Quantum Information Processing and Machine Learning

Dear Colleagues,

Quantum information processing is a field that combines the principles of quantum mechanics and information science to study the processing, analysis, and transmission of information. It covers both theoretical and experimental aspects of quantum physics, including the limits that quantum information can reach. Moreover, driven by ever-increasing computer power and algorithmic advances, machine learning techniques have become a powerful tool for finding patterns in data. Machine learning has become a ubiquitous and effective technique for data processing and classification. Due to the advantages and advances in quantum computing in many fields (e.g., cryptography, machine learning, healthcare), the combination of classical machine learning and quantum information processing has established a new field called, quantum machine learning, which has become an important research topic in academia.

This Special Issue aims to curate original research and review articles from academia and industry-relevant researchers in the fields of quantum machine learning, quantum information processing, machine learning, and deep learning. image processing, computer vision, natural language processing, and recommendation system. Researchers and industry practitioners from academia are invited to submit their innovative research on technical challenges and state-of-the-art findings related to quantum information processing and machine learning. This Special Issue encourages authors to discuss and express their views on current trends, challenges, and state-of-the-art solutions to various problems in quantum machine learning.

Topics of interest include but are not limited to:

1. Quantum machine learning;
2. Quantum computing;
3. Quantum cryptography and communications;
4. Quantum algorithms;
5. Machine learning;
6. Deep learning;
7. Image processing;
8. Computer vision;
9. Natural language processing;
10. Recommendations system.

Dr. Wenbin Yu
Dr. Yadang Chen
Dr. Chengjun Zhang
Guest Editors

Manuscript Submission Information

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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. Information is an international peer-reviewed open access monthly journal published by MDPI.

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• quantum machine learning
• quantum information
• quantum computation
• quantum communication
• machine learning
• deep learning
• computer vision

Title: Entropic alternatives to initialization
Authors: Daniele Musso
Affiliation: --
Abstract: Local entropic loss functions provide a versatile framework to define architecture-aware regularization procedures. Besides the possibility of being anisotropic in the synaptic space, the local entropic smoothening of the loss function can vary during training, thus yielding a tunable model complexity. A scoping protocol where the regularization is strong in the early-stage of the training and then fades progressively away constitutes an alternative to standard initialization procedures for deep convolutional neural networks, nonetheless, it has wider applicability. We analyze anisotropic, local entropic smoothenings in the language of statistical physics and information theory, providing insight into both their interpretation and workings. We comment some aspects related to the physics of renormalization and the spacetime structure of convolutional networks.

Title: The Impact of Quantum Data Science on Software Engineers' Collaboration Soft Skills Assessment
Authors: Itzhak Aviv; Havana Rika
Affiliation: --
Abstract: Collaboration soft skills (CSS) are essential for software engineers working on software platforms like Jira or Asana, as these tools are designed to facilitate teamwork, communication, and project management in software development. Effective collaboration in these platforms can significantly contribute to the success of software development projects. Current research assesses data from software platforms for CSS analytics by classical data science. However, it reached a glass ceiling, suffering from the drawbacks of classical probability theory since its reliance on rational decision-making, while people are characterized by irrational decision-making bias. In this vision research, we attempted to overcome the limitations of classical data science by developing the Quantum Data Science Approach for Collaborative Skills Assessment (QDSA-CSA). QDSA-CSA proposes a mathematical model using principles from quantum mechanics, such as superposition, contextuality, interference, complementarity and entanglement. CSS states are assessed by utilizing the quantum superposition of multiple possibilities, reflecting the uncertainty and ambiguity in human decision-making. Quantum contextuality implements for order effect analysis, where the sequence in which information is presented influences the engineer's judgments and decisions. Quantum interference uses to map the relevant CSS onto the elements of the quantum mathematical model. Quantum complementarity uses to assess software engineers' ambiguity aversion, detecting incompatible or mutually exclusive CSS. The approach also used quantum entanglement to model the states of two or more interdependent CSS, where the state of one skill cannot be described independently of the state of the other skill, even when separated by large distances. We demonstrate QDSA-CSA theoretical implementation and proof through software teams' collaboration case study. The results demonstrated that QDSA-CSA could better capture software engineers' soft skills' complexities, nuances, and uncertainties than classical data science.

Title: Binary Classifier on the integrated photonics platform
Authors: Hexiang Lin, Hui Zhang, Yuanchen Zhan, Huihui Zhu, Aiqun Liu, Leong Chuan Kwek
Affiliation: --
Abstract: Integrated photonics is a promising platform for the large-scale implementation of optical and quantum computation. This recent advances in silicon photonic chips have made huge progress in optical computing. In this paper, we integrated the Mach Zehnder Interferometer (MZI) on the silicon photonic chip to manipulate the relative phase and amplitude distribution of the input light and thus perform unitary transformations. Input data is encoded in the light amplitude distribution and go through the optical network to perform binary classification.

Title: Are loopholes in Bell's theorem a threat to quantum cryptography and communications?
Authors: Richard D. Gill
Affiliation: --
Abstract: Thanks to experimental and theoretical progress, various forms of quantum communication are approaching technological implementation. In this paper I would like to focus on DIQKD (device independent quantum key distribution) which offers a way for two distant users to generate shared random keys which are guaranteed to be unknown to others, and hence suitable for use as cryptographic keys in classical communication protocols. The idea is to interleave the successive trials of a classical Bell experiment (testing entanglement) and of a key generation experiment (using entanglement). If the first experiment succeeds to a sufficient degree then Alice and Bob can hopefully have confidence in the results of the second experiment. There are many subtle issues here, and many different fields are involved. Debate continues as to whether Bell experiments do actually prove what they are claimed to prove. Key aspects of Bell experiments involve statistical considerations which might be less known in the cryptography world, and vice versa. Those who want to promote the new technology need to understand the reasoning of sceptical opponents, and also to avoid making promises which they cannot fulfil.

Title: Quantum Convolutional Long Short-Term Memory Network Based on Variational Quantum Algorithm in the Era of NISQ
Authors: Zeyu Xu, Wenbin Yu*, Chengjun Zhang
Affiliation: Nanjing University of Information Science and Technology, Nanjing 210044, China
Abstract: In the era of NISQ (Noisy Intermediate-Scale Quantum) computing, the field of quantum machine learning has emerged as a promising solution for addressing complex computational challenges. The synergy between quantum and classical computing models has become apparent. Long Short-Term Memory (LSTM), a popular network for sequential data modeling, has been widely recognized for its effectiveness. However, as the volume of data and the need for spatial feature extraction increase, the training cost of LSTM grows exponentially. In this study, we propose the Quantum Convolutional Long Short-Term Memory (QConv-LSTM) model. By combining a classical Convolutional LSTM network with quantum variational algorithms, our model leverages the acceleration properties of quantum states to improve training efficiency. Through experimentation, we demonstrate that our proposed model achieves lower loss values compared to the classical version while achieving better accuracy. Due to the variational nature of the circuit, it reduces the requirements for quantum bit counting and circuit depth, thereby saving computational resources to some extent. Moreover, the intrinsic noise resilience in variational quantum algorithms makes this model suitable for spatiotemporal prediction tasks on NISQ devices.