Copertina

Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Thudumu, Srikanth, Fisher, Jason, Du, Hung
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Quantum Physics Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

OpenAg: Democratizing Agricultural Intelligence
di: Thudumu, Srikanth, et al.
Pubblicazione: (2025)

A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directions
di: Du, Hung, et al.
Pubblicazione: (2024)

Pretraining Objective Matters in Extreme Low-Data FGVC: A Backbone-Controlled Study
di: Hackett, Alexander, et al.
Pubblicazione: (2026)

Quantum Machine Learning in Multi-Qubit Phase-Space Part I: Foundations
di: Heightman, Timothy, et al.
Pubblicazione: (2025)

Contextual Knowledge Sharing in Multi-Agent Reinforcement Learning with Decentralized Communication and Coordination
di: Du, Hung, et al.
Pubblicazione: (2025)

Goal-Oriented Multi-Agent Reinforcement Learning for Decentralized Agent Teams
di: Du, Hung, et al.
Pubblicazione: (2025)

Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures
di: Russo, Enrico, et al.
Pubblicazione: (2024)

Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning
di: Qi, Jun, et al.
Pubblicazione: (2024)

Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
di: Du, Yuxuan, et al.
Pubblicazione: (2025)

Cavity-Enhanced Collective Quantum Processing with Polarization-Encoded Qubits
di: Wereszczyński, Kamil, et al.
Pubblicazione: (2026)

Digital-Analog Quantum Machine Learning
di: Lamata, Lucas
Pubblicazione: (2024)

Quantum Sidecar Architectures for Hybrid AI Training and Inference: Stateful Protected Registers, Stateless Reset-and-Reprepare Circuits and Quantum Weight-State Outlook
di: Mo, Y., et al.
Pubblicazione: (2026)

Overcoming Semantic Dilution in Transformer-Based Next Frame Prediction
di: Nguyen, Hy, et al.
Pubblicazione: (2025)

Optimal Layout Synthesis for Deep Quantum Circuits on NISQ Processors with 100+ Qubits
di: Shaik, Irfansha, et al.
Pubblicazione: (2024)

Quantum-Assisted Simulation: A Framework for Developing Machine Learning Models in Quantum Computing
di: Rath, Minati, et al.
Pubblicazione: (2023)

Enhancement of Quantum Semi-Supervised Learning via Improved Laplacian and Poisson Methods
di: Gholipour, Hamed, et al.
Pubblicazione: (2025)

Benchmarking data encoding methods in Quantum Machine Learning
di: Zang, Orlane, et al.
Pubblicazione: (2025)

From Classical to Quantum Reinforcement Learning and Its Applications in Quantum Control: A Beginner's Tutorial
di: Sen, Abhijit, et al.
Pubblicazione: (2026)

Generalization Bounds in Hybrid Quantum-Classical Machine Learning Models
di: Wu, Tongyan, et al.
Pubblicazione: (2025)

A Primer on Quantum Machine Learning
di: Chang, Su Yeon, et al.
Pubblicazione: (2025)

Regression and Classification with Single-Qubit Quantum Neural Networks
di: Souza, Leandro C., et al.
Pubblicazione: (2024)

QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning
di: Nguyen, Kien X., et al.
Pubblicazione: (2026)

Trustworthy Quantum Machine Learning: A Roadmap for Reliability, Robustness, and Security in the NISQ Era
di: Catak, Ferhat Ozgur, et al.
Pubblicazione: (2025)

Quantum Processing Unit (QPU) processing time Prediction with Machine Learning
di: Xing, Lucy, et al.
Pubblicazione: (2025)

Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning
di: Sanjaya, Sahan, et al.
Pubblicazione: (2026)

Image Denoising with Machine Learning: A Novel Approach to Improve Quantum Image Processing Quality and Reliability
di: Zhou, Yifan, et al.
Pubblicazione: (2024)

Transition Role of Entangled Data in Quantum Machine Learning
di: Wang, Xinbiao, et al.
Pubblicazione: (2023)

Continuous-Variable Quantum Encoding Techniques: A Comparative Study of Embedding Techniques and Their Impact on Machine Learning Performance
di: Rath, Minati, et al.
Pubblicazione: (2025)

Large Language Model-Assisted Superconducting Qubit Experiments
di: Li, Shiheng, et al.
Pubblicazione: (2026)

Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning
di: Fan, Fan, et al.
Pubblicazione: (2026)

Quantum Architecture Search for Solving Quantum Machine Learning Tasks
di: Kölle, Michael, et al.
Pubblicazione: (2025)

Reinforcement Learning Generation of 4-Qubits Entangled States
di: Giordano, Sara, et al.
Pubblicazione: (2022)

A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
di: Wang, Yunfei, et al.
Pubblicazione: (2024)

Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU
di: Zi, Wei, et al.
Pubblicazione: (2024)

Identification and Mitigating Bias in Quantum Machine Learning
di: Swaminathan, Nandhini, et al.
Pubblicazione: (2024)

Quantum Machine Unlearning: Foundations, Mechanisms, and Taxonomy
di: Shaik, Thanveer, et al.
Pubblicazione: (2025)

Optimal Layout-Aware CNOT Circuit Synthesis with Qubit Permutation
di: Shaik, Irfansha, et al.
Pubblicazione: (2024)

Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning
di: Ordóñez, Sebastián Andrés Cajas, et al.
Pubblicazione: (2025)

Local Control Networks (LCNs): Optimizing Flexibility in Neural Network Data Pattern Capture
di: Nguyen, Hy, et al.
Pubblicazione: (2025)

QAMA: Scalable Quantum Annealing Multi-Head Attention Operator for Deep Learning
di: Du, Peng, et al.
Pubblicazione: (2025)