Implementing machine learning methods on QICK hardware for qubit readout & control

Conference · Tue May 23 04:00:00 EDT 2023

DOI:https://doi.org/10.2172/1974720· OSTI ID:1974720

Yesilyurt, O.; Campos, J.; Di Guglielmo, G.; Boltasseva, A.; Bowring, D.; Cancelo, G.; Du, B.; Fahim, F.; Herwig, C.; Ma, R.; Perdue, G.; Shalaev, V.; Stefanazzi, L.; Tran, N.; Uemura, S.

Quantum readout and control is a fundamental aspect of quantum computing that requires accurate measurement of qubit states. Errors emerge in all stages, from initialization to readout, and identifying errors in post-processing necessitates resource-intensive statistical analysis. In our work, we use a lightweight fully-connected neural network (NN) to classify states of a transmon system with no prior processing. Our NN accelerator yields higher fidelities (92%) than the classical matched filter method (84%). By exploiting the natural parallelism of NNs and their placement near the source of data on field-programmable gate arrays (FPGAs), we can achieve ultra-low latency on the Quantum Instrumentation Control Kit (QICK). Integrating machine learning methods on QICK opens several pathways for efficient real-time processing of quantum circuits.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
• DOE Contract Number: AC02-07CH11359
• OSTI ID: 1974720
• Report Number(s): FERMILAB-POSTER-23-062-CSAID; oai:inspirehep.net:2661815
• Country of Publication: United States
• Language: English

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