Optimal Control of Families of Quantum Gates
Open accessAbstract
Quantum optimal control (QOC) enables the realization of accurate operations, such as quantum gates, and supports the development of quantum technologies. To date, many QOC frameworks have been developed, but those remain only naturally suited to optimize a single targeted operation at a time. We extend this concept to optimal control with a continuous family of targets, and demonstrate that an optimization based on neural networks can find families of time-dependent Hamiltonians realizing desired classes of quantum gates in minimal time.
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Quantum gates can perform operations that classical gates cannot, such as teleportation, entanglement, and superdense coding.
“Quantum gates can also perform operations that classical gates cannot, such as teleportation, entanglement, and superdense coding.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [26]
The paper discusses a quantum gate set that includes a two-qubit entangling gate (p. 1), supporting the entanglement example. It does not discuss teleportation or superdense coding, or compare quantum gates with classical gates, so those parts of the claim are unsupported by our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe most common type of quantum gate is the CNOT gate.
“The most common type of quantum gate is the CNOT gate.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [26]
On p. 1, the paper calls realizing a controlled-NOT gate a common example of a quantum optimal-control task. It does not say that CNOT is the most common type of quantum gate. (LLM, conf=0.78)
Experimental — accuracy not yet measuredQuantum gates can process much larger datasets than classical gates.
“In short, quantum gates can perform operations that classical gates cannot and can process much larger datasets than classical gates [23, 26].”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [26]
The introduction (p. 1) discusses reduced gate counts and potentially more expressive computations, while the results discussion (p. 4) compares quantum-gate implementation times. Neither compares the size of datasets quantum and classical gates can process; searches for larger datasets and classical-gate comparisons found no supporting passage. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Sauvage, F., & Mintert, F.
- Affiliations
- Los Alamos National Laboratory (US)
- Imperial College London (GB)
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2022-07-29
- Volume
- 129
- Issue
- 5
- Pages
- 050507-050507
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W3212765762
- Cited by
- 31
- References
- 64
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 9 · 2023: 7 · 2022: 1
- FWCI
- 2.13
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Physical Review Letters (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Engineering and Physical Sciences Research Council (EP/P510257/1, EP/R044082/1)
- QuantERA
- Horizon 2020 Framework Programme
- U.S. Department of Energy
- Office of Science
- Office of Advanced Scientific Computing Research
- Accelerated Research in Quantum Computing
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
- Keywords
- quantum optimal control, quantum gates, time-dependent Hamiltonian, neural network optimization, minimal time control
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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