Events

Abstract: Complex systems are among the most challenging to study due to their intricate interplay of numerous components, encompassing the rich dynamics of many interacting particles, chemical reactions, biological processes, and pharmacological interactions. These systems reflect the complexity of life and the physical world, making their study vital across all branches of science.   Complex systems are challenging to model because they involve a vast number of interacting degrees of freedom. Capturing and predicting these interactions with high accuracy is daunting due to the sheer scale and complexity of behaviors and phases that can emerge. This diversity makes it difficult to create comprehensive models that can reliably predict system behavior across different scenarios.   Physicists often approach the challenge of complex systems through a technique known as coarse-graining. This method involves selectively simplifying a system by focusing on key variables while averaging out less critical internal degrees of freedom. By sacrificing some detail, this approach allows for greater predictability and understanding of the system’s behavior at larger length and timescales, as well as at higher levels of complexity.   Traditionally, coarse-graining has been an art, guided by the intuition and insight of experienced researchers. While this approach can yield powerful results, it often relies on strokes of genius and a fair share of luck. Now, machine learning is democratizing such coarse-graining through a process called tokenization. This tokenization equips researchers with the tools to identify optimal coarse-graining strategies efficiently and effectively. This technological advance empowers even the non-expert to uncover insights that were once accessible only to the most seasoned scientists.   In this talk, I will share the experience of how our students and postdocs, equipped with machine learning tools, have embarked on such journeys of data-driven discovery in complex systems. From uncovering novel spatiotemporal motifs in quantum materials, non-reciprocal interactions between biological cells, deciphering the language of functional disorder in piezoelectric materials, unveiling the structural origins of vibrant colors in butterfly wing scales, to creating novel computational lenses for cryo-electron microscopy. They have collectively pushed the boundaries of how we think about complex systems.   These experiences, I believe, show us how AI has become indispensable for understanding and discovering the secrets in complex systems.

Abstract: This introductory talk visits the ultraviolet catastrophe, atomic bombs, atomic clocks, transistors, superconductivity, quantum entanglement and more. Find out how quantum physics has given us modern technologies and what future applications of quantum technologies could look like. There will be time left at the end of the talk for a Q&A session. Come with your burning questions!https://www.nlb.gov.sg/main/whats-on/event-detail?event-id=174165952596

Abstract: In this talk, I will introduce the multi-armed bandit problem, a fundamental challenge in reinforcement learning and decision-making. It captures the trade-off between exploration—gathering information about an unknown environment—and exploitation—using what has already been learned to maximize rewards. I will then discuss a specific instance: the multi-armed quantum bandit problem, where we have sequential oracle access to an unknown quantum state. In each round, we choose an observable from a given set of actions to maximize its expectation value on the state—our notion of reward. I will outline strategies based on the principle of Optimism in the Face of Uncertainty (OFU) and adaptive confidence regions, which guide decision-making under uncertainty. This framework has practical applications, such as optimizing energy transfer in quantum batteries. Specifically, when we have sequential access to a finite number of copies of an unknown pure qubit state, the goal is to design measurement strategies that both maximize energy extraction and improve our knowledge of the state, enabling more efficient charging in future rounds.

Abstract:QCamp is a one-week summer school for curious and inquisitive minds who wish to explore and understand the physical laws that govern the world's smallest scales: quantum physics. The scope of QCamp is to provide vivid demonstrations of these laws, offer career advice, as well as to help students build a technical foundation for independent exploration of this world. QCamp is organised by the Centre for Quantum Technologies (CQT) at the National University of Singapore.
 

The one-day Flash QCamp for curious and inquisitive minds to get a taster in quantum technologies.