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About
Artificial Intelligence (AI) and Quantum Science are among the most active areas in cutting-edge science and technology, with both addressing the computational complexity frontier. Although these two domains have evolved separately in the past, there are growing efforts to leverage recent breakthroughs in each field and tackle outstanding challenges through AI-Quantum synergy. The breakthroughs in language model (LLM) are enroute to establishing LLMs as new computational languages and breaking down barriers between domains. QI science is entering a new era, approaching error-corrected logical qubits and logical quantum processors, enabling quantum algorithms of unprecedented complexity. This conference aims to bring together researchers from academia and industry, leading the movement of AI-Quantum interdisciplinary research to address bottleneck issues and propel progress.
Topics
Topics that will be covered at this conference include:
Application of LLM for state characterization and error correction on quantum hardware.
Bootstrapping classical computing for quantum simulation.
Using quantum hardware to explore improvements in AI’s learning dynamics.
Using Quantum many-body physics research tasks as a testing ground for LLM’s.
Confirmed Speakers
Adam Brown
Andrew Senior
Google Deep Mind
Benjamin Lev
Stanford University
David Pfau
Google Deep Mind
Di Luo
UCLA
Eun-Ah Kim
Cornell University
Gabriel Kotliar
Rutgers University
Hsin-Yuan (Robert) Huang
Caltech
Ichiro Takeuchi
University of Maryland
Johannes Bausch
Google Deep Mind
Jungsang Kim
Duke University
Junyu Liu
University of Pittsburgh
Masako Yamada
IonQ
Matthias Troyer
ETH Zurich and Microsoft Quantum
Misha Lukin
Harvard University
Paul Ginsparg
Cornell University
Roger Melko
University of Waterloo
Sona Najafi
IBM Quantum
Soonwon Choi
MIT
Stephen Wolfram
Wolfram Research
Victor Albert
University of Maryland & NIST
Yi-Zhuang You
UC San Diego
Yiqing Zhou
Cornell University
Yuri Lensky
Google Quantum AI
Zlatko Minev
IBM Quantum
Schedule
Conference Organizers
Eun-Ah Kim
Cornell University
Xiaoliang Qi
Stanford University
Victor Galitsky
University of Maryland
Michael Brenner
Harvard University and Google ResearchTalks
Adam Brown (Google) Towards AI quantum physicists [slides]
Andrea Gentile (PASCAL) Hardware-informed applications, application-informed hardware: neutral atoms for Quantum Machine Learning [slides]
Andrew Senior (Google Deep Mind) Learning high-accuracy error decoding for quantum processors [slides]
Benjamin Lev (Stanford University) An Experimental Quantum-Optical Spin Glass: From Ultrametricity to Associative Memory [slides]
David Pfau (Google Deep Mind) Pushing the Boundaries of Classical Computation of Quantum Systems with Deep Learning [slides]
Di Luo (UCLA) Quantum Simulation Meets Machine Learning [slides]
Eun-Ah Kim (Cornell University) Data-centric learning from Quantum Simulators [slides]
Gabriel Kotliar (Rutgers University) Deep Learning Based Superconductivity: Prediction and Experimental Tests [slides]
Hsin-Yuan Huang (Caltech) What Quantum AI Can't Learn [slides]
Ichiro Takeuchi (University of Maryland) AI for Quantum Materials Exploration [slides]
Johannes Bausch (Google Deep Mind) Quantum Circuit Optimization with AlphaTensor [slides]
Jungsang Kim (Duke University) Opportunities for Quantum Machine Learning in Near-Term Quantum Devices [slides]
Junyu Liu (University of Pittsburgh) On the boundary of quantum computing advantage [slides]
Masako Yamada (IonQ) IonQ Customer Use-Cases in AIML [slides]
Matthias Troyer (ETH Zurich and Microsoft Quantum) Quantum, AI, and the Path to Commercial Quantum Advantage [slides]
Marcin Kalinowski (Harvard University) Probing topological matter and fermion dynamics on a neutral-atom quantum computer [slides]
Paul Ginsparg (Cornell University) Title [slides]
Roger Melko (University of Waterloo) Artificial Intelligence and the Age of Emergence [video]
Sona Najafi (IBM Quantum) Toward quantum supercomputing, opportunities and challenges [slides]
Soonwon Choi (MIT) Quantum Convolutional Neural Network [slides]
Steven Wolfram (Wolfram Research) Title
Victor Albert (University of Maryland & NIST) AI + Quantum Coding [slides]
Yi-Zhuang You (UC San Diego) Can GPT Learn to Speak the Quantum Language? [slides]
Yiqing Zhou (Cornell University) Learning from Dynamical Quantum Measurement Data [slides]
Yuri Lensky (Google Quantum AI) Anyons for engineers: gates and decoding [slides]
Zlatko Minev (IBM Quantum) Machine learning for practical quantum error mitigation [slides]
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