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J.S. van Leeuwen
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Conference paper
(2026)
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E. Hua, Steven van Ommen, K.Y. Yu, J.S. van Leeuwen, R.K. Bishnoi, Heba Abunahla, S. Nur, S. Feld, R. Ishihara
Accurate quantum state tomography (QST) is vital for calibrating quantum processors but faces exponential scaling challenges. We benchmark seven neural architectures—FCN, CNN, CGAN, Transformer, RNN, RBM, and SVAE—for QST reconstruction using expectation- and probability-based measurements. CNN and CGAN achieve high fidelity (F > 0.99), while SVAE enables efficient event-driven learning. To enhance scalability, memristor-based computation-in-memory (CiM) acceleration is proposed for CNN and SVAE, leveraging analog matrix–vector multiplication in HfO2 crossbars. The fabricated arrays show stable bipolar switching and STDP behavior, advancing energy-efficient, real-time quantum diagnostics through algorithm–hardware co-design.
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Accurate quantum state tomography (QST) is vital for calibrating quantum processors but faces exponential scaling challenges. We benchmark seven neural architectures—FCN, CNN, CGAN, Transformer, RNN, RBM, and SVAE—for QST reconstruction using expectation- and probability-based measurements. CNN and CGAN achieve high fidelity (F > 0.99), while SVAE enables efficient event-driven learning. To enhance scalability, memristor-based computation-in-memory (CiM) acceleration is proposed for CNN and SVAE, leveraging analog matrix–vector multiplication in HfO2 crossbars. The fabricated arrays show stable bipolar switching and STDP behavior, advancing energy-efficient, real-time quantum diagnostics through algorithm–hardware co-design.