave new paths for implementing large-scale QML, and provide potential connections between QML and theory of quantum metrology and quantum dynamics. Meanwhile, I will also introduce an implementable QML framework, NISQ Reservoir Computing (NISQRC), which leverages state-of-the-art superconducting quantum technologies to experimentally run QML algorithms with persistent temporal memory far longer than the coherence time (Hu et al., Nat. Commun. 2024).