Fangjun Hu (Princeton University)

Abstract:

The practical implementation of many quantum algorithms known today is believed to be limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum systems that enables processing of temporal data over durations unconstrained by the finite coherence times of constituent qubits. NISQRC strikes a balance between input encoding steps and mid-circuit measurements with reset to endow the quantum system with an appropriate-length persistent temporal memory to capture the time-domain correlations in the streaming data. This enables NISQRC to overcome not only limitations imposed by finite coherence, but also information scrambling or thermalization in monitored circuits.

Bio:

Fangjun Hu is a fifth-year PhD candidate advised by Hakan Türeci at Princeton University. His research interest lies in the theory of quantum computing and quantum machine learning, most recently focusing on quantum reservoir computing, quantum optimization, and the intersection between machine learning and statistical mechanics.