The IEEE Quantum Week is a leading event for presenting research and innovations in quantum computing and engineering. Key individuals involved include Yuri Alexeev from Argonne National Laboratories and Sarah Sheldon from IBM Quantum. The best papers from the conference will be recommended to journals like IEEE Transactions on Quantum Engineering and ACM Transactions on Quantum Computing. The conference will cover a wide range of topics, including quantum algorithms, quantum applications, quantum photonics, quantum system software, quantum technologies and systems engineering, quantum machine learning, and quantum networking & communications.
IEEE Quantum Week: A Platform for Quantum Computing and Engineering Research
IEEE Quantum Week is a conference that provides a platform for presenting high-quality research, innovative ideas, and insightful discussions in the field of quantum computing and engineering. The conference accepts technical papers on a wide range of topics related to quantum computing and engineering, which are peer-reviewed and considered for various technical paper tracks.
Technical Paper Submission and Guidelines
The conference accepts different types of papers, including full papers, short papers, and artifact papers. Full papers, which are 8-10 pages long, can be either research papers that describe the paper’s contributions and innovations, or survey papers that provide a comprehensive overview of a research topic in quantum computing and engineering. Short papers, which are 4-6 pages long, can be New Ideas and Emergent Results (NIER) papers that describe novel and promising ideas, Experience and Application (EXAP) papers that describe experiences gained from applying quantum computing and engineering research results in practice, or Artifact papers (ARTI) that describe useful resources for the broader quantum computing and engineering community. All papers must be original and not simultaneously submitted to another journal or conference.
Technical Paper Tracks
The conference features several technical paper tracks, each focusing on a specific area of quantum computing and engineering. These include Quantum Algorithms (QALG), Quantum Applications (QAPP), Quantum Photonics (QPHO), Quantum System Software (QSYS), Quantum Technologies and Systems Engineering (QTEM), Quantum Machine Learning (QML), and Quantum Networking & Communications (QNET). Each track is chaired by experts in the field and covers a wide range of topics, from the theory and practice of solving problems with quantum computers to the design and architecture of quantum technologies and systems engineering.
Quantum Algorithms (QALG)
The Quantum Algorithms track focuses on the theory of solving problems with quantum computers. It covers a wide range of topics, including quantum information science, quantum algorithm structures and patterns, new NISQ-friendly algorithms, error correction and mitigation algorithms, advances in hybrid variational algorithms, quantum cryptography, and secure quantum computing.
Quantum Applications (QAPP)
The Quantum Applications track focuses on the practice of solving problems with quantum computers. It covers a wide range of topics, including quantum machine learning applications, quantum simulation of physical systems, applications of quantum annealing, integrated high-performance computing and quantum applications, and quantum medical applications.
Quantum Photonics (QPHO)
The Quantum Photonics track focuses on the design and architecture of quantum photonic technologies and systems engineering. It covers a wide range of topics, including quantum photonic information science and technology, quantum computing with photonic systems, optical quantum computing, integrated quantum photonics, and quantum sensing and metrology.
Quantum System Software (QSYS)
The Quantum System Software track focuses on the design, architecture, and operation of full-stack quantum computing systems. It covers a wide range of topics, including quantum programming, quantum languages and intermediate representations, quantum simulators, quantum software engineering, and testing, validation, and verification of quantum programs and systems.
Quantum Technologies and Systems Engineering (QTEM)
The Quantum Technologies and Systems Engineering track focuses on the design and architecture of quantum technologies and systems engineering for computation and sensing. It covers a wide range of topics, including superconducting quantum technologies, trapped ion quantum technologies, quantum dot technologies, and quantum electronics.
Quantum Machine Learning (QML)
The Quantum Machine Learning track focuses on the practice of combining quantum computing and machine learning for innovative application development. It covers a wide range of topics, including quantum algorithms for machine learning tasks, quantum-enhanced machine learning, quantum neural networks, and quantum machine learning applications.
Quantum Networking & Communications (QNET)
The Quantum Networking & Communications track focuses on quantum techniques and technologies for networking and communications. It covers a wide range of topics, including quantum internet, quantum networking, secure communication in quantum networks, quantum cryptography, and distributed quantum computing.
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