Cover Image

Online learning of quantum processes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Raza, Asad, Caro, Matthias C., Eisert, Jens, Khatri, Sumeet
Format:	Preprint
Published:	2024
Subjects:	Quantum Physics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Understanding quantum machine learning also requires rethinking generalization
by: Gil-Fuster, Elies, et al.
Published: (2023)

A PAC-Bayesian approach to generalization for quantum models
by: Rodriguez-Grasa, Pablo, et al.
Published: (2026)

Perturbative gadgets for gate-based quantum computing: Non-recursive constructions without subspace restrictions
by: Cichy, Simon, et al.
Published: (2022)

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

Opportunities and limitations of explaining quantum machine learning
by: Gil-Fuster, Elies, et al.
Published: (2024)

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

Exponentially tighter bounds on limitations of quantum error mitigation
by: Quek, Yihui, et al.
Published: (2022)

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

Towards provably efficient quantum algorithms for large-scale machine-learning models
by: Liu, Junyu, et al.
Published: (2023)

Complexity of geometrically local stoquastic Hamiltonians
by: Raza, Asad, et al.
Published: (2024)

Reinforcement learning of quantum circuit architectures for molecular potential energy curves
by: Krumtünger, Maureen, et al.
Published: (2025)

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

Learning Quantum Processes and Hamiltonians via the Pauli Transfer Matrix
by: Caro, Matthias C.
Published: (2022)

Quantum reinforcement learning of classical rare dynamics: Enhancement by intrinsic Fourier features
by: Wilms, Alissa, et al.
Published: (2025)

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

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

Stochastic noise can be helpful for variational quantum algorithms
by: Liu, Junyu, et al.
Published: (2022)

Towards efficient quantum algorithms for diffusion probabilistic models
by: Wang, Yunfei, et al.
Published: (2025)

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

Towards a General Framework for Practical Quantum Network Protocols
by: Khatri, Sumeet
Published: (2024)

Provable and scalable quantum Gaussian processes for quantum learning
by: Jäger, Jonas, et al.
Published: (2026)

Online learning of a panoply of quantum objects
by: Bansal, Akshay, et al.
Published: (2024)

Quantum metrology in the finite-sample regime
by: Meyer, Johannes Jakob, et al.
Published: (2023)

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

Artificially intelligent Maxwell's demon for optimal control of open quantum systems
by: Erdman, Paolo Andrea, et al.
Published: (2024)

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

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

Fast and reliable entanglement distribution with quantum repeaters: principles for improving protocols using reinforcement learning
by: Haldar, Stav, et al.
Published: (2023)

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

Cloning is as Hard as Learning for Stabilizer States
by: Bansal, Nikhil, et al.
Published: (2026)

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

The computational two-way quantum capacity
by: Meyer, Johannes Jakob, et al.
Published: (2026)

Reinforcement learning-based architecture search for quantum machine learning
by: Rapp, Frederic, et al.
Published: (2024)

On exploring the potential of quantum auto-encoder for learning quantum systems
by: Du, Yuxuan, et al.
Published: (2021)

Single-shot quantum machine learning
by: Recio-Armengol, Erik, et al.
Published: (2024)

Maximum channel entropy principle and microcanonical channels
by: Faist, Philippe, et al.
Published: (2025)

Thermalization with partial information
by: Faist, Philippe, et al.
Published: (2025)

Covert Quantum Learning: Privately and Verifiably Learning from Quantum Data
by: Anand, Abhishek, et al.
Published: (2025)

Is data-efficient learning feasible with quantum models?
by: Sakhnenko, Alona, et al.
Published: (2025)