QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity

Stein, Samuel A.; Baheri, Betis; Chen, Daniel; Mao, Ying; Guan, Qiang; Li, Ang; Xu, Sheng; Ding, Caiwen

doi:10.48550/arxiv.2103.11307

Cited by 5 publications

(7 citation statements)

References 28 publications

(34 reference statements)

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“…Quantum algorithms are proven to have exponential or quadratic operational efficiency improvements in solving specific problems compared to classical algorithms 32,33 , such as integer factorization 34 and unstructured database searches 35 . Recent studies in variational quantum algorithms (VQA) have applied quantum computing to many scientific domains, including molecular dynamical studies 36 , quantum optimization 37,38 and various quantum machine learning (QML) applications such as regression [39][40][41] , classification 40,[42][43][44][45][46][47][48][49][50][51][52][53][54][55][56] , generative modeling [57][58][59][60][61][62] , deep reinforcement learning [63][64][65][66][67][68][69] , sequence modeling 39,70,71 , speech identification 72 , distance metric learning 73,74 , transfer learning…”

Section: Quantum Architecture Search Via Truly Proximal Policy Optimi...mentioning

confidence: 99%

Quantum architecture search via truly proximal policy optimization

Zhu

Hou

2023

Sci Rep

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Quantum Architecture Search (QAS) is a process of voluntarily designing quantum circuit architectures using intelligent algorithms. Recently, Kuo et al. (Quantum architecture search via deep reinforcement learning. arXiv preprint arXiv:2104.07715, 2021) proposed a deep reinforcement learning-based QAS (QAS-PPO) method, which used the Proximal Policy Optimization (PPO) algorithm to automatically generate the quantum circuit without any expert knowledge in physics. However, QAS-PPO can neither strictly limit the probability ratio between old and new policies nor enforce well-defined trust domain constraints, resulting in poor performance. In this paper, we present a new deep reinforcement learning-based QAS method, called Trust Region-based PPO with Rollback for QAS (QAS-TR-PPO-RB), to automatically build the quantum gates sequence from the density matrix only. Specifically, inspired by the research work of Wang, we employ an improved clipping function to implement the rollback behavior to limit the probability ratio between the new strategy and the old strategy. In addition, we use the triggering condition of the clipping based on the trust domain to optimize the policy by restricting the policy within the trust domain, which leads to guaranteed monotone improvement. Experiments on several multi-qubit circuits demonstrate that our presented method achieves better policy performance and lower algorithm running time than the original deep reinforcement learning-based QAS method.

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Section: Quantum Architecture Search Via Truly Proximal Policy Optimi...mentioning

confidence: 99%

Quantum architecture search via truly proximal policy optimization

Zhu

Hou

2023

Sci Rep

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“…A previous study [15] used QuClassi, the construction of multi-class and high-count classification with a certain number of qubits. Experiments were conducted on quantum stimulators, and the performance of IonQ and IBM-Q quantum platforms was determined by accessing Microsoft's Azure Quantum platform.…”

Section: Review Of Existing Workmentioning

confidence: 99%

A Quantum Computing-Based Accelerated Model for Image Classification Using a Parallel Pipeline Encoded Inception Module

et al. 2023

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Image classification is typically a research area that trains an algorithm for accurately identifying subjects in images that have never been seen before. Training a model to recognize images within a dataset is significant as image classification generally has several applications in medicine, face detection, image reconstruction, etc. In spite of such applications, the main difficulty in this area involves the computation in the classification process, which is vast, leading to slow speed of classification. Moreover, as conventional image classification approaches have fallen short in terms of attaining high accuracy, an optimal model is needed. To resolve this, quantum computing has been developed. Due to their parallel computing ability, quantum-based algorithms could accomplish the classification of vast amounts of image data. This has theoretically confirmed the feasibility and advantages of incorporating a quantum computing-based system with traditional image classification methodologies. Considering this, the present study quantizes the layers of the proposed parallel encoded Inception module to improvise the network performance. This study exposes the flexibility of DL (deep learning)-based quantum state computational methodologies for missing computations by creating a pipeline for denoising, state estimation, and imputation. Furthermore, controlled parameterized rotations are regarded for entanglement, a vital component in quantum perceptron structure. The proposed approach not only possesses the unique features of quantum mechanics, but it also maintains the weight sharing of the kernel. Finally, the MNIST (Modified National Institute of Standards and Technology) and Fashion MNIST image classification outcomes are attained by measuring the quantum state. Overall performance is assessed to prove its effectiveness in image classification.

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“…Zhao and Gao [15] train a quantum neural network on a classical computer to classify two digits from the MNIST dataset which is downsized to size 8×8. Stein et al [16] construct a hybrid quantum neural network with a quantum state fidelity-based cost function. They train a binary classifier and a 10-class classifier with the MNIST dataset.…”

Section: Related Workmentioning

confidence: 99%

RGB Image Classification with Quantum Convolutional Ansaetze

Jing,

Li,

Yang

et al. 2021

Preprint

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With the rapid growth of qubit numbers and coherence times in quantum hardware technology, implementing shallow neural networks on the so-called Noisy Intermediate-Scale Quantum (NISQ) devices has attracted a lot of interest. Many quantum (convolutional) circuit ansaetze are proposed for grayscale images classification tasks with promising empirical results. However, when applying these ansaetze on RGB images, the intra-channel information that is useful for vision tasks is not extracted effectively. In this paper, we propose two types of quantum circuit ansaetze to simulate convolution operations on RGB images, which differ in the way how inter-channel and intra-channel information are extracted. To the best of our knowledge, this is the first work of a quantum convolutional circuit to deal with RGB images effectively, with a higher test accuracy compared to the purely classical CNNs. We also investigate the relationship between the size of quantum circuit ansatz and the learnability of the hybrid quantum-classical convolutional neural network. Through experiments based on CIFAR-10 and MNIST datasets, we demonstrate that a larger size of the quantum circuit ansatz improves predictive performance in multiclass classification tasks, providing useful insights for near term quantum algorithm developments.

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QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity

Cited by 5 publications

References 28 publications

Quantum architecture search via truly proximal policy optimization

Quantum architecture search via truly proximal policy optimization

A Quantum Computing-Based Accelerated Model for Image Classification Using a Parallel Pipeline Encoded Inception Module

RGB Image Classification with Quantum Convolutional Ansaetze

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