Cover Image

Generative quantum advantage for classical and quantum problems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Huang, Hsin-Yuan, Broughton, Michael, Eassa, Norhan, Neven, Hartmut, Babbush, Ryan, McClean, Jarrod R.
Format:	Preprint
Published:	2025
Subjects:	Quantum Physics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Exponential quantum advantage in processing massive classical data
by: Zhao, Haimeng, et al.
Published: (2026)

Learning shallow quantum circuits
by: Huang, Hsin-Yuan, et al.
Published: (2024)

Quantum advantage for learning shallow neural networks with natural data distributions
by: Lewis, Laura, et al.
Published: (2025)

The vast world of quantum advantage
by: Huang, Hsin-Yuan, et al.
Published: (2025)

Exponential learning advantages with conjugate states and minimal quantum memory
by: King, Robbie, et al.
Published: (2024)

On quantum backpropagation, information reuse, and cheating measurement collapse
by: Abbas, Amira, et al.
Published: (2023)

Exponential Quantum Communication Advantage in Distributed Inference and Learning
by: Gilboa, Dar, et al.
Published: (2023)

Accelerating Quantum Algorithms with Precomputation
by: Huggins, William J., et al.
Published: (2023)

Barren Plateaus in Variational Quantum Computing
by: Larocca, Martin, et al.
Published: (2024)

Consumable Data via Quantum Communication
by: Gilboa, Dar, et al.
Published: (2024)

Predicting adaptively chosen observables in quantum systems
by: Huang, Jerry, et al.
Published: (2024)

Quantum oracles give an advantage for identifying classical counterfactuals
by: Gilligan-Lee, Ciarán M., et al.
Published: (2025)

Quantum Error Mitigation
by: Cai, Zhenyu, et al.
Published: (2022)

Prospects for quantum advantage in machine learning from the representability of functions
by: Masot-Llima, Sergi, et al.
Published: (2025)

Learning quantum symmetries with interactive quantum-classical variational algorithms
by: Lu, Jonathan Z., et al.
Published: (2022)

Efficient quantum-enhanced classical simulation for patches of quantum landscapes
by: Lerch, Sacha, et al.
Published: (2024)

Certifying almost all quantum states with few single-qubit measurements
by: Huang, Hsin-Yuan, et al.
Published: (2024)

Enhancing variational quantum algorithms by balancing training on classical and quantum hardware
by: Bhowmick, Rahul, et al.
Published: (2025)

The Grand Challenge of Quantum Applications
by: Babbush, Ryan, et al.
Published: (2025)

Hybrid quantum-classical algorithm for near-optimal planning in POMDPs
by: Cunha, Gilberto, et al.
Published: (2025)

Anonymous Quantum Tokens with Classical Verification
by: Gavinsky, Dmytro, et al.
Published: (2025)

Exponential separations between classical and quantum learners
by: Gyurik, Casper, et al.
Published: (2023)

Entanglement-induced provable and robust quantum learning advantages
by: Zhao, Haimeng, et al.
Published: (2024)

Non-iid hypothesis testing: from classical to quantum
by: De Palma, Giacomo, et al.
Published: (2025)

Securing Elliptic Curve Cryptocurrencies against Quantum Vulnerabilities: Resource Estimates and Mitigations
by: Babbush, Ryan, et al.
Published: (2026)

Expressive equivalence of classical and quantum restricted Boltzmann machines
by: Demidik, Maria, et al.
Published: (2025)

On the similarity of bandwidth-tuned quantum kernels and classical kernels
by: Flórez-Ablan, Roberto, et al.
Published: (2025)

Limits of quantum generative models with classical sampling hardness
by: Herbst, Sabrina, et al.
Published: (2025)

Dynamical simulation via quantum machine learning with provable generalization
by: Gibbs, Joe, et al.
Published: (2022)

A general learning scheme for classical and quantum Ising machines
by: Schmid, Ludwig, et al.
Published: (2023)

Learning quantum states and unitaries of bounded gate complexity
by: Zhao, Haimeng, et al.
Published: (2023)

A brief history of quantum vs classical computational advantage
by: LaRose, Ryan
Published: (2024)

QGAN-based data augmentation for hybrid quantum-classical neural networks
by: He, Run-Ze, et al.
Published: (2025)

The power and limitations of learning quantum dynamics incoherently
by: Jerbi, Sofiene, et al.
Published: (2023)

Comparing concepts of quantum and classical neural network models for image classification task
by: Potempa, Rafal, et al.
Published: (2021)

Better than classical? The subtle art of benchmarking quantum machine learning models
by: Bowles, Joseph, et al.
Published: (2024)

Closing the problem of which causal structures of up to six total nodes have a classical-quantum gap
by: Khanna, Shashaank, et al.
Published: (2025)

Sample-efficient quantum error mitigation via classical learning surrogates
by: Liao, Wei-You, et al.
Published: (2025)

Bridging quantum and classical computing for partial differential equations through multifidelity machine learning
by: Jacob, Bruno, et al.
Published: (2025)

Learning from imperfect quantum data via unsupervised domain adaptation with classical shadows
by: Ito, Kosuke, et al.
Published: (2026)