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Quantum Machine Learning for Protein Folding

Proteins are complex 3-dimensional structures that play a vital role in human health. In their unfolded state, proteins can have a wide variety of structural forms; however, they must refold into their native, functional structure in order to function properly. This process is both a biological mystery and a critical step in drug discovery, as many of our most effective medicines are protein-based.

Despite its crucial importance, folding a single protein is an enormous computational challenge. The inherent asymmetry and complexity of proteins creates a rugged energy landscape that must be optimized using highly accurate models, which requires significant computational resources. The advent of quantum computing has the potential to dramatically accelerate the folding of complex proteins, paving the way for new therapies and diagnostics.

Quantum machine learning for protein folding has already yielded impressive results, demonstrating the power of quantum computation to tackle a diverse set of biochemical problems. The superposition of qubits allows for simultaneous simulation of multiple solutions, enabling exponential speedups compared to classical computers. These speedups, coupled with the ability to probe more complex energy states of proteins, can reveal novel insights into folding pathways and help accelerate drug discovery timelines.

Recent studies have used hybrid quantum-classical computer systems to study the problem of protein folding. Casares et al 2021 combined quantum walks on a quantum computer with deep learning on a classical computer to create a hybrid algorithm called QFold, which achieves a polynomial speedup over the best classical algorithms. Outeiral et al 2020 used a similar approach, combining quantum annealing with a genetic algorithm on a classical computer to find low-energy configurations of lattice protein models.

Other approaches techogle.co have been explored using variational quantum learning, a form of approximate inference in which the optimization algorithm is informed by an empirical error model. For example, Roney and Ovchinnikov use an error model to guide their adiabatic quantum protein-folding algorithm to start from the lowest-energy conformation of a given amino acid sequence. Their algorithm then uses a heuristic search to locate a likely 3D protein structure.

While most experimental work to date has focused on simple proteins, recent theoretical developments have opened the door to applying quantum machine learning to more challenging proteins. In particular, researchers at Zhejiang University in Hangzhou have proposed a model that describes protein folding as a quantum walk on a definite graph, without relying on any simple assumptions of protein structure at the outset.

This work is an technology website exciting advancement in the field of quantum biology, but it is important to emphasize that it is still far from a clinically relevant model of protein folding. To be applicable to the study of real proteins, the model must be tested in simulations on a large scale. Currently available quantum computational devices have between 14 and 15 qubits; to simulate a protein of 50 amino acids, the number of qubits would need to grow to 98 or more. Quantum computers with higher qubit counts are under intensive development, and their application to protein folding may soon become a reality.