Our quantum computing research develops practical algorithmic and circuit-level methods for near-term and future devices. We combine theoretical design with implementation-aware optimization so that new methods remain relevant under realistic hardware constraints.
Current work includes variational algorithms, architecture-aware circuit design, algorithm synthesis, and quantum machine learning pipelines. We also investigate automated and evolutionary approaches to circuit discovery, enabling scalable design patterns for tasks such as quantum phase recognition, classification, and quantum simulation.
Physical Review Research
We present a theoretical framework for a quantized memristive Leaky Integrate-and-Fire (LIF) neuron, uniting principles from neuromorphic engineering and open quantum systems. Starting from a classical memristive LIF cir…
View publicationEPJ Quantum Technology
We present a computational method to automatically design then-qubit realisations of quantum algorithms. Our approach leverages a domain-specific language (DSL) that enables the construction of quantum circuits via modul…
View publicationarXiv preprint arXiv:2602.01165
Sample-based quantum diagonalization (SQD) offers a powerful route to accurate quantum chemistry on noisy intermediate-scale quantum (NISQ) devices by combining quantum sampling with classical diagonalization. Here we in…
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