Sample-efficient verification of continuously-parameterized quantum gates for small quantum processors Article Swipe
Ryan Shaffer
,
Hang Ren
,
Emiliia Dyrenkova
,
Christopher G. Yale
,
Daniel Lobser
,
Ashlyn D. Burch
,
Matthew N. H. Chow
,
Melissa Revelle
,
Susan Clark
,
Hartmut Häffner
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2205.13074
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2205.13074
Most near-term quantum information processing devices will not be capable of implementing quantum error correction and the associated logical quantum gate set. Instead, quantum circuits will be implemented directly using the physical native gate set of the device. These native gates often have a parameterization (e.g., rotation angles) which provide the ability to perform a continuous range of operations. Verification of the correct operation of these gates across the allowable range of parameters is important for gaining confidence in the reliability of these devices. In this work, we demonstrate a procedure for sample-efficient verification of continuously-parameterized quantum gates for small quantum processors of up to approximately 10 qubits. This procedure involves generating random sequences of randomly-parameterized layers of gates chosen from the native gate set of the device, and then stochastically compiling an approximate inverse to this sequence such that executing the full sequence on the device should leave the system near its initial state. We show that fidelity estimates made via this technique have a lower variance than fidelity estimates made via cross-entropy benchmarking. This provides an experimentally-relevant advantage in sample efficiency when estimating the fidelity loss to some desired precision. We describe the experimental realization of this technique using continuously-parameterized quantum gate sets on a trapped-ion quantum processor from Sandia QSCOUT and a superconducting quantum processor from IBM Q, and we demonstrate the sample efficiency advantage of this technique both numerically and experimentally.
Related Topics
Concepts
Quantum circuit
Computer science
Quantum gate
Quantum computer
Quantum error correction
Qubit
Controlled NOT gate
Algorithm
Parameterized complexity
Quantum
Computer engineering
Electronic engineering
Physics
Quantum mechanics
Engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2205.13074
- https://arxiv.org/pdf/2205.13074
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281636398
All OpenAlex metadata
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https://openalex.org/W4281636398Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2205.13074Digital Object Identifier
- Title
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Sample-efficient verification of continuously-parameterized quantum gates for small quantum processorsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-05-25Full publication date if available
- Authors
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Ryan Shaffer, Hang Ren, Emiliia Dyrenkova, Christopher G. Yale, Daniel Lobser, Ashlyn D. Burch, Matthew N. H. Chow, Melissa Revelle, Susan Clark, Hartmut HäffnerList of authors in order
- Landing page
-
https://arxiv.org/abs/2205.13074Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2205.13074Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2205.13074Direct OA link when available
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Quantum circuit, Computer science, Quantum gate, Quantum computer, Quantum error correction, Qubit, Controlled NOT gate, Algorithm, Parameterized complexity, Quantum, Computer engineering, Electronic engineering, Physics, Quantum mechanics, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.These | 38 |
| abstract_inverted_index.error | 13 |
| abstract_inverted_index.gates | 40, 66, 97, 118 |
| abstract_inverted_index.leave | 148 |
| abstract_inverted_index.lower | 166 |
| abstract_inverted_index.often | 41 |
| abstract_inverted_index.range | 56, 70 |
| abstract_inverted_index.small | 99 |
| abstract_inverted_index.these | 65, 82 |
| abstract_inverted_index.using | 29, 200 |
| abstract_inverted_index.which | 48 |
| abstract_inverted_index.work, | 86 |
| abstract_inverted_index.(e.g., | 45 |
| abstract_inverted_index.QSCOUT | 212 |
| abstract_inverted_index.Sandia | 211 |
| abstract_inverted_index.across | 67 |
| abstract_inverted_index.chosen | 119 |
| abstract_inverted_index.device | 146 |
| abstract_inverted_index.layers | 116 |
| abstract_inverted_index.native | 32, 39, 122 |
| abstract_inverted_index.random | 112 |
| abstract_inverted_index.sample | 181, 225 |
| abstract_inverted_index.should | 147 |
| abstract_inverted_index.state. | 154 |
| abstract_inverted_index.system | 150 |
| abstract_inverted_index.ability | 51 |
| abstract_inverted_index.angles) | 47 |
| abstract_inverted_index.capable | 9 |
| abstract_inverted_index.correct | 62 |
| abstract_inverted_index.desired | 190 |
| abstract_inverted_index.device, | 127 |
| abstract_inverted_index.device. | 37 |
| abstract_inverted_index.devices | 5 |
| abstract_inverted_index.gaining | 76 |
| abstract_inverted_index.initial | 153 |
| abstract_inverted_index.inverse | 134 |
| abstract_inverted_index.logical | 18 |
| abstract_inverted_index.perform | 53 |
| abstract_inverted_index.provide | 49 |
| abstract_inverted_index.quantum | 2, 12, 19, 23, 96, 100, 202, 208, 216 |
| abstract_inverted_index.qubits. | 107 |
| abstract_inverted_index.Instead, | 22 |
| abstract_inverted_index.circuits | 24 |
| abstract_inverted_index.describe | 193 |
| abstract_inverted_index.devices. | 83 |
| abstract_inverted_index.directly | 28 |
| abstract_inverted_index.fidelity | 158, 169, 186 |
| abstract_inverted_index.involves | 110 |
| abstract_inverted_index.physical | 31 |
| abstract_inverted_index.provides | 176 |
| abstract_inverted_index.rotation | 46 |
| abstract_inverted_index.sequence | 137, 143 |
| abstract_inverted_index.variance | 167 |
| abstract_inverted_index.advantage | 179, 227 |
| abstract_inverted_index.allowable | 69 |
| abstract_inverted_index.compiling | 131 |
| abstract_inverted_index.estimates | 159, 170 |
| abstract_inverted_index.executing | 140 |
| abstract_inverted_index.important | 74 |
| abstract_inverted_index.near-term | 1 |
| abstract_inverted_index.operation | 63 |
| abstract_inverted_index.procedure | 90, 109 |
| abstract_inverted_index.processor | 209, 217 |
| abstract_inverted_index.sequences | 113 |
| abstract_inverted_index.technique | 163, 199, 230 |
| abstract_inverted_index.associated | 17 |
| abstract_inverted_index.confidence | 77 |
| abstract_inverted_index.continuous | 55 |
| abstract_inverted_index.correction | 14 |
| abstract_inverted_index.efficiency | 182, 226 |
| abstract_inverted_index.estimating | 184 |
| abstract_inverted_index.generating | 111 |
| abstract_inverted_index.parameters | 72 |
| abstract_inverted_index.precision. | 191 |
| abstract_inverted_index.processing | 4 |
| abstract_inverted_index.processors | 101 |
| abstract_inverted_index.approximate | 133 |
| abstract_inverted_index.demonstrate | 88, 223 |
| abstract_inverted_index.implemented | 27 |
| abstract_inverted_index.information | 3 |
| abstract_inverted_index.numerically | 232 |
| abstract_inverted_index.operations. | 58 |
| abstract_inverted_index.realization | 196 |
| abstract_inverted_index.reliability | 80 |
| abstract_inverted_index.trapped-ion | 207 |
| abstract_inverted_index.Verification | 59 |
| abstract_inverted_index.experimental | 195 |
| abstract_inverted_index.implementing | 11 |
| abstract_inverted_index.verification | 93 |
| abstract_inverted_index.approximately | 105 |
| abstract_inverted_index.benchmarking. | 174 |
| abstract_inverted_index.cross-entropy | 173 |
| abstract_inverted_index.stochastically | 130 |
| abstract_inverted_index.experimentally. | 234 |
| abstract_inverted_index.superconducting | 215 |
| abstract_inverted_index.parameterization | 44 |
| abstract_inverted_index.sample-efficient | 92 |
| abstract_inverted_index.randomly-parameterized | 115 |
| abstract_inverted_index.experimentally-relevant | 178 |
| abstract_inverted_index.continuously-parameterized | 95, 201 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile.value | 0.05375822 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |