Mitigasi Quantum Error Berbasis Machine Learning pada Simulasi Variational Quantum Eigensolver (VQE) untuk Perhitungan Energi Molekul H₂, HeH⁺, dan HeHe²⁺

MAUDINA ROHMAH, . (2026) Mitigasi Quantum Error Berbasis Machine Learning pada Simulasi Variational Quantum Eigensolver (VQE) untuk Perhitungan Energi Molekul H₂, HeH⁺, dan HeHe²⁺. Sarjana thesis, UNIVERSITAS NEGERI JAKARTA.

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Abstract

Penelitian ini bertujuan menerapkan mitigasi quantum error berbasis machine learning pada simulasi Variational Quantum Eigensolver (VQE) untuk perhitungan energi dasar molekul H₂, HeH⁺, dan HeHe²⁺. VQE merupakan algoritma hibrida kuantum-klasik yang sesuai untuk perangkat Noisy Intermediate-Scale Quantum (NISQ), namun akurasinya masih dipengaruhi oleh berbagai sumber noise. Pada penelitian ini digunakan metode mitigasi error berbasis supervised learning, yaitu Random Forest dan XGB, serta dibandingkan dengan metode Zero Noise Extrapolation (ZNE). Simulasi dilakukan menggunakan Qiskit Nature dan PySCF dengan basis set STO-3G dan ansatz TwoLocal. Data noisy diperoleh melalui simulasi backend FakeAthensV2 yang merepresentasikan karakteristik perangkat kuantum nyata. Kinerja metode mitigasi dievaluasi menggunakan Mean Square Error (MSE) dan koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa metode machine learning mampu meningkatkan akurasi hasil simulasi VQE dengan menghasilkan energi yang lebih mendekati nilai ideal. Di antara metode yang diuji, XGB memberikan performa terbaik dengan nilai MSE terendah dan R² tertinggi dibandingkan Random Forest dan ZNE. Hasil ini menunjukkan bahwa machine learning berpotensi menjadi metode mitigasi error yang efektif untuk meningkatkan performa komputasi kuantum pada era NISQ. ***** This research discusses about the application of machine learning-based quantum error mitigation in Variational Quantum Eigensolver (VQE) simulations for calculating the ground-state energies of H₂, HeH⁺, and HeHe²⁺ molecules. VQE is a hybrid quantum-classical algorithm designed for Noisy Intermediate-Scale Quantum (NISQ) devices, but its performance is limited by various noise sources. In this work, supervised learning approaches, namely Random Forest and XGB, are employed for error mitigation and compared with the conventional Zero Noise Extrapolation (ZNE) method. Simulations are performed using Qiskit Nature and PySCF with the STO-3G basis set and a TwoLocal ansatz. Noisy data are generated using the FakeAthensV2 backend, which emulates the characteristics of real quantum hardware. The mitigation performance is evaluated using Mean Square Error (MSE) and the coefficient of determination (R²). The results demonstrate that machine learning-based mitigation improves the accuracy of VQE simulations by producing energies closer to ideal values. Among the evaluated methods, XGB achieves the best performance, yielding the lowest MSE and the highest R². These findings indicate that machine learning is a promising and effective approach for quantum error mitigation in NISQ-era quantum computing.

Item Type:	Thesis (Sarjana)
Additional Information:	1). Dr. Teguh Budi Prayitno M. Si; 2). Yanoar Pribadi Sarwono, Ph. D.
Subjects:	Sains > Fisika
Divisions:	FMIPA > S1 Fisika
Depositing User:	Maudina Rohmah .
Date Deposited:	11 Aug 2026 07:49
Last Modified:	11 Aug 2026 07:49
URI:	http://repository.unj.ac.id/id/eprint/69668

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