Quantum algorithms
We design variational and feedback-based algorithms that stay practical under today’s hardware constraints — limited circuit depth, noisy gates, and scarce qubits.
Our work spans quantum compilation, state preparation, trainable feature maps, and machine-learning-assisted optimization, with an emphasis on methods that degrade gracefully rather than fail outright as problems scale up.
Related publications
Flexible genetic algorithm for quantum support vector machines
Machine Learning: Science and Technology 7, 045030 (2026)
Benchmarking loss functions for trainable quantum feature maps
arXiv:2607.12487
Feedback-based quantum control for safe and synergistic drug combination design
Journal of Computational Science 100, 102965 (2026)
Exact gradient for general cost functions in variational quantum algorithms
Physical Review A 113, 042435 (2026)
Advancing quantum process tomography through quantum compilation
Advanced Quantum Technologies 9, e00494 (2026)
Entangled state preparation via cluster states on quantum computers with <qo|op> software
2025 IEEE International Conference on Quantum Software (QSW), 116-122 (2025)
Multi-target quantum compilation algorithm
Machine Learning: Science and Technology 5, 045057 (2024)
<qo|op>: a quantum object optimizer
SoftwareX 26, 101726 (2024)
Universal compilation for quantum state tomography
Scientific Reports 13, 3750 (2023)
Variational preparation of entangled states on quantum computers
arXiv:2306.17422
Qsun: an open-source platform towards practical quantum machine learning applications
Machine Learning: Science and Technology 3, 015034 (2022)
tqix: a toolbox for quantum in X
Computer Physics Communications 263, 107902 (2021)