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Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuits

15 November 2021

Papers citing "Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuits"

23 / 23 papers shown

Title
Gradient-based Sample Selection for Faster Bayesian Optimization Qiyu Wei Haowei Wang Zirui Cao Songhao Wang Richard Allmendinger Mauricio A Álvarez 56 1 0 10 Apr 2025
Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers Samuele Pedrielli Christopher J. Anders L. Funcke K. Jansen K. Nicoli Shinichi Nakajima 69 0 0 04 Feb 2025
CONGO: Compressive Online Gradient Optimization Jeremy Carleton Prathik Vijaykumar Divyanshu Saxena Dheeraj Narasimha Srinivas Shakkottai Aditya Akella 89 0 0 08 Jul 2024
Exponential Error Convergence in Data Classification with Optimized Random Features: Acceleration by Quantum Machine Learning H. Yamasaki Sho Sonoda 55 6 0 16 Jun 2021
Optimal training of variational quantum algorithms without barren plateaus Tobias Haug M. S. Kim 90 35 0 29 Apr 2021
Variational Quantum Algorithms M. Cerezo A. Arrasmith Ryan Babbush S. Benjamin Suguru Endo ... Jarrod R. McClean K. Mitarai Xiao Yuan L. Cincio Patrick J. Coles 107 2,537 0 16 Dec 2020
Predicting Many Properties of a Quantum System from Very Few Measurements Hsin-Yuan Huang R. Kueng J. Preskill 55 1,115 0 18 Feb 2020
Stochastic gradient descent for hybrid quantum-classical optimization R. Sweke Frederik Wilde Johannes Jakob Meyer Maria Schuld Paul K. Fährmann Barthélémy Meynard-Piganeau Jens Eisert 55 240 0 02 Oct 2019
Quantum Natural Gradient J. Stokes J. Izaac N. Killoran Giuseppe Carleo 54 409 0 04 Sep 2019
Optimizing quantum heuristics with meta-learning M. Wilson Rachel Stromswold Filip Wudarski Stuart Hadfield N. Tubman E. Rieffel 44 75 0 08 Aug 2019
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces Johannes Kirschner Mojmír Mutný N. Hiller R. Ischebeck Andreas Krause 76 149 0 08 Feb 2019
Tight Analyses for Non-Smooth Stochastic Gradient Descent Nicholas J. A. Harvey Christopher Liaw Y. Plan Sikander Randhawa 55 138 0 13 Dec 2018
Supervised learning with quantum enhanced feature spaces Vojtěch Havlíček A. Córcoles K. Temme A. Harrow A. Kandala J. Chow J. Gambetta 84 1,833 0 30 Apr 2018
Barren plateaus in quantum neural network training landscapes Jarrod R. McClean Sergio Boixo V. Smelyanskiy Ryan Babbush Hartmut Neven 95 1,825 0 29 Mar 2018
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups Paul Rolland Jonathan Scarlett Ilija Bogunovic Volkan Cevher 71 115 0 20 Feb 2018
A Progressive Batching L-BFGS Method for Machine Learning Raghu Bollapragada Dheevatsa Mudigere J. Nocedal Hao-Jun Michael Shi P. T. P. Tang ODL 80 153 0 15 Feb 2018
Adaptive Sampling Strategies for Stochastic Optimization Raghu Bollapragada R. Byrd J. Nocedal 44 116 0 30 Oct 2017
Coupling Adaptive Batch Sizes with Learning Rates Lukas Balles Javier Romero Philipp Hennig ODL 130 110 0 15 Dec 2016
Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci Philipp Hennig ODL 68 126 0 10 Feb 2015
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir Tong Zhang 160 576 0 08 Dec 2012
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek Hugo Larochelle Ryan P. Adams 373 7,957 0 13 Jun 2012
Hybrid Deterministic-Stochastic Methods for Data Fitting M. Friedlander Mark Schmidt 199 388 0 13 Apr 2011
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design Niranjan Srinivas Andreas Krause Sham Kakade Matthias Seeger 154 1,624 0 21 Dec 2009