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Enhancing a Convolutional Autoencoder with a Quantum Approximate Optimization Algorithm for Image Noise Reduction
Published 12 Jan 2024 in quant-ph | (2401.06367v1)
Abstract: Image denoising is essential for removing noise in images caused by electric device malfunctions or other factors during image acquisition. It helps preserve image quality and interpretation. Many convolutional autoencoder algorithms have proven effective in image denoising. Owing to their promising efficiency, quantum computers have gained popularity. This study introduces a quantum convolutional autoencoder (QCAE) method for improved image denoising. This method was developed by substituting the representative latent space of the autoencoder with a quantum circuit. To enhance efficiency, we leveraged the advantages of the quantum approximate optimization algorithm (QAOA)-incorporated parameter-shift rule to identify an optimized cost function, facilitating effective learning from data and gradient computation on an actual quantum computer. The proposed QCAE method outperformed its classical counterpart as it exhibited lower training loss and a higher structural similarity index (SSIM) value. QCAE also outperformed its classical counterpart in denoising the MNIST dataset by up to 40% in terms of SSIM value, confirming its enhanced capabilities in real-world applications. Evaluation of QAOA performance across different circuit configurations and layer variations showed that our technique outperformed other circuit designs by 25% on average.
- Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 .
- State-of-the-art in artificial neural network applications: A survey. Heliyon 4.
- Autoencoders, unsupervised learning, and deep architectures, in: Proceedings of ICML workshop on unsupervised and transfer learning, JMLR Workshop and Conference Proceedings. pp. 37–49.
- Parameterized quantum circuits as machine learning models. Quantum Science and Technology 4, 043001.
- Noisy intermediate-scale quantum algorithms. Reviews of Modern Physics 94, 015004.
- Noise and the frontier of quantum supremacy, in: 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS), IEEE. pp. 1308–1317.
- Quantum autoencoders with enhanced data encoding. Machine Learning: Science and Technology 2, 035028.
- A tutorial on quantum approximate optimization algorithm (qaoa): Fundamentals and applications, in: 2019 International Conference on Information and Communication Technology Convergence (ICTC), IEEE. pp. 138–142.
- Loading classical data into a quantum computer. arXiv preprint arXiv:1803.01958 .
- Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition. arXiv preprint arXiv:1905.13311 .
- A review on ct image noise and its denoising. Biomedical Signal Processing and Control 42, 73–88.
- Image denoising: The deep learning revolution and beyond—a survey paper. SIAM Journal on Imaging Sciences 16, 1594–1654.
- A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028 .
- Quantum supremacy through the quantum approximate optimization algorithm. arXiv preprint arXiv:1602.07674 .
- Medical image denoising using convolutional denoising autoencoders, in: 2016 IEEE 16th international conference on data mining workshops (ICDMW), IEEE. pp. 241–246.
- Qaoa for max-cut requires hundreds of qubits for quantum speed-up. Scientific reports 9, 6903.
- A quantum convolutional network and resnet (50)-based classification architecture for the mnist medical dataset. Biomedical Signal Processing and Control 87, 105560.
- Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation. Science China Physics, Mechanics & Astronomy 66, 250302.
- Evaluation of parameterized quantum circuits: on the relation between classification accuracy, expressibility, and entangling capability. Quantum Machine Intelligence 3, 1–19.
- Quantum convolutional neural network for classical data classification. Quantum Machine Intelligence 4, 3.
- Image denoising in the deep learning era. Artificial Intelligence Review 56, 5929–5974.
- Classical-to-quantum convolutional neural network transfer learning. Neurocomputing 555, 126643.
- Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 .
- Enabling efficient real-time calibration on cloud quantum machines. IEEE Transactions on Quantum Engineering .
- Efficient z gates for quantum computing. Physical Review A 96, 022330.
- Machine learning technology-based heart disease detection models. Journal of Healthcare Engineering 2022.
- Image classification using quantum inference on the d-wave 2x, in: 2018 IEEE International Conference on Rebooting Computing (ICRC), IEEE. pp. 1–7.
- Quantum computation and quantum information. Cambridge university press.
- Quantum circuits for quantum convolutions: A quantum convolutional autoencoder. Transactions on Computational Science and Computational Intelligence. Springer .
- Quantum angle encoding with learnable rotation applied to quantum–classical convolutional neural networks. Applied Soft Computing 141, 110307.
- Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32.
- Wavelet enabled convolutional autoencoder based deep neural network for hyperspectral image denoising. Multimedia tools and applications , 1–27.
- Modified convolutional neural network with pseudo-cnn for removing nonlinear noise in digital images. Displays 74, 102258.
- Qiskit contributors, 2023. Qiskit: An open-source framework for quantum computing.
- Hybrid quantum variational autoencoders for representation learning, in: 2021 International Conference on Computational Science and Computational Intelligence (CSCI), IEEE. pp. 52–57.
- Quantum autoencoders for efficient compression of quantum data. Quantum Science and Technology 2, 045001.
- Stacked denoising autoencoders for sentiment analysis: a review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 7, e1212.
- A review on medical image denoising algorithms. Biomedical signal processing and control 61, 102036.
- Autoencoders-a comparative analysis in the realm of anomaly detection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1986–1992.
- Quantum computing: progress and prospects .
- Convolution filter embedded quantum gate autoencoder. arXiv preprint arXiv:1906.01196 .
- A hybrid quantum enabled rbm advantage: convolutional autoencoders for quantum image compression and generative learning, in: Quantum information science, sensing, and computation XII, SPIE. pp. 23–38.
- A hybrid system for learning classical data in quantum states, in: 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC), IEEE. pp. 1–7.
- Visual-psnr measure of image quality. Journal of Visual Communication and Image Representation 25, 874–878.
- Image denoising using convolutional autoencoder. arXiv preprint arXiv:2207.11771 .
- An adaptive learning image denoising algorithm based on eigenvalue extraction and the gan model. Computational Intelligence and Neuroscience 2022.
- Qoc: quantum on-chip training with parameter shift and gradient pruning, in: Proceedings of the 59th ACM/IEEE Design Automation Conference, pp. 655–660.
- Development of variational quantum deep neural networks for image recognition. Neurocomputing 501, 566–582.
- General parameter-shift rules for quantum gradients. Quantum 6, 677.
- Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises. Robotics and Computer-Integrated Manufacturing 79, 102441.
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