Hardware-Efficient Ansatz Variational Quantum Regression for Molecular Energy Prediction

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Rasyid Ustman Ramadhan, Luthfiya Kurnia Permatahati, Teguh Budi Prayitno, Yanoar P. Sarwono

2026 ChemistrySelect Vol. 11 Issue 5 Article Cited by 2 SDG 7 Quartile

Abstract

We propose a variational quantum regression (VQR) algorithm using a hardware-efficient ansatz (HEA) structure. This approach enables the quantum state to directly encode classical tabular data with variational parameters corresponding to real-valued regression coefficients, ensuring high interpretability and efficient optimization without sacrificing expressiveness. By combining a variational quantum circuit with a classical optimizer, our method predicts the ground-state energy of a hydrogen molecule using full configuration interaction (FCI) data. We quantify the expressibility of the VQR HEA circuit via the Kullback–Leibler (KL) divergence DKL and show the advantages of our Ry − Rx gate sequence in balancing expressibility. Performed for pennylane using an idealized quantum simulator, our 4-qubit, 5-layer HEA-based VQR model achieves an accuracy of ∼0.99, mean squared error (MSE) < 10−6 Hartree2, and mean absolute error (MAE) around 0.1 × 10−2 Hartree compared to FCI benchmarks. We further demonstrate how qubit number and layer depth influence model accuracy, providing insights for task-specific quantum circuit design. Our results advance practical applications of quantum computing for electronic structure problems by introducing an expressive, interpretable ansatz tailored for high-precision simulations. © 2026 Wiley-VCH GmbH.

Affiliations

Research Center for Quantum Physics, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia; Department of Physics, Faculty of Mathematics and Natural Science, Universitas Sebelas Maret, Central Java, Surakarta, Indonesia; Department of Physics, Faculty of Mathematics and Natural Science, Universitas Negeri Jakarta, East Jakarta, Indonesia

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