Ellis John Wilson

College of Engineering

Works (3)

Updated: April 5th, 2024 13:54

2022 article

Combining Hard and Soft Constraints in Quantum Constraint-Satisfaction Systems

Wilson, E., Mueller, F., & Pakin, S. (2022, November 1).

By: E. Wilson n, F. Mueller n & S. Pakin *

author keywords: circuit-model quantum computing; quantum annealing; programming models
topics (OpenAlex): Quantum Computing Algorithms and Architecture; Quantum Information and Cryptography; Cloud Computing and Resource Management
TL;DR: This enhanced version of NchooseK enables problems to be expressed in a more concise, less error-prone manner than if these problems were encoded manually for quantum execution, and includes an empirical evaluation of performance, scalability, and fidelity on both a large IBM Q system and a large D- Wave system. (via Semantic Scholar)
Source: Web Of Science
Added: June 12, 2023

2021 article

Mapping Constraint Problems onto Quantum Gate and Annealing Devices

Wilson, E., Mueller, F., & Pakin, S. (2021, November 1).

By: E. Wilson n, F. Mueller n & S. Pakin *

author keywords: circuit-model quantum computing; quantum annealing; programming models
topics (OpenAlex): Quantum Computing Algorithms and Architecture; Computability, Logic, AI Algorithms; Quantum Information and Cryptography
TL;DR: This work presents NchooseK, a unified programming model for constraint satisfaction problems that can be mapped to both quantum circuit and annealing devices through Quadratic Unconstrained Binary Operators (QUBOs). (via Semantic Scholar)
Source: Web Of Science
Added: February 28, 2022

2020 article

Just-in-time Quantum Circuit Transpilation Reduces Noise

Wilson, E., Singh, S., & Mueller, F. (2020, October 1).

By: E. Wilson n, S. Singh  n & F. Mueller n

author keywords: quantum computing; errors; dynamic compilation
topics (OpenAlex): Quantum Computing Algorithms and Architecture; Quantum Information and Cryptography; Low-power high-performance VLSI design
TL;DR: Experiments indicate that the accuracy of circuit results improves by 3–304% on average and up to 400% with on-the-fly circuit mappings based on error measurements just prior to application execution, which is improved over IBM's default mappings. (via Semantic Scholar)
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Source: Web Of Science
Added: June 10, 2021

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