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Noise-tolerant parity learning with one quantum bit
Open accessAbstract
Demonstrating quantum advantage with less powerful but more realistic devices is of great importance in modern quantum information science. Recently, a significant quantum speedup was achieved in the problem of learning a hidden parity function with noise. However, if all data qubits at the query output are completely depolarized, the algorithm fails. In this work, we present a quantum parity learning algorithm that exhibits quantum advantage as long as one qubit is provided with nonzero polarization in each query. In this scenario, the quantum parity learning naturally becomes deterministic quantum computation with one qubit. Then the hidden parity function can be revealed by performing a set of operations that can be interpreted as measuring nonlocal observables on the auxiliary result qubit having nonzero polarization and each data qubit. We also discuss the source of the quantum advantage in our algorithm from the resource-theoretic point of view.
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“it is computationally intractable for classical approaches [101]”
From QASMBench: A Low-Level Quantum Benchmark Suite for NISQ Evaluation and Simulation (2023) · cites this paper as [101]
Citations
Cites
Noise-tolerant learning, the parity problem, and the statistical query model
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Cited by
Sorted by citations
QASMBench: A Low-Level Quantum Benchmark Suite for NISQ Evaluation and Simulation
Li, A., Stein, S., Krishnamoorthy, S., & Ang, J. (2023) · ACM Transactions on Quantum Computing · doi:10.1145/3550488 · Cited by 183
Metadata
- Authors
- Park, D. K., Rhee, J.-K. K., & Lee, S.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Venue
- Physical Review A
- Publisher
- American Physical Society (APS)
- Published
- 2018-03-20
- Volume
- 97
- Issue
- 3
- ISSN
- 0556-2791 (ISSN-L)
- 2469-9926
- 2469-9934
- OpenAlex
- W2780642250
- Cited by
- 12
- References
- 1
- Citations by year
- 2026: 1 · 2025: 2 · 2023: 1 · 2022: 2 · 2021: 2 · 2020: 4
- FWCI
- 0.84
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review A (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Research Foundation of Korea (2015R1A2A2A01006251, 2016R1A5A1008184)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
- Keywords
- quantum advantage, noise immunity, depolarization, non-local observables, resource theory, quantum machine learning
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- The paper
- Park, D. K., Rhee, J.-K. K., & Lee, S. (2018). Noise-tolerant parity learning with one quantum bit. Physical Review A. https://doi.org/10.1103/physreva.97.032327
