Cover Image

Deep reinforcement learning for near-deterministic preparation of cubic- and quartic-phase gates in photonic quantum computing

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Anteneh, Amanuel, Brunel, Léandre, González-Arciniegas, Carlos, Pfister, Olivier
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

Machine learning for efficient generation of universal hybrid quantum computing resources
by: Anteneh, Amanuel, et al.
Published: (2023)

Gradient-descent methods for scalable quantum detector tomography
by: Anteneh, Amanuel, et al.
Published: (2025)

Quantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles
by: Anteneh, Amanuel
Published: (2025)

Sample efficient graph classification using binary Gaussian boson sampling
by: Anteneh, Amanuel, et al.
Published: (2023)

Laser interferometry as a robust neuromorphic platform for machine learning
by: Anteneh, Amanuel, et al.
Published: (2026)

Expressivity of deterministic quantum computation with one qubit
by: Kim, Yujin, et al.
Published: (2024)

All-optical quantum computing using cubic phase gates
by: Budinger, Niklas, et al.
Published: (2022)

Automatic re-calibration of quantum devices by reinforcement learning
by: Crosta, T., et al.
Published: (2024)

Implementing the Koopman-von Neumann approach on continuous-variable photonic quantum computers
by: Gao, Xinfeng, et al.
Published: (2025)

Discovering autonomous quantum error correction via deep reinforcement learning
by: Yin, Yue, et al.
Published: (2025)

Full Bell-basis measurement of an atom-photon 2-qubit state and its application for quantum networks
by: Arenskötter, Elena, et al.
Published: (2023)

Biclustering a dataset using photonic quantum computing
by: Borle, Ajinkya, et al.
Published: (2024)

Spectral methods: crucial for machine learning, natural for quantum computers?
by: Belis, Vasilis, et al.
Published: (2026)

Reinforcement learning for ion shuttling on trapped-ion quantum computers
by: Schier, Maximilian, et al.
Published: (2026)

Towards Heisenberg limit without critical slowing down via quantum reinforcement learning
by: Xu, Hang, et al.
Published: (2025)

Scalable high-fidelity and near-deterministic preparation of large photon-number states
by: Xiong, Mo, et al.
Published: (2026)

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)

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

Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardware
by: Kundu, Akash
Published: (2025)

Quantum reinforcement learning in continuous action space
by: Wu, Shaojun, et al.
Published: (2020)

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

What is my quantum computer good for? Quantum capability learning with physics-aware neural networks
by: Hothem, Daniel, et al.
Published: (2024)

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

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

Learning a quantum computer's capability
by: Hothem, Daniel, et al.
Published: (2023)

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

Machine learning discovery of new phases in programmable quantum simulator snapshots
by: Miles, Cole, et al.
Published: (2021)

Hybrid model of the kernel method for quantum computers
by: de Borba, Jhordan Silveira, et al.
Published: (2024)

Discovering highly efficient low-weight quantum error-correcting codes with reinforcement learning
by: He, Austin Yubo, et al.
Published: (2025)

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

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

Probabilistic modeling over permutations using quantum computers
by: Belis, Vasilis, et al.
Published: (2026)

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

Exploring polymer classification with a hybrid single-photon quantum approach
by: Stoyanova, Alexandrina, et al.
Published: (2025)

Online learning of quantum processes
by: Raza, Asad, et al.
Published: (2024)

Noise tolerance via reinforcement: Learning a reinforced quantum dynamics
by: Ramezanpour, Abolfazl
Published: (2025)

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

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

Kernel-based optimization of measurement operators for quantum reservoir computers
by: Gross, Markus, et al.
Published: (2026)