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Basically, my research areas focus on Artificial Intelligence and Cross-disciplinary Topics.

My primary research focuses on exploring how to demonstrate the practical advantages of quantum technology. I am particularly interested in methods that efficiently encode classical information as quantum information using fewer quantum resources. For example, to make quantum federated learning viable in real-world applications, the number of communications in quantum networks should be minimised, and the quantum data obtained from other participants must be robust.

I am also exploring how to incorporate the benefits of quantum technology into Trustworthy AI. I believe that trusted models are one of the most important areas of direction in satisfying the need for trustworthiness in finance and material analysis.

Research interests:

Quantum information, Quantum Computing, Quantum AI, Quantum Machine Learning, Quantum Federated Learning, Cybersecurity, Cryptography

Publication:

Hybrid Quantum-Classical Neural Network for LAB Color Space Image...

Parameterized quantum circuits (PQCs) are essential in many variational quantum algorithms. Quantum convolutional neural networks (QCNNs), which are structurally similar to classical convolutional...