1Middlebury College, Middlebury, VT, USA
2Williams College, Williamstown, MA, USA
3Brown University, Providence, RI, USA
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Abstract
Quantum span program algorithms for function evaluation sometimes have reduced query complexity when promised that the input has a certain structure. We design a modified span program algorithm to show these improvements persist even without a promise ahead of time, and we extend this approach to the more general problem of state conversion. As an application, we prove exponential and superpolynomial quantum advantages in average query complexity for several search problems, generalizing Montanaro’s Search with Advice [Montanaro, TQC 2010].
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Cited by
[1] Stacey Jeffery, Shelby Kimmel, and Alvaro Piedrafita, “Quantum Algorithm for Path-Edge Sampling”, arXiv:2303.03319, (2023).
[2] Michael Czekanski, Shelby Kimmel, and R. Teal Witter, “Robust and Space-Efficient Dual Adversary Quantum Query Algorithms”, arXiv:2306.15040, (2023).
The above citations are from SAO/NASA ADS (last updated successfully 2024-04-11 15:45:18). The list may be incomplete as not all publishers provide suitable and complete citation data.
On Crossref’s cited-by service no data on citing works was found (last attempt 2024-04-11 15:45:17).
This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Copyright remains with the original copyright holders such as the authors or their institutions.
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- Source: https://quantum-journal.org/papers/q-2024-04-08-1309/
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