The NEASQC project is organising a series of webinars that are not a simple overview of the NISQ Quantum Computing use cases investigated by our project, but a platform to learn from and exchange with the best experts in the fields covered by NEASQC. Our Work Package 5 prepared a programme of four weekly webinars from 5 to 24 November, dedicated to Machine learning and optimization methods. Each week, a distinguished guest speaker will share their insights on the day’s topic, and NEASQC experts will then explain how NEASQC is addressing the issue through a specific use case.
Dr Vedran Dunjko, Leader of the Work Package 5 (Machine Learning & Optimisation) in the NEASQC project, Assistant Professor at Leiden University
Dr Alejandro Perdomo-Ortiz, Associate Director of Quantum AI at Zapata Computing, Inc. – Toronto
Enhancing Machine Learning and Combinatorial Optimization with Quantum Generative Models
Abstract: Generating high-quality data (e.g. images or video) is one of the most exciting and challenging frontiers in unsupervised machine learning. Utilizing quantum computers in such tasks to potentially enhance conventional machine learning algorithms has emerged as a promising application, but poses big challenges due to the limited number of qubits and the level of gate noise in available devices. In this talk, we provide the first practical and experimental implementation of a quantum-classical generative algorithm capable of generating high-resolution images of handwritten digits with state-of-the-art gate-based quantum computers. In the second part of my talk, we focus on combinatorial optimization; another key candidates in the race for practical quantum advantage. Here we introduce a new family of quantum-enhanced optimizers and demonstrate how quantum generative models can find lower minima than those found by means of stand-alone state-of-the-art classical solvers. We illustrate our findings in the context of the portfolio optimization problem by constructing instances from the S&P 500 stock market index. We show that our quantum-inspired generative models based on tensor networks generalize to unseen candidates with lower cost function values than any of the candidates seen by the classical solvers. This is the first demonstration of the generalization capabilities of quantum generative models that brings real value in the context of an industrial-scale application.
Dr Wim Van Ackooij, Expert Researcher at EDF
Computational hurdle in energy management optimization problems
Abstract: In this talk we will briefly go through key classes of energy management optimization problems. We will state their general purpose as well as their underlying mathematical structures. These structures will allow us to highlight computational difficulties related to both time and memory requirements. We will briefly discuss classic computational approaches that have shown quite effective, yet have also shown limits.