Quantum analytic descent
Variational algorithms have particular relevance for near-term quantum computers but require nontrivial parameter optimizations. Here we propose analytic descent: Given that the energy landscape must have a certain simple form in the local region around any reference point, it can be efficiently app...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2022-04-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.4.023017 |
| Summary: | Variational algorithms have particular relevance for near-term quantum computers but require nontrivial parameter optimizations. Here we propose analytic descent: Given that the energy landscape must have a certain simple form in the local region around any reference point, it can be efficiently approximated in its entirety by a classical model—we support these observations with rigorous, complexity-theoretic arguments. One can classically analyze this approximate function to directly jump to the (estimated) minimum before determining a more refined function, if necessary. We derive an optimal measurement strategy and generally prove that the asymptotic resource cost of a jump corresponds to only a single gradient vector evaluation. |
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| ISSN: | 2643-1564 |