Error-Resilient Quantum Machine Learning for Real-World Optimization Problems in Finance and Supply Chain Networks
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: quantum computing; quantum machine learning; hybrid algorithms; error resilience; optimization challenges; financial modeling.
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: komputasi kuantum; pembelajaran mesin kuantum; algoritma hibrida; ketahanan kesalahan; tantangan optimasi; pemodelan keuangan.
2020MSC: 81P68, 90C59, 91G60.
Keywords
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DOI: 10.15408/inprime.v7i1.44960
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