Protein structure prediction and design on near-term quantum computers

Doctoral thesis, 2025

DNA is transcribed into mRNA, which then forms a chain of amino acids. But what shape does this chain take when it folds into a functional protein? And how can we design one with a specific shape?
Simulating the folding process is highly complex, so we often try to predict the final structure instead. Modern AI methods do this well, but some cases remain challenging—where quantum algorithms might help.

Why predict protein structure at all?
Why simulate folding at all? Proteins begin as amino acid chains and fold into low-energy 3D shapes that define their function. The number of possible folds is astronomically large—checking them all would take longer than the age of the universe! Simulating every atom is daunting, so we group atoms to reduce complexity, at the cost of detail.

Quantum mechanics, through wave-particle duality and entanglement, enables the development of quantum computers through qubits. In theory, quantum algorithms may one day surpass classical ones in terms of speed and efficiency. For now, quantum computers are small and noisy, so we use hybrid algorithms, where quantum and classical parts work together.

This thesis explores such approaches, highlighting trade-offs, and clarifying where quantum computing might truly make an impact—and where it still falls short.