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Quantum Machine Learning
Quantum Advantage with Faulty Oracle
arXiv
Authors: David Rasmussen Lolck, Laura ManĨinska, Manaswi Paraashar
Year
2024
Paper ID
37039
Status
Preprint
Abstract Read
~2 min
Abstract Words
124
Citations
N/A
Abstract
This paper investigates the impact of noise in the quantum query model, a fundamental framework for quantum algorithms. We focus on the scenario where the oracle is subject to non-unitary (or irreversible) noise, specifically under the faulty oracle model, where the oracle fails with a constant probability and acts as identity. Regev and Schiff (ICALP'08) showed that quantum advantage is lost for the search problem under this noise model. Our main result shows that every quantum query algorithm can be made robust in this noise model with a roughly quadratic blow-up in query complexity, thereby preserving quantum speedup for all problems where the quantum advantage is super-cubic. This is the first non-trivial robustification of quantum query algorithms against an oracle that is noisy.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2024 reference point for readers tracking recent quantum research.
- This paper investigates the impact of noise in the quantum query model, a fundamental framework for quantum algorithms.
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