Digitized-Counterdiabatic Quantum Algorithm for Protein Folding

Abstract

We propose a hybrid classical-quantum digitized-counterdiabatic algorithm to tackle the protein folding problem on a tetrahedral lattice. Digitized-counterdiabatic quantum computing is a paradigm developed to compress quantum algorithms via the digitization of the counterdiabatic acceleration of a given adiabatic quantum computation. Finding the lowest energy configuration of the amino acid sequence is an NP-hard optimization problem that plays a prominent role in chemistry, biology, and drug design. We outperform state-of-the-art quantum algorithms using problem-inspired and hardware-efficient variational quantum circuits. We apply our method to proteins with up to 9 amino acids, using up to 17 qubits on quantum hardware. Specifically, we benchmark our quantum algorithm with Quantinuum's trapped ions, Google's and IBM's superconducting circuits, obtaining high success probabilities with low-depth circuits as required in the NISQ era.

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• Taming quantum systems: A tutorial for using shortcuts-to-adiabaticity, quantum optimal control, and reinforcement learning
• Convergence of Digitized-Counterdiabatic QAOA: circuit depth versus free parameters
• Resource analysis of quantum algorithms for coarse-grained protein folding models
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• Benchmarking hybrid digitized-counterdiabatic quantum optimization
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• Efficient Large-Scale Quantum Optimization via Counterdiabatic Ansatz
• Symmetry-enhanced Counterdiabatic Quantum Algorithm for Qudits
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• Noise Effects on Diabatic Quantum Annealing Protocols
• Digitized Counter-Diabatic Quantum Optimization for Bin Packing Problem
• Counterdiabatic ADAPT-VQE for molecular simulation
• Exploring Quantum Annealing for Coarse-Grained Protein Folding
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