In this study, we explore how quantum pathfinding algorithm called Gaussian Amplitude Amplification (GAA) can be used to solve the DNA sequencing problem. To do this, sequencing by hybridization was assumed wherein short fragments of the nucleic acids called oligonucleotides of length l were gathered and were then assembled. The process of reassembling the sequence was then abstracted into a graph problem of finding the Hamiltonian path with the least cost. The constructed directed graph was then converted into sequential bipartite graphs in order to use GAA. The results of our simulation revealed that for the case where l = 2 and spectrum size of |S| = 4, the probability of finding the optimal solution (with the least cost) is approximately 70.92% - a significant improvement compared to 4.17% when the path is chosen randomly. While this study only focused on the ideal scenario where there are no errors in the spectrum, the outcomes presented here demonstrate the plausibility of using GAA as a genome sequencing method.
In this study, we explore how quantum pathfinding algorithm called Gaussian Amplitude Amplification (GAA) can be used to solve the DNA sequencing problem. To do this, sequencing by hybridization was assumed wherein short fragments of the nucleic acids called oligonucleotides of length l were gathered and were then assembled. The process of reassembling the sequence was then abstracted into a graph problem of finding the Hamiltonian path with the least cost. The constructed directed graph was then converted into sequential bipartite graphs in order to use GAA. The results of our simulation revealed that for the case where l = 2 and spectrum size of |S| = 4, the probability of finding the optimal solution (with the least cost) is approximately 70.92% - a significant improvement compared to 4.17% when the path is chosen randomly. While this study only focused on the ideal scenario where there are no errors in the spectrum, the outcomes presented here demonstrate the plausibility of using GAA as a genome sequencing method.
Rapid advances in DNA sequencing have converted genomics and made large strides in our expertise in genetic records and the way it pertain to evolution, health, and illness. DNA fragment meeting is the method of reassembling the small, fragmented reads that might be produced by using DNA sequencing to rebuild the authentic genome. DNA fragment meeting is a primary computational issue, especially for huge genomes, due to the full-size extent of statistics and the intrinsic complexity of correctly assembling genomes. The want for innovative solutions is highlighted by using the truth that traditional computational techniques, such as graph-primarily based totally and heuristic approaches, are resource-intensive, have scaling problems, and can be erroneous in complex genome regions. Because of its exquisite capability to do positive computations at formerly unheard-of quotes and efficiently manipulate hard optimization issues, quantum computing has simply come to mild as a likely road for overcoming those computational barriers. In computationally annoying responsibilities like combinatorial optimization and large-scale statistics processing, quantum algorithms just like the Quantum Approximate Optimization Algorithm (QAOA) and Grover`s set of rules for search optimization have verified the capacity to carry out higher than classical approaches. DNA fragments meeting with quantum computing have the potential to boost accuracy and performance even as additionally develop new possibilities for large-scale genomic study. The essential thoughts of quantum computing, current tendencies in quantum-primarily based totally algorithms and their relevance to the meeting system are all included in this chapter, which investigates the capability of quantum computing as an innovative device for DNA fragment assembly. This chapter intends to explore on how quantum strategies would possibly enhance the precision and effectiveness of DNA assembly, starting the door for brand new tendencies inside the subject of genomics with the aid of using reading the capability and constraints of quantum computing for genomics.
Bayesian Optimization (BO) is a powerful technique for optimizing black-box functions, frequently employed in areas such as machine learning hyperparameter tuning and experimental design. However, its performance can degrade significantly in high-dimensional spaces, leading to slow convergence and increased computational cost. This paper introduces a novel approach to Bayesian Optimization inspired by quantum amplitude amplification, a technique rooted in quantum computing. Our method utilizes a probabilistic representation of candidate solutions within a Hilbert space, employing amplitude amplification to systematically bias the search towards promising regions of the solution space. This approach aims to accelerate the optimization process, particularly in scenarios where traditional BO methods struggle due to the curse of dimensionality. The core claim of this work is to demonstrate a speedup in convergence compared to standard BO algorithms, leveraging the principles of amplitude amplification for enhanced exploration and exploitation. The theoretical framework is presented, alongside a detailed explanation of the algorithm's components, and the results are discussed, highlighting the potential for improved efficiency in complex optimization problems. We focus on the core mechanics of using amplitude amplification to enhance the probability of finding optimal solutions.
BACKGROUND: The assembly task is an indispensable step in sequencing genomes of new organisms and studying structural genomic changes. In recent years, the dynamic development of next-generation sequencing (NGS) methods raises hopes for making whole-genome sequencing a fast and reliable tool used, for example, in medical diagnostics. However, this is hampered by the slowness and computational requirements of the current processing algorithms, which raises the need to develop more efficient algorithms. One possible approach, still little explored, is the use of quantum computing. RESULTS: We present a proof of concept of de novo assembly algorithm, using the Genomic Signal Processing approach, detecting overlaps between DNA reads by calculating the Pearson correlation coefficient and formulating the assembly problem as an optimization task (Traveling Salesman Problem). Computations performed on a classic computer were compared with the results achieved by a hybrid method combining CPU and QPU calculations. For this purpose quantum annealer by D-Wave was used. The experiments were performed with artificially generated data and DNA reads coming from a simulator, with actual organism genomes used as input sequences. To our knowledge, this work is one of the few where actual sequences of organisms were used to study the de novo assembly task on quantum annealer. CONCLUSIONS: Proof of concept carried out by us showed that the use of quantum annealer (QA) for the de novo assembly task might be a promising alternative to the computations performed in the classical model. The current computing power of the available devices requires a hybrid approach (combining CPU and QPU computations). The next step may be developing a hybrid algorithm strictly dedicated to the de novo assembly task, using its specificity (e.g. the sparsity and bounded degree of the overlap-layout-consensus graph).
Genome sequencing is essential to decode genetic information, identify organisms, understand diseases and advance personalized medicine. A critical step in any genome sequencing technique is genome assembly. However, de novo genome assembly, which involves constructing an entire genome sequence from scratch without a reference genome, presents significant challenges due to its high computational complexity, affecting both time and accuracy. In this study, we propose a hybrid approach utilizing a quantum computing-based optimization algorithm integrated with classical pre-processing to expedite the genome assembly process. Specifically, we present a method to solve the Hamiltonian and Eulerian paths within the genome assembly graph using gate-based quantum computing through a Higher-Order Binary Optimization (HOBO) formulation with the Variational Quantum Eigensolver algorithm (VQE), in addition to a novel bitstring recovery mechanism to improve optimizer traversal of the solution space. A comparative analysis with classical optimization techniques was performed to assess the effectiveness of our quantum-based approach in genome assembly. The results indicate that, as quantum hardware continues to evolve and noise levels diminish, our formulation holds a significant potential to accelerate genome sequencing by offering faster and more accurate solutions to the complex challenges in genomic research.
Recent advances in DNA sequencing open prospects to make whole-genome analysis rapid and reliable, which is promising for various applications including personalized medicine. However, existing techniques for de novo genome assembly, which is used for the analysis of genomic rearrangements, chromosome phasing, and reconstructing genomes without a reference, require solving tasks of high computational complexity. Here we demonstrate a method for solving genome assembly tasks with the use of quantum and quantum-inspired optimization techniques. Within this method, we present experimental results on genome assembly using quantum annealers both for simulated data and the [Formula: see text]X 174 bacteriophage. Our results pave a way for a significant increase in the efficiency of solving bioinformatics problems with the use of quantum computing technologies and, in particular, quantum annealing might be an effective method. We expect that the new generation of quantum annealing devices would outperform existing techniques for de novo genome assembly. To the best of our knowledge, this is the first experimental study of de novo genome assembly problems both for real and synthetic data on quantum annealing devices and quantum-inspired techniques.
The task of aligning DNA sequences has been pivotal in bioinformatics since the Needleman-Wunsch algorithm in 1970 [1]. Recent advances in sequencing technology have dramatically reduced the cost of DNA data acquisition, outpacing Moore's Law for computing costs. The COVID-19 pandemic further intensified data generation from viral and human samples, creating significant computational challenges. This study investigates the potential of near-term quantum computing to address these bottlenecks. We introduce two quantum algorithms tailored for crucial bioinformatics tasks. The first algorithm applies a quantum dynamic programming approach, inspired by Ambainis et al. [2], to pairwise Sequence Alignment, demonstrating minimal speedup. The second algorithm employs a quantum search via random walk, based on Magniez et al. [3], for the Center String problem, achieving a near-quadratic speedup over the best classical algorithm for unbounded alphabets and superior performance for bounded alphabets in key parameter regimes. Our findings suggest that while quantum computing offers promising speedups for certain bioinformatics problems, the extent of its benefits varies significantly across different tasks. Future work will investigate the potential of quantum computing in analysing graph-based representations of DNA data, such as pangenome models.