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Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges



Axel Hoffmann, Shriram Ramanathan, Julie Grollier, Andrew Kent, Marcelo Rozenberg, Ivan Schuller, Oleg Shpyrko, Robert Dynes, Yeshaiahu Fainman, Alex Frano, Eric Fullerton, Giulia Galli, Vitaliy Lomakin, Shyue Ping Ong, Amanda K. Petford-Long, Jonathan A. Schuller, Mark Stiles, Yayoi Takamura, Yimei Zhu


Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short and long-term plasticity transitions. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This paper discusses select examples of these approaches, and provides a perspective for the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.
APL Materials


Neuromorphic computing, spintronics, quantum materials, memristor, neuristor, metal insulator transition, plasticity, magnetization dynamics


Hoffmann, A. , Ramanathan, S. , Grollier, J. , Kent, A. , Rozenberg, M. , Schuller, I. , Shpyrko, O. , Dynes, R. , Fainman, Y. , Frano, A. , Fullerton, E. , Galli, G. , Lomakin, V. , Ong, S. , Petford-Long, A. , Schuller, J. , Stiles, M. , Takamura, Y. and Zhu, Y. (2022), Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges, APL Materials, [online],, (Accessed May 30, 2024)


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Created July 19, 2022, Updated November 29, 2022