Basic quantum subroutines: finding multiple marked elements and summing numbers
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929274342080512 |
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| author | van Apeldoorn, Joran Gribling, Sander Nieuwboer, Harold |
| author_facet | van Apeldoorn, Joran Gribling, Sander Nieuwboer, Harold |
| contents | We show how to find all $k$ marked elements in a list of size $N$ using the optimal number $O(\sqrt{N k})$ of quantum queries and only a polylogarithmic overhead in the gate complexity, in the setting where one has a small quantum memory. Previous algorithms either incurred a factor $k$ overhead in the gate complexity, or had an extra factor $\log(k)$ in the query complexity.
We then consider the problem of finding a multiplicative $δ$-approximation of $s = \sum_{i=1}^N v_i$ where $v=(v_i) \in [0,1]^N$, given quantum query access to a binary description of $v$. We give an algorithm that does so, with probability at least $1-ρ$, using $O(\sqrt{N \log(1/ρ) / δ})$ quantum queries (under mild assumptions on $ρ$). This quadratically improves the dependence on $1/δ$ and $\log(1/ρ)$ compared to a straightforward application of amplitude estimation. To obtain the improved $\log(1/ρ)$ dependence we use the first result. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_10244 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Basic quantum subroutines: finding multiple marked elements and summing numbers van Apeldoorn, Joran Gribling, Sander Nieuwboer, Harold Quantum Physics Data Structures and Algorithms We show how to find all $k$ marked elements in a list of size $N$ using the optimal number $O(\sqrt{N k})$ of quantum queries and only a polylogarithmic overhead in the gate complexity, in the setting where one has a small quantum memory. Previous algorithms either incurred a factor $k$ overhead in the gate complexity, or had an extra factor $\log(k)$ in the query complexity. We then consider the problem of finding a multiplicative $δ$-approximation of $s = \sum_{i=1}^N v_i$ where $v=(v_i) \in [0,1]^N$, given quantum query access to a binary description of $v$. We give an algorithm that does so, with probability at least $1-ρ$, using $O(\sqrt{N \log(1/ρ) / δ})$ quantum queries (under mild assumptions on $ρ$). This quadratically improves the dependence on $1/δ$ and $\log(1/ρ)$ compared to a straightforward application of amplitude estimation. To obtain the improved $\log(1/ρ)$ dependence we use the first result. |
| title | Basic quantum subroutines: finding multiple marked elements and summing numbers |
| topic | Quantum Physics Data Structures and Algorithms |
| url | https://arxiv.org/abs/2302.10244 |