Imagen de Portada

Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Layden, David, Sweke, Ryan, Havlíček, Vojtěch, Chowdhury, Anirban, Neklyudov, Kirill
Formato:	Preprint
Publicado:	2025
Materias:	Quantum Physics Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Quantum complexity of the Kronecker coefficients
por: Bravyi, Sergey, et al.
Publicado: (2023)

Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition
por: Carrasquilla, Juan, et al.
Publicado: (2023)

Classical Verification of Quantum Learning
por: Caro, Matthias C., et al.
Publicado: (2023)

Quantum Algorithms for Representation-Theoretic Multiplicities
por: Larocca, Martin, et al.
Publicado: (2024)

Riemannian MeanFlow
por: Woo, Dongyeop, et al.
Publicado: (2026)

Quantum Normalizing Flows for Anomaly Detection
por: Rosenhahn, Bodo, et al.
Publicado: (2024)

Potential and limitations of random Fourier features for dequantizing quantum machine learning
por: Sweke, Ryan, et al.
Publicado: (2023)

Quantum Flow Matching
por: Cui, Zidong, et al.
Publicado: (2025)

Efficient Quantum Gibbs Sampling with Local Circuits
por: Hahn, Dominik, et al.
Publicado: (2025)

On the average-case complexity of learning output distributions of quantum circuits
por: Nietner, Alexander, et al.
Publicado: (2023)

FlowQ-Net: A Generative Framework for Automated Quantum Circuit Design
por: Dai, Jun, et al.
Publicado: (2025)

Quantum Neural Networks for Power Flow Analysis
por: Kaseb, Zeynab, et al.
Publicado: (2023)

Interactive proofs for verifying (quantum) learning and testing
por: Caro, Matthias C., et al.
Publicado: (2024)

From Classical to Hybrid: A Practical Framework for Quantum-Enhanced Learning
por: Illésová, Silvie, et al.
Publicado: (2025)

On the hardness of cloning and connections to representation theory
por: Havlíček, Vojtěch, et al.
Publicado: (2024)

Learning topological states from randomized measurements using variational tensor network tomography
por: Teng, Yanting, et al.
Publicado: (2024)

Functional Neural Wavefunction Optimization
por: Armegioiu, Victor, et al.
Publicado: (2025)

Distributed Quantum Property Testing with Communication Constraints
por: Doosti, Mina, et al.
Publicado: (2026)

A Computational Framework for Solving Wasserstein Lagrangian Flows
por: Neklyudov, Kirill, et al.
Publicado: (2023)

Tensor network to learn the wavefunction of data
por: Dymarsky, Anatoly, et al.
Publicado: (2021)

Quantum Power Flows: From Theory to Practice
por: Liu, Junyu, et al.
Publicado: (2022)

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

On the Complexity of Decoded Quantum Interferometry
por: Marwaha, Kunal, et al.
Publicado: (2025)

Variational Quantum Optimization with Continuous Bandits
por: Wanner, Marc, et al.
Publicado: (2025)

Simulating Non-Markovian Open Quantum Dynamics with Neural Quantum States
por: Cao, Long, et al.
Publicado: (2024)

Quantum Convolutional Neural Networks are Effectively Classically Simulable
por: Bermejo, Pablo, et al.
Publicado: (2024)

Classical and quantum algorithms for characters of the symmetric group
por: Bravyi, Sergey, et al.
Publicado: (2025)

New perspectives on quantum kernels through the lens of entangled tensor kernels
por: Shin, Seongwook, et al.
Publicado: (2025)

A Resource Efficient Quantum Kernel
por: Singh, Utkarsh, et al.
Publicado: (2025)

Resource-Efficient Variational Quantum Classifier
por: Ptáček, Petr, et al.
Publicado: (2025)

Uncertainty Quantification for Quantum Computing
por: Bennink, Ryan, et al.
Publicado: (2026)

RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations
por: Kumar, Swagat, et al.
Publicado: (2025)

Fragmentation is Efficiently Learnable by Quantum Neural Networks
por: Mints, Mikhail, et al.
Publicado: (2025)

A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning
por: Cowlessur, Hevish, et al.
Publicado: (2025)

Quantum Neural Physics: Solving Partial Differential Equations on Quantum Simulators using Quantum Convolutional Neural Networks
por: Zhai, Jucai, et al.
Publicado: (2026)

AQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks for Carbon-Efficient Climate Modeling
por: Dutta, Siddhant, et al.
Publicado: (2024)

SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization
por: Rahman, Ratun, et al.
Publicado: (2025)

Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
por: Atanackovic, Lazar, et al.
Publicado: (2024)

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

Efficient Quantum Agnostic Improper Learning of Decision Trees
por: Chatterjee, Sagnik, et al.
Publicado: (2022)