Zobrazeno 1 - 10
of 296
pro vyhledávání: '"Liu, Chen Yu"'
In this paper, we introduce Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning (Dist-QTRL), a novel approach to addressing the scalability challenges of traditional Reinforcement Learning (RL) by integrating quantum computing principl
Externí odkaz:
http://arxiv.org/abs/2412.08845
In this study, the Quantum-Train Quantum Fast Weight Programmer (QT-QFWP) framework is proposed, which facilitates the efficient and scalable programming of variational quantum circuits (VQCs) by leveraging quantum-driven parameter updates for the cl
Externí odkaz:
http://arxiv.org/abs/2412.01173
Quantum-centric supercomputing presents a compelling framework for large-scale hybrid quantum-classical tasks. Although quantum machine learning (QML) offers theoretical benefits in various applications, challenges such as large-size data encoding in
Externí odkaz:
http://arxiv.org/abs/2410.09846
The rise of deepfake technologies has posed significant challenges to privacy, security, and information integrity, particularly in audio and multimedia content. This paper introduces a Quantum-Trained Convolutional Neural Network (QT-CNN) framework
Externí odkaz:
http://arxiv.org/abs/2410.09250
This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroim
Externí odkaz:
http://arxiv.org/abs/2409.08584
In the Quantum-Train (QT) framework, mapping quantum state measurements to classical neural network weights is a critical challenge that affects the scalability and efficiency of hybrid quantum-classical models. The traditional QT framework employs a
Externí odkaz:
http://arxiv.org/abs/2409.06992
Autor:
Musedinovic, R., Blokland, L. S., Cude-Woods, C. B., Singh, M., Blatnik, M. A., Callahan, N., Choi, J. H., Clayton, S., Filippone, B. W., Fox, W. R., Fries, E., Geltenbort, P., Gonzalez, F. M., Hayen, L., Hickerson, K. P., Holley, A. T., Ito, T. M., Komives, A., Lin, S, Liu, Chen-Yu, Makela, M. F., O'Shaughnessy, C. M., Pattie Jr, R. W., Ramsey, J. C., Salvat, D. J., Saunders, A., Seestrom, S. J., Sharapov, E. I., Tang, Z., Uhrich, F. W., Vanderwerp, J., Walstrom, P., Wang, Z., Young, A. R., Morris, C. L.
Here we publish three years of data for the UCNtau experiment performed at the Los Alamos Ultra Cold Neutron Facility at the Los Alamos Neutron Science Center. These data are in addition to our previously published data. Our goals in this paper are t
Externí odkaz:
http://arxiv.org/abs/2409.05560
Autor:
Liu, Chen-Yu, Chen, Samuel Yen-Chi
In this work, we introduce the Federated Quantum-Train (QT) framework, which integrates the QT model into federated learning to leverage quantum computing for distributed learning systems. Quantum client nodes employ Quantum Neural Networks (QNNs) an
Externí odkaz:
http://arxiv.org/abs/2409.02763
This paper introduces a noise-aware distributed Quantum Approximate Optimization Algorithm (QAOA) tailored for execution on near-term quantum hardware. Leveraging a distributed framework, we address the limitations of current Noisy Intermediate-Scale
Externí odkaz:
http://arxiv.org/abs/2407.17325
We present novel path-slicing strategies integrated with quantum local search to optimize solutions for the Traveling Salesman Problem (TSP), addressing the limitations of current Noisy Intermediate-Scale Quantum (NISQ) technologies. Our hybrid quant
Externí odkaz:
http://arxiv.org/abs/2407.13616