Quantum autoencoders for image classification

• URL: http://arxiv.org/abs/2502.15254v1
• Date: Fri, 21 Feb 2025 07:13:38 GMT
• Title: Quantum autoencoders for image classification
• Authors: Hinako Asaoka, Kazue Kudo,
• Abstract summary: Quantum autoencoders (QAEs) leverage classical optimization solely for parameter tuning.<n>QAEs can serve as efficient classification models with fewer parameters and highlight the potential of utilizing quantum circuits for complete end-to-end learning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Classical machine learning often struggles with complex, high-dimensional data. Quantum machine learning offers a potential solution, promising more efficient processing. While the quantum convolutional neural network (QCNN), a hybrid quantum-classical algorithm, is suitable for current noisy intermediate-scale quantum-era hardware, its learning process relies heavily on classical computation. Future large-scale, gate-based quantum computers could unlock the full potential of quantum effects in machine learning. In contrast to QCNNs, quantum autoencoders (QAEs) leverage classical optimization solely for parameter tuning. Data compression and reconstruction are handled entirely within quantum circuits, enabling purely quantum-based feature extraction. This study introduces a novel image-classification approach using QAEs, achieving classification without requiring additional qubits compared with conventional QAE implementations. The quantum circuit structure significantly impacts classification accuracy. Unlike hybrid methods such as QCNN, QAE-based classification emphasizes quantum computation. Our experiments demonstrate high accuracy in a four-class classification task, evaluating various quantum-gate configurations to understand the impact of different parameterized quantum circuit (ansatz) structures on classification performance. Our results reveal that specific ansatz structures achieve superior accuracy, and we provide an analysis of their effectiveness. Moreover, the proposed approach achieves performance comparable to that of conventional machine-learning methods while significantly reducing the number of parameters requiring optimization. These findings indicate that QAEs can serve as efficient classification models with fewer parameters and highlight the potential of utilizing quantum circuits for complete end-to-end learning, a departure from hybrid approaches such as QCNN.

Related papers

• Quantum parallel information exchange (QPIE) hybrid network with transfer learning [18.43273756128771]
  Quantum machine learning (QML) has emerged as an innovative framework with the potential to uncover complex patterns. We introduce quantum parallel information exchange (QPIE) hybrid network, a new non-sequential hybrid classical quantum model architecture. We develop a dynamic gradient selection method that applies the parameter shift rule on quantum processing units.
  arXiv  Detail & Related papers  (2025-04-05T17:25:26Z)
• Quantum phase classification via quantum hypothesis testing [0.39102514525861415]
  We propose a classification algorithm based on the quantum Neyman-Pearson test, which is theoretically optimal for distinguishing between two quantum states. Our results show that the proposed method achieves lower classification error probabilities and significantly reduces the training cost. These findings highlight the potential of quantum hypothesis testing as a powerful tool for quantum phase classification.
  arXiv  Detail & Related papers  (2025-04-05T08:23:45Z)
• Tensor-Based Binary Graph Encoding for Variational Quantum Classifiers [3.5051814539447474]
  We propose a novel quantum encoding framework for graph classification using Variational Quantums (VQCs)<n>By constructing slightly more complex circuits tailored for graph encoding, we demonstrate that VQCs can effectively classify graphs within the constraints of current quantum hardware.
  arXiv  Detail & Related papers  (2025-01-24T02:26:21Z)
• NN-AE-VQE: Neural network parameter prediction on autoencoded variational quantum eigensolvers [1.7400502482492273]
  In recent years, the field of quantum computing has become significantly more mature. We present an auto-encoded VQE with neural-network predictions: NN-AE-VQE. We demonstrate these methods on a $H$ molecule, achieving chemical accuracy.
  arXiv  Detail & Related papers  (2024-11-23T23:09:22Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Large-scale quantum reservoir learning with an analog quantum computer [45.21335836399935]
  We develop a quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning.
  arXiv  Detail & Related papers  (2024-07-02T18:00:00Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Quantum machine learning for image classification [39.58317527488534]
  This research introduces two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era. A second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process.
  arXiv  Detail & Related papers  (2023-04-18T18:23:20Z)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning [0.5999777817331318]
  We propose a quantum machine learning approach based on quantum convolutional neural networks for solving multiclass classification problems. We use the proposed approach to demonstrate the 4-class classification for the case of the MNIST dataset using eight qubits for data encoding and four acnilla qubits. Our results demonstrate comparable accuracy of our solution with classical convolutional neural networks with comparable numbers of trainable parameters.
  arXiv  Detail & Related papers  (2022-03-29T09:07:18Z)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier [0.0]
  We propose a novel hybrid variational quantum classifier that combines the quantum gradient descent method with steepest gradient descent to optimise the parameters of the network. We find that this algorithm has a better learning outcome than a classical neural network or a quantum machine learning method trained with a non-quantum optimisation method.
  arXiv  Detail & Related papers  (2020-10-14T18:05:49Z)
• Efficient Discrete Feature Encoding for Variational Quantum Classifier [3.7576442570677253]
  Variational quantum classification (VQC) is one of such methods with possible quantum advantage. We introduce the use of quantum random-access coding (QRAC) to map discrete features efficiently into limited number of qubits for VQC. We experimentally show that QRAC can help speeding up the training of VQC by reducing its parameters via saving on the number of qubits for the mapping.
  arXiv  Detail & Related papers  (2020-05-29T04:43:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.