Cover Image

Separable Power of Classical and Quantum Learning Protocols Through the Lens of No-Free-Lunch Theorem

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Xinbiao, Du, Yuxuan, Liu, Kecheng, Luo, Yong, Du, Bo, Tao, Dacheng
Format:	Preprint
Published:	2024
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

MG-Net: Learn to Customize QAOA with Circuit Depth Awareness
by: Qian, Yang, et al.
Published: (2024)

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

Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors
by: Liao, Wei-You, et al.
Published: (2025)

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

AiDE-Q: Synthetic Labeled Datasets Can Enhance Learning Models for Quantum Property Estimation
by: Wang, Xinbiao, et al.
Published: (2025)

Rethink the Role of Neural Decoders in Quantum Error Correction
by: Yan, Ge, et al.
Published: (2026)

No-Free-Lunch Theories for Tensor-Network Machine Learning Models
by: Wu, Jing-Chuan, et al.
Published: (2024)

Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits
by: Du, Yuxuan, et al.
Published: (2024)

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

A No Free Lunch Theorem for Human-AI Collaboration
by: Peng, Kenny, et al.
Published: (2024)

Learning from models beyond fine-tuning
by: Zheng, Hongling, et al.
Published: (2023)

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

Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation
by: Hasan, Mohammad Junayed, et al.
Published: (2023)

Celo2: Towards Learned Optimization Free Lunch
by: Moudgil, Abhinav, et al.
Published: (2026)

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

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

AQER: a scalable and efficient data loader for digital quantum computers
by: Zhang, Kaining, et al.
Published: (2026)

SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models
by: Tang, Anke, et al.
Published: (2024)

Drawback of Enforcing Equivariance and its Compensation via the Lens of Expressive Power
by: Chen, Yuzhu, et al.
Published: (2025)

Quantum Entanglement as Super-Confounding: From Bell's Theorem to Robust Machine Learning
by: Kang, Pilsung
Published: (2025)

Data Curation Through the Lens of Spectral Dynamics: Static Limits, Dynamic Acceleration, and Practical Oracles
by: Zhang, Yizhou, et al.
Published: (2025)

Quantum-Enhanced Generative Adversarial Networks: Comparative Analysis of Classical and Hybrid Quantum-Classical Generative Adversarial Networks
by: Goh, Kun Ming
Published: (2025)

Boosting In-Context Learning in LLMs Through the Lens of Classical Supervised Learning
by: Gundem, Korel, et al.
Published: (2025)

Flood Prediction Using Classical and Quantum Machine Learning Models
by: Grzesiak, Marek, et al.
Published: (2024)

A Geometric-Aware Perspective and Beyond: Hybrid Quantum-Classical Machine Learning Methods
by: Alavia, Azadeh, et al.
Published: (2025)

No Free Lunch: Fundamental Limits of Learning Non-Hallucinating Generative Models
by: Wu, Changlong, et al.
Published: (2024)

The Role of Quantum in Hybrid Quantum-Classical Neural Networks: A Realistic Assessment
by: Freinberger, Dominik, et al.
Published: (2026)

Integer Scale: A Free Lunch for Faster Fine-grained Quantization of LLMs
by: Li, Qingyuan, et al.
Published: (2024)

GoQuant: Geometric Orthogonal Residual Projection for Multiplier-Free Power-of-Two Transformer Quantization
by: Xiang, Maoyang, et al.
Published: (2026)

QIXAI: A Quantum-Inspired Framework for Enhancing Classical and Quantum Model Transparency and Understanding
by: Willis, John M.
Published: (2024)

QARIMA: A Quantum Approach To Classical Time Series Analysis
by: Mohanty, Nishikanta, et al.
Published: (2026)

No Free Lunch: Rethinking Internal Feedback for LLM Reasoning
by: Zhang, Yanzhi, et al.
Published: (2025)

FusionBench: A Unified Library and Comprehensive Benchmark for Deep Model Fusion
by: Tang, Anke, et al.
Published: (2024)

Q-Newton: Hybrid Quantum-Classical Scheduling for Accelerating Neural Network Training with Newton's Gradient Descent
by: Li, Pingzhi, et al.
Published: (2024)

A Hybrid Quantum-Classical Framework for Financial Volatility Forecasting Based on Quantum Circuit Born Machines
by: Chen, Yixiong
Published: (2026)

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

HiQ-Lip: A Hierarchical Quantum-Classical Method for Global Lipschitz Constant Estimation of ReLU Networks
by: He, Haoqi, et al.
Published: (2025)

No Free Lunch Theorem for Privacy-Preserving LLM Inference
by: Zhang, Xiaojin, et al.
Published: (2024)

Quantitative Evaluation of Quantum/Classical Neural Network Using a Game Solver Metric
by: Kamei, Suzukaze, et al.
Published: (2025)

FreeBind: Free Lunch in Unified Multimodal Space via Knowledge Fusion
by: Wang, Zehan, et al.
Published: (2024)