Invited Speaker Confirmed for TEMC 2024

The MARCH organised workshop TEMC (https://www.cs.york.ac.uk/nature/temc/TEMC2024-UCNC24/) has confirmed Quoc Hoan Tran as the invited speaker for 2024.

Quoc Hoan Tran is a senior researcher at the Quantum Laboratory of Fujitsu Research, Fujitsu Limited. His current research focuses on novel architectures and intersecting applications of emerging quantum systems for machine learning within the unconventional computing framework. He received his predoctoral education and PhD from the University of Tokyo in 2020, with a background in applied mathematics and machine learning methods for dynamic systems. Before his current position at Fujitsu Research, he was a Postdoctoral Scholar and Specially Appointed Assistant Professor at the University of Tokyo in Professor Kohei Nakajima's group, working in the field of physical reservoir computing. He has also been a member of the MEXT - Quantum Leap Flagship Program (MEXT Q-LEAP) for the development of quantum software through intelligent quantum system design and its applications.

The abstract for his talk is:

Quantum computing stands as a pivotal force in the exploration of unconventional computing territories. Reflecting on Feynman's perspective: given the significant difficulty of simulating quantum mechanics on classical computers, shouldn't we instead embrace the complexity and develop computers directly from quantum systems? Meanwhile, neural networks in classical systems play a central role in performing machine learning tasks. In this talk, inspired by both domains, I present the general structure of quantum neural networks that utilize natural quantum dynamics for machine learning tasks, particularly with high-dimensional and complex data that classical machine learning methods cannot handle well. I will discuss one of the major challenges in quantum computing: the inevitable buildup of quantum error in constructing quantum algorithms. Interestingly, quantum neural networks can help mitigate these quantum errors by training on noisy quantum data. Finally, I offer a complementary perspective to the current trends and approaches in quantum machine learning on leveraging prior knowledge about the data and quantum hardware to develop neural networks that exploit the inherent complexity of noisy intermediate-scale quantum devices.

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