Protein folding takes a step forward with Quantum Computing

Protein Folding Takes A Step Forward With Quantum Computing

The proposed hybrid classical-quantum algorithm aims to solve the protein folding problem using quantum computing techniques. Protein folding problems involve finding the lowest energy configuration of a protein’s amino acid sequence and is a computationally challenging optimization problem that is important in fields such as chemistry, biology, and drug discovery.

The research paper “Digitized-Counterdiabatic Quantum Algorithm for Protein Folding” was written by Pranav Chandarana, Narendra N. Hegade, Iraitz Montalban, Enrique Solano, and Xi Chen. The authors are affiliated with various institutions, including the University of the Basque Country UPV/EHU in Spain, Kipu Quantum in Germany, the International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Physics Department at Shanghai University in China, and the Basque Foundation for Science in Spain.

The proposed algorithm uses digitized-counterdiabatic quantum computing, a method for compressing quantum algorithms, to outperform existing quantum algorithms for solving the protein folding problem. The algorithm has been tested using up to seventeen qubits on quantum hardware from different companies, including Quantinuum’s trapped Ion device and Google’s and IBM’s superconducting device. Both have achieved high success rates with low-depth circuits, which are suitable for use in the NISQ (Noisy Intermediate-Scale Quantum) regime.

VQA’s and DCQC

Variational quantum algorithms (VQAs) are hybrid classical-quantum algorithms that aim to solve problems with noisy qubits on near-term quantum computers. They consist of a parameterized quantum circuit (PQC) or circuit ansatz, which produces trial quantum states, and a classical optimization routine that finds the optimal parameters to solve the problem. PQCs can be divided into two categories: problem-inspired, which utilizes the properties of the problem Hamiltonian to reach the expected state efficiently, and hardware-efficient, which takes into account the device connections to reduce noise from deep circuits and unimplementable connections. VQAs can be challenging to implement due to shot noise, measurement noise, and the phenomenon of barren plateaus, where the gradients vanish with increasing system size.

Recently, digitized-counterdiabatic quantum computation (DCQC) has been used to improve and compress state-of-the-art quantum algorithms and has demonstrated improvements in industrial applications such as portfolio optimization and integer factorization. DCQC uses counterdiabatic (CD) protocols to accelerate adiabatic quantum algorithms and generate many-body ground states, but has challenges including finding suitable initial parameters and optimal CD terms. Other quantum control protocols like quantum optimal control (QOC) have also been studied in the context of VQAs.