Solar Irradiance Forecasting Using a Hybrid Quantum Neural Network: A Comparison on GPU-Based Workflow Development Platforms Article Swipe
Ying‐Yi Hong
,
Dylan Josh Domingo Lopez
,
Yun-Yuan Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3472053
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3472053
Modern renewable power operations can be enhanced by integrating deep neural networks, particularly for forecasting solar irradiance. Recent advancements in quantum computing have shown potential improvements in classical deep neural networks. However, current challenges with quantum hardware, such as susceptibility to noise and decoherence, pose risks to its practicality. Hybrid quantum neural networks (HQNNs) are found to mitigate these issues, especially when integrated with graphics processing unit (GPU)-based pipelines. This paper presents a comparative study of different software platforms for developing HQNNs, using multi-location very short-term solar irradiance forecasting as an example. A classical benchmark model is initially designed based on statistical analysis of a 10-minute resolution solar irradiance dataset, with its parameters further optimized using Bayesian Optimization. The experimental design of this paper includes a loss comparison between classical neural networks and HQNNs across different seasons and a performance comparison between Pennylane, Torchquantum, and CUDA Quantum (CUDA-Q) as HQNN development platforms. Experimental results show that HQNNs achieve up to a 92.30% improvement in testing loss compared to classical neural networks. Regarding HQNN development platforms, Pennylane shows an 81.54% testing loss reduction from classical models, Torchquantum shows a 90.34% improvement, and CUDA-Q shows a 92.30% improvement in testing loss. Implementing hardware acceleration libraries for GPU-based state vector simulation demonstrates an approximate 275% speedup in average latency per epoch, a 218% speedup in inference time, and a 10.20% improvement in testing loss compared to CPU-based simulations. CUDA-Q achieves a training time 2.7 times shorter and an inference time 2.9 times shorter compared to Pennylane, while it is 32.3 times faster in training and 31 times faster in inference compared to Torchquantum.
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Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3472053
- OA Status
- gold
- Cited By
- 7
- References
- 52
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403059058Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3472053Digital Object Identifier
- Title
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Solar Irradiance Forecasting Using a Hybrid Quantum Neural Network: A Comparison on GPU-Based Workflow Development PlatformsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Ying‐Yi Hong, Dylan Josh Domingo Lopez, Yun-Yuan WangList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3472053Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/access.2024.3472053Direct OA link when available
- Concepts
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Computer science, Workflow, Artificial neural network, Irradiance, Solar irradiance, Artificial intelligence, Real-time computing, Meteorology, Database, Optics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
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2025: 7Per-year citation counts (last 5 years)
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52Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.218% | 219 |
| abstract_inverted_index.275% | 211 |
| abstract_inverted_index.32.3 | 256 |
| abstract_inverted_index.CUDA | 145 |
| abstract_inverted_index.HQNN | 149, 172 |
| abstract_inverted_index.This | 69 |
| abstract_inverted_index.deep | 9, 28 |
| abstract_inverted_index.from | 182 |
| abstract_inverted_index.have | 22 |
| abstract_inverted_index.loss | 126, 165, 180, 230 |
| abstract_inverted_index.pose | 44 |
| abstract_inverted_index.show | 154 |
| abstract_inverted_index.such | 37 |
| abstract_inverted_index.that | 155 |
| abstract_inverted_index.this | 122 |
| abstract_inverted_index.time | 239, 246 |
| abstract_inverted_index.unit | 66 |
| abstract_inverted_index.very | 84 |
| abstract_inverted_index.when | 61 |
| abstract_inverted_index.with | 34, 63, 110 |
| abstract_inverted_index.HQNNs | 133, 156 |
| abstract_inverted_index.based | 99 |
| abstract_inverted_index.found | 55 |
| abstract_inverted_index.loss. | 198 |
| abstract_inverted_index.model | 95 |
| abstract_inverted_index.noise | 41 |
| abstract_inverted_index.paper | 70, 123 |
| abstract_inverted_index.power | 2 |
| abstract_inverted_index.risks | 45 |
| abstract_inverted_index.shown | 23 |
| abstract_inverted_index.shows | 176, 186, 192 |
| abstract_inverted_index.solar | 15, 86, 107 |
| abstract_inverted_index.state | 205 |
| abstract_inverted_index.study | 74 |
| abstract_inverted_index.these | 58 |
| abstract_inverted_index.time, | 223 |
| abstract_inverted_index.times | 241, 248, 257, 263 |
| abstract_inverted_index.using | 82, 115 |
| abstract_inverted_index.while | 253 |
| abstract_inverted_index.10.20% | 226 |
| abstract_inverted_index.81.54% | 178 |
| abstract_inverted_index.90.34% | 188 |
| abstract_inverted_index.92.30% | 161, 194 |
| abstract_inverted_index.CUDA-Q | 191, 235 |
| abstract_inverted_index.HQNNs, | 81 |
| abstract_inverted_index.Hybrid | 49 |
| abstract_inverted_index.Modern | 0 |
| abstract_inverted_index.Recent | 17 |
| abstract_inverted_index.across | 134 |
| abstract_inverted_index.design | 120 |
| abstract_inverted_index.epoch, | 217 |
| abstract_inverted_index.faster | 258, 264 |
| abstract_inverted_index.neural | 10, 29, 51, 130, 169 |
| abstract_inverted_index.vector | 206 |
| abstract_inverted_index.(HQNNs) | 53 |
| abstract_inverted_index.Quantum | 146 |
| abstract_inverted_index.achieve | 157 |
| abstract_inverted_index.average | 214 |
| abstract_inverted_index.between | 128, 141 |
| abstract_inverted_index.current | 32 |
| abstract_inverted_index.further | 113 |
| abstract_inverted_index.issues, | 59 |
| abstract_inverted_index.latency | 215 |
| abstract_inverted_index.models, | 184 |
| abstract_inverted_index.quantum | 20, 35, 50 |
| abstract_inverted_index.results | 153 |
| abstract_inverted_index.seasons | 136 |
| abstract_inverted_index.shorter | 242, 249 |
| abstract_inverted_index.speedup | 212, 220 |
| abstract_inverted_index.testing | 164, 179, 197, 229 |
| abstract_inverted_index.(CUDA-Q) | 147 |
| abstract_inverted_index.Bayesian | 116 |
| abstract_inverted_index.However, | 31 |
| abstract_inverted_index.achieves | 236 |
| abstract_inverted_index.analysis | 102 |
| abstract_inverted_index.compared | 166, 231, 250, 267 |
| abstract_inverted_index.dataset, | 109 |
| abstract_inverted_index.designed | 98 |
| abstract_inverted_index.enhanced | 6 |
| abstract_inverted_index.example. | 91 |
| abstract_inverted_index.graphics | 64 |
| abstract_inverted_index.hardware | 200 |
| abstract_inverted_index.includes | 124 |
| abstract_inverted_index.mitigate | 57 |
| abstract_inverted_index.networks | 52, 131 |
| abstract_inverted_index.presents | 71 |
| abstract_inverted_index.software | 77 |
| abstract_inverted_index.training | 238, 260 |
| abstract_inverted_index.10-minute | 105 |
| abstract_inverted_index.CPU-based | 233 |
| abstract_inverted_index.GPU-based | 204 |
| abstract_inverted_index.Pennylane | 175 |
| abstract_inverted_index.Regarding | 171 |
| abstract_inverted_index.benchmark | 94 |
| abstract_inverted_index.classical | 27, 93, 129, 168, 183 |
| abstract_inverted_index.computing | 21 |
| abstract_inverted_index.different | 76, 135 |
| abstract_inverted_index.hardware, | 36 |
| abstract_inverted_index.inference | 222, 245, 266 |
| abstract_inverted_index.initially | 97 |
| abstract_inverted_index.libraries | 202 |
| abstract_inverted_index.networks, | 11 |
| abstract_inverted_index.networks. | 30, 170 |
| abstract_inverted_index.optimized | 114 |
| abstract_inverted_index.platforms | 78 |
| abstract_inverted_index.potential | 24 |
| abstract_inverted_index.reduction | 181 |
| abstract_inverted_index.renewable | 1 |
| abstract_inverted_index.Pennylane, | 142, 252 |
| abstract_inverted_index.challenges | 33 |
| abstract_inverted_index.comparison | 127, 140 |
| abstract_inverted_index.developing | 80 |
| abstract_inverted_index.especially | 60 |
| abstract_inverted_index.integrated | 62 |
| abstract_inverted_index.irradiance | 87, 108 |
| abstract_inverted_index.operations | 3 |
| abstract_inverted_index.parameters | 112 |
| abstract_inverted_index.pipelines. | 68 |
| abstract_inverted_index.platforms, | 174 |
| abstract_inverted_index.platforms. | 151 |
| abstract_inverted_index.processing | 65 |
| abstract_inverted_index.resolution | 106 |
| abstract_inverted_index.short-term | 85 |
| abstract_inverted_index.simulation | 207 |
| abstract_inverted_index.(GPU)-based | 67 |
| abstract_inverted_index.approximate | 210 |
| abstract_inverted_index.comparative | 73 |
| abstract_inverted_index.development | 150, 173 |
| abstract_inverted_index.forecasting | 14, 88 |
| abstract_inverted_index.improvement | 162, 195, 227 |
| abstract_inverted_index.integrating | 8 |
| abstract_inverted_index.irradiance. | 16 |
| abstract_inverted_index.performance | 139 |
| abstract_inverted_index.statistical | 101 |
| abstract_inverted_index.Experimental | 152 |
| abstract_inverted_index.Implementing | 199 |
| abstract_inverted_index.Torchquantum | 185 |
| abstract_inverted_index.acceleration | 201 |
| abstract_inverted_index.advancements | 18 |
| abstract_inverted_index.decoherence, | 43 |
| abstract_inverted_index.demonstrates | 208 |
| abstract_inverted_index.experimental | 119 |
| abstract_inverted_index.improvement, | 189 |
| abstract_inverted_index.improvements | 25 |
| abstract_inverted_index.particularly | 12 |
| abstract_inverted_index.simulations. | 234 |
| abstract_inverted_index.Optimization. | 117 |
| abstract_inverted_index.Torchquantum, | 143 |
| abstract_inverted_index.Torchquantum. | 269 |
| abstract_inverted_index.practicality. | 48 |
| abstract_inverted_index.multi-location | 83 |
| abstract_inverted_index.susceptibility | 39 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.93145344 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |