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Quantum Machine Learning for Protein Folding

Proteins are linear sequences of amino acid residues that perform many essential functions vital to sustaining life, such as signaling, cell cycle regulation and production of hormones and enzymes. Each individual protein has a specific three-dimensional shape, which is called its native conformation. However, proteins often misfold, which can lead to a variety of serious diseases. This is why understanding how proteins fold into their native structures is a major challenge in biology, chemistry and medicine.

Fortunately, recent research into machine learning and quantum computing has shown that these techniques may help us solve the protein-folding problem and shed light on the physical principles that dictate this process. Specifically, by incorporating physical symmetries into machine-learning algorithms, we can create more accurate and efficient models for protein folding. Additionally, the speedups achieved by quantum computer simulations have opened new avenues for exploring this complex process.

In a new study, scientists have combined machine learning with a hybrid classical-quantum algorithm to improve the performance of protein folding on quantum computers. The researchers developed a parametrized quantum circuit inspired by counterdiabatic (CD) quantum algorithms and used it to implement a variational quantum eigensolver (VQE) routine, which techogle.co searches for the lowest-energy protein structure on a tetrahedral lattice. The algorithm was then tested using up to 17 qubits on different quantum hardware platforms including Quantinuum’s trapped ions and superconducting circuits from Google and IBM.

The results showed that the algorithm outperformed other CD-based quantum programs in its ability to find the protein ground state with high probability. They also found that the system was able to rank candidate structures in a manner consistent with physical protein-folding models. These findings suggest that the combination of CD-based quantum algorithms with a VQE routine can effectively solve the protein-folding problem on gate-based quantum computers, which are prone to noise and have limited qubits.

Quantum Parallelism and Speedup The computational speedup resulting from the use of quantum computers to simulate biochemical processes is largely due to their ability to utilize quantum parallelism, in which each qubit can act as a replica of multiple states simultaneously. This allows the computation to explore countless possible protein conformational spaces in a fraction of the time required by classical computer methods.

This advantage was leveraged in the current study, which used a variational VQE routine that was implemented on a tetrahedral quantum lattice to search for the best protein structure for a given amino-acid sequence. The tetrahedral architecture provides an efficient representation of a protein’s conformational space, and this was complemented by the technology website use of an adiabatic quantum annealing (QQA) technique that simulates the gradual transition of qubits to states representing the desired protein solution.

The tetrahedral QQA method, which utilizes the natural adiabatic properties of qubits to efficiently sample conformational space, is the foundation of all future work in the area of quantum-aided protein-folding and related machine learning applications. In the future, researchers hope to utilize this technique in conjunction with other quantum-aided algorithms to tackle even more complex sequences and 3D structures.