Topic Editors

Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI 49931, USA
Institute of Electronic Structure & Laser, Foundation for Research and Technology-Hellas (FORTH), GR-71110 Heraklion, Greece

Quantum Information and Quantum Computing, 2nd Volume

Abstract submission deadline
30 November 2024
Manuscript submission deadline
31 March 2025
Viewed by
6818

Topic Information

Dear Colleagues,

Although a few decades have passed since the first concept of quantum computing was established by Richard Feynman and Paul Benioff, it has only recently attracted the attention of the wider community, including large technology companies such as Google, IBM, Microsoft, Amazon, and others. This has resulted in an important milestone in the field, leading to the first demonstrations of quantum supremacy. Superconducting, photonic, solid-state, and trapped neutral atom and charged ion platforms are considered among the most promising approaches. However, there has been no clear winner so far, since each approach has come with its own severe deficiency. In both fields of science and engineering, it is a challenge to manufacture a large-scale quantum computer that can reliably process information to solve practical problems relevant to the real needs of society.

Research that reports new advances that meaningfully contribute to the state-of-the-art quantum computing and current body of knowledge on quantum information is encouraged. This may include research on scalable manufacturing and quantum control of qubit systems, hybrid classical and quantum computing approaches, algorithms for noisy intermediate scale quantum computers, demonstrations of quantum advantage, novel materials, technology milestones in quantum networks and infrastructures, quantum fault-tolerance and noise, miniaturization, quantum imaging and sensing, quantum statistical learning, quantum information processes in biological systems, and foundations of quantum mechanics.

An additional topic of interest is the training of the quantum workforce for highly demanding quantum information and quantum computing technologies.

Dr. Durdu Guney
Dr. David Petrosyan
Topic Editors

Keywords

  • quantum supremacy
  • superconducting quantum computing
  • photonic quantum computing
  • solid-state quantum computing
  • trapped-ion quantum computing
  • Rydberg-atom quantum computing and simulations
  • scalable manufacturing of qubit systems
  • scalable quantum control
  • hybrid classical and quantum computing
  • noisy intermediate scale quantum computers
  • quantum communications
  • quantum machine learning
  • quantum imaging and sensing
  • quantum biology
  • fault-tolerant quantum computing
  • quantum education

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400 Submit
Electronics
electronics
2.9 4.7 2012 15.6 Days CHF 2400 Submit
Entropy
entropy
2.7 4.7 1999 20.8 Days CHF 2600 Submit
Mathematics
mathematics
2.4 3.5 2013 16.9 Days CHF 2600 Submit
Symmetry
symmetry
2.7 4.9 2009 16.2 Days CHF 2400 Submit
Technologies
technologies
3.6 5.5 2013 19.7 Days CHF 1600 Submit
Chips
chips
- - 2022 15.0 days * CHF 1000 Submit

* Median value for all MDPI journals in the second half of 2023.


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Published Papers (5 papers)

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14 pages, 701 KiB  
Article
Non-Hermitian Quantum Rényi Entropy Dynamics in Anyonic-PT Symmetric Systems
by Zhihang Liu and Chao Zheng
Symmetry 2024, 16(5), 584; https://doi.org/10.3390/sym16050584 - 9 May 2024
Viewed by 484
Abstract
We reveal the continuous change of information dynamics patterns in anyonic-PT symmetric systems that originates from the continuity of anyonic-PT symmetry. We find there are three information dynamics patterns for anyonic-PT symmetric systems: damped oscillations with an overall decrease (increase) and asymptotically stable [...] Read more.
We reveal the continuous change of information dynamics patterns in anyonic-PT symmetric systems that originates from the continuity of anyonic-PT symmetry. We find there are three information dynamics patterns for anyonic-PT symmetric systems: damped oscillations with an overall decrease (increase) and asymptotically stable damped oscillations, which are three-fold degenerate and are distorted using the Hermitian quantum Rényi entropy or distinguishability. It is the normalization of the non-unitary evolved density matrix that causes the degeneracy and distortion. We give a justification for non-Hermitian quantum Rényi entropy being negative. By exploring the mathematics and physical meaning of the negative entropy in open quantum systems, we connect negative non-Hermitian quantum Rényi entropy and negative quantum conditional entropy, paving the way to rigorously investigate negative entropy in open quantum systems. Full article
(This article belongs to the Topic Quantum Information and Quantum Computing, 2nd Volume)
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16 pages, 5250 KiB  
Article
Modeling Robotic Thinking and Creativity: A Classic–Quantum Dialogue
by Maria Mannone, Antonio Chella, Giovanni Pilato, Valeria Seidita, Filippo Vella and Salvatore Gaglio
Mathematics 2024, 12(5), 642; https://doi.org/10.3390/math12050642 - 22 Feb 2024
Viewed by 1164
Abstract
The human mind can be thought of as a black box, where the external inputs are elaborated in an unknown way and lead to external outputs. D’Ariano and Faggin schematized thinking and consciousness through quantum state dynamics. The complexity of mental states can [...] Read more.
The human mind can be thought of as a black box, where the external inputs are elaborated in an unknown way and lead to external outputs. D’Ariano and Faggin schematized thinking and consciousness through quantum state dynamics. The complexity of mental states can be formalized through the entanglement of the so-called qualia states. Thus, the interaction between the mind and the external world can be formalized as an interplay between classical and quantum-state dynamics. Since quantum computing is more and more often being applied to robots, and robots constitute a benchmark to test schematic models of behavior, we propose a case study with a robotic dance, where the thinking and moving mechanisms are modeled according to quantum–classic decision making. In our research, to model the elaboration of multi-sensory stimuli and the following decision making in terms of movement response, we adopt the D’Ariano–Faggin formalism and propose a case study with improvised dance based on a collection of poses, whose combination is presented in response to external and periodic multi-sensory stimuli. We model the dancer’s inner state and reaction to classic stimuli through a quantum circuit. We present our preliminary results, discussing further lines of development. Full article
(This article belongs to the Topic Quantum Information and Quantum Computing, 2nd Volume)
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16 pages, 1027 KiB  
Article
Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation
by Naihua Ji, Rongyi Bao, Zhao Chen, Yiming Yu and Hongyang Ma
Appl. Sci. 2024, 14(4), 1392; https://doi.org/10.3390/app14041392 - 8 Feb 2024
Viewed by 740
Abstract
In this study, we present an innovative approach to quantum image classification, specifically designed to mitigate the impact of noise interference. Our proposed method integrates key technologies within a hybrid variational quantum neural network architecture, aiming to enhance image classification performance and bolster [...] Read more.
In this study, we present an innovative approach to quantum image classification, specifically designed to mitigate the impact of noise interference. Our proposed method integrates key technologies within a hybrid variational quantum neural network architecture, aiming to enhance image classification performance and bolster robustness in noisy environments. We utilize a convolutional autoencoder (CAE) for feature extraction from classical images, capturing essential characteristics. The image information undergoes transformation into a quantum state through amplitude coding, replacing the coding layer of a traditional quantum neural network (QNN). Within the quantum circuit, a variational quantum neural network optimizes model parameters using parameterized quantum gate operations and classical–quantum hybrid training methods. To enhance the system’s resilience to noise, we introduce a quantum autoencoder for error mitigation. Experiments conducted on FashionMNIST datasets demonstrate the efficacy of our classification model, achieving an accuracy of 92%, and it performs well in noisy environments. Comparative analysis with other quantum algorithms reveals superior performance under noise interference, substantiating the effectiveness of our method in addressing noise challenges in image classification tasks. The results highlight the potential advantages of our proposed quantum image classification model over existing alternatives, particularly in noisy environments. Full article
(This article belongs to the Topic Quantum Information and Quantum Computing, 2nd Volume)
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22 pages, 10974 KiB  
Article
Comparing Classical and Quantum Generative Learning Models for High-Fidelity Image Synthesis
by Siddhant Jain, Joseph Geraci and Harry E. Ruda
Technologies 2023, 11(6), 183; https://doi.org/10.3390/technologies11060183 - 18 Dec 2023
Viewed by 2465
Abstract
The field of computer vision has long grappled with the challenging task of image synthesis, which entails the creation of novel high-fidelity images. This task is underscored by the Generative Learning Trilemma, which posits that it is not possible for any image synthesis [...] Read more.
The field of computer vision has long grappled with the challenging task of image synthesis, which entails the creation of novel high-fidelity images. This task is underscored by the Generative Learning Trilemma, which posits that it is not possible for any image synthesis model to simultaneously excel at high-quality sampling, achieve mode convergence with diverse sample representation, and perform rapid sampling. In this paper, we explore the potential of Quantum Boltzmann Machines (QBMs) for image synthesis, leveraging the D-Wave 2000Q quantum annealer. We undertake a comprehensive performance assessment of QBMs in comparison to established generative models in the field: Restricted Boltzmann Machines (RBMs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DDPMs). Our evaluation is grounded in widely recognized scoring metrics, including the Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Scores. The results of our study indicate that QBMs do not significantly outperform the conventional models in terms of the three evaluative criteria. Moreover, QBMs have not demonstrated the capability to overcome the challenges outlined in the Trilemma of Generative Learning. Through our investigation, we contribute to the understanding of quantum computing’s role in generative learning and identify critical areas for future research to enhance the capabilities of image synthesis models. Full article
(This article belongs to the Topic Quantum Information and Quantum Computing, 2nd Volume)
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13 pages, 1209 KiB  
Article
Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder
by Aoqing Li, Fan Li, Qidi Gan and Hongyang Ma
Appl. Sci. 2023, 13(17), 9689; https://doi.org/10.3390/app13179689 - 27 Aug 2023
Cited by 1 | Viewed by 1315
Abstract
Topological quantum error-correcting codes are an important tool for realizing fault-tolerant quantum computers. Heavy hexagonal coding is a new class of quantum error-correcting coding that assigns physical and auxiliary qubits to the vertices and edges of a low-degree graph. The layout of heavy [...] Read more.
Topological quantum error-correcting codes are an important tool for realizing fault-tolerant quantum computers. Heavy hexagonal coding is a new class of quantum error-correcting coding that assigns physical and auxiliary qubits to the vertices and edges of a low-degree graph. The layout of heavy hexagonal codes is particularly suitable for superconducting qubit architectures to reduce frequency conflicts and crosstalk. Although various topological code decoders have been proposed, constructing the optimal decoder remains challenging. Machine learning is an effective decoding scheme for topological codes, and in this paper, we propose a machine learning heavy hexagonal decoder based on a convolutional neural network (CNN) to obtain the decoding threshold. We test our method on heavy hexagonal codes with code distance of three, five, and seven, and increase it to five, seven, and nine by optimizing the RestNet network architecture. Our results show that the decoder thresholding accuracies are about 0.57% and 0.65%, respectively, which are about 25% higher than the conventional decoding scheme under the depolarizing noise model. The proposed decoding architecture is also applicable to other topological code families. Full article
(This article belongs to the Topic Quantum Information and Quantum Computing, 2nd Volume)
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