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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2301.13842 |
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| _version_ | 1866929363969114112 |
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| author | Benedetti, Claudia Gianani, Ilaria |
| author_facet | Benedetti, Claudia Gianani, Ilaria |
| contents | Control and characterization of networks is a paramount step for the development of many quantum technologies. Even for moderate-sized networks, this amounts to explore an extremely vast parameters space in search for the couplings defining the network topology. Here we explore the use of a genetic algorithm to retrieve the topology of a network from the measured probability distribution obtained from the evolution of a continuous-time quantum walk on the network. Our result shows that the algorithm is capable of efficiently retrieving the required information even in the presence of noise. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_13842 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Identifying network topologies via quantum walk distributions Benedetti, Claudia Gianani, Ilaria Quantum Physics Control and characterization of networks is a paramount step for the development of many quantum technologies. Even for moderate-sized networks, this amounts to explore an extremely vast parameters space in search for the couplings defining the network topology. Here we explore the use of a genetic algorithm to retrieve the topology of a network from the measured probability distribution obtained from the evolution of a continuous-time quantum walk on the network. Our result shows that the algorithm is capable of efficiently retrieving the required information even in the presence of noise. |
| title | Identifying network topologies via quantum walk distributions |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2301.13842 |