T-Gate Enabled Fault-Tolerant Ansatz Circuit Design for Variational Quantum Algorithms in Peptide Folding on Aria-1

Protein folding poses a significant challenge in biology due to its combinatorial nature and NP-completeness, making it a prime candidate for quantum computing solutions. Quantum computers offer the potential to expedite protein folding predictions by leveraging superposition and entanglement properties for efficient exploration of protein structure space. This study focuses on enhancing peptide folding predictions through an innovative Ansatz circuit design incorporating T-gates alongside Clifford gates to reduce noise in quantum algorithms. The study benchmarks existing tools like MD simulation and stochastic techniques against the Variational Quantum Eigensolver (VQE) using quantum simulators and QPUs. Detailed qubit configurations and peptide folding processes are outlined, highlighting the use of efficient quantum structures and circuit designs. The experimental setup includes running the circuit for multiple replicas to analyze error rates and energy minimization for different peptide sequences. The results demonstrate a significant difference in gate performance between Clifford gates alone and Clifford + T gates, with T gates leading to reduced noise and improved performance. Statistical analyses reveal a negative mean difference and a high effect size, emphasizing the impact of T gates on noise reduction in quantum algorithms for peptide folding predictions. The study showcases consistent results across multiple replicas, indicating the reliability and efficiency of the proposed Ansatz circuit design. The study highlights the importance of gate sequencing and optimization in quantum computing applications for protein structure prediction. Future directions include expanding the model to larger proteins, refining ansatz circuits, and collaborating with experts to advance quantum algorithms for complex biological systems.