Enhanced Quantum-Based DNA Sequence Alignment with Noise Handling and Error Detection

This paper presents an enhanced quantum computing approach for DNA sequence alignment, integrating advanced noise management and robust error detection. We augment existing quantum alignment methodologies by incorporating supplementary qubits for each base, enabling improved alignment in noisy quantum environments. Our method models quantum circuits for matching reference and read DNA sequences using a hardware-calibrated noise model, specifically FakeJakartaV2, which incorporates depolarizing and thermal relaxation error characteristics. A key enhancement involves the integration of Zero-Noise Extrapolation (ZNE) for active error mitigation, with ZNE confidence serving as an early stopping criterion to enhance computational efficiency. Our error detection mechanism complements ZNE, providing an additional layer of verification for alignment integrity even when ZNE confidence is high. Experimental results demonstrate that this combined strategy effectively addresses sequence discrepancies, enhances error resilience, and produces more reliable alignment outcomes in quantum-based DNA alignment tasks compared to classical techniques. We validate our approach using IBM Quantum simulators, showcasing its potential for advancing large-scale genomic data analysis, addressing challenges of noise and errors in quantum DNA sequence alignment, and offering practical value through actionable error mitigation.