IJCNN Special Session on
Quantum Machine Learning Algorithms
and Applications
on NISQ Devices
2024
Quantum Machine Learning Algorithms
and Applications
on NISQ Devices
2024
co-located with IEEE WCCI-IJCNN-2024
| Program | CFP | Dates | Organization | Venue |
The landscape of quantum computing has seen remarkable strides, progressing from its inception in the 1980s to tangible hardware prototypes in the 2020s, capable of managing hundreds of qubits. Amidst these advancements, the potential ascendancy of Noisy-Intermediate Scale Quantum (NISQ) devices over classical computers has sparked considerable interest, notably through algorithms like the Variational Quantum Eigensolver (VQE). These algorithms, operating within limited qubits while resilient to noise, blend classical machine learning with Variational Quantum Circuits (VQC), birthing Quantum Neural Networks (QNNs) deployed in diverse models from image classification to natural language processing and reinforcement learning. This Special Session seeks to delve deeply into VQC-based and other potential Quantum Machine Learning (QML) algorithms, aiming to redefine the frontiers of this field and explore their myriad applications across machine learning and artificial intelligence. We invite submissions encompassing fundamental training algorithms, privacy-preserving or trustworthy QML, distributed QML and a spectrum of applications spanning scientific discovery to commercial and industrial domains. Join us in shaping the future of quantum-driven machine learning and its vast applications.
In this workshop, we invite the research community in quantum information science and machine learning/artificial intelligence to submit works related to the proposed integration of quantum computing and machine learning/ artificial intelligence, revolving around the following topic areas:
The list above is by no means exhaustive, as the aim is to foster the debate around all aspects of the suggested integration.
Papers should be formatted according to the IEEE-WCCI-IJCNN-2024 formatting guidelines and submitted as a single PDF file. We welcome submissions across the full spectrum of theoretical and practical work including research ideas, methods, tools, simulations, applications or demos, practical evaluations, and surveys. All papers will be peer-reviewed in a double-blind process and assessed based on their novelty, technical quality, potential impact, clarity, and reproducibility (when applicable). Special Session submissions will be handled by EDAS; the submission link is as follows: https://edas.info/N31614 Please select the respective special session title ( Quantum Machine Learning Algorithms and Applications on NISQ Devices) under the list of research topics in the submission system.
Be mindful of the following dates:
Note: all deadlines are AoE (Anywhere on Earth).
The accepted papers will appear on the Special Session website and are included in the IJCNN conference proceedings.
TBA