Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inverses Article Swipe
Swarnadeep Majumder
,
Christopher G. Yale
,
Titus Morris
,
Daniel Lobser
,
Ashlyn D. Burch
,
Matthew N. H. Chow
,
Melissa Revelle
,
Susan Clark
,
Raphael C. Pooser
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2205.14225
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2205.14225
Quantum computing testbeds exhibit high-fidelity quantum control over small collections of qubits, enabling performance of precise, repeatable operations followed by measurements. Currently, these noisy intermediate-scale devices can support a sufficient number of sequential operations prior to decoherence such that near term algorithms can be performed with proximate accuracy (like chemical accuracy for quantum chemistry). While the results of these algorithms are imperfect, these imperfections can help bootstrap quantum computer testbed development. Demonstrations of these algorithms over the past few years, coupled with the idea that imperfect algorithm performance can be caused by several dominant noise sources in the quantum processor, which can be measured and calibrated during algorithm execution or in post-processing, has led to the use of noise mitigation to improve computational results. Conversely, benchmark algorithms coupled with noise mitigation can help diagnose the nature of noise, whether systematic or purely random. Here, we outline the use of coherent noise mitigation techniques as a characterization tool in trapped-ion testbeds. We perform model-fitting of the noisy data to determine the noise source based on realistic noise models and demonstrate that systematic noise amplification coupled with error mitigation schemes provides useful data for noise model deduction. Further, in order to connect lower level noise model details with application specific performance of near term algorithms, we experimentally construct the loss landscape of a variational algorithm under various injected noise sources coupled with error mitigation techniques. This type of connection enables application-aware hardware codesign, in which the most important noise sources in specific applications, like quantum chemistry, become foci of improvement in subsequent hardware generations.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2205.14225
- https://arxiv.org/pdf/2205.14225
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281693217
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4281693217Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2205.14225Digital Object Identifier
- Title
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Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inversesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-05-27Full publication date if available
- Authors
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Swarnadeep Majumder, Christopher G. Yale, Titus Morris, Daniel Lobser, Ashlyn D. Burch, Matthew N. H. Chow, Melissa Revelle, Susan Clark, Raphael C. PooserList of authors in order
- Landing page
-
https://arxiv.org/abs/2205.14225Publisher landing page
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https://arxiv.org/pdf/2205.14225Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2205.14225Direct OA link when available
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Noise (video), Computer science, Quantum computer, Qubit, Algorithm, Quantum decoherence, Benchmark (surveying), Environmental noise, Quantum noise, Computer engineering, Quantum, Artificial intelligence, Physics, Geography, Image (mathematics), Acoustics, Quantum mechanics, Geodesy, Sound (geography)Top 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.type | 234 |
| abstract_inverted_index.with | 45, 81, 128, 184, 205, 229 |
| abstract_inverted_index.(like | 48 |
| abstract_inverted_index.Here, | 143 |
| abstract_inverted_index.While | 54 |
| abstract_inverted_index.based | 172 |
| abstract_inverted_index.error | 185, 230 |
| abstract_inverted_index.level | 201 |
| abstract_inverted_index.lower | 200 |
| abstract_inverted_index.model | 193, 203 |
| abstract_inverted_index.noise | 94, 118, 129, 150, 170, 175, 181, 192, 202, 226, 246 |
| abstract_inverted_index.noisy | 23, 165 |
| abstract_inverted_index.order | 197 |
| abstract_inverted_index.prior | 34 |
| abstract_inverted_index.small | 8 |
| abstract_inverted_index.these | 22, 58, 62, 73 |
| abstract_inverted_index.under | 223 |
| abstract_inverted_index.which | 100, 242 |
| abstract_inverted_index.become | 254 |
| abstract_inverted_index.caused | 90 |
| abstract_inverted_index.during | 106 |
| abstract_inverted_index.models | 176 |
| abstract_inverted_index.nature | 135 |
| abstract_inverted_index.noise, | 137 |
| abstract_inverted_index.number | 30 |
| abstract_inverted_index.purely | 141 |
| abstract_inverted_index.source | 171 |
| abstract_inverted_index.useful | 189 |
| abstract_inverted_index.years, | 79 |
| abstract_inverted_index.Quantum | 0 |
| abstract_inverted_index.connect | 199 |
| abstract_inverted_index.control | 6 |
| abstract_inverted_index.coupled | 80, 127, 183, 228 |
| abstract_inverted_index.details | 204 |
| abstract_inverted_index.devices | 25 |
| abstract_inverted_index.enables | 237 |
| abstract_inverted_index.exhibit | 3 |
| abstract_inverted_index.improve | 121 |
| abstract_inverted_index.outline | 145 |
| abstract_inverted_index.perform | 161 |
| abstract_inverted_index.quantum | 5, 52, 67, 98, 252 |
| abstract_inverted_index.qubits, | 11 |
| abstract_inverted_index.random. | 142 |
| abstract_inverted_index.results | 56 |
| abstract_inverted_index.schemes | 187 |
| abstract_inverted_index.several | 92 |
| abstract_inverted_index.sources | 95, 227, 247 |
| abstract_inverted_index.support | 27 |
| abstract_inverted_index.testbed | 69 |
| abstract_inverted_index.various | 224 |
| abstract_inverted_index.whether | 138 |
| abstract_inverted_index.Further, | 195 |
| abstract_inverted_index.accuracy | 47, 50 |
| abstract_inverted_index.chemical | 49 |
| abstract_inverted_index.coherent | 149 |
| abstract_inverted_index.computer | 68 |
| abstract_inverted_index.diagnose | 133 |
| abstract_inverted_index.dominant | 93 |
| abstract_inverted_index.enabling | 12 |
| abstract_inverted_index.followed | 18 |
| abstract_inverted_index.hardware | 239, 260 |
| abstract_inverted_index.injected | 225 |
| abstract_inverted_index.measured | 103 |
| abstract_inverted_index.precise, | 15 |
| abstract_inverted_index.provides | 188 |
| abstract_inverted_index.results. | 123 |
| abstract_inverted_index.specific | 207, 249 |
| abstract_inverted_index.testbeds | 2 |
| abstract_inverted_index.algorithm | 86, 107, 222 |
| abstract_inverted_index.benchmark | 125 |
| abstract_inverted_index.bootstrap | 66 |
| abstract_inverted_index.codesign, | 240 |
| abstract_inverted_index.computing | 1 |
| abstract_inverted_index.construct | 215 |
| abstract_inverted_index.determine | 168 |
| abstract_inverted_index.execution | 108 |
| abstract_inverted_index.imperfect | 85 |
| abstract_inverted_index.important | 245 |
| abstract_inverted_index.landscape | 218 |
| abstract_inverted_index.performed | 44 |
| abstract_inverted_index.proximate | 46 |
| abstract_inverted_index.realistic | 174 |
| abstract_inverted_index.testbeds. | 159 |
| abstract_inverted_index.Currently, | 21 |
| abstract_inverted_index.algorithms | 41, 59, 74, 126 |
| abstract_inverted_index.calibrated | 105 |
| abstract_inverted_index.chemistry, | 253 |
| abstract_inverted_index.connection | 236 |
| abstract_inverted_index.deduction. | 194 |
| abstract_inverted_index.imperfect, | 61 |
| abstract_inverted_index.mitigation | 119, 130, 151, 186, 231 |
| abstract_inverted_index.operations | 17, 33 |
| abstract_inverted_index.processor, | 99 |
| abstract_inverted_index.repeatable | 16 |
| abstract_inverted_index.sequential | 32 |
| abstract_inverted_index.subsequent | 259 |
| abstract_inverted_index.sufficient | 29 |
| abstract_inverted_index.systematic | 139, 180 |
| abstract_inverted_index.techniques | 152 |
| abstract_inverted_index.Conversely, | 124 |
| abstract_inverted_index.algorithms, | 212 |
| abstract_inverted_index.application | 206 |
| abstract_inverted_index.chemistry). | 53 |
| abstract_inverted_index.collections | 9 |
| abstract_inverted_index.decoherence | 36 |
| abstract_inverted_index.demonstrate | 178 |
| abstract_inverted_index.improvement | 257 |
| abstract_inverted_index.performance | 13, 87, 208 |
| abstract_inverted_index.techniques. | 232 |
| abstract_inverted_index.trapped-ion | 158 |
| abstract_inverted_index.variational | 221 |
| abstract_inverted_index.development. | 70 |
| abstract_inverted_index.generations. | 261 |
| abstract_inverted_index.amplification | 182 |
| abstract_inverted_index.applications, | 250 |
| abstract_inverted_index.computational | 122 |
| abstract_inverted_index.high-fidelity | 4 |
| abstract_inverted_index.imperfections | 63 |
| abstract_inverted_index.measurements. | 20 |
| abstract_inverted_index.model-fitting | 162 |
| abstract_inverted_index.Demonstrations | 71 |
| abstract_inverted_index.experimentally | 214 |
| abstract_inverted_index.characterization | 155 |
| abstract_inverted_index.post-processing, | 111 |
| abstract_inverted_index.application-aware | 238 |
| abstract_inverted_index.intermediate-scale | 24 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.05509233 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |