Quantum Machine Learning

by Prof. Hsi-Sheng Goan (Center for Quantum Science and Engineering National Taiwan University)

Tuesday 9 Jun 2026, 12:00 → 13:30 Europe/London

OLL/3-337 (Liverpool Physics)

Quantum computing and machine learning (the core of contemporary artificial intelligence) are emerging, promising technologies that will significantly impact human life and society. It is interesting to explore the interaction between quantum computing and machine learning and to study how to apply the results and technologies of one field to solve problems in the other. We are currently in the so-called noisy intermediate scale quantum (NISQ) era. Due to the lack of quantum error correction, NISQ machines suffer from errors in state preparation, measurements, and gate operations, making them unsuitable for deep quantum circuit architectures. A hybrid quantum-classical variational approach that leverages the strengths of both quantum and classical computation is suited to NISQ machines. In this talk, I will present results for some machine learning tasks using hybrid variational quantum algorithms. After that, I will introduce the quantum-train (QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to tackle significant challenges in data encoding, model compression, and inference hardware requirements. If time allows, I will also discuss our recent work on quantum variational activation functions and efficient hybrid quantum-classical models for time-series forecasting.