Virtual AI/ML seminar: Deep Neural Networks for Qubit Readout

  • Oct. 11, 2022, 11:00 am US/Central

Title: Deep Neural Networks for Qubit Readout

Time and date: Tuesday, Oct. 11, at 11 a.m. CDT

Registration: Click here for the Zoom info; look for AI/ML Seminar Series (Services login required)

Speaker: Benjamin Lienhard, Princeton University

Abstract: Quantum computers hold the promise to solve specific problems significantly faster than classical computers. However, the quantum processor’s constituent components, control, and readout must be very well-calibrated to realize a practical quantum computer. Over the last few decades, infrastructure and protocols have been developed to operate small-scale quantum processors efficiently. However, the operation of medium- to large-scale quantum processors presents new engineering challenges. Among those challenges are efficient and high-fidelity multi-qubit control and readout. In particular, qubit-state readout is a significant error source in contemporary superconducting quantum processors. In this talk, I will discuss control and readout software tools for multiple superconducting qubits and their suitability for implementation on FPGAs at room and cryogenic temperatures. We demonstrate deep machine learning techniques to improve frequency-multiplexed superconducting qubit readout pulse shapes and discrimination for a five-qubit system. Compared with currently employed readout methods, these novel techniques reduce the required measurement time, the readout resonator reset, and the discrimination error rate by about 20% each. Lastly, I will discuss the potential to employ these techniques using fast processing electronics such as FPGAs at room and cryogenic temperatures. The developed readout techniques are a significant step towards efficiently implementing near-term quantum algorithms based on iterative optimization and quantum error correction protocols necessary for future universal quantum processors.

Bio: Benjamin Lienhard is an SNF postdoctoral research fellow at Princeton University, where he focuses on efficient quantum processor calibration and control. He received his PhD in 2021 from the Electrical Engineering and Computer Science Department at MIT and an MS and BS from the Department of Information Technology and Electrical Engineering at ETH Zurich. While at MIT, he worked on logical qubits using solid-state quantum emitters and readout and control of superconducting qubits.