Quantum Circuit Optimization for Protein Structure Prediction
Xinpeng Li, Vinooth Rao Kulkarni, Jai Nana, Shusen Pu, Ning Xie, Qiang Guan, Ruihao Li, Shulei Zhang, Shuai Xu, Daniel Blankenberg, Vipin Chaudhary · 2025
Quantum computing holds immense potential for solving complex problems. However, current Noisy Intermediate-Scale Quantum (NISQ) devices are limited by high error rates and restricted qubit counts. Variational Quantum Algorithms (VQAs) have emerged as promising approaches for utilizing NISQ hardware, particularly in applications like protein structure prediction. Nonetheless, their practical implementation re-mains challenging due to the large number of gates required in quantum circuits. In this paper, we employ two methods: quantum circuit cutting, which makes circuits more hardware-friendly, and Initial-State-Dependent Optimization (ISDO), which dynamically prunes unnecessary gates to optimize the ansatz for protein structure prediction. Together, these techniques reduce circuit gate count, enhancing fidelity and improving the practi-cality of real-world quantum applications.