Workshop II: Mathematical Aspects of Quantum Learning - IPAM

Workshop II: Mathematical Aspects of Quantum Learning

Part of the Long Program Mathematical and Computational Challenges in Quantum Computing
October 16 - 20, 2023

Schedule

All times in this Schedule are Pacific Time (PT)

Monday, October 16, 2023

Morning Session

08:00-08:50
Check-In/Breakfast (Hosted by IPAM)
08:55-09:00
Welcome & Opening Remarks
SESSSION CHAIR: Jens Eisert
09:00-09:50
Nathan Wiebe (University of Toronto)
Title not available
10:00-10:15
Break
10:15-11:05
Maria Schuld (Xanadu Quantum Technologies)
How to rethink quantum machine learning
11:15-11:30
Break
11:30-12:20
Juan Carrasquilla (Vector Institute)
Training Binary Neural Networks in Quantum Superposition

Afternoon Session

12:30-14:30
Lunch (on your own)
SESSSION CHAIR: Nathan Wiebe
14:30-15:20
Yihui Quek (Harvard University)
The signal and the noise: learning with random quantum circuits and other agents of chaos
15:30-16:00
Break
16:00-16:50
Patrick Rebentrost (National University of Singapore)
Learning of neural networks with quantum computers and learning of quantum states with graphical models.
17:00-17:15
Lightning Poster Session
17:15-18:30
Poster Session & Reception (Hosted by IPAM)

Tuesday, October 17, 2023

Morning Session

08:00-09:00
Check-In/Breakfast (Hosted by IPAM)
SESSSION CHAIR: Maria Kieferova
09:00-09:50
Jens Eisert (Freie Universität Berlin)
Do quantum computers have applications in machine learning and combinatorial optimization?
10:00-10:15
Break
10:15-11:05
Matthias Caro (Freie Universität Berlin)
Classical Verification of Quantum Learning
11:15-11:30
Break
11:30-12:20
Ryan Sweke (IBM Research - Almaden )
Should we use parameterized quantum circuits for machine learning?

Afternoon Session

12:30-14:30
Lunch (on your own)
SESSSION CHAIR: Maria Schuld
14:30-15:20
Marco Cerezo (Los Alamos National Laboratory)
A Unified Theory of Barren Plateaus for Deep Parametrized Quantum Circuits
15:30-16:00
Break
16:00-16:50
Zoe Holmes (EPFL (Ecole Polytechnique Fédérale de Lausanne))
Exponential Concentration in Quantum Generative Modeling and Quantum Kernel Methods

Wednesday, October 18, 2023

Morning Session

08:00-09:00
Check-In/Breakfast (Hosted by IPAM)
SESSION CHAIR: Matthias Caro
09:00-09:50
Srinivasan Arunachalam (IBM Research - Almaden )
Overview of learning structured quantum states
10:00-10:15
Break
10:15-11:05
Daniel Liang (Rice University)
Learning Beyond Stabilizer States
11:15-11:30
Break
11:30-12:20
Hsin-Yuan (Robert) Huang (California Institute of Technology)
(Remote Speaker) Learning to predict arbitrary quantum processes

Afternoon Session

12:30-12:30
Group Photo
12:30-14:30
Lunch (on your own)
SESSION CHAIR: Ryan Sweke
14:30-15:20
Marika (Maria) Kieferova (University of Technology, Sydney)
Generating Approximate Ground States of Molecules Using Quantum Machine Learning
15:30-16:00
Break
16:00-16:50
Tongyang Li (Peking University)
(Remote Speaker) On Quantum Speedups for Nonconvex Optimization via Quantum Tunneling Walks

Thursday, October 19, 2023

Morning Session

08:00-09:00
Check-In/Breakfast (Hosted by IPAM)
SESSSION CHAIR: Srinivasan Anurachalam
09:00-09:50
Vojtěch Havlíček (IBM Thomas J. Watson Research Center)
Quantum Statistical Query Learning I.
10:00-10:15
Break
10:15-11:05
Louis Schatzki (University of Illinois at Urbana-Champaign)
Quantum Statistical Query Learning II.
11:15-11:30
Break
11:30-12:20
Vedran Dunjko (Leiden University)
On provable separations between classical and quantum learners

Afternoon Session

12:30-14:30
Lunch (on your own)
14:30-14:30
SESSSION CHAIR: Srinivasan Anurachalam
14:30-15:20
Carlos Bravo Prieto (Freie Universität Berlin)
Understanding quantum machine learning also requires rethinking generalization
15:30-16:00
Break
16:00-16:50
Panel Discussion (Moderator: Jens Eisert)
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