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Quantum Artificial Intelligence for Sensor Data Analysis, Classification and Forecasting

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 2271

Special Issue Editor

Department of Electrical & Computer Engineering, University of Patras, 26504 Patras, Greece
Interests: quantum AI; quantum computing; quantum deep neural networks; quantum machine learning; quantum mechanics; quantum pattern recognition; quantum sensors; quantum supervized learning; quantum unsupervized learning

Special Issue Information

Dear Colleagues,

Researchers are investigating how quantum computing could help to improve classical machine learning algorithms. Quantum machine learning can take logarithmic time, in both the number of vectors and their dimensions in a model’s architecture, inside a quantum environment. This is an exponential speedup over classical algorithms, but with both quantum input and quantum output. Quantum sensing has become a rapidly growing branch of research in the sector of quantum science and technology. In recent years, a different set of applications has emerged which employ quantum mechanical systems on quantum sensing for various physical quantities. Quantum sensing capitalizes on the central weakness of quantum machines or quantum simulation—that is, their strong sensitivity to external noise. The technological implementation of quantum computing is emerging, and it is only a matter of time until every theoretical proposal in every scientific field will be tested on quantum systems. Despite this growing level of interest in the field, a comprehensive theory of quantum learning, or how quantum information can in principle be applied to intelligent forms of computing, is only in the very first stages of development. It is important to determine the need for future works on quantum machine learning that concentrate on how the actual learning part of machine learning methods could be improved using the power of quantum computing in information processing. There are many open questions related to what an efficient quantum learning procedure could look like. How can we efficiently implement an optimization problem that is usually solved by iterative and dissipative methods, such as gradient descent, on a coherent quantum machine? How can we process structural information such as metrics using quantum states? How do we formulate a decision strategy in terms of quantum mechanics? Finally, the overall question, is there a general way in which quantum physics could in principle speed up certain problems of machine learning?

Prof. Dr. Nikos D. Fakotakis
Guest Editor

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Keywords

  • quantum physics
  • quantum mechanics
  • quantum computing
  • quantum machine learning
  • quantum simulation
  • quantum sensing
  • quantum sensors
  • feature extraction
  • quantum deep learning models

Published Papers (1 paper)

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Research

16 pages, 1024 KiB  
Article
Quantum Neural Network Based Distinguisher on SPECK-32/64
by Hyunji Kim, Kyungbae Jang, Sejin Lim, Yeajun Kang, Wonwoong Kim and Hwajeong Seo
Sensors 2023, 23(12), 5683; https://doi.org/10.3390/s23125683 - 18 Jun 2023
Cited by 1 | Viewed by 1592
Abstract
As IoT technology develops, many sensor devices are being used in our life. To protect such sensor data, lightweight block cipher techniques such as SPECK-32 are applied. However, attack techniques for these lightweight ciphers are also being studied. Block ciphers have differential characteristics, [...] Read more.
As IoT technology develops, many sensor devices are being used in our life. To protect such sensor data, lightweight block cipher techniques such as SPECK-32 are applied. However, attack techniques for these lightweight ciphers are also being studied. Block ciphers have differential characteristics, which are probabilistically predictable, so deep learning has been utilized to solve this problem. Since Gohr’s work at Crypto2019, many studies on deep-learning-based distinguishers have been conducted. Currently, as quantum computers are developed, quantum neural network technology is developing. Quantum neural networks can also learn and make predictions on data, just like classical neural networks. However, current quantum computers are constrained by many factors (e.g., the scale and execution time of available quantum computers), making it difficult for quantum neural networks to outperform classical neural networks. Quantum computers have higher performance and computational speed than classical computers, but this cannot be achieved in the current quantum computing environment. Nevertheless, it is very important to find areas where quantum neural networks work for technology development in the future. In this paper, we propose the first quantum neural network based distinguisher for the block cipher SPECK-32 in an NISQ. Our quantum neural distinguisher successfully operated for up to 5 rounds even under constrained conditions. As a result of our experiment, the classical neural distinguisher achieved an accuracy of 0.93, but our quantum neural distinguisher achieved an accuracy of 0.53 due to limitations in data, time, and parameters. Due to the constrained environment, it cannot exceed the performance of classical neural networks, but it can operate as a distinguisher because it has obtained an accuracy of 0.51 or higher. In addition, we performed an in-depth analysis of the quantum neural network’s various factors that affect the performance of the quantum neural distinguisher. As a result, it was confirmed that the embedding method, the number of the qubit, and quantum layers, etc., have an effect. It turns out that if a high-capacity network is needed, we have to properly tune properly to take into account the connectivity and complexity of the circuit, not just by adding quantum resources. In the future, if more quantum resources, data, and time become available, it is expected that an approach to achieve better performance can be designed by considering the various factors presented in this paper. Full article
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