Quantum Information and Quantum Computing, 2nd Volume

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

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 (4 papers)

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All Applied Sciences Electronics Entropy Mathematics Symmetry Technologies Chips

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16 pages, 5250 KiB  

Open AccessArticle

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 1095

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  

Open AccessArticle

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 - 08 Feb 2024

Viewed by 622

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  

Open AccessArticle

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 2260

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  

Open AccessArticle

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 1182

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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