Tensor Network Contraction Algorithm
| dc.contributor.author | Gallagher, Kiana | |
| dc.date.accessioned | 2023-09-19T20:34:31Z | |
| dc.date.available | 2023-09-19T20:34:31Z | |
| dc.date.copyright | 2023 | en_US |
| dc.date.issued | 2023-09-19 | |
| dc.description.abstract | Tensor networks are mathematical objects that can be used to model many-body systems in physics. Without the use of tensors, the computations of these models are exponential. Different operations can be applied on tensors to find information regarding the system. One of these operations is tensor contraction. My research project was to create an algorithm that is able to contract a tensor network into a single tensor. Contracting tensors naively will result in exponential memory requirements which will cause the average computer to run out of memory quickly or it will take a long time to compute. Thus, a contraction algorithm will decide which tensors to contract to minimize the time for contraction and the memory required. | en_US |
| dc.description.reviewstatus | Reviewed | en_US |
| dc.description.scholarlevel | Undergraduate | en_US |
| dc.description.sponsorship | Valerie Kuehne Undergraduate Research Awards (VKURA) | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/15412 | |
| dc.language.iso | en | en_US |
| dc.subject | tensor | en_US |
| dc.subject | contraction | en_US |
| dc.subject | greedy algorithm | en_US |
| dc.subject | tensor network | en_US |
| dc.title | Tensor Network Contraction Algorithm | en_US |
| dc.type | Poster | en_US |