Stereoscopic scalable quantum convolutional neural networks

Baek, Hankyul; Yun, Won Joon; Park, Soo-Hyun; Kim, Joongheon

doi:10.1016/j.neunet.2023.06.027

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…Previous studies have primarily focused on either purely quantum solutions [53][54][55] for image recognition or various hybrid models [36]. Yet, the specific potential of seamlessly integrating quantum circuits with classical neural networks remains an under-explored area.…”

Section: Introductionmentioning

confidence: 99%

Quantum machine learning for image classification

Senokosov,

Sedykh,

Sagingalieva

et al. 2024

Mach. Learn.: Sci. Technol.

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Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing 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, where circuits with a large number of qubits are currently infeasible. This model demonstrated a record-breaking classification accuracy of 99.21% on the full MNIST dataset, surpassing the performance of known quantum-classical models, while having eight times fewer parameters than its classical counterpart. Also, the results of testing this hybrid model on a Medical MNIST (classification accuracy over 99%), and on CIFAR-10 (classification accuracy over 82%), can serve as evidence of the generalizability of the model and highlights the efficiency of quantum layers in distinguishing common features of input data. Our second model introduces a Hybrid Quantum Neural Network with a Quanvolutional layer, reducing image resolution via a convolution process. The model matches the performance of its classical counterpart, having four times fewer trainable parameters, and outperforms a classical model with equal weight parameters. These models represent advancements in quantum machine learning research and illuminate the path towards more accurate image classification systems.

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Section: Introductionmentioning

confidence: 99%

Quantum machine learning for image classification

Senokosov,

Sedykh,

Sagingalieva

et al. 2024

Mach. Learn.: Sci. Technol.

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“…The QC-FSelQUBO Feature selection technique is based on quadratic unconstrained binary optimization (QUBO) followed by a CatBoost (CB) classifier to classify the nodule into benign and malignant classes. The existing framework of quantum-based devices has significantly improved in the past few years, and now the technology has entered the NISQ (noisy intermediate-scale quantum) era [8]. The ability of the quantum-based algorithm to comprehend complex relationships in the high-dimensional dataset is higher due to its quantum characteristics.…”

Section: Introductionmentioning

confidence: 99%

Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment

Shivwanshi,

Nirala

2024

Biomed. Phys. Eng. Express

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The intricate nature of lung cancer treatment poses considerable challenges upon diagnosis. Early detection plays a pivotal role in mitigating its escalating global mortality rates. Consequently, there are pressing demands for robust and dependable early detection and diagnostic systems. However, the technological limitations and complexity of the disease make it challenging to implement an efficient lung cancer screening system. AI-based CT image analysis techniques are showing significant contributions to the development of computer-assisted detection (CAD) systems for lung cancer screening. Various existing research groups are working on implementing CT image analysis systems for assessing and classifying lung cancer. However, the complexity of different structures inside the CT image is high and comprehension of significant information inherited by them is more complex even after applying advanced feature extraction and feature selection techniques. Traditional and classical feature selection techniques may struggle to capture complex interdependencies between features. They may get stuck in local optima and sometimes require additional exploration strategies. When applied to a prominent feature space, traditional techniques may also struggle with combinatorial optimization problems. This paper proposed a methodology to overcome the existing challenges by applying feature extraction using Vision Transformer (FexViT) and Feature selection using the Quadratic unconstrained binary optimization (FSelQUBO) technique. This algorithm shows better performance when compared with other existing techniques. The proposed methodology showed better performance as compared to other existing techniques when evaluated by applying necessary output measures, such as accuracy, Area under roc (receiver operating characteristics) curve, precision, sensitivity, and specificity, obtained as 94.28%, 99.10%, 96.17%, 90.16% and 97.46%. The further advancement of CAD systems is essential to meet the demand for more reliable detection and diagnosis of cancer, which can be addressed by leading the proposed quantum computation and growing AI-based technology ahead.

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“…Quantum computing [23][24][25], which incorporates the superposition principle of quantum states, possesses parallel computing capabilities. In comparison to traditional neural networks, quantum neural networks [26][27][28] offer a higher storage capacity, effectively mitigating the vanishing gradient problem. This makes them particularly well-suited for high-demand computations involving large-scale datasets.…”

Section: Introductionmentioning

confidence: 99%

Fast Coordinated Predictive Control for Renewable Energy Integrated Cascade Hydropower System Based on Quantum Neural Network

Ye,

Chen,

Zhu

et al. 2024

Electronics

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The increasing penetration of renewable energy poses intractable uncertainties in cascade hydropower systems, such that excessively conservative operations and unnecessary curtailment of clean energies can be incurred. To address these challenges, a quantum neural network (QNN)-based coordinated predictive control approach is proposed. It manipulates coordinated dispatch of multiple clean energy sources, including hydro, wind, and solar power, leverages QNN to conquer intricate multi-uncertainty and learn intraday predictive control patterns, by taking renewable power, load, demand response (DR), and optimal unit commitment as observations. This enables us to exploit the stability and exponential memory capacity of QNN to extrapolate diversified dispatch policies in a reliable manner, which can be hard to reach for traditional learning algorithms. A closed-loop warm start framework is finally presented to enhance the dispatch quality, where the decisions by QNN are fed to initialize the optimizer, and the optimizer returns optimal solutions to quickly evolve the QNN. A real-world case in the ZD sub-grid of the Sichuan power grid in China demonstrates that the proposed method hits a favorable balance between operational cost, accuracy, and efficiency. It realizes second-level elapsed time for intraday predictive control.

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Stereoscopic scalable quantum convolutional neural networks

Cited by 9 publications

References 25 publications

Quantum machine learning for image classification

Quantum machine learning for image classification

Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment

Fast Coordinated Predictive Control for Renewable Energy Integrated Cascade Hydropower System Based on Quantum Neural Network

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