양자 기계 학습을 활용하여 양자 동역학을 효율적으로 컴파일할 수 있는 방법을 제시한다.
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.
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.
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。
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.
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.
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.
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 and improper quantum PAC learning complexities are compared, with a focus on the Coupon Collector problem.