Cover Image

Shadows of quantum machine learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jerbi, Sofiene, Gyurik, Casper, Marshall, Simon C., Molteni, Riccardo, Dunjko, Vedran
Format:	Preprint
Published:	2023
Subjects:	Quantum Physics Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Exponential quantum advantages in learning quantum observables from classical data
by: Molteni, Riccardo, et al.
Published: (2024)

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

Reinforcement Learning Assisted Recursive QAOA
by: Patel, Yash J., et al.
Published: (2022)

On the relation between trainability and dequantization of variational quantum learning models
by: Gil-Fuster, Elies, et al.
Published: (2024)

Testing the presence of balanced and bipartite components in a sparse graph is QMA1-hard
by: Incudini, Massimiliano, et al.
Published: (2024)

Quantum machine learning advantages beyond hardness of evaluation
by: Molteni, Riccardo, et al.
Published: (2025)

On Bounded Advice Classes
by: Marshall, Simon, et al.
Published: (2024)

A quantum-classical reinforcement learning model to play Atari games
by: Freinberger, Dominik, et al.
Published: (2024)

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

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

Curriculum reinforcement learning for quantum architecture search under hardware errors
by: Patel, Yash J., et al.
Published: (2024)

Enhancing variational quantum state diagonalization using reinforcement learning techniques
by: Kundu, Akash, et al.
Published: (2023)

Variational measurement-based quantum computation for generative modeling
by: Majumder, Arunava, et al.
Published: (2023)

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

Differential equation quantum solvers: engineering measurements to reduce cost
by: Paine, Annie, et al.
Published: (2025)

On the expressivity of embedding quantum kernels
by: Gil-Fuster, Elies, et al.
Published: (2023)

Training-efficient density quantum machine learning
by: Coyle, Brian, et al.
Published: (2024)

Computational complexity of the homology problem with orientable filtration: MA-completeness
by: Hayakawa, Ryu, et al.
Published: (2025)

Generative quantum machine learning via denoising diffusion probabilistic models
by: Zhang, Bingzhi, et al.
Published: (2023)

Detecting underdetermination in parameterized quantum circuits
by: Kempkes, Marie, et al.
Published: (2025)

Optimal algorithmic complexity of inference in quantum kernel methods
by: Gil-Fuster, Elies, et al.
Published: (2026)

Improved separation between quantum and classical computers for sampling and functional tasks
by: Marshall, Simon C., et al.
Published: (2024)

Quantum-inspired tensor networks in machine learning models
by: Valverde, Guillermo, et al.
Published: (2026)

Training quantum machine learning models on cloud without uploading the data
by: He, Guang Ping
Published: (2024)

An unconditional distribution learning advantage with shallow quantum circuits
by: Pirnay, N., et al.
Published: (2024)

Characterizing Trainability of Instantaneous Quantum Polynomial Circuit Born Machines
by: Shen, Kevin, et al.
Published: (2026)

Reinforcement learning for quantum processes with memory
by: Lumbreras, Josep, et al.
Published: (2026)

Reinforcement learning-assisted quantum architecture search for variational quantum algorithms
by: Kundu, Akash
Published: (2024)

The Efficiency Frontier: Classical Shadows versus Quantum Footage
by: Ma, Shuowei, et al.
Published: (2025)

Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
by: Kundu, Akash, et al.
Published: (2024)

Universal scaling laws in quantum-probabilistic machine learning by tensor network towards interpreting representation and generalization powers
by: Bai, Sheng-Chen, et al.
Published: (2024)

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

Double descent in quantum kernel methods
by: Kempkes, Marie, et al.
Published: (2025)

Analogy between Boltzmann machines and Feynman path integrals
by: Iyengar, Srinivasan S., et al.
Published: (2023)

BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
by: Ikhtiarudin, Azhar, et al.
Published: (2025)

Privacy-preserving machine learning with tensor networks
by: Pozas-Kerstjens, Alejandro, et al.
Published: (2022)

Artificial intelligence for representing and characterizing quantum systems
by: Du, Yuxuan, et al.
Published: (2025)

Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver
by: Minami, Shunya, et al.
Published: (2025)

Quantum computing and artificial intelligence: status and perspectives
by: Acampora, Giovanni, et al.
Published: (2025)

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