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Algorithms
Special Issues
Quantum and Classical Artificial Intelligence
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Quantum and Classical Artificial Intelligence

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 2236

Special Issue Editors

Dr. Hua-Lei Yin

E-Mail Website
Guest Editor
School of Physics, National Laboratory of Solid State Microstructures, Nanjing University, Nanjing 210093, China
Interests: quantum communication and information security; quantum blockchain and privacy protection; quantum algorithm and artificial intelligence; basic problems of quantum mechanics and quantum gravity
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nan-Run Zhou

E-Mail Website
Guest Editor
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
Interests: quantum machine learning; quantum deep learning; quantum generative adversarial network; federated learning; quatum image encryption; quantum image watermarking

Special Issue Information

Dear Colleagues,

Quantum artificial intelligence (QAI), an interdisciplinary field that combines quantum information technology and artificial intelligence, has emerged as a promising avenue for harnessing the power of quantum computing in the NISQ era. In recent years, QAI has experienced significant growth, demonstrating its prospective advantages over its classical counterpart. Concurrently, classical artificial intelligence (CAI) has achieved remarkable breakthroughs, driving advancements in the field.

This Special Issue seeks to compile cutting-edge research articles that showcase the latest theoretical developments and experimental innovations in both quantum artificial intelligence and classical artificial intelligence. We welcome submissions that address various areas, including quantum machine learning, quantum deep learning, quantum generative adversarial networks, quantum graph machine learning, deep learning, federated learning, computer vision, generative models, natural language processing, and graph neural networks. By bridging the gap between theory and practical applications, this Special Issue aims to emphasize the significant strides made in QAI and CAI, garnering attention from researchers and practitioners alike.

Dr. Hua-Lei Yin
Prof. Dr. Nan-Run Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantum artificial intelligence
  • quantum algorithms for machine learning and optimization
  • quantum machine learning
  • quantum graph machine learning
  • quantum deep learning
  • quantum generative adversarial network
  • computer vision
  • deep learning
  • federated learning
  • generative model
  • natural language processing
  • graph neural networks

Published Papers (3 papers)

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Research

42 pages, 790 KiB  
Article
Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
by Neeraj Anand Sharma, Rishal Ravikesh Chand, Zain Buksh, A. B. M. Shawkat Ali, Ambreen Hanif and Amin Beheshti
Algorithms 2024, 17(6), 227; https://doi.org/10.3390/a17060227 - 24 May 2024
Viewed by 300
Abstract
This study delves into the realm of Explainable Artificial Intelligence (XAI) frameworks, aiming to empower researchers and practitioners with a deeper understanding of these tools. We establish a comprehensive knowledge base by classifying and analyzing prominent XAI solutions based on key attributes like [...] Read more.
This study delves into the realm of Explainable Artificial Intelligence (XAI) frameworks, aiming to empower researchers and practitioners with a deeper understanding of these tools. We establish a comprehensive knowledge base by classifying and analyzing prominent XAI solutions based on key attributes like explanation type, model dependence, and use cases. This resource equips users to navigate the diverse XAI landscape and select the most suitable framework for their specific needs. Furthermore, the study proposes a novel framework called XAIE (eXplainable AI Evaluator) for informed decision-making in XAI adoption. This framework empowers users to assess different XAI options based on their application context objectively. This will lead to more responsible AI development by fostering transparency and trust. Finally, the research identifies the limitations and challenges associated with the existing XAI frameworks, paving the way for future advancements. By highlighting these areas, the study guides researchers and developers in enhancing the capabilities of Explainable AI. Full article
(This article belongs to the Special Issue Quantum and Classical Artificial Intelligence)
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17 pages, 5558 KiB  
Article
Improving 2–5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
by Charles Woodrum, Torrey Wagner and David Weeks
Algorithms 2024, 17(5), 214; https://doi.org/10.3390/a17050214 - 15 May 2024
Viewed by 386
Abstract
Quantum computing has the potential to solve problems that are currently intractable to classical computers with algorithms like Quantum Phase Estimation (QPE); however, noise significantly hinders the performance of today’s quantum computers. Machine learning has the potential to improve the performance of QPE [...] Read more.
Quantum computing has the potential to solve problems that are currently intractable to classical computers with algorithms like Quantum Phase Estimation (QPE); however, noise significantly hinders the performance of today’s quantum computers. Machine learning has the potential to improve the performance of QPE algorithms, especially in the presence of noise. In this work, QPE circuits were simulated with varying levels of depolarizing noise to generate datasets of QPE output. In each case, the phase being estimated was generated with a phase gate, and each circuit modeled was defined by a randomly selected phase. The model accuracy, prediction speed, overfitting level and variation in accuracy with noise level was determined for 5 machine learning algorithms. These attributes were compared to the traditional method of post-processing and a 6x–36 improvement in model performance was noted, depending on the dataset. No algorithm was a clear winner when considering these 4 criteria, as the lowest-error model (neural network) was also the slowest predictor; the algorithm with the lowest overfitting and fastest prediction time (linear regression) had the highest error level and a high degree of variation of error with noise. The XGBoost ensemble algorithm was judged to be the best tradeoff between these criteria due to its error level, prediction time and low variation of error with noise. For the first time, a machine learning model was validated using a 2-qubit datapoint obtained from an IBMQ quantum computer. The best 2-qubit model predicted within 2% of the actual phase, while the traditional method possessed a 25% error. Full article
(This article belongs to the Special Issue Quantum and Classical Artificial Intelligence)
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24 pages, 4920 KiB  
Article
Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems
by Yuan Chen and Abdul Khaliq
Algorithms 2024, 17(4), 163; https://doi.org/10.3390/a17040163 - 19 Apr 2024
Viewed by 814
Abstract
In this study, we investigate Quantum Long Short-Term Memory and Quantum Gated Recurrent Unit integrated with Variational Quantum Circuits in modeling complex dynamical systems, including the Van der Pol oscillator, coupled oscillators, and the Lorenz system. We implement these advanced quantum machine learning [...] Read more.
In this study, we investigate Quantum Long Short-Term Memory and Quantum Gated Recurrent Unit integrated with Variational Quantum Circuits in modeling complex dynamical systems, including the Van der Pol oscillator, coupled oscillators, and the Lorenz system. We implement these advanced quantum machine learning techniques and compare their performance with traditional Long Short-Term Memory and Gated Recurrent Unit models. The results of our study reveal that the quantum-based models deliver superior precision and more stable loss metrics throughout 100 epochs for both the Van der Pol oscillator and coupled harmonic oscillators, and 20 epochs for the Lorenz system. The Quantum Gated Recurrent Unit outperforms competing models, showcasing notable performance metrics. For the Van der Pol oscillator, it reports MAE 0.0902 and RMSE 0.1031 for variable x and MAE 0.1500 and RMSE 0.1943 for y; for coupled oscillators, Oscillator 1 shows MAE 0.2411 and RMSE 0.2701 and Oscillator 2 MAE is 0.0482 and RMSE 0.0602; and for the Lorenz system, the results are MAE 0.4864 and RMSE 0.4971 for x, MAE 0.4723 and RMSE 0.4846 for y, and MAE 0.4555 and RMSE 0.4745 for z. These outcomes mark a significant advancement in the field of quantum machine learning. Full article
(This article belongs to the Special Issue Quantum and Classical Artificial Intelligence)
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