Quantum mechanics has advanced analytical studies beyond the constraints of classical mechanics in recent years. Quantum Machine Learning (QML) is one of these fields of study. QML currently offers working solutions comparable to (traditional) machine learning, such as classification and prediction tasks utilizing quantum classifiers (q-classifiers).
We investigated a variety of these q-classifiers and reported the outcomes of our research in comparison to their classical counterparts. Paper: [ bib | .pdf ]
Quantum computing is an emerging interdisciplinary research area at the intersection of mathematics, physics, and engineering. Quantum computing requires experts, and specialists from STEM areas to assure scientific rigor and to keep up with technological advances.
The main goal of organizing ICEQT’22 is to share knowledge about the recent advancements in the field of QML and build a forum for discussions on this topic for researchers working in this field as well as machine learning researchers, attending The 2022 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE’22), who are interested in applying AI to enhance quantum computing algorithms.
In recent years, we have observed a significant amount of published research papers in the quantum machine learning domain. There is an increasing interest from machine learning researchers to apply AI to the quantum computing domain (and vice versa). Therefore, we invite all contributions in the following areas:
AI for Quantum * Machine learning for improved quantum algorithm performance * Machine learning for quantum control * Machine learning for building better quantum hardware Quantum technologies and applications * Quantum computing: models and paradigms * Fairness/ethics with quantum machine learning * Quantum algorithms for hyperparameter tuning (Quantum computing for AutoML) * Theory of Quantum-enhanced Machine Learning * Quantum Machine Learning Algorithms based on Grover search * Quantum-enhanced Reinforcement Learning * Quantum computing, models and paradigms such as Quantum Annealing, * Quantum Sampling Quantum computing foundations * Quantum computing: models and paradigms * Applications of Quantum Machine Learning * Quantum Tensor Networks and their Applications in QML * Quantum algorithms for Linear Systems of Equations, and other algorithms such as Quantum Neural Networks, Quantum Hidden Markov Models, Quantum PCA, Quantum SVM, Quantum Autoencoders, Quantum Transfer Learning, Quantum Boltzmann machines, Grover, Shor, and others.
You are invited to submit a paper for consideration. ALL ACCEPTED PAPERS will be published in the corresponding proceedings by Publisher: Springer Nature – Book Series: Transactions on Computational Science & Computational Intelligence https://www.springer.com/series/11769
Prospective authors are invited to submit their papers by uploading them to the evaluation website at: https://american-cse.org/drafts/
March 31, 2022: Submission of papers: https://american-cse.org/drafts/ – Full/Regular Research Papers (maximum of 10 pages) – Short Research Papers (maximum of 6 pages) – Abstract/Poster Papers (maximum of 3 pages)
April 18, 2022: Notification of acceptance (+/- two days)
May 12, 2022: Final papers + Registration
June 22, 2022: Hotel Room reservation (for those who are physically attending the conference).
July 25-28, 2022: The 2022 World Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE’22: USA) Which includes the International Conference on Emergent and Quantum Technologies (ICEQT’22)
Chairs: Dr. Javier Orduz, Baylor University Dr. Pablo Rivas, Baylor University
Interested in how quantum algorithms work, or what does the quantum circuit looks like? Read our recent paper on Grover’s algorithm covering rudimentary to intermediate quantum computation with machine learning descriptions. [pdf, bib]
Quantum circuit for Grover algorithm with the user-defined clauses for an oracle.
