Copertina

Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Yu, Zhan, Chen, Qiuhao, Jiao, Yuling, Li, Yinan, Lu, Xiliang, Wang, Xin, Yang, Jerry Zhijian
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Quantum Physics Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Approximation Error Upper and Lower Bounds for Hölder Class with Transformers
di: He, Xin, et al.
Pubblicazione: (2026)

Near-optimal Prediction Error Estimation for Quantum Machine Learning Models
di: Chen, Qiuhao, et al.
Pubblicazione: (2025)

Online Quantum State Tomography via Stochastic Gradient Descent
di: Cai, Jian-Feng, et al.
Pubblicazione: (2025)

Deep Transfer Learning: Model Framework and Error Analysis
di: Jiao, Yuling, et al.
Pubblicazione: (2024)

GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs
di: Jiao, Yuling, et al.
Pubblicazione: (2023)

Quantum Compiling with Reinforcement Learning on a Superconducting Processor
di: Wang, Z. T., et al.
Pubblicazione: (2024)

Special-Unitary Parameterization for Trainable Variational Quantum Circuits
di: Chen, Kuan-Cheng, et al.
Pubblicazione: (2025)

Latent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum States
di: Tran, Quoc Hoan, et al.
Pubblicazione: (2026)

DRM Revisited: A Complete Error Analysis
di: Jiao, Yuling, et al.
Pubblicazione: (2024)

Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits
di: Nguyen, Hoang-Quan, et al.
Pubblicazione: (2024)

Quantum Generative Modeling using Parameterized Quantum Circuits
di: Sarkar, Soumyadip
Pubblicazione: (2023)

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation
di: Barta, Daniel, et al.
Pubblicazione: (2025)

Approximation Bounds for Recurrent Neural Networks with Application to Regression
di: Jiao, Yuling, et al.
Pubblicazione: (2024)

Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
di: Aktar, Shamminuj, et al.
Pubblicazione: (2024)

Robust Fuzzy local k-plane clustering with mixture distance of hinge loss and L1 norm
di: Huang, Junjun, et al.
Pubblicazione: (2026)

Statistics-Informed Parameterized Quantum Circuit via Maximum Entropy Principle for Data Science and Finance
di: Zhuang, Xi-Ning, et al.
Pubblicazione: (2024)

Approximation Bounds for Transformer Networks with Application to Regression
di: Jiao, Yuling, et al.
Pubblicazione: (2025)

Deep conditional distribution learning via conditional Föllmer flow
di: Chang, Jinyuan, et al.
Pubblicazione: (2024)

Adv-SSL: Adversarial Self-Supervised Representation Learning with Theoretical Guarantees
di: Duan, Chenguang, et al.
Pubblicazione: (2024)

Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification
di: Komini, Ralntion, et al.
Pubblicazione: (2026)

Taming Barren Plateaus in Arbitrary Parameterized Quantum Circuits without Sacrificing Expressibility
di: Chen, Zhenyu, et al.
Pubblicazione: (2025)

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

Semi-Supervised Deep Sobolev Regression: Estimation and Variable Selection by ReQU Neural Network
di: Ding, Zhao, et al.
Pubblicazione: (2024)

Calibrated Language Models and How to Find Them with Label Smoothing
di: Huang, Jerry, et al.
Pubblicazione: (2025)

Leveraging Nested MLMC for Sequential Neural Posterior Estimation with Intractable Likelihoods
di: Yang, Xiliang, et al.
Pubblicazione: (2024)

Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits
di: Lee, Yu-Ting, et al.
Pubblicazione: (2026)

Q-Tag: Watermarking Quantum Circuit Generative Models
di: Yang, Yang, et al.
Pubblicazione: (2026)

Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits
di: Qi, Jun, et al.
Pubblicazione: (2023)

Inference-Time Alignment for Diffusion Models via Variationally Stable Doob's Matching
di: Chang, Jinyuan, et al.
Pubblicazione: (2026)

Active Learning with Variational Quantum Circuits for Quantum Process Tomography
di: Yang, Jiaqi, et al.
Pubblicazione: (2024)

Sampling via Stochastic Interpolants by Langevin-based Velocity and Initialization Estimation in Flow ODEs
di: Duan, Chenguang, et al.
Pubblicazione: (2026)

Generalization Error Bound for Quantum Machine Learning in NISQ Era -- A Survey
di: Khanal, Bikram, et al.
Pubblicazione: (2024)

Reinforcement Learning for Parameterized Quantum State Preparation: A Comparative Study
di: Stenzel, Gerhard, et al.
Pubblicazione: (2026)

Nonlinear Assimilation via Score-based Sequential Langevin Sampling
di: Ding, Zhao, et al.
Pubblicazione: (2024)

An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation
di: Xiong, Yifei, et al.
Pubblicazione: (2023)

Random-Matrix-Induced Simplicity Bias in Over-parameterized Variational Quantum Circuits
di: Qi, Jun, et al.
Pubblicazione: (2026)

Parameterized quantum comb and simpler circuits for reversing unknown qubit-unitary operations
di: Mo, Yin, et al.
Pubblicazione: (2024)

Maximising Quantum-Computing Expressive Power through Randomised Circuits
di: Yang, Yingli, et al.
Pubblicazione: (2023)

Domain-Aware Quantum Circuit for QML
di: Singh, Gurinder, et al.
Pubblicazione: (2025)

On Quantum Circuits for Discrete Graphical Models
di: Piatkowski, Nico, et al.
Pubblicazione: (2022)