Motivation: Pairwise sequence alignment is a core component of multiple sequencing-data analysis tools. Recent advancements in sequencing technologies have enabled the generation of longer sequences at a much lower price. Thus, long-read sequencing technologies have become increasingly popular in sequencing-based studies. However, classical sequence analysis algorithms face significant scalability challenges when aligning long sequences. As a result, several heuristic methods have been developed to improve performance at the expense of accuracy, as they often fail to produce the optimal alignment. Results: This paper introduces QuickEd, a sequence alignment algorithm based on a bound-and-align strategy. First, QuickEd effectively bounds the maximum alignment-score using efficient heuristic strategies. Then, QuickEd utilizes this bound to reduce the computations required to produce the optimal alignment. Compared to Oðn2Þ complexity of traditional dynamic programming algorithms, QuickEd’s bound-and-align strategy achieves OðnbsÞ complexity, where n is the sequence length and bs is an estimated upper bound of the alignment-score between the sequences. As a result, QuickEd is consistently faster than other state-of-the-art implementations, such as Edlib and BiWFA, achieving performance speedups of 4:2-5:9× and 3:8-4:4×, respectively, aligning long and noisy datasets. In addition, QuickEd maintains a stable memory footprint below 35 MB while aligning sequences up to 1 Mbp. Availability and implementation: QuickEd code and documentation are publicly available at https://github.com/maxdoblas/QuickEd.