Quantum Algorithms (QA), Quantum Artificial Intelligence (QAI), and Quantum Machine Learning (QML), hold the potential to revolutionize classical artificial intelligence (CAI) and machine learning (CML) and emerging applications. This potential is expected to grow alongside the development of more advanced quantum computing devices. Currently, quantum devices are at the noise intermediate-scale quantum (NISQ) level, supporting up to a few hundred physical qubits. Despite the current limitations, early fault-tolerant quantum hardware in the NISQ phase has already demonstrated the quantum advantages of QA, QAI, and QML. For instance, the quantum approximate optimization algorithm (QAOA) has demonstrated the ability to solve combinatorial optimization problems in polynomial time.
This capability is crucial for coordinating massive systems such as self-driving cars, mega-constellation satellites, and complex infrastructure required by the forthcoming hyper-connected 6G communication networks. Additionally, parameterized quantum circuits (PQCs) used in QML can implement various functionalities of classical neural network (NN) architectures, such as recurrent neural network (RNN) and Transformer, with significantly fewer parameters and physical resources. This can substantially reduce the latency and memory requirements of current large-sized classical NN frameworks, including diffusion and large language models.
According to current roadmaps for quantum computer development, the number of qubits is expected to greatly increase, and the beyond-NISQ era is set to emerge by around 2026. This highlights the need for early contributions to explore the potential impact of QA/QML on ML design and future applications. Towards unleashing the full potential of QAI/QML, this workshop focuses on QAI/QML principles, algorithms, and use cases and seeks original contributions to various aspects of QAI/QML-based system architectures, protocols, resource management, error correction, and other technologies. On the other hand, there is also an increasing interest in applying classical AI techniques for solving problems within QC (AI4QC), such as in quantum software engineering, quantum circuit design, and optimizing quantum optimization algorithms. Consequently, we also seek contributions that apply classical AI techniques in various aspects of QC.
Topics of interest include, but are not limited to:
QAs such as QAOA for optimizing classical/quantum ML and applications
QML architectures such as PQC for generative AI, multimodal data, and language understanding
QA complexity and quantum information/coding-theoretic analyses
Optimization and kernel kernel methods for QML convergence analysis
Quantum differential privacy and quantum security analysis
Distributed QA, such as distributed quantum information processing and entanglement manipulation
Distributed QML such as quantum federated learning and quantum multi-agent reinforcement learning
QA and QML for quantum sensing
Experimental/simulation designs of QA, QAI, and QML for emerging use cases
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
Important Deadlines (AoE)
Submission Deadline: Apr 26, 2024
Notification: June 4, 2024
Submission Link: https://easychair.org/my/conference?conf=qai2024
Workshop Webpage: https://sites.google.com/view/qai2024ijcaiworkshop
Submission Instructions
We allow two types of papers:
Regular papers up to 7 pages with two additional pages for references
Extended abstracts up to 2 pages with one additional page for references
The papers must be formatted based on the IJCAI'24 template and submitted as a single PDF file.
The papers will NOT be published as part of IJCAI proceedings. However, with the authors of the accepted papers, we will discuss the possibility of postproceedings. We allow single anonymous submissions.
At least, one of the authors of the accepted papers must register for the workshop and travel to IJCAI venue in person for presentation.
Main Contact Points
Joongheon Kim (Korea University, Korea)
Shaukat Ali (Simula Research Laboratory and Oslo Metropolitan University, Oslo, Norway)
General Chairs
Shaukat Ali (Simula Research Laboratory and Oslo Metropolitan University, Oslo, Norway)
Joongheon Kim (Korea University, Korea)
Soohyun Park (Sookmyung Women's University, Korea)
Jihong Park (Deakin University, Australia)
Advisory Board
Shaukat Ali (Simula Research Laboratory and Oslo Metropolitan University, Oslo, Norway)
Joongheon Kim (Korea University, Korea)
Soohyun Park (Sookmyung Women's University, Korea)
Jihong Park (Deakin University, Australia)
Francisco Chicano (University of Malaga, Spain)
Lei Ma (The University of Tokyo, Japan / University of Alberta, Canada)