Representing a given problem as a QUBO problem implies the possibility of running it in a quantum computational environment (generic or specific). The well-known problem of looking for similar functions in biological structures, especially of proteins, is of great interest in the field of Bioinformatics. We give a QUBO formulation for CMO protein problem. Experimental results validate this approach as an alternative to classical methods via combinatorial optimization. For the accomplishment of such experiments, we use the qbsolv tool.

Distance-based Quantum Classifier (DBQC) is a quantum machine learning model for pattern recognition. However, DBQC has a low accuracy on real noisy quantum processors. We present a modification of DBQC named Quantum One-class Classifier (QOCC) to improve accuracy on NISQ (Noisy Intermediate-Scale Quantum) computers. Experimental results were obtained by running the proposed classifier on a computer provided by IBM Quantum Experience and show that QOCC has improved accuracy over DBQC.

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