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Springer eBooks

Integrating Neural Networks with Quantum Chips: Converging Two Cutting-Edge Frontiers

Shugufta Fatima, Shaik Amna Hilal, Marlia Mohd Hanafiah

Published Jan 1, 2025• 1 citation
Publisher
DOI
10.1007/978-3-031-87931-9_17

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This chapter describes the mesmerizing journey of artificial intelligence (AI) artificial intelligence (AI) , from classical systems to modern cutting-edge innovation cutting-edge innovation s. The era begins with deep roots in the historical milestones of AI; it all extends out to the inception of the quantum era quantum era – when quantum mechanics and quantum computation meet the already ambitious world of AI to overcome technological boundaries. quantum computing (QC) Quantum computing is not only the next frontier in computing; it is the next frontier for AI experts to explore. Particular emphasis is placed on generative adversarial networks (GANs) – an innovative method to ML machine learning (ML) that employs adversarial mechanisms to produce astonishingly realistic data. The section allows an analysis on the intricacies of training the neural networks of GANs and also an exploration of quantum generative adversarial network (QGAN), which has the potential for improving generative performance through quantum advantage. Other topics also covered include quantum computing, AI, and the metaphorical phase of machine intelligence, the balance between human creativity and machine intelligence, with quantum computing’s parallel processing and algorithm knowledge being a complementary force. Collectively, they provide a shimmering and soon-to-be-mating pair of building blocks for the next generation of applications that will transfigure enterprises and set human beings loose – evolution and revolution in the era of digital transformation digital transformation .

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Quantum computing represents a cutting-edge frontier in artificial intelligence. It makes use of hybrid quantum-classical computation which tries to leverage quantum mechanic principles that allow us to use a different approach to deep learning classification problems. The work presented here falls within the context of the AGILE space mission, launched in 2007 by the Italian Space Agency. We implement different Quantum Convolutional Neural Networks (QCNN) that analyze data acquired by the instruments onboard AGILE to detect Gamma-Ray Bursts from sky maps or light curves. We use several frameworks such as TensorFlow-Quantum, Qiskit and Penny-Lane to simulate a quantum computer. We achieved an accuracy of 95.1% on sky maps with QCNNs, while the classical counterpart achieved 98.8% on the same data, using however hundreds of thousands more parameters.

arXiv:2404.14133•Feb 28, 2026•0 citations
This chapter describes the mesmerizing journey of artificial intelligence (AI) artificial intelligence (AI) , from classical systems to modern cutting-edge innovation cutting-edge innovation s. The era begins with deep roots in the historical milestones of AI; it all extends out to the inception of the quantum era quantum era – when quantum mechanics and quantum computation meet the already ambitious world of AI to overcome technological boundaries. quantum computing (QC) Quantum computing is not only the next frontier in computing; it is the next frontier for AI experts to explore. Particular emphasis is placed on generative adversarial networks (GANs) – an innovative method to ML machine learning (ML) that employs adversarial mechanisms to produce astonishingly realistic data. The section allows an analysis on the intricacies of training the neural networks of GANs and also an exploration of quantum generative adversarial network (QGAN), which has the potential for improving generative performance through quantum advantage. Other topics also covered include quantum computing, AI, and the metaphorical phase of machine intelligence, the balance between human creativity and machine intelligence, with quantum computing’s parallel processing and algorithm knowledge being a complementary force. Collectively, they provide a shimmering and soon-to-be-mating pair of building blocks for the next generation of applications that will transfigure enterprises and set human beings loose – evolution and revolution in the era of digital transformation digital transformation .
On the one hand, artificial neural networks have many successful applications in the field of machine learning and optimization. On the other hand, interferometers are integral parts of any field that deals with waves such as optics, astronomy, and quantum physics. Here, we introduce neural networks composed of interferometers and then build generative adversarial networks from them. Our networks do not have any classical layer and can be realized on quantum computers or photonic chips. We demonstrate their applicability for combinatorial optimization, image classification, and image generation. For combinatorial optimization, our network consistently converges to the global optimum or remains within a narrow range of it. In multi-class image classification tasks, our networks achieve accuracies of 93% and 83%. Lastly, we show their capability to generate images of digits from 0 to 9 as well as human faces.
Quantum computing represents a cutting-edge frontier in artificial intelligence. It makes use of hybrid quantum-classical computation which tries to leverage quantum mechanic principles that allow us to use a different approach to deep learning classification problems. The work presented here falls within the context of the AGILE space mission, launched in 2007 by the Italian Space Agency. We implement different Quantum Convolutional Neural Networks (QCNN) that analyze data acquired by the instruments onboard AGILE to detect Gamma-Ray Bursts from sky maps or light curves. We use several frameworks such as TensorFlow-Quantum, Qiskit and Penny-Lane to simulate a quantum computer. We achieved an accuracy of 95.1% on sky maps with QCNNs, while the classical counterpart achieved 98.8% on the same data, using however hundreds of thousands more parameters.