We use artificial intelligence to accelerate quantum science workflows, from model fitting and prediction to design automation. Our approach combines classical machine learning with quantum-aware methods where appropriate.
Research includes learning-driven optimization of quantum circuits, data-driven modeling of open-system dynamics, and hybrid pipelines that improve performance on complex scientific datasets. We also study how AI can be integrated into quantum communication and neuromorphic settings.
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 publicationQuantum Machine Intelligence
We construct a hybrid quantum-classical approach for the K-Nearest Neighbour algorithm, where the information is embedded in a phase-distributed multimode coherent state with the assistance of a single photon. The task o…
View publication2025 IEEE International Conference on Quantum Artificial Intelligence (QAI)
Quantum computing has recently inspired many applications in machine learning. In this paper, we present a novel idea of combining neuromorphic computing and quantum computing to develop an advanced application for study…
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