March 3, 2025
Philadelphia, Pennsylvania
Description of the Workshop
Quantum computers, albeit on a small scale, are becoming more accessible to the public, e.g., through IBM, Google, and D-Wave. Naturally, this calls for exploiting quantum computers to enhance classical Artificial Intelligence (AI), e.g., to improve their prediction performance or enable faster training by exploiting quantum mechanical principles such as superposition and entanglement. To this end, there is a growing interest in quantum artificial intelligence (QAI) to exploit quantum computing (QC) to enhance classical AI techniques. This workshop focuses on seeking contributions encompassing theoretical and applied advances in QAI. On the other hand, there is also an increasing interest in the application of classical AI techniques for solving problems within QC (AI4QC), such as in quantum software engineering, quantum circuit design, and optimizing quantum optimization approaches (e.g., minor embedding in quantum annealing). Consequently, we also seek contributions that apply classical AI techniques in various aspects of QC. Many AI problems can be cast as optimization problems, and we also welcome contributions formulating AI problems as optimization tasks, e.g., Quadratic Unconstrained Binary Optimization (QUBO) to be solved by quantum annealers.
Topics
Theoretical foundations of quantum AI algorithms
Quantum AI applications in any domain, e.g., transportation, chemistry, simulations, physics, etc
Classical AI techniques in the area of quantum circuit design, such as optimizing quantum circuit compilation and transpilation
Quantum noise reduction with classical AI techniques
Classical AI techniques for quantum software engineering, including quantum software testing, debugging, and repair
Applications of large-language models for quantum circuit design and quantum software engineering
Quantum AI techniques for quantum software engineering
Classical AI techniques for optimizing quantum search and optimization algorithms such as QAOA
Quantum annealing and its applications
QUBO models of AI problems
Novel quantum machine learning algorithms such as theory and applications of quantum reservoir computing and quantum extreme learning machines
Format of Workshop
It will be a one-day workshop. We plan to have an invited keynote speaker, paper presentations, and a panel discussion. We may invite additional speakers if the number of paper submissions is low.
Attendance
Everyone interested is welcome!
Submission requirements
Please follow AAAI formatting instructions.
https://aaai.org/conference/aaai/aaai-25/submission-instructions/
Full Paper: 8 Pages
Work in Progress: 4 pages maximum
Lightning Talks: Only abstract
Submission Site Information: https://easychair.org/my/conference?conf=qcai2025
Publication: The publication plan will be discussed with the authors of the accepted papers for post-proceedings.
Important Deadlines
Submission: November 24, 2024 (AoE)
Notification: December 9, 2024 (AoE)
Workshop Chairs
Shaukat Ali, Simula Research Laboratory Norway, shaukat@simula.no,
Francisco Chicano, University of Malaga Spain, chicano@uma.es
Alberto Moraglio, University of Exeter UK, A.Moraglio@exeter.ac.uk
PC Members
Paolo Arcaini, National Institute of Informatics
Samuel Yen-Chi Chen, Wells Fargo
Eneko Osaba, TECNALIA Research & Innovation
Joongheon, Kim Korea University
Gabriel Luque, University of Málaga
Aritra Sarkar, Fujitsu
Philippe Codognet, JFLI - CNRS / Sorbonne University / University of Tokyo
Ofer Shir, Tel-Hai College
Sebastian Feld, Delft University of Technology
Marco Baioletti, Universita` degli Studi di Perugia
Muhammad Usman, CSIRO
Bilel Derbel, CRIStAL (Univ. Lille)
Shinobu Saito, NTT Corporation