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Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

Quantum neurocomputation

Hamid R. Eghbalnia, Amir H. Assadi

Published Jul 13, 2000• 0 citations
Publisher

Abstract

Neural Networks and Neurocomputing provide a natural paradigm for parallel and distributed processing. Neurocomputing within the context of classical computation have been used for approximation and classification tasks with some success. In this paper we propose a model for Quantum Neurocomputation and explore some of its properties and potential applications to pattern recognition.

DOI
10.1117/12.391963

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Neural Networks and Neurocomputing provide a natural paradigm for parallel and distributed processing. Neurocomputing within the context of classical computation have been used for approximation and classification tasks with some success. In this paper we propose a model for Quantum Neurocomputation and explore some of its properties and potential applications to pattern recognition.
We consider a quantum computational algorithm that can be used to determine (probabilistically) how close a given signal is to one of a set of previously observed signals stored in the state of a quantum neurocomputional machine. The realization of a new quantum algorithm for factorization of integers by Shor and its implication to cryptography has created a rapidly growing field of investigation. Although no physical realization of a quantum computer is available, a number of software systems simulating a quantum computation process exist. In light of the rapidly increasing power of desktop computers and their ability to carry out these simulations, it is worthwhile to investigate possible advantages as well as realizations of quantum algorithms in signal processing applications. The algorithm presented offers a glimpse of the potential of this approach. Neural networks (NN) provide a natural paradigm for parallel and distributed processing of a wide class of signals. Neural networks within the context of classical computation have been used for approximation and classification tasks with some success. We propose a model for quantum neurocomputation (QN) and explore some of its properties and potential applications to signal processing in an information theoretic context.
Quantum computing and neural networks have revolutionized computational intelligence and have the potential to bring revolution in a wide range of scientific, industrial, and technological fields. In some computational activities, quantum computing utilizes the concepts of superposition, entanglement, and quantum parallelism and performs better than classical computers by processing complicated data with high speed and efficiency. Neural networks have transformed decision-making, natural language processing, and pattern recognition especially deep learning models. By incorporating quantum computing into neural network architectures, Quantum Neural Networks (QNNs) open new paradigms in data encoding, feature representation, and learning dynamics by overcoming the computational constraints and scaling related problems of classical models. In this chapter, the theoretical basis, latest developments and possible applications of a quantum neural network have been discussed. This chapter covers the constraints of near-term quantum devices, quantum noise, data encoding techniques, and practical difficulties in hardware implementation. Real-world applications in quantum chemistry, financial modeling, cryptography, and biological imaging have been studied. To fully realize the promise of quantum neural processing, this chapter concludes by describing current research shortcomings and providing opportunities for multi-disciplinary collaboration among computer scientists, physicists, and AI researchers.
This paper investigates the application of quantum neural networks (QNNs) for pattern recognition. Traditional neural networks face limitations in processing complex data due to their reliance on classical computing principles. QNNs leverage quantum mechanics phenomena, specifically superposition and entanglement, to potentially overcome these limitations. This research proposes a framework for constructing a QNN-based pattern recognition system. The core claim is to enhance pattern recognition accuracy and efficiency by utilizing these quantum properties. We detail the conversion process from classical neural networks to QNNs, highlighting the computational advantages offered by quantum computing. This work contributes to the nascent field of quantum machine learning, exploring the potential of QNNs to revolutionize pattern recognition tasks. The theoretical foundation presented here forms a basis for future research and development in this area.
Three computational models are considered: classical, hybrid (NISQ) and quantum computational models, their pros, cons, possibilities of implementation in modern realities and the problem of image classification and its solution using neural networks in these computational models. Three computational experiments were carried out using the described image recognition approaches with visualization of the learning process and a comparison of the final metrics was carried out aimed at clarifying the prospects of the applied approach based on quantum computing.
Neural networks have achieved impressive breakthroughs in both industry and academia. How to effectively develop neural networks on quantum computing devices is a challenging open problem. Here, we propose a new quantum neural network model for quantum neural computing using (classically controlled) single-qubit operations and measurements on real-world quantum systems with naturally occurring environment-induced decoherence, which greatly reduces the difficulties of physical implementations. Our model circumvents the problem that the state-space size grows exponentially with the number of neurons, thereby greatly reducing memory requirements and allowing for fast optimization with traditional optimization algorithms. We benchmark our model for handwritten digit recognition and other nonlinear classification tasks. The results show that our model has an amazing nonlinear classification ability and robustness to noise. Furthermore, our model allows quantum computing to be applied in a wider context and inspires the earlier development of a quantum neural computer than standard quantum computers.