Ryoichi Ishihara

Associate Professor, Group leader

Qutech, Dep. Quantum and Computer Engineering, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology

Ishihara-lab focuses on the integration technologies for unconventional electronic systems; quantum computers, quantum sensors, neuromorphic computers, and biodegradable sensors. Our work involves new materials, scalable fabrication of electronic and photonic devices, and 3D heterogeneous integration, aiming to realize unconventional electronic systems.

Memristor-Based SNN Emulated on FPGA Platform for Implementation of Quantum State Tomography

In recent years, quantum computing and neuromorphic computing have emerged as two forefront research areas in artificial intelligence[1, 2]. Quantum state tomography is a crucial step in quantum computing, but its complexity makes traditional computational methods inefficient[3]. Meanwhile, memristors and Spiking Neural Networks (SNNs) have shown great potential in neuromorphic computing, yet their application is still constrained by hardware resources[4]. This proposal aims to develop a memristor-based SNN accelerator on an FPGA platform to efficiently compute quantum state tomography.

Methodology：

• Theoretical Analysis: Conduct an in-depth study of the potential applications of memristors and SNNs in quantum state tomography, exploring their advantages and challenges.
• System Design: Design and implement an SNN accelerator based on the FPGA platform, considering the characteristics of memristors, optimizing circuit structure, and algorithms.
• Simulation and Validation: Verify the designed accelerator using simulation tools to ensure its performance and functionality meet expectations including the energy efficiency, accuracy (Fidelity),computation time compared with traditional ways.
• Experimental Validation: Perform experimental validation on actual hardware platforms to evaluate the performance and energy consumption of the accelerator.
• Application Cases: Utilize the developed accelerator for practical applications of quantum state tomography, validating its effectiveness and practicality in quantum computing.

References:

[1] M.O. Brown, S.R. Muleady, W.J. Dworschack, R.J. Lewis-Swan, A.M. Rey, O. Romero-Isart, C.A. Regal, Time-of-flight quantum tomography of an atom in an optical tweezer, Nat Phys, 19 (2023) 569-+.

[2] D. Ielmini, H.S.P. Wong, In-memory computing with resistive switching devices, Nat Electron, 1 (2018) 333-343.

[3] D. Koutny, L. Motka, Z. Hradil, J. Rehácek, L.L. Sánchez-Soto, Neural-network quantum state tomography, Phys Rev A, 106 (2022).

[4] P. Mannocci, M. Farronato, N. Lepri, L. Cattaneo, A. Glukhov, Z. Sun, D. Ielmini, In-memory computing with emerging memory devices: Status and outlook, APL Machine Learning, 1 (2023).

Interested? Please contact Ryoichi Ishihara r.ishihara@tudelft.nl or Erbing Hua <E.Hua@tudelft.nl>