Cover Image

Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Pedrielli, Samuele, Anders, Christopher J., Funcke, Lena, Jansen, Karl, Nicoli, Kim A., Nakajima, Shinichi
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Quantum Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adaptive Observation Cost Control for Variational Quantum Eigensolvers
by: Anders, Christopher J., et al.
Published: (2025)

Bias Analysis and Regularization of Sequential Minimal Optimization in Variational Quantum Eigensolvers
by: Pedrielli, Samuele, et al.
Published: (2026)

Machine-Learning-Enhanced Optimization of Noise-Resilient Variational Quantum Eigensolvers
by: Nicoli, Kim A., et al.
Published: (2025)

Physics-Informed Bayesian Optimization of Variational Quantum Circuits
by: Nicoli, Kim A., et al.
Published: (2024)

VQEzy: An Open-Source Dataset for Parameter Initialization in Variational Quantum Eigensolvers
by: Zhang, Chi, et al.
Published: (2025)

Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects
by: Bulgarelli, Andrea, et al.
Published: (2024)

Multilevel Generative Samplers for Investigating Critical Phenomena
by: Singha, Ankur, et al.
Published: (2025)

SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows
by: Kreit, Janik, et al.
Published: (2025)

Generative Quantum-inspired Kolmogorov-Arnold Eigensolver
by: Lin, Yu-Cheng, et al.
Published: (2026)

Variational Quantum Eigensolver with Constraints (VQEC): Solving Constrained Optimization Problems via VQE
by: Le, Thinh Viet, et al.
Published: (2023)

Variational Quantum Eigensolver Approach to Prime Factorization on IBM's Noisy Intermediate Scale Quantum Computer
by: Sobhani, Mona, et al.
Published: (2024)

QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits
by: Kulshrestha, Ankit, et al.
Published: (2024)

DyPP: Dynamic Parameter Prediction to Accelerate Convergence of Variational Quantum Algorithms
by: Kundu, Satwik, et al.
Published: (2023)

Optimal Parameter Configurations for Sequential Optimization of Variational Quantum Eigensolver
by: Endo, Katsuhiro, et al.
Published: (2023)

Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training
by: Sahin, M. Emre, et al.
Published: (2024)

Q-MAML: Quantum Model-Agnostic Meta-Learning for Variational Quantum Algorithms
by: Lee, Junyong, et al.
Published: (2025)

Warm-Start Variational Quantum Policy Iteration
by: Meyer, Nico, et al.
Published: (2024)

Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
by: Meyer, Nico, et al.
Published: (2025)

Quantum Shadow Gradient Descent for Variational Quantum Algorithms
by: Heidari, Mohsen, et al.
Published: (2023)

Simulating the Hubbard Model with Equivariant Normalizing Flows
by: Schuh, Dominic, et al.
Published: (2025)

Simulating Correlated Electrons with Symmetry-Enforced Normalizing Flows
by: Schuh, Dominic, et al.
Published: (2025)

Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits
by: Kölle, Michael, et al.
Published: (2025)

Barren Plateaus in Variational Quantum Computing
by: Larocca, Martin, et al.
Published: (2024)

An Introduction to Variational Quantum Eigensolver Applied to Chemistry
by: Fonseca, Matheus da S., et al.
Published: (2025)

DiffQ: Unified Parameter Initialization for Variational Quantum Algorithms via Diffusion Models
by: Zhang, Chi, et al.
Published: (2025)

Small-scale Hamiltonian optimization of interpolating operators for Lagrangian lattice quantum field theory
by: Avkhadiev, Artur, et al.
Published: (2024)

Noise-Induced Barren Plateaus in Variational Quantum Algorithms
by: Wang, Samson, et al.
Published: (2020)

Resource-Efficient Variational Quantum Classifier
by: Ptáček, Petr, et al.
Published: (2025)

Variational Quantum Optimization with Continuous Bandits
by: Wanner, Marc, et al.
Published: (2025)

(2+1)D Quantum Electrodynamics at Finite Density on a Quantum Computer
by: Rosanowski, Emil Otis, et al.
Published: (2025)

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

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

Towards determining the (2+1)-dimensional Quantum Electrodynamics running coupling with Monte Carlo and quantum computing methods
by: Crippa, Arianna, et al.
Published: (2024)

Classical Regularization in Variational Quantum Eigensolvers
by: Chernyak, Yury, et al.
Published: (2026)

Physically motivated improvements of Variational Quantum Eigensolvers
by: Vaquero-Sabater, Nonia, et al.
Published: (2024)

Exponential Scaling Barriers for Variational Quantum Eigensolvers
by: Hagelueken, Manuel, et al.
Published: (2026)

Trainability Beyond Linearity in Variational Quantum Objectives
by: Ma, Gordon, et al.
Published: (2026)

Reinforcement Learning for Variational Quantum Circuits Design
by: Foderà, Simone, et al.
Published: (2024)

Spectral Bias in Variational Quantum Machine Learning
by: Duffy, Callum, et al.
Published: (2025)

Expressive equivalence of classical and quantum restricted Boltzmann machines
by: Demidik, Maria, et al.
Published: (2025)