Quantum pattern recognition techniques have recently raised attention as potential candidates in analyzing vast amount of data. The necessity to obtain faster ways to process data is imperative where data generation is rapid. The ever-growing size of sequence databases caused by the development of high throughput sequencing is unprecedented. Current alignment methods have blossomed overnight but there is still the need for more efficient methods that preserve accuracy in high levels. In this work, a complex method is proposed to treat the alignment problem better than its classical counterparts by means of quantum computation. The basic principal of the standard dot-plot method is combined with a quantum algorithm, giving insight into the effect of quantum pattern recognition on pairwise alignment. The central feature of quantum algorithmic -quantum parallelism- and the diffraction patterns of x-rays are synthesized to provide a clever array indexing structure on the growing sequence databases. A completely different approach is considered in contrast to contemporary conventional aligners and a variety of competitive classical counterparts are classified and organized in order to compare with the quantum setting. The proposed method seems to exhibit high alignment quality and prevail among the others in terms of time and space complexity.

A novel quantum algorithm for string‐matching is introduced that significantly enhances the complexity of this fundamental operation, essential in numerous computing applications. The algorithm is designed as a composite quantum denoising procedure applied to a quantum‐generated dot‐matrix plot, which is treated as an image. This approach effectively identifies regions of similarity between two input strings of lengths N and M . For strings of equal length, the algorithm achieves a time complexity of and a space complexity of , demonstrating a clear advantage in quantum computational efficiency.

Vast amounts of genomic, proteomic, transcriptomics and other forms, commonly referred to as -omics data, are generated daily in an unprecedented way thanks to high-throughput Next Generation Sequencing technologies. One of the main processes to generate value and insights from this data in bioinformatics is “sequence alignment”, an algorithmic routine that matches a “reference” sequence to a larger sequence. Despite the plethora of approaches, pairwise and multiple sequence alignment remain a complex problem that requires high computational power. In this article, the most prominent sequence alignment approaches of the past three decades are reviewed and categorized, examining different aspects, such as their overall algorithmic synthesis, alignment quality and performance benchmarking tests in a uniform way. The latest trends reveal an increased specialization on biology-based directions, the need for alternative heuristic approaches and a promise in optics-enabled approaches and the quantum computing paradigm shift.

A circuit design interface for simulation of quantum circuits is presented. This simple but powerful tool aims to enable and improve experimentation in the field of quantum computing, either for professional or amateur usage. It provides an intuitive, automated, easy-to-use environment for building algorithms using the circuit model of quantum computation, while freeing the experimenter from technical procedures, such as coding or program installations. It provides a wide range of tools to the user, from a universal gate-set, to custom gate definitions, post-selected measurements, complex compositions of sub-circuits in a recursive way, good modularity and nice output results. Moreover, it is combined with popular backend simulators due to its circuit-parsing capabilities.

Quantum Multiple-Valued Decision Diagrams have proven their utility in efficiently representing and manipulating reversible and quantum functions. This work, acting as a mini-review of the topic, aims to provide a comprehensive guide for implementing reversible and quantum circuit simulators using such diagrams by integrating existing methodologies into a single cohesive framework. The operation and purpose of all relevant algorithms and routines are explained, and pseudo-code schematics are also detailed for each. A proof-of-concept implementation is presented at the end, made openly available to facilitate comprehension and research in the field of decision-diagram-based simulation of Quantum Computing.

Quantum image processing in qubit-based image representations for more than two dimensions seems to offer great opportunities for advanced data analysis and processing in quantum computers. Of fundamental importance is cycle shifting an image along an arbitrary direction, as various operations are based on it. In this work, a widely used image representation scheme is generalized into a single, multidimensional encoding. Within this framework, we define a cycle shifting method capable of translating a representation along any linear combination of its dimensional axes. The algorithmic procedure is appropriately redefined to accommodate arbitrary shifts in the Hilbert space. The corresponding computational complexity is optimized, achieving an exponential reduction in quantum resource requirements with respect to the shift-step magnitude. A comparative complexity analysis substantiates the efficiency gain, while simulation results validate the proposed multidimensional framework for two- and three-dimensional representations.