Iordanis Kerenidis
Quantum Signals and CNRS, France
Iordanis Kerenidis
Quantum Signals and CNRS, France
Quantum Machine Learning and applications
Iordanis Kerenidis received his Ph.D. from the Computer Science Department at the University of California, Berkeley, in 2004, under Umesh Vazirani. After a two-year postdoctoral position at the Massachusetts Institute of Technology with Peter Shor, he joined the Centre National de Recherche Scientifique CNRS in Paris as a research director. He has been the coordinator of a number of EU-funded projects including an ERC Grant, and he is the founder and director of the Paris Centre for Quantum Computing. His research is focused on quantum algorithms for machine learning and optimization, including one of the first end-to-end quantum machine learning applications for recommendation systems, demonstration of quantum classification on trapped ion hardware and NISQ algorithms for Monte Carlo methods in Finance. Between 2019-2024 he was the Senior VP of Quantum Algorithms at QC Ware Corp. Currently he is the co-founder and CTO of Quantum Signals working on quantum and AI methods for Finance.This talk describes some recent work in quantum machine learning: (i) hardware-aware intelligent agents that can discover quantum algorithms (e.g., QFT, Grover; nonlocal-game strategies) via reinforcement learning, and (ii) quantum/quantum-inspired neural networks for applications in finance.
Jeanette Miriam Lorenz
Head of Department @ Fraunhofer IKS
Jeanette Miriam Lorenz
Head of Department @ Fraunhofer IKS
How applications can guide developments in quantum computing
The recent years have seen significant process towards error-corrected qubits, but presently available quantum computers remain limited in their number of qubits and quality. In parallel, significant efforts were put into exploring applications of quantum computing, but without resulting in any practical, industrially relevant quantum advantage yet. In principle, quantum computing is expected to lead to disruptive changes in a variety of different industries – e.g., quantum computing could speed up drug development.
Still, investigating applications of quantum computing already provided us with very important, although mostly theoretical insights. For example, investigating variational quantum algorithms pointed us to the difficulties of controlling their sampling overhead and their stability in general. We also realized that quantum computers are likely to work synergistically as quantum accelerators alongside classical computers, putting us into the situation that we need to control, optimize and improve their interplay. We are also required to understand when and how a prospective quantum advantage will result in an overall benefit for quantum-classical algorithms. This includes topics such as generalization properties of quantum-classical algorithms and how properties of the data will influence the overall performance of a quantum algorithm. Eventually, these findings guide us to both the necessity to advance quantum algorithms, as well as to advance quantum hardware beyond just realizing logical qubits.
This talk will expand on these aspects and will additionally bring forward the idea of systematic benchmarks at all layers of the quantum software stack to be able to also quantify prospective benefits of quantum computing practically.
Amir Pourabdollah
Nottingham Trent University, UK
Amir Pourabdollah
Nottingham Trent University, UK
Ethics in the Post-Quantum AI
This talk looks at how the emerging quantum-AI (QAI) could change the way we think about the AI ethics and our responsibilities. Today’s AI systems are powerful but becoming opaque and uncontrollable. Adding quantum technology might make them more powerful, yet also harder to understand or control. I will explore some very QAI-specific ethical and explainability challenges and ask open questions. Particularly, what might happen if we supercharge AI when it already faces serious ethical challenges? If quantum systems are uncertain by nature and one can’t even see what happens between states, can we really expect more fairness, trust, or transparency? Or alternatively, in a future when the AI boom settles, can QAI helps rebuilding an ethical framework like never before?!