{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-in-quantum-control-high-dimensional","title":"Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics","arxiv_id":"1607.03428","date":"2016-07-12","proceeding":null,"authors":["Pantita Palittapongarnpim","Peter Wittek","Ehsan Zahedinejad","Shakib Vedaie","Barry C. Sanders"],"abstract":"Quantum control is valuable for various quantum technologies such as\nhigh-fidelity gates for universal quantum computing, adaptive quantum-enhanced\nmetrology, and ultra-cold atom manipulation. Although supervised machine\nlearning and reinforcement learning are widely used for optimizing control\nparameters in classical systems, quantum control for parameter optimization is\nmainly pursued via gradient-based greedy algorithms. Although the quantum\nfitness landscape is often compatible with greedy algorithms, sometimes greedy\nalgorithms yield poor results, especially for large-dimensional quantum\nsystems. We employ differential evolution algorithms to circumvent the\nstagnation problem of non-convex optimization. We improve quantum control\nfidelity for noisy system by averaging over the objective function. To reduce\ncomputational cost, we introduce heuristics for early termination of runs and\nfor adaptive selection of search subspaces. Our implementation is massively\nparallel and vectorized to reduce run time even further. We demonstrate our\nmethods with two examples, namely quantum phase estimation and quantum gate\ndesign, for which we achieve superior fidelity and scalability than obtained\nusing greedy algorithms.","url_abs":"http://arxiv.org/abs/1607.03428v3","url_pdf":"http://arxiv.org/pdf/1607.03428v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-in-quantum-control-high-dimensional","repo_url":"https://github.com/PanPalitta/phase_estimation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"global-optimization","task_name":"global-optimization"},{"task_slug":"quantum-gate-design","task_name":"quantum gate design"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}