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arXiv:2502.17790 · Physical Review Applied

Quantum neural compressive sensing for ghost imaging

Xinliang Zhai, Tailong Xiao, Jingzheng Huang, Jianping Fan B, Guihua Zeng

Published Jan 7, 2025• 8 citations
arXivHTMLPDF

Abstract

Demonstrating the utility of quantum algorithms is a long-standing challenge, where quantum machine learning becomes one of the most promising candidates that can be resorted to. In this study, we investigate a quantum neural compressive-sensing algorithm for ghost imaging to showcase its utility. The algorithm utilizes the variational quantum circuits to reparameterize the inverse problem of ghost imaging and uses the inductive bias of the physical forward model to perform optimization. To validate the algorithm's effectiveness, we conduct optical ghost-imaging experiments, capturing signals from objects at different physical sampling rates and detection signal-to-noise ratios. The experimental results show that our proposed algorithm surpasses conventional methods in both visual appearance and quantitative metrics, achieving state-of-the-art performance. Of note, we observe that the quantum neural network, guided by prior knowledge of physics, effectively overcomes the challenge of barren plateau in the optimization process. The proposed algorithm demonstrates robustness against various quantum noise levels, making it suitable for near-term quantum devices. Our study leverages a physical inductive-bias-guided variational quantum algorithm, underscoring the potential of quantum computation in tackling a broad range of optimization and inverse problems.

DOI
10.1103/physrevapplied.23.014018

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Demonstrating the utility of quantum algorithms is a long-standing challenge, where quantum machine learning becomes one of the most promising candidates that can be resorted to. In this study, we investigate a quantum neural compressive-sensing algorithm for ghost imaging to showcase its utility. The algorithm utilizes the variational quantum circuits to reparameterize the inverse problem of ghost imaging and uses the inductive bias of the physical forward model to perform optimization. To validate the algorithm's effectiveness, we conduct optical ghost-imaging experiments, capturing signals from objects at different physical sampling rates and detection signal-to-noise ratios. The experimental results show that our proposed algorithm surpasses conventional methods in both visual appearance and quantitative metrics, achieving state-of-the-art performance. Of note, we observe that the quantum neural network, guided by prior knowledge of physics, effectively overcomes the challenge of barren plateau in the optimization process. The proposed algorithm demonstrates robustness against various quantum noise levels, making it suitable for near-term quantum devices. Our study leverages a physical inductive-bias-guided variational quantum algorithm, underscoring the potential of quantum computation in tackling a broad range of optimization and inverse problems.
Abstract Demonstrating the practical advantage of quantum computation remains a long-standing challenge whereas quantum machine learning becomes a promising application that can be resorted to. In this work, we investigate the practical advantage of quantum machine learning in ghost imaging by overcoming the limitations of classical methods in blind object identification and imaging. We propose two hybrid quantum-classical machine learning algorithms and a physical-inspired patch strategy to allow distributed quantum learning with parallel variational circuits. In light of the algorithm, we conduct experiments for imaging-free object identification and blind ghost imaging under different physical sampling rates. We further quantitatively analyze the advantage through the lens of information geometry and generalization capability. The numerical results showcase that quantum machine learning can restore high-quality images but classical machine learning fails. The advantage of identification rate are up to 10% via fair comparison with the classical machine learning methods. Our work explores a physics-related application capable of practical quantum advantage, which highlights the prospect of quantum computation in the machine learning field.
Exploiting the utility of near-term quantum devices is a long-standing challenge whereas hybrid quantum machine learning emerges as a promising candidate. Here we propose a quantum-enhanced deep generative algorithm based on programmable quantum circuit-induced quantum latent codes. To validate the effectiveness of the algorithm, we conduct optical ghost imaging experiments, collecting dataset under varying physical sampling rates. Leveraging a physically enhanced loss function and a pretrained neural network, the quantum algorithm exhibits superior reconstruction performance compared to conventional algorithms. We observe the learnable quantum latent space enhances the out-of-distribution generalization capability of the pretrained model. In the context of computer vision problems, numerical results indicate that quantum latent space can increase the generation diversity of the pretrained model. Furthermore, the algorithm outperforms classical counterparts, particularly in image inpainting and colorization, by a significant margin. Our study demonstrates the utility of hybrid quantum-classical algorithms in enhancing generalization capability, highlighting the potential of near-term quantum devices in large-scale generative artificial intelligence. Quantum circuits can generate hard distributions. Exploiting this known fact, the authors train a mixture of parametric quantum circuits and classical neural networks and show that the addition of the quantum part can enhance the expressive power of the model for generative AI.
Ghost imaging enables nonlocal image reconstruction and exhibits strong robustness against interference, but achieving high-fidelity recognition at ultra-low sampling rates remains challenging. Quantum machine learning offers a novel approach for efficient feature extraction on noisy medium-scale quantum devices; however, existing methods generally suffer from low recognition accuracy and weak noise resistance. This paper proposes a ghost imaging recognition method based on the simultaneous optimization of speckle patterns and quantum network parameters. By leveraging the mathematical equivalence between classical convolution and speckle-object dot product operations in ghost imaging, a speckle consistency regularization mechanism is introduced to achieve end-to-end joint optimization of optical coding and quantum feature extractors. A parallel 8-qubit quantum circuit employing block coding and a star-shaped entanglement structure is designed to extract higher-order features from bucket signals. Simulation results on the MNIST and Fashion-MNIST datasets show that at an ultra-low sampling rate of 1.5625%, the proposed framework achieves recognition accuracies of 90.1% and 81.7%, respectively, representing a 2.6% improvement over classical convolutional neural networks and a maximum improvement of 14.2% over traditional hybrid quantum machine learning models. This method also exhibits strong robustness to quantum noise and has been validated on a real optical ghost imaging system, achieving an average recognition accuracy of 84.8%. These results confirm that the joint optimization of speckle patterns and quantum network parameters provides a reliable and practical solution for low-sampling ghost imaging recognition.
This work experimentally demonstrates that the imaging quality of quantum ghost imaging (GI) with entangled photons can be significantly improved by properly handling the errors caused by the imperfection of optical devices. We also consider compressive GI to reduce the number of measurements and thereby the data acquisition time. The image reconstruction is formulated as a sparse total least square problem which is solved with an iterative algorithm. Our experiments show that, compared with existing methods, the new method can achieve a significant performance gain in terms of mean square error and peak signal⁻noise ratio.
In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multi-layer neural networks to model complex data. Simultaneously, quantum computing (QC) promises to address classically intractable problems via quantum parallelism, motivating research in quantum machine learning (QML). Among QML techniques, quantum autoencoders show promise for compressing high-dimensional quantum and classical data. However, designing effective quantum circuit architectures for quantum autoencoders remains challenging due to the complexity of selecting gates, arranging circuit layers, and tuning parameters. This paper proposes a neural architecture search (NAS) framework that automates the design of quantum autoencoders using a genetic algorithm (GA). By systematically evolving variational quantum circuit (VQC) configurations, our method seeks to identify high-performing hybrid quantum-classical autoencoders for data reconstruction without becoming trapped in local minima. We demonstrate effectiveness on image datasets, highlighting the potential of quantum autoencoders for efficient feature extraction within a noise-prone, near-term quantum era. Our approach lays a foundation for broader application of genetic algorithms to quantum architecture search, aiming for a robust, automated method that can adapt to varied data and hardware constraints.
Quantum machine learning stands out as one of the most promising applications of quantum computing, widely believed to possess potential quantum advantages. In the era of noisy intermediate-scale quantum, the scale and quality of quantum computers are limited, and quantum algorithms based on fault-tolerant quantum computing paradigms cannot be experimentally verified in the short term. The variational quantum algorithm design paradigm can better adapt to the practical characteristics of noisy quantum hardware and is currently one of the most promising solutions. However, variational quantum algorithms, due to their highly entangled nature, encounter the phenomenon known as the “barren plateau” during the optimization and training processes, making effective optimization challenging. This paper addresses this challenging issue by researching a variational quantum neural network optimization method based on collective intelligence algorithms. The aim is to overcome optimization difficulties encountered by traditional methods such as gradient descent. We study two typical applications of using quantum neural networks: random 2D Hamiltonian ground state solving and quantum phase recognition. We find that the collective intelligence algorithm shows a better optimization compared to gradient descent. The solution accuracy of ground energy and phase classification is enhanced, and the optimization iterations are also reduced. We highlight that the collective intelligence algorithm has great potential in tackling the optimization of variational quantum algorithms.