ScholarWorks@UMassAmherst
We are now able to accept submissions directly in ScholarWorks. For submissions that are not doctoral dissertations or masters theses, please log in with your NetID, click the + (plus) in to the top left corner, and select the Submit Research option.
Graduate students filing for February 2025 degrees: We are now accepting submissions directly to ScholarWorks. Directions for submissions can be found in this guide. Please email scholarworks@library.umass.edu if you have any questions.
Request forms are functional. If you do not receive a reply to a submitted request, please email scholarworks@library.umass.edu.
This site is still under construction, please see our ScholarWorks guide for updates.
Recent Submissions
Publication CIES Northeast Regional Conference at UMass(2013-10)In Fall 2013 CIE hosted the Northeast Regional Conference of CIES at the Campus Center.Publication Publication CIE Retreat - 1979?(1979-09)That year the annual CIE retreat was held at Fox Run, a resort hotel in western Massachusetts as we looked for more suitable locations for future retreats.Publication CIE Retreat - Camp Bement - 1980(1980-09)This was the first retreat held at Camp Bement, a summer camp in central Massachusetts owned by a group of churches, that CIE used for many years thereafter for its annual retreats. This version features a complete list of names of those in the picture.Publication Multi-Objective Evolutionary Architecture Search for Parameterized Quantum Circuits(2023)Recent work on hybrid quantum-classical machine learning systems has demonstrated success in utilizing parameterized quantum circuits (PQCs) to solve the challenging reinforcement learning (RL) tasks, with provable learning advantages over classical systems, e.g., deep neural networks. While existing work demonstrates and exploits the strength of PQC-based models, the design choices of PQC architectures and the interactions between different quantum circuits on learning tasks are generally underexplored. In this work, we introduce a Multi-objective Evolutionary Architecture Search framework for parameterized quantum circuits (MEAS-PQC), which uses a multi-objective genetic algorithm with quantum-specific configurations to perform efficient searching of optimal PQC architectures. Experimental results show that our method can find architectures that have superior learning performance on three benchmark RL tasks, and are also optimized for additional objectives including reductions in quantum noise and model size. Further analysis of patterns and probability distributions of quantum operations helps identify performance-critical design choices of hybrid quantum-classical learning systems.
Communities in ScholarWorks
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