With small-scale quantum processors transitioning from experimental physics labs to industrial products, these processors in a few years are expected to scale up and be more robust for efficiently computing important algorithms in various fields. In this paper, we propose a quantum algorithm to address the challenging field of data processing for genome sequence reconstruction. This research describes an architecture-aware implementation of a quantum algorithm for sub-sequence alignment. A new algorithm named QiBAM (quantum indexed bidirectional associative memory) is proposed, which uses approximate pattern-matching based on Hamming distances. QiBAM extends the Grover’s search algorithm in two ways, allowing: (1) approximate matches needed for read errors in genomics, and (2) a distributed search for multiple solutions over the quantum encoding of DNA sequences. This approach gives a quadratic speedup over the classical algorithm. A full implementation of the algorithm is provided and verified using the OpenQL compiler and QX Simulator framework. Our implementation represents a first exploration towards a full-stack quantum accelerated genome sequencing pipeline design.
With small-scale quantum processors transitioning from experimental physics labs to industrial products, these processors in a few years are expected to scale up and be more robust for efficiently computing important algorithms in various fields. In this paper, we propose a quantum algorithm to address the challenging field of data processing for genome sequence reconstruction. This research describes an architecture-aware implementation of a quantum algorithm for sub-sequence alignment. A new algorithm named QiBAM (quantum indexed bidirectional associative memory) is proposed, which uses approximate pattern-matching based on Hamming distances. QiBAM extends the Grover’s search algorithm in two ways, allowing: (1) approximate matches needed for read errors in genomics, and (2) a distributed search for multiple solutions over the quantum encoding of DNA sequences. This approach gives a quadratic speedup over the classical algorithm. A full implementation of the algorithm is provided and verified using the OpenQL compiler and QX Simulator framework. Our implementation represents a first exploration towards a full-stack quantum accelerated genome sequencing pipeline design.
With small-scale quantum processors transitioning from experimental physics labs to industrial products, these processors allow us to efficiently compute important algorithms in various fields. In this paper, we propose a quantum algorithm to address the challenging field of big data processing for genome sequence reconstruction. This research describes an architecture-aware implementation of a quantum algorithm for sub-sequence alignment. A new algorithm named QiBAM (quantum indexed bidirectional associative memory) is proposed, that uses approximate pattern-matching based on Hamming distances. QiBAM extends the Grover's search algorithm in two ways to allow for: (1) approximate matches needed for read errors in genomics, and (2) a distributed search for multiple solutions over the quantum encoding of DNA sequences. This approach gives a quadratic speedup over the classical algorithm. A full implementation of the algorithm is provided and verified using the OpenQL compiler and QX simulator framework. This represents a first exploration towards a full-stack quantum accelerated genome sequencing pipeline design. The open-source implementation can be found on https://github.com/prince-ph0en1x/QAGS.
Sequence matching in genomics can be approached by quantum computing through Grover’s algorithm offering a quadratic speed-up versus the fastest classical algorithms. Previous research using quantum computing focused on long DNA sequences, but application to complex RNA secondary structures with hairpin, bulge, ring, and stem forms has not been possible. This paper developed new formulations for RNA structures using quantum pattern recognition and quantum Hamming distance on Grover’s framework by extracting linear regions from the complex forms. To best leverage the quantum speedup, methods are developed to create long sequences by concatenating different linearized parts of the complex RNA structures. The mechanism is simulated on IBM’s Qiskit quantum simulator using RNA examples from GenBank and mirBase. Comparisons of the iteration complexity indeed shows the potential for quadratic speedups from quantum computing versus classical. Simulation results also show that search accuracy is very good when using the quantum pattern recognition method.
Fast sequencing and analysis of (microorganism, plant or human) genomes will open up new vistas in fields like personalised medication, food yield and epigenetic research. Current state-of-the-art DNA pattern matching techniques use heuristic algorithms on computing clusters of CPUs, GPUs and FPGAs. With genomic data set to eclipse social and astronomical big data streams within a decade, the alternate computing paradigm of quantum computation is explored to accelerate genome-sequence reconstruction. The inherent parallelism of quantum superposition of states is harnessed to design a quantum kernel for accelerating the search process. The project explores the merger of these two domains and identifies ways to fit these together to design a genome-sequence analysis pipeline with quantum algorithmic speedup. The design of a genome-sequence analysis pipeline with a quantum kernel is tested with a proof-of-concept demonstration using a quantum simulator.
Motivation: The exponential growth of publicly available genomic data has created unprecedented opportunities for sequence-based discovery. Locating specific k-mers is fundamental to diverse applications, including metagenomic classification, pathogen and cancer detection, and variant calling yet efficient identification of multiple k-mer patterns across large sequencing data and massive databases remains a significant computational challenge. Method: We implement two quantum algorithms for DNA multi pattern string matching for k-mer detection based on Grover's amplitude amplification with quantum random access memory (QRAM). The first algorithm uses an enumerate-m oracle that sequentially checks a loaded text substring against all m patterns achieving O(√S) query complexity for S text positions but requiring O(m·L) work per oracle call. The second algorithm employs nested Grover search with an outer loop over text positions and an inner loop over pattern space, reducing oracle complexity to O(L) while performing O(√S · √m) in total. Results: We present two quantum implementations of multi-pattern string matching tailored for k-mer detection. Leveraging quantum parallelism and Grover-inspired search primitives, our methods accelerate dictionary-based pattern matching, particularly in contexts involving large sequences, such as genomic data, and extensive pattern sets. Conclusions: While implementation challenges such as QRAM overhead remain, this study demonstrates both the promise and current limitations of quantum-enhanced string matching, establishing a foundational step toward quantum readiness in bioinformatics.
The integration of quantum computing into bioinformatics offers transformative advancements in genome sequencing, particularly in addressing computational bottlenecks associated with large-scale genomic data. This study introduces the QuASeR (Quantum Accelerated Sequence Reconstruction) framework, leveraging quantum algorithms such as Grover's search and quantum Fourier transform for DNA sequence reconstruction. Hypothetical simulations were conducted on datasets of varying sizes, ranging from 10 million to 1 billion base pairs, comparing the performance of the proposed quantum approach with state-of-the-art classical algorithms. Results indicate that QuASeR achieves a 40% reduction in processing time for datasets of 10 million base pairs and an 80% reduction for datasets of 1 billion base pairs, highlighting its scalability and efficiency. Moreover, the framework achieved a 95% accuracy rate in sequence alignment tasks, closely matching classical methods while significantly outperforming them in speed. These results underscore the potential of quantum computing in bioinformatics, paving the way for rapid and accurate genome sequencing, which is critical for advancing personalized medicine and large-scale genomic studies. As quantum hardware matures, the proposed framework is expected to achieve even greater practical utility, offering a paradigm shift in computational genomics.
The exponential growth of genomic data presents significant computational challenges in the field of bioinformatics, particularly in genome sequencing. Traditional methods struggle to meet the increasing demand for speed, accuracy, and efficiency. This paper introduces a novel framework that leverages quantum computing to accelerate genome sequencing processes. The proposed framework integrates quantum algorithms, such as Grover's search and quantum Fourier transform, with classical bioinformatics techniques to optimize key tasks, including sequence alignment, variant detection, and large-scale data analysis. By exploiting the inherent parallelism and computational power of quantum systems, the framework achieves substantial reductions in processing time while maintaining high accuracy. A case study on large-scale genomic datasets demonstrates the framework's ability to outperform state-of-the-art classical approaches, particularly in handling complex and large-scale sequencing tasks. Furthermore, this research highlights the potential of quantum computing in advancing bioinformatics, paving the way for more efficient and scalable solutions to address the computational bottlenecks of modern genomics. The implications of this interdisciplinary approach extend beyond genomics, offering transformative possibilities in data-driven life sciences.