Ji Liu

College of Engineering

Works (5)

Updated: November 4th, 2024 08:52

2022 conference paper

Exploiting Quantum Assertions for Error Mitigation and Quantum Program Debugging

2022 IEEE 40TH INTERNATIONAL CONFERENCE ON COMPUTER DESIGN (ICCD 2022), 124–131.

By: P. Li n, J. Liu n, Y. Li * & H. Zhou n 

author keywords: quantum computing; error mitigation; debugging; assertion
topics (OpenAlex): Quantum Computing Algorithms and Architecture; Quantum Information and Cryptography; Parallel Computing and Optimization Techniques
TL;DR: This paper presents the development of quantum assertion schemes and shows how they are used for hardware error mitigation and software debugging, and shows that besides detecting program bugs, dynamic assertion circuits can mitigate noise effects via post-selection of the assertion results. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: March 20, 2023

2022 article

Not All SWAPs Have the Same Cost: A Case for Optimization-Aware Qubit Routing

2022 IEEE INTERNATIONAL SYMPOSIUM ON HIGH-PERFORMANCE COMPUTER ARCHITECTURE (HPCA 2022), pp. 709–725.

By: J. Liu n, P. Li n & H. Zhou n 

author keywords: quantum computing; compiler optimization; qubit routing
topics (OpenAlex): Quantum Computing Algorithms and Architecture; Quantum-Dot Cellular Automata; Quantum Information and Cryptography
TL;DR: NASSC (Not All Swaps have the Same Cost) is the first algorithm that considers the subsequent optimizations during the routing step, and optimization-aware qubit routing leads to better routing decisions and benefits subsequent optimizations. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: August 29, 2022

2021 article

Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuits

CGO '21: PROCEEDINGS OF THE 2021 IEEE/ACM INTERNATIONAL SYMPOSIUM ON CODE GENERATION AND OPTIMIZATION (CGO), pp. 301–314.

By: J. Liu n, L. Bello* & H. Zhou n 

author keywords: quantum computing; peephole optimization
topics (OpenAlex): Quantum Computing Algorithms and Architecture; Quantum Information and Cryptography; Quantum-Dot Cellular Automata
TL;DR: A novel quantum compiler optimization, named relaxed peephole optimization (RPO) for quantum computers, which leverages the single-qubit state information that can be determined statically by the compiler and extends the approach to optimize the quantum gates when some input qubits are in known pure states. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: July 26, 2021

2021 article

Systematic Approaches for Precise and Approximate Quantum State Runtime Assertion

2021 27TH IEEE INTERNATIONAL SYMPOSIUM ON HIGH-PERFORMANCE COMPUTER ARCHITECTURE (HPCA 2021), pp. 179–193.

By: J. Liu n & H. Zhou n 

author keywords: quantum computing; runtime assertion
topics (OpenAlex): Quantum Computing Algorithms and Architecture; Quantum Information and Cryptography; Quantum Mechanics and Applications
TL;DR: This work proposes two systematic approaches for dynamic quantum state assertion and they can assert a much broader range of quantum states including both pure states and mixed states and introduces the idea of approximate quantumstate assertion for the cases where the programmers only have limited knowledge of the quantum states. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: July 26, 2021

2020 article

Reliability Modeling of NISQ-Era Quantum Computers

2020 IEEE INTERNATIONAL SYMPOSIUM ON WORKLOAD CHARACTERIZATION (IISWC 2020), pp. 94–105.

By: J. Liu n & H. Zhou n 

author keywords: NISQ quantum computer; reliability model; neural network
topics (OpenAlex): Quantum Computing Algorithms and Architecture; Advancements in Semiconductor Devices and Circuit Design; Quantum Information and Cryptography
TL;DR: This paper treats the NISQ quantum computer as a black box and derives a reliability estimation model using polynomial fitting and a shallow neural network, and proposes randomized benchmarks with random numbers of qubits and basic gates to generate a large data set for neural network training. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: June 10, 2021

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