Carbon market risk estimation using quantum conditional generative adversarial network and amplitude estimation Article Swipe
Xiyuan Zhou
,
Huan Zhao
,
Yuji Cao
,
Fei Xiang
,
Gaoqi Liang
,
Junhua Zhao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1049/enc2.12122
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1049/enc2.12122
Accurately and efficiently estimating the carbon market risk is paramount for ensuring financial stability, promoting environmental sustainability, and facilitating informed decision‐making. Although classical risk estimation methods are extensively utilized, the implicit pre‐assumptions regarding distribution are predominantly contained and challenging to balance accuracy and computational efficiency. A quantum computing‐based carbon market risk estimation framework is proposed to address this problem with the quantum conditional generative adversarial network‐quantum amplitude estimation (QCGAN‐QAE) algorithm. Specifically, quantum conditional generative adversarial network (QCGAN) is employed to simulate the future distribution of the generated return rate, whereas quantum amplitude estimation (QAE) is employed to measure the distribution. Moreover, the quantum circuit of the QCGAN improved by reordering the data interaction layer and data simulation layer is coupled with the introduction of the quantum fully connected layer. The binary search method is incorporated into the QAE to bolster the computational efficiency. The simulation results based on the European Union Emissions Trading System reveals that the proposed framework markedly enhances the efficiency and precision of value‐at‐risk and conditional value‐at‐risk compared to original methods.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/enc2.12122
- OA Status
- gold
- Cited By
- 8
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401471069
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- OpenAlex ID
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https://openalex.org/W4401471069Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/enc2.12122Digital Object Identifier
- Title
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Carbon market risk estimation using quantum conditional generative adversarial network and amplitude estimationWork 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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2024-08-01Full publication date if available
- Authors
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Xiyuan Zhou, Huan Zhao, Yuji Cao, Fei Xiang, Gaoqi Liang, Junhua ZhaoList of authors in order
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https://doi.org/10.1049/enc2.12122Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1049/enc2.12122Direct OA link when available
- Concepts
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Estimation, Generative grammar, Econometrics, Amplitude, Generative adversarial network, Mathematics, Computer science, Artificial intelligence, Statistics, Economics, Deep learning, Physics, Management, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 3, 2024: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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