The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Automated Search for new Quantum Experiments
Open accessAbstract
Quantum mechanics predicts a number of, at first sight, counterintuitive phenomena. It therefore remains a question whether our intuition is the best way to find new experiments. Here, we report the development of the computer algorithm Melvin which is able to find new experimental implementations for the creation and manipulation of complex quantum states. Indeed, the discovered experiments extensively use unfamiliar and asymmetric techniques which are challenging to understand intuitively. The results range from the first implementation of a high-dimensional Greenberger-Horne-Zeilinger state, to a vast variety of experiments for asymmetrically entangled quantum states-a feature that can only exist when both the number of involved parties and dimensions is larger than 2. Additionally, new types of high-dimensional transformations are found that perform cyclic operations. Melvin autonomously learns from solutions for simpler systems, which significantly speeds up the discovery rate of more complex experiments. The ability to automate the design of a quantum experiment can be applied to many quantum systems and allows the physical realization of quantum states previously thought of only on paper.
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Cites
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Cited by
A Survey on quantum computing technology
Gyongyosi, L., & Imre, S. (2019) · Computer Science Review · doi:10.1016/j.cosrev.2018.11.002 · Cited by 646
Quantum-Enhanced Machine Learning
Dunjko, V., Taylor, J. M., & Briegel, H. J. (2016) · Physical Review Letters · doi:10.1103/physrevlett.117.130501 · Cited by 460
Ghosh, S., Opala, A., Matuszewski, M., Paterek, T., & Liew, T. C. H. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0149-8 · Cited by 171
Artificial intelligence and machine learning for quantum technologies
Krenn, M., Landgraf, J., Foesel, T., & Marquardt, F. (2023) · Physical Review A · doi:10.1103/physreva.107.010101 · Cited by 162
Self-citationMachine learning meets quantum foundations: A brief survey
Bharti, K., Haug, T., Vedral, V., & Kwek, L.-C. (2020) · AVS Quantum Science · doi:10.1116/5.0007529 · Cited by 59
Metadata
- Authors
- Krenn, M., Malik, M., Fickler, R., Lapkiewicz, R., & Zeilinger, A.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2016-03-04
- Volume
- 116
- Issue
- 9
- Pages
- 090405-090405
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2244690429
- Cited by
- 238
- References
- 13
- FWCI
- 11.27
- Type
- journal article
- Language
- en
- Open access
- green
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- Krenn, M., Malik, M., Fickler, R., Lapkiewicz, R., & Zeilinger, A. (2016). Automated Search for new Quantum Experiments. Physical Review Letters. https://doi.org/10.1103/physrevlett.116.090405
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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