2026/01/08 Joint CQSE-NCTS-CACIB Seminars
2026/01/08 Joint CQSE-NCTS-CACIB Seminars
Seminar 1
Time: Jan. 08, 11:00 ~ 12:00Title: An area law for metastable states
Speaker: Dr. Chi-Fang Chen, Postdoc at Simons Institute for the Theory of Computing
Place: NCTS Physics Lecture Hall, 4F, Chee-Chun Leung Cosmology Hall, NTU
Online Link: 1/8 (11:00) Joint CQSE-NCTS-CACIB Seminars Hosted by TG1
Abstract
Statistical mechanics assumes that a quantum many-body system at low temperature can be effectively described by its Gibbs state. However, many complex quantum systems exist only as metastable states of dissipative open system dynamics, which appear stable and robust yet deviate substantially from true thermal equilibrium. In this work, we model metastable states as approximate stationary states of a quasi-local, (KMS)-detailed-balanced master equation representing Markovian system-bath interaction, and unveil a universal structural theory: all metastable states satisfy an area law of mutual information and a Markov property. The more metastable the states are, the larger the regions to which these structural results apply. Therefore, the hallmark correlation structure and noise resilience of Gibbs states are not exclusive to true equilibrium but emerge dynamically. Behind our structural results lies a systematic framework encompassing sharp equivalences between local minima of free energy, a non-commutative Fisher information, and approximate detailed balance conditions. Our results build towards a comprehensive theory of thermal metastability and, in turn, formulate a well-defined, feasible, and repeatable target for quantum thermal simulation. (with Thiago Bergamaschi and Umesh Vazirani, https://arxiv.org/pdf/2510.08538)
Biography
I am Chi-Fang Chen (陳麒⽅), a postdoc at the Simons Institute for the Theory of Computing. I received a PhD in physics from Caltech, working on theoretical problems with broad practical impact, such as finding quantum computational advantage. Topics include quantum dynamic bounds, random matrix theory, thermodynamics, and quantum simulation.
Seminar 2
Time: Jan. 08, 14:30 ~ 15:30Title: Generative Quantum Advantage for Classical and Quantum Problems
Speaker: Prof. Hsin-Yuan Huang (Robert), Assistant Professor Theoretical Physics at Caltech and a Senior Research Scientist at Google Quantum AI
Place: NCTS Physics Lecture Hall, 4F, Chee-Chun Leung Cosmology Hall, NTU
Online Link: 1/8 (14:30) Joint CQSE-NCTS-CACIB Seminars Hosted by TG1
Abstract
Recent breakthroughs in generative machine learning, powered by massive computational resources, have demonstrated unprecedented human-like capabilities. While beyond-classical quantum experiments can generate samples from classically intractable distributions, their complexity has thwarted all efforts toward efficient learning. This challenge has hindered demonstrations of generative quantum advantage: the ability of quantum computers to learn and generate desired outputs substantially better than classical computers. We resolve this challenge by introducing families of generative quantum models that are hard to simulate classically, are efficiently trainable, exhibit no barren plateaus or proliferating local minima, and can learn to generate distributions beyond the reach of classical computers. Using a 68-qubit superconducting quantum processor, we demonstrate these capabilities in two scenarios: learning classically intractable probability distributions and learning quantum circuits for accelerated physical simulation. Our results establish that both learning and sampling can be performed efficiently in the beyond-classical regime, opening new possibilities for quantum-enhanced generative models with provable advantage.
Biography
Hsin-Yuan Huang (Robert) is an Assistant Professor of Theoretical Physics at Caltech and a Senior Research Scientist at Google Quantum AI. He completed his Ph.D. at Caltech under John Preskill and Thomas Vidick. His research leverages learning theory to advance quantum computation, physics, and information science, with contributions including classical shadow tomography, machine learning algorithms for quantum many-body problems, and quantum advantages in learning from experiments. His work has appeared in premier venues including Nature, Science, FOCS, and STOC, and has delivered over 160 invited talks. His doctoral thesis "Learning in the Quantum Universe" earned the Milton and Francis Clauser Doctoral Prize for the most original research among all 2024 Caltech graduates, and he holds additional honors including the Google Ph.D. Fellowship, Boeing Quantum Creator Prize, and the William H. Hurt Scholar endowed professorship.
