Name
Abstract | ||
|---|---|---|
In recent years, tremendous efforts from both the industrial and the academic research communities have been put into bringing forth quantum computing technologies. With the potential proliferation of universal quantum computers on the horizon, quantum computing, however, is still severely grounded by numerous grave barriers, which lead to its low accessibility and practicality. For example, the vastly different underlying computing models, combined with the steep background knowledge requirements, makes it extremely difficult, if possible at all, for most software engineering researchers and practitioners to even begin to design or implement quantum algorithms or softwares in practice. To overcome this problem, we, in this paper, propose a design that largely circumvents said accessibility and practicality barriers, by providing an end-to-end quantum computing framework for solving NP-complete problems via reduction. We fully implemented a toolkit under our design framework. With this toolkit, software engineering researchers and practitioners would be able to enjoy the speedup and scalability benefits of universal quantum computers without necessarily having to have prior knowledge on quantum computing.
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Year | Venue | DocType |
|---|---|---|
2018 | ICSE (Companion Volume) | Conference |
ISBN | Citations | PageRank |
978-1-4503-5663-3 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaohan Hu | 1 | 33 | 4.93 |
| Peng Liu | 2 | 0 | 0.68 |
| Chun-Fu Chen | 3 | 6 | 6.52 |
| Marco Pistoia | 4 | 896 | 58.12 |