Copertina

Entanglement-induced provable and robust quantum learning advantages

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhao, Haimeng, Deng, Dong-Ling
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Quantum Physics Computational Complexity Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Learning to erase quantum states: thermodynamic implications of quantum learning theory
di: Zhao, Haimeng, et al.
Pubblicazione: (2025)

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

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

Quantum-Classical Separations in Shallow-Circuit-Based Learning with and without Noises
di: Zhang, Zhihan, et al.
Pubblicazione: (2024)

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

Proper vs Improper Quantum PAC learning
di: Nayak, Ashwin, et al.
Pubblicazione: (2024)

Learning unitaries with quantum statistical queries
di: Angrisani, Armando
Pubblicazione: (2023)

Information-theoretic generalization bounds for learning from quantum data
di: Caro, Matthias, et al.
Pubblicazione: (2023)

Learning quantum states prepared by shallow circuits in polynomial time
di: Landau, Zeph, et al.
Pubblicazione: (2024)

Learning depth-3 circuits via quantum agnostic boosting
di: Arunachalam, Srinivasan, et al.
Pubblicazione: (2025)

Peaked quantum advantage using error correction
di: Deshpande, Abhinav, et al.
Pubblicazione: (2025)

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

A learning theory for quantum photonic processors and beyond
di: Rosati, Matteo
Pubblicazione: (2022)

Efficiently verifiable quantum advantage on near-term analog quantum simulators
di: Liu, Zhenning, et al.
Pubblicazione: (2024)

Adversarially robust quantum state learning and testing
di: Aliakbarpour, Maryam, et al.
Pubblicazione: (2025)

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

Reconquering Bell sampling on qudits: stabilizer learning and testing, quantum pseudorandomness bounds, and more
di: Allcock, Jonathan, et al.
Pubblicazione: (2025)

Has quantum advantage been achieved?
di: Hangleiter, Dominik
Pubblicazione: (2026)

Noise-tolerant learnability of shallow quantum circuits from statistics and the cost of quantum pseudorandomness
di: Wadhwa, Chirag, et al.
Pubblicazione: (2024)

Quantum automated learning with provable and explainable trainability
di: Ye, Qi, et al.
Pubblicazione: (2025)

Generalization Error Bound for Quantum Machine Learning in NISQ Era -- A Survey
di: Khanal, Bikram, et al.
Pubblicazione: (2024)

Learning Robust Observable to Address Noise in Quantum Machine Learning
di: Khanal, Bikram, et al.
Pubblicazione: (2024)

Quantum computing and persistence in topological data analysis
di: Gyurik, Casper, et al.
Pubblicazione: (2024)

Spacetime-Efficient Low-Depth Quantum State Preparation with Applications
di: Gui, Kaiwen, et al.
Pubblicazione: (2023)

Quantum Learning Theory Beyond Batch Binary Classification
di: Mohan, Preetham, et al.
Pubblicazione: (2023)

Gradient Scalability and Taylor Surrogation of Quantum Cost Landscapes
di: Meyer, Sabri, et al.
Pubblicazione: (2025)

Low-Stabilizer-Complexity Quantum States Are Not Pseudorandom
di: Grewal, Sabee, et al.
Pubblicazione: (2022)

Scalable Quantum Error Mitigation with Neighbor-Informed Learning
di: Chen, Zhenyu, et al.
Pubblicazione: (2025)

Reducing the Complexity of Matrix Multiplication to $O(N^2log_2N)$ by an Asymptotically Optimal Quantum Algorithm
di: Yao, Jiaqi, et al.
Pubblicazione: (2026)

Learning Quantum Processes with Quantum Statistical Queries
di: Wadhwa, Chirag, et al.
Pubblicazione: (2023)

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

Quantum information advantage based on Bell inequalities
di: Jain, Rahul, et al.
Pubblicazione: (2026)

Quantum advantage from measurement-induced entanglement in random shallow circuits
di: Watts, Adam Bene, et al.
Pubblicazione: (2024)

Testing classical properties from quantum data
di: Caro, Matthias C., et al.
Pubblicazione: (2024)

An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory
di: Pirnay, Niklas, et al.
Pubblicazione: (2022)

When does Metropolized Hamiltonian Monte Carlo provably outperform Metropolis-adjusted Langevin algorithm?
di: Chen, Yuansi, et al.
Pubblicazione: (2023)

Quantum advantage from random geometrically-two-local Hamiltonian dynamics
di: Quek, Yihui
Pubblicazione: (2025)

Efficient Pauli channel estimation with logarithmic quantum memory
di: Chen, Sitan, et al.
Pubblicazione: (2023)

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

Empirical Sample Complexity of Neural Network Mixed State Reconstruction
di: Zhao, Haimeng, et al.
Pubblicazione: (2023)