Schedule

Program 

9:00 am - 10:30 am: Welcome, Keynote, and Paper Presentations  (Session Chair: Francisco Chicano) 

9:00 am - 9:10 am: Welcome (slides)

9:10 am - 10:00 am: Keynote by Samuel Yen-Chi Chen (Senior Research Scientist at Wells Fargo), Quantum Machine Learning: Bridging Quantum Computing and Artificial Intelligence (slides)

Abstract: Quantum Machine Learning (QML) represents a transformative frontier at the intersection of quantum computing and classical machine learning. This talk will explore how QML leverages the unique principles of quantum mechanics to address complex problems and unlock new capabilities in computational intelligence. A particular focus will be on the role of variational quantum circuits (VQCs) in designing QML architectures for noisy intermediate-scale quantum (NISQ) devices. Key findings from our recent research will be presented, highlighting the innovative applications of QML in advancing both fields. Additionally, we will examine the symbiotic relationship between artificial intelligence and quantum computing, showcasing how their convergence drives progress across disciplines. Finally, the talk will consider the future of QML, discussing both its transformative potential and the challenges that lie ahead.

Bio: Dr. Samuel Yen-Chi Chen received the Ph.D. and B.S. degree in physics and the M.D. degree in medicine from National Taiwan University, Taipei City, Taiwan. He is now a senior research scientist at Wells Fargo Bank. Prior to that, he was an assistant computational scientist in the Computational Science Initiative, Brookhaven National Laboratory. He is the first one to use variational quantum circuits to perform deep reinforcement learning and the inventor of quantum LSTM. His research interests include building quantum machine learning algorithms as well as applying classical machine learning techniques to solve quantum computing challenges such as quantum error correction and quantum architecture search. He is involved in multiple advanced privacy-preserving quantum AI research project and is an experienced distributed computing researcher and developer. He won the First Prize In the Software Competition (Research Category) from Xanadu Quantum Technologies, in 2019.

10:00 am - 10:30 am: Paper Presentations

10:00 am - 10:07 am: [Short] Rey Guadarrama, Sergei Gleyzer, Kyoungchul Kong, Konstantin T. Matchev, Katia Matcheva, Gopal Ramesh Dahale, Isabel Pedraza and Haydee Hernández-Arellano. Quantum generative adversarial networks for gluon-initiated jets generation (slides)

10:08 am - 10:15 am: [Lighting Talk] Shunya Minami, Kouhei Nakaji, Yohichi Suzuki and Tadashi Kadowaki. Quantum circuit generation for combinatorial optimization problems (slides)

10:30 am - 11:00 am: Coffee Break

11:00 am - 12:30 pm: Quantum Machine Learning (Session Chair: Francisco Chicano)

11:00 am - 11:13 am: [Full] Ruhan Wang, Ye Wang, Jing Liu and Toshiaki Koike-Akino. Quantum Diffusion Models for Few-Shot Learning (slides)

11:15 am - 11:28 am: [Full] Joan Lo, José A. Lázaro, Samael Sarmiento, Adolfo Lerin, Jaume Alexandre Solé, Javier Ruiz-Hidalgo, Josep R. Casas, Ricardo Martinez and Josep M. Fàbrega. Quantum Machine Learning Techniques for Network Intrusion Detection in Software-Defined Networks (slides)

11:30 am - 11:43 am: [Full] Bikram Khanal and Pablo Rivas. The Theory of Learning from Data as a Function of Noise (slides)

11:45 am - 11:58 am: [Full] Flavjo Xhelollari and Juntao Chen. Ensembling Personalized Quantum Models with Local Differential Privacy via Meta-Learning (slides)

12:00 pm - 12:13 pm: [Full] Jeihee Cho, Junyong Lee, Daniel Justice and Shiho Kim. Enhancing Circuit Trainability with Selective Gate Activation Strategy (slides) 

12:30 pm - 2:00 pm Lunch (on your own; no sponsored lunch provided)

2:00 pm - 3:30 pm: Quantum Neural Networks (Session Chair: Toshiaki Koike-Akino)

2:00 pm - 2:13 pm: [Full] James Steck and Elizabeth Behrman. Quantum Generative Adversarial Networks: Generating and Detecting Quantum Product States (slides)

2:15 pm - 2:28 pm: [Full] Sparsh Gupta, Debanjan Konar and Vaneet Aggarwal. A Scalable Quantum-enhanced Neural Network with Non-local Connections (slides)

2:30 pm - 2:43 pm: [Full ] Alessandro Tesi, Sergei Gleyzer, Konstantin T. Matchev, Katia Matcheva, Kyoungchul Kong, Tom Magorsch and Gopal Ramesh Dahale. Quantum Attention for Vision Transformers in High Energy Physics (slides)

2:45 pm - 2:58 pm: [Full] Erik Connerty, Ethan Evans, Gerasimos Angelatos and Vignesh Narayanan. Predicting Chaotic Systems with Quantum Echo-state Networks (slides)

3:00 pm - 3:13 pm: [Full ]Takuya Fujihashi and Toshiaki Koike-Akino. Quantum Implicit Neural Compression (slides)

3:15 pm - 3:28 pm: [Full] Haemanth Velmurugan, Roy T. Forestano, Sergei Gleyzer, Kyoungchul Kong, Konstantin T. Matchev and Katia Matcheva. Quantum-Classical Graph Neural Networks and Jet Tagging (slides)

3:30 pm - 4:00 pm: Coffee Break 

4:00 pm - 5:00 pm: Optimization and Search (Session Chair: José A. Lázaro)

4:00 pm - 4:13 pm:  [Full] Dheeraj Peddireddy, Gurcan Comert, Mashrur Chowdhury and Vaneet Aggarwal. Arc Interdiction Vehicle Routing Problem using Quantum Annealing (slides)

4:15 pm - 4:28 pm: [Full ] Harshit Dhankhar and Tristan Cazenave. Nested Qubit Routing

4:30 pm - 4:43 pm:  [Full ] Zain Hafeez, Debanjan Konar and Vaneet Aggarwal. A Bi-directional Quantum Search Algorithm (slides)

4:45 pm - 5:00 pm: Closing