Can quantum computers solve protein folding?
Protein folding is a problem that can be solved through quantum computing, and more specifically quantum annealing so that the lowest energy path to creating a protein can be found much more quickly, and without limitations.
How was Levinthal’s paradox resolved?
He suggested that the paradox can be resolved if “protein folding is sped up and guided by the rapid formation of local interactions which then determine the further folding of the peptide; this suggests local amino acid sequences which form stable interactions and serve as nucleation points in the folding process”.
How many qubits does it take to fold a protein model?
These methods were used to fold a coarse-grained protein model with 6 and 8 amino acid sequences on 2D and 3D lattices, respectively, using a quantum annealer 14. These experiments required 81 and 200 qubits and led to a final population of 0.13\% and 0.024\% for the corresponding ground state structures, using divide and conquer strategies.
Can quantum algorithms be used in noisy state-of-the-art quantum computers?
On the other hand, while fault-tolerant quantum computers are beyond reach for state-of-the-art quantum technologies, there is evidence that quantum algorithms can be successfully used in noisy state-of-the-art quantum computers to accelerate energy optimization in frustrated systems.
How many qubits does it take to fold a neuropeptide?
The same method is also successfully applied to the study of the folding of a 7 amino acid neuropeptide using 9 qubits on an IBM 20-qubit quantum computer.
What is Perdomo-Ortiz spin Hamiltonian?
Perdomo-Ortiz et al. paved the way towards the construction of spin Hamiltonians to find the on-lattice heteropolymer’s low-energy conformations using quantum devices, but with unattainable high costs for near-term quantum computers 10, 11.