Memristor-Accelerated Neural Network Architectures for Energy-Efficient Quantum State Tomography

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Memristor-Accelerated Neural Network Architectures for Energy-Efficient Quantum State Tomography

Conference Paper (2026)

Author(s)

E. Hua (TU Delft - Electrical Engineering, Mathematics and Computer Science, TU Delft - QuTech Advanced Research Centre)

Steven van Ommen (Student TU Delft)

K.Y. Yu (TU Delft - QuTech Advanced Research Centre, TU Delft - QID/Ishihara Lab)

J.S. van Leeuwen (TU Delft - Electrical Engineering, Mathematics and Computer Science)

R.K. Bishnoi (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Heba Abunahla (TU Delft - Electrical Engineering, Mathematics and Computer Science)

S. Nur (TU Delft - QuTech Advanced Research Centre, TU Delft - Electrical Engineering, Mathematics and Computer Science)

S. Feld (TU Delft - QuTech Advanced Research Centre, TU Delft - QCD/Feld Group, TU Delft - Electrical Engineering, Mathematics and Computer Science)

R. Ishihara (TU Delft - QuTech Advanced Research Centre, TU Delft - QID/Ishihara Lab, TU Delft - Electrical Engineering, Mathematics and Computer Science)

Research Group

Computer Engineering

Neural networks Memristors Quantum state tomography

DOI related publication

https://doi.org/10.1109/EDTM65772.2026.11496763 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:5ba3a9ff-e94b-46ce-b29d-5f792193886b

More Info

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Publication Year

2026

Language

English

Research Group

Computer Engineering

Publisher

IEEE

ISBN (print)

979-8-3315-8599-0

ISBN (electronic)

979-8-3315-8598-3

Event

2026 10th IEEE Electron Devices Technology & Manufacturing Conference (EDTM) (2026-03-01 - 2026-03-04), Penang, Malaysia

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Abstract

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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