Machine Learning to Advance Quantum Technologies (ML4QT) Schedule

Welcoming remarks 

Invited Speaker

Christine Muschik, University of Waterloo (IQC) 

• Leveraging Machine Learning for Quantum Technologies - Digital Twins and Beyond

Invited Speaker

Pierre-Emmanuel Emeriau, Quandela 

• Machine Learning and Photonic Quantum Computing

Contributed Talk

Christian Tutschku, Fraunhofer IAO 

• Grokking and Epoch-wise Double Descent in Quantum Neural Networks

Break

Invited Speaker

Justyna Zwolak, National Institute of Standards and Technology (NIST)

• Closing the Loop on Quantum Dot Control: Real-Time Feedback for Scalable Spin-Quibit Arrays

Invited Speaker 

Yuval Baum, Q-CTRL

• AI Enhanced Infrastructure Software: Integrating Machine Learning Across the Quantum Computing Stack

Contributed Talk

Zach Merino, National Institute of Standards and Technology (NIST)

• Machine Learning for Autonomous Characterization and Control of Semiconductor Quantum Devices

Lunch 

Invited Speaker

Connor van Rossum, University of Queensland

• Can Noise Help Quantum Algorithms? Insights from Sampling and Variational Methods

Invited Speaker

Tak Hur, Yonsei University 

• Scalable Neural Decoders for Practical Real-Time Quantum Error Correction

Contributed Talk

João F. Bravo, Fraunhofer

• Ravines in Quantum Cost Landscapes: Opportunities for Improved VQA Predictions

Break

Invited Speaker

Rui Jie Tang, University of Toronto

• Encoded Quantum Signal Processing for Heisenberg-Limited Metrology

Contributed Talk

Carlos Benavides-Riveros, IQM Quantum Computers

• Neural Quantum Propagators: Operator Learning for Open Quantum Dynamics

Contributed Talk

Prateek P. Kulkarni, PES University

• How Fine Can You Slice It? Quantum Speedups for Certifying Noisy Linear Classifiers

Invited Speaker

Lirandë Pira, National University of Singapore

• Computational Structure of Learning in Quantum Systems

Invited Speaker

Debankan Sannamoth, University of Waterloo (IQC)

• Data-Driven Belief Propagation Decoding of Quantum LDPC Codes Under Realistic Noise

Contributed Talk

Andrew Zhao, Sandia National Laboratories

• Learning Fermionic Linear Optics with Heisenberg Scaling and Physical Operations

Break

Invited Speaker

Manas Mukherjee, National University of Singapore

• Seeking Advantage in Quantum-Classical Machine Learning with Trapped Ion System

Invited Speaker 

Wolfgang Mauerer, OTH Regensburg

• Machine Learning, Quanta, and the Extraction of Physical Compute Power

Contributed Talk

Einar Gabbassov, University of Waterloo (IQC)

• Exact Stochastic Schrödinger Equations for Quantum Reverse Diffusion

Lunch 

Invited Speaker

Barry Sanders, University of Calgary

• Artificial Intelligence for Representing and Characterizing Quantum Systems

Invited Speaker

Roman Krems, University of British Columbia

• Quantum Advantage of Fidelity Kernels and Molecular Descriptors of Quantum Models

Contributed Talk

Sreeraj Rajindran Nair, University of Technology Sydney

• Local Tensor-Train Surrogates for Quantum Learning Models

Break

Poster Session

15 Poster Displays/Presentations - QNC Atrium

Reception & Conference Dinner

Invited Speaker

Jem Guhit, Quantinuum

• Learning Quantum State Preparation with Generative AI

Invited Speaker

Aida Ahmadzadegan-Shapiro, D-WAVE

• Beyond Optimization: Quantum Annealing for Generative Machine Learning

Contributed Talk

Jun Dai, Mila/University of Montreal

• Generative Learning of Optimal Quantum Measurements

Break

Invited Speaker

Kohei Nakaji, NVIDIA

• Toward ML for Quantum Algorithms at Practical Scale

Invited Speaker 

Estelle Maeva Inack, Perimeter Institute for Theoretical Physics

• Data-Enhanced Self-Learning Projection Quantum Monte Carlo

Contributed Talk

Youngseok Lee, NORMA

• Trainability and Mode Separation of Mixed IQP Circuits

Lunch 

Panel Discussion

Closing Remarks