Automatic qubit control for large-scale quantum computers enabled by neuromorphic computing and cryogenic bio-inspired hardware

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The latest major breakthrough in quantum computing (QC) has been the demonstration of quantum systems with more than 50 superconducting qubits allowing to demonstrate quantum supremacy for the first time. Although significant advances have been made in quantum chip fabrication and quantum algorithms, qubit control and error corrections are still performed mostly by hand with bulky classical electronics located outside the cryostat. This approach, characterized by a "wiring bottleneck" between the qubits and the control electronics, makes it impossible to fabricate truly large-scale quantum computers. One of the next major breakthroughs in QC is to automate the control and error corrections of very large numbers of qubits using integrated cryogenic electronics located inside the cryostat (i.e. in-situ). In recent years, several studies have shown that machine learning (ML) could solve or automate difficult data-driven quantum problems. Artificial intelligence (AI) could thus become a key tool for in-situ qubit control, but major research efforts have first to be conducted on AI-dedicated cryo-electronics and ultralow-power AI solutions with high-performance and minimum heat dissipation. Spiking neural networks (SNN) can achieve state-of-the-art energy efficiency and accuracy with unsupervised learning and reservoir computing. Exploring SNN-based solutions for quantum problems is thus a very promising approach. However, current computing hardware based on the serial and digital von Neumann architecture is fundamentally different from the highly paralleled, analog and asynchronous nature of SNNs. This major software/hardware mismatch induces performance and energy efficiency issues. This problem could be overcome by fully embracing neuromorphic engineering to create circuits emulating brain-like functions in hardware. This approach should lead to energy-efficient SNN-based computing suitable for cryogenic technologies with tight thermal constraints. Before QC can benefit from SNN-based in-situ AI, novel cryo-compatible nanodevices emulating synaptic functions are required. In that scope, emerging multi-terminal resistive memories (i.e. memristors) recently appeared as one of the most interesting candidates to emulate synaptic functions in hardware.  RESEARCH PROGRAM In the long term, I plan to use my expertise in neuromorphic computing, AI-dedicated emerging hardware and quantum engineering to develop fully automatic in-situ control of qubits using ultralow-power SNN solutions running entirely on cryo-compatible memristor-based neuromorphic hardware. The outcomes of this research program would allow Canada to stay ahead in the race for the first general-purpose quantum computers. In the short term, I will kickstart 3 interdisciplinary research projects aiming to develop key enabling technologies for AI-based in-situ qubit control by following a unique research path combining memristor-based neuromorphic engineering and quantum technology.