2 citations · 6 across the 10 of their papers we have counts for
12 papers
An Empirical Study of Bugs in Quantum Machine Learning Frameworks
Pengzhan Zhao, Xiongfei Wu, Junjie Luo +2
Quantum computing has emerged as a promising domain for the machine learning (ML) area, offering significant computational advantages over classical counterparts. With the growing…
Formalization of Quantum Intermediate Representations for Code Safety
Junjie Luo, Jianjun Zhao
Quantum Intermediate Representation (QIR) is a Microsoft-developed, LLVM-based intermediate representation for quantum program compilers. QIR aims to provide a general solution for…
Fair and Efficient Multi-Resource Allocation for Cloud Computing
Xiaohui Bei, Zihao Li, Junjie Luo
We study the problem of allocating multiple types of resources to agents with Leontief preferences. The classic Dominant Resource Fairness (DRF) mechanism satisfies several desired…
Multivariate Algorithmics for Eliminating Envy by Donating Goods
Niclas Boehmer, Robert Bredereck, Klaus Heeger +2
Fairly dividing a set of indivisible resources to a set of agents is of utmost importance in some applications. However, after an allocation has been implemented the preferences of…
A Comprehensive Study of Bug Fixes in Quantum Programs
Junjie Luo, Pengzhan Zhao, Zhongtao Miao +2
As quantum programming evolves, more and more quantum programming languages are being developed. As a result, debugging and testing quantum programs have become increasingly import…
On Improving Resource Allocations by Sharing
Robert Bredereck, Andrzej Kaczmarczyk, Junjie Luo +2
Given an initial resource allocation, where some agents may envy others or where a different distribution of resources might lead to higher social welfare, our goal is to improve t…