Description of the workshop
Quantum computers, though still small-scale, are becoming increasingly accessible to the public through companies like IBM, Google, and D-Wave. This calls for leveraging quantum computers to enhance classical Artificial Intelligence (AI), for example, to improve its prediction performance or enable faster training by exploiting quantum mechanical principles such as superposition and entanglement. Interest in quantum artificial intelligence (QAI) is growing as researchers explore how quantum computing (QC) can enhance classical AI techniques. This workshop seeks contributions spanning both theoretical and applied advances in QAI.
Meanwhile, classical AI techniques are increasingly being applied to solve problems in QC (AI4QC), such as quantum software engineering, quantum circuit design, and improving quantum optimization approaches (e.g., minor embedding in quantum annealing). Contributions that apply classical AI techniques to various aspects of QC are also welcome.
Finally, many AI problems can be cast as optimization problems, and we also invite 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 half-day workshop. We plan to have paper presentations. We may invite additional speakers if the number of paper submissions is low.
Attendance
The workshop is open to all conference attendees.
Submission requirements
We plan to publish the proceedings in the Communications in Computer and Information Science (CCIS) Springer series (to be confirmed). Please follow the CCIS formatting instructions. We allow three kinds of submissions: full papers, short papers (work in progress) and lightning talks. Papers do not need to be anonymized.
Full Paper: 9-16 Pages
Work in Progress: 8 pages maximum
Lightning Talks: Only abstract
Submission Site Information: https://easychair.org/conferences/?conf=qcai2027
Publication: (proceedings to be confirmed)
Important Deadlines
Submission: TBD (AoE)
Notification: TBD (AoE)
Camera Ready: TBD (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
TBD