Title |
Programming languages and compiler design for realistic quantum hardware
|
---|---|
Published in |
Nature, September 2017
|
DOI | 10.1038/nature23459 |
Pubmed ID | |
Authors |
Frederic T. Chong, Diana Franklin, Margaret Martonosi |
Abstract |
Quantum computing sits at an important inflection point. For years, high-level algorithms for quantum computers have shown considerable promise, and recent advances in quantum device fabrication offer hope of utility. A gap still exists, however, between the hardware size and reliability requirements of quantum computing algorithms and the physical machines foreseen within the next ten years. To bridge this gap, quantum computers require appropriate software to translate and optimize applications (toolflows) and abstraction layers. Given the stringent resource constraints in quantum computing, information passed between layers of software and implementations will differ markedly from in classical computing. Quantum toolflows must expose more physical details between layers, so the challenge is to find abstractions that expose key details while hiding enough complexity. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 8 | 7% |
Japan | 7 | 6% |
United Kingdom | 6 | 5% |
Mexico | 6 | 5% |
Canada | 3 | 3% |
Spain | 3 | 3% |
India | 2 | 2% |
Denmark | 1 | <1% |
South Africa | 1 | <1% |
Other | 12 | 11% |
Unknown | 62 | 56% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 84 | 76% |
Scientists | 23 | 21% |
Science communicators (journalists, bloggers, editors) | 3 | 3% |
Practitioners (doctors, other healthcare professionals) | 1 | <1% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 234 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 53 | 23% |
Researcher | 40 | 17% |
Student > Master | 20 | 9% |
Student > Bachelor | 18 | 8% |
Student > Postgraduate | 12 | 5% |
Other | 41 | 18% |
Unknown | 50 | 21% |
Readers by discipline | Count | As % |
---|---|---|
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Computer Science | 42 | 18% |
Engineering | 18 | 8% |
Chemistry | 10 | 4% |
Biochemistry, Genetics and Molecular Biology | 5 | 2% |
Other | 19 | 8% |
Unknown | 53 | 23% |