Error-Resilient Quantum Machine Learning for Real-World Optimization Problems in Finance and Supply Chain Networks
Bibliographic record
Abstract
The advent of quantum computing has introduced a revolutionary shift in computational paradigms, with the potential to address complex problems beyond the reach of classical systems. However, the practical deployment of quantum solutions is challenged by inherent issues such as noise and decoherence, which hinder their reliability. This study presents a novel error-resilient framework tailored for quantum machine learning (QML) to address optimization problems in finance and supply chain networks. By utilizing hybrid quantum-classical algorithms, the framework mitigates the detrimental effects of quantum noise and enhances computational robustness through advanced techniques like quantum variational circuits. Comprehensive experiments on real-world datasets highlight the framework's ability to outperform conventional methods in solving intricate optimization challenges. The findings demonstrate the transformative potential of quantum-assisted optimization for tackling critical issues in financial modeling and supply chain resilience. In this work, I propose a novel hybrid quantum-classical framework that integrates variational quantum circuits with noise mitigation strategies such as Zero Noise Extrapolation (ZNE), tailored for real-world optimization in finance and supply chain domains. This integrated approach—addressing error resilience and practical scalability together—sets the work apart from existing studies focused solely on theoretical or idealized scenarios. Keywords: Error resilience; Financial modeling; Hybrid algorithms; Optimization challenges; Quantum computing; Quantum machine learning. Abstrak Munculnya komputasi kuantum telah memperkenalkan pergeseran revolusioner dalam paradigma komputasi, dengan potensi untuk mengatasi masalah kompleks di luar jangkauan sistem klasik. Namun, penerapan praktis solusi kuantum ditantang oleh masalah inheren seperti kebisingan dan dekoherensi, yang menghambat keandalannya. Studi ini menyajikan kerangka kerja baru mengenai ketahanan kesalahan (error-resilient) yang dirancang untuk pembelajaran mesin kuantum (QML) dalam mengatasi masalah optimasi pada jaringan keuangan dan rantai pasokan. Dengan memanfaatkan algoritma kuantum-klasik hibrida, kerangka kerja tersebut mengurangi efek merugikan dari kebisingan kuantum dan meningkatkan ketahanan komputasi melalui teknik canggih seperti sirkuit variasional kuantum. Eksperimen komprehensif pada kumpulan data dunia nyata menyoroti kemampuan kerangka kerja untuk mengungguli metode konvensional dalam memecahkan tantangan pengoptimalan yang rumit. Temuan ini menunjukkan potensi transformatif pengoptimalan berbantuan kuantum (quantum-assisted) untuk mengatasi masalah kritis dalam pemodelan keuangan dan ketahanan rantai pasokan. Pada artikel ini, kami mengusulkan kerangka kerja kuantum-klasik hibrida baru yang memadukan sirkuit kuantum variasional dengan strategi mitigasi derau seperti Zero Noise Extrapolation (ZNE), yang dirancang khusus untuk pengoptimalan dunia nyata dalam domain keuangan dan rantai pasokan. Pendekatan terpadu ini—yang menangani ketahanan kesalahan dan skalabilitas praktis secara bersamaan—hal inilah yang menjadi pembeda penelitian ini dengan studi-studi sebelumnya yang hanya berfokus pada skenario teoritis atau ideal. Kata Kunci: Ketahanan kesalahan; Pemodelan keuangan; Algoritma hibrida; Tantangan optimasi; Komputasi kuantum; Pembelajaran mesin kuantum. 2020MSC: 81P68, 90C59, 91G60
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".