Parameterized quantum algorithms for closest string problems

Parameterized complexity enables the practical solution of generally intractable NP-hard problems when certain parameters are small, making it particularly useful in real-world applications. The study of string problems in this framework has been particularly fruitful, yielding many state-of-the-art classical algorithms that run efficiently in certain parameter regimes contrary to their worst- or average-case performance. Motivated by the dramatic increase in genomic data and its growing computational demands, we initiate the study of the quantum parameterized complexity of the Closest String Problem (CSP) and the related Closest Substring Problem (CSSP). We present three quantum algorithms for the CSP and one for the CSSP. Each algorithm demonstrates improved performance over classical counterparts in specific parameter regimes, highlighting the promise of quantum approaches in structured combinatorial settings. We also derive a conditional lower bound for the CSP with binary alphabets, showing that our first algorithm is tight in its dominant scaling factor.