Quantum Accelerated Pattern Matching for Genome Sequencing in Complex RNA Secondary Structures

Sequence matching in genomics can be approached by quantum computing through Grover’s algorithm offering a quadratic speed-up versus the fastest classical algorithms. Previous research using quantum computing focused on long DNA sequences, but application to complex RNA secondary structures with hairpin, bulge, ring, and stem forms has not been possible. This paper developed new formulations for RNA structures using quantum pattern recognition and quantum Hamming distance on Grover’s framework by extracting linear regions from the complex forms. To best leverage the quantum speedup, methods are developed to create long sequences by concatenating different linearized parts of the complex RNA structures. The mechanism is simulated on IBM’s Qiskit quantum simulator using RNA examples from GenBank and mirBase. Comparisons of the iteration complexity indeed shows the potential for quadratic speedups from quantum computing versus classical. Simulation results also show that search accuracy is very good when using the quantum pattern recognition method.