Quantum algorithm for position weight matrix matching

We propose two quantum algorithms for a problem in bioinformatics, position weight matrix (PWM) matching, which aims to find segments (sequence motifs) biological sequence such as DNA and protein that have high scores defined by the PWM are thus of informational importance related function. The proposed algorithms, naive iteration method Monte-Carlo-based method, output matched segments, given oracular accesses entries PWM. former uses amplitude amplification (QAA) motif search, resulting query complexity scaling on length n , xmlns:xlink="http://www.w3.org/1999/xlink">m number PWMs xmlns:xlink="http://www.w3.org/1999/xlink">K $O(m\sqrt{Kn})$ means speedup over existing classical with respect . latter also QAA, further, Monte Carlo integration segment score calculation, instead iteratively operating circuits arithmetic method; then it provides additional some situation. As drawback, these use random access memories their initialization takes xmlns:xlink="http://www.w3.org/1999/xlink">O ( ) time. Nevertheless, our keep advantage especially when we search matches many parallel.