Imagen de Portada

Machine Learning Framework for Efficient Prediction of Quantum Wasserstein Distance

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Feng, Changchun, Qiu, Xinyu, Tao, Laifa, Chen, Lin
Formato:	Preprint
Publicado:	2025
Materias:	Quantum Physics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Physics-Informed Neural Networks with Adaptive Constraints for Multi-Qubit Quantum Tomography
por: Feng, Changchun, et al.
Publicado: (2025)

Critical Scaling of the Quantum Wasserstein Distance
por: Camacho, Gonzalo, et al.
Publicado: (2025)

Wasserstein Distances on Quantum Structures: an Overview
por: Beatty, Emily
Publicado: (2025)

Efficiently catching entangled microwave photons from a quantum transducer with shaped optical pumps
por: Zhong, Changchun
Publicado: (2024)

Quantum Wasserstein Compilation: Unitary Compilation using the Quantum Earth Mover's Distance
por: Richter, Marvin, et al.
Publicado: (2024)

An Egorov Theorem for Wasserstein Distances
por: Cotler, Jordan, et al.
Publicado: (2025)

Device-independent quantum state discrimination
por: Qiu, Xinyu, et al.
Publicado: (2024)

Capability of anti-degradable quantum channel with additional entanglement
por: Zhong, Changchun
Publicado: (2025)

Security Framework for Quantum Distance-Bounding
por: Bogner, Kevin, et al.
Publicado: (2026)

Efficient State Preparation for Quantum Machine Learning
por: Nakhl, Chris, et al.
Publicado: (2026)

Multipartite entangling power by von Neumann entropy
por: Qiu, Xinyu, et al.
Publicado: (2024)

Near-optimal Prediction Error Estimation for Quantum Machine Learning Models
por: Chen, Qiuhao, et al.
Publicado: (2025)

Enhancing Drug Discovery: Quantum Machine Learning for QSAR Prediction with Incomplete Data
por: Chiang, Wei-Yin, et al.
Publicado: (2025)

Dual-Capability Machine Learning Models for Quantum Hamiltonian Parameter Estimation and Dynamics Prediction
por: An, Zheng, et al.
Publicado: (2024)

Predicting the Onset of Quantum Synchronization Using Machine Learning
por: Mahlow, Felipe, et al.
Publicado: (2023)

Low Depth Distributed Quantum Algorithms for Unordered Database Search
por: Huang, Huaijing, et al.
Publicado: (2026)

Quantum Random Features: A Spectral Framework for Quantum Machine Learning
por: Sakurai, Akitada, et al.
Publicado: (2026)

Quantum Wasserstein distance for Gaussian states
por: Hertz, Anaelle, et al.
Publicado: (2025)

On the Role of Controllability in Pulse-based Quantum Machine Learning Models
por: Tao, Han-Xiao, et al.
Publicado: (2024)

Data-Efficient Quantum Noise Modeling via Machine Learning
por: Ji, Yanjun, et al.
Publicado: (2025)

Training Classical Neural Networks by Quantum Machine Learning
por: Liu, Chen-Yu, et al.
Publicado: (2024)

Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning
por: Qi, Jun, et al.
Publicado: (2024)

Quantum Resonant Dimensionality Reduction and Its Application in Quantum Machine Learning
por: Yang, Fan, et al.
Publicado: (2024)

On the mixed UDA states and additivity
por: Qiu, Xinyu, et al.
Publicado: (2025)

Circuit Harmonic Matrices: A Spectral Framework for Quantum Machine Learning
por: Campbell, Kyle James Stuart, et al.
Publicado: (2026)

A Resource-Efficient Variational Quantum Framework for the Traveling Salesman Problem
por: Lin, Yuefeng, et al.
Publicado: (2026)

Quantum Boltzmann Machines for Sample-Efficient Reinforcement Learning
por: Gerlach, Thore, et al.
Publicado: (2025)

Efficient Mutation Testing of Quantum Machine Learning Models
por: Andrews, Emma, et al.
Publicado: (2026)

Enhancing Microwave-Optical Bell Pairs Generation for Quantum Transduction Using Kerr Nonlinearity
por: Li, Fangxin, et al.
Publicado: (2025)

Quantum Machine Learning
por: Usman, Muhammad
Publicado: (2025)

Integrating Machine Learning and Quantum Circuits for Proton Affinity Predictions
por: Jin, Hongni, et al.
Publicado: (2024)

Wasserstein Complexity of Quantum Circuits
por: Li, Lu, et al.
Publicado: (2022)

Machine Learning Framework for Quantum Sampling of Highly-Constrained, Continuous Optimization Problems
por: Wilson, Blake A., et al.
Publicado: (2021)

Efficient Quantum Digital Signatures over Long Distances with Likely Bit Strings
por: Qin, Ji-Qian, et al.
Publicado: (2024)

Adaptive Interpolating Quantum Transform: A Quantum-Native Framework for Efficient Transform Learning
por: Budiutama, Gekko, et al.
Publicado: (2025)

Improved Lower Bounds for Learning Quantum Channels in Diamond Distance
por: Oufkir, Aadil, et al.
Publicado: (2026)

Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage
por: Wang, Maida, et al.
Publicado: (2025)

Quantum-Train: Rethinking Hybrid Quantum-Classical Machine Learning in the Model Compression Perspective
por: Liu, Chen-Yu, et al.
Publicado: (2024)

Quantum Wasserstein distance and its relation to several types of fidelities
por: Tóth, Géza, et al.
Publicado: (2025)

Triangle Inequality for a Quantum Wasserstein Divergence
por: Wirth, Melchior
Publicado: (2025)