효율적인 양자 동역학 컴파일을 위한 양자 기계 학습 기법
양자 기계 학습을 활용하여 양자 동역학을 효율적으로 컴파일할 수 있는 방법을 제시한다.
Hybrid VAE-QWGAN: Enhancing Quantum Generative Adversarial Networks for High-Quality Image Generation
The proposed hybrid VAE-QWGAN model combines the strengths of classical Variational AutoEncoder (VAE) and quantum Wasserstein Generative Adversarial Network (QWGAN) to generate high-quality and diverse images from classical datasets.
Accelerating Quantum Support Vector Machines with cuTensorNet: Achieving Exponential Speedups for Large-Scale Data Classification
The paper presents a simulation workflow that substantially diminishes the computational overhead of Quantum Support Vector Machines (QSVMs) from exponential to quadratic cost, enabled by the integration of NVIDIA's cuQuantum SDK and the cuTensorNet library.
量子機械学習アルゴリズムの性能向上のための座標変換の活用
座標変換を用いることで、量子機械学習アルゴリズムの収束性と性能を大幅に改善できる。
Quantum Convolutional Neural Networks for Multi-Class Classification of Classical Data
A quantum convolutional neural network (QCNN) is proposed for multi-class classification of classical data, demonstrating improved performance over classical convolutional neural networks (CNNs) for 6, 8, and 10 class scenarios.
Embedding Quantum Kernels: Universality and Efficient Approximations
All kernel functions can be approximated as embedding quantum kernels, and efficient embedding quantum kernels are universal within the classes of shift-invariant and composition kernels.
Vulnerability of Quantum-Enhanced Support Vector Machines to Adversarial Attacks and Strategies for Robust Defense
Quantum-enhanced support vector machines (QSVMs) are vulnerable to adversarial attacks, where small perturbations to input data can deceive the classifier. However, simple defense strategies based on data augmentation with crafted adversarial samples can make the QSVM classifier robust against new attacks.
Optimizing Quantum Convolutional Neural Networks for Arbitrary Data Dimensions
The core message of this article is to propose an efficient QCNN architecture capable of handling arbitrary data dimensions by optimizing the allocation of quantum resources such as ancillary qubits and quantum gates.
Proper vs Improper Quantum PAC Learning: Sample Complexity Analysis and Algorithm Development
Proper and improper quantum PAC learning complexities are compared, with a focus on the Coupon Collector problem.
