Quantum Resource Estimation for Protein Conformation Prediction

In this work, we investigate the use of quantum computing for protein conformation prediction and develop a resource estimation framework for quantum algorithms in this context. Our approach involves mapping the problem to a quantum circuit, characterizing the circuit in terms of the number of gates and qubits required, applying different levels of circuit optimization and analyzing the number of CNOT gates required for execution. We derived our estimates using the sequence of an experimentally verified protein structure (membrane-proximal region of aIIb cytoplasmic domain tail of platelet integrin aIIb-b3 cytoplasmic domain, then apply a regression model to extrapolate the estimates. Our results demonstrate the potential of quantum computing for protein conformation prediction and provide insights into the resource requirements for quantum algorithms.