We propose a modified MSA algorithm on quantum annealers with applications in areas of bioinformatics and genetic sequencing.To understand the human genome, researchers compare extensive sets of these genetic sequences -or their protein counterparts -to identify patterns.This comparison begins with the alignment of the set of (multiple) sequences.However, this alignment problem is considered nondeterministically-polynomial time complete and, thus, current classical algorithms at best rely on brute force or heuristic methods to find solutions.Quantum annealing algorithms are able to bypass this need for sheer brute force due to their use of quantum mechanical properties.However, due to the novelty of these algorithms, many are rudimentary in nature and limited by hardware restrictions.We apply progressive alignment techniques to modify annealing algorithms, achieving a linear reduction in spin usage whilst introducing more complex heuristics to the algorithm.This opens the door for further exploration into quantum computing-based bioinformatics, potentially allowing for a deeper understanding of disease detection and monitoring.
We propose a modified MSA algorithm on quantum annealers with applications in areas of bioinformatics and genetic sequencing.To understand the human genome, researchers compare extensive sets of these genetic sequences -or their protein counterparts -to identify patterns.This comparison begins with the alignment of the set of (multiple) sequences.However, this alignment problem is considered nondeterministically-polynomial time complete and, thus, current classical algorithms at best rely on brute force or heuristic methods to find solutions.Quantum annealing algorithms are able to bypass this need for sheer brute force due to their use of quantum mechanical properties.However, due to the novelty of these algorithms, many are rudimentary in nature and limited by hardware restrictions.We apply progressive alignment techniques to modify annealing algorithms, achieving a linear reduction in spin usage whilst introducing more complex heuristics to the algorithm.This opens the door for further exploration into quantum computing-based bioinformatics, potentially allowing for a deeper understanding of disease detection and monitoring.
Multiple sequence alignment (MSA) reveals homology in biological sequences, which is crucial for phylogenetics, medicine, and molecular biology. Many heuristic MSA algorithms use guide trees to determine sequence alignment order, but finding an optimal guide tree is an NP-hard problem. Conventional guide tree algorithms are greedy heuristics designed for better scalability at the cost of accuracy. By utilizing quantum algorithms, such as quantum annealing, we can overcome the problem of local minima that occurs in greedy methods. We propose a scalable guide tree algorithm that achieves scalability through quantum annealing. The theoretical foundations of our method are both minimum evolution and molecular clock. Unlike classical greedy approaches, we directly map the minimum evolution problem to a traveling salesperson problem (TSP), which quantum annealing can solve efficiently. The TSP solution enables the guide tree to be constructed in linear time using the molecular clock. Even with only a single sample from a D-Wave hybrid solver, our guide tree generally performed comparably to classical trees on the BAliBASE 3.0 benchmark. With a single iterative refinement, no statistically significant performance differences were found between our method and classical guide trees. Our scalable guide tree algorithm is practical in the sense that only a single sampling can be enough for constructing a good guide tree. Further performance analysis requires larger-scale benchmark tests. Fortunately, rapid advances in quantum hardware may soon enable these tests. While the practical application of quantum algorithms in bioinformatics has been relatively overlooked, this study highlights the potential of quantum algorithms for targeting computational bottlenecks in the field.
This paper explores the application of principles from quantum biology to design novel bioinformatics algorithms. Traditional bioinformatics relies heavily on classical computational models, often neglecting the potential influence of quantum effects observed within biological systems. This research proposes a framework that leverages these quantum phenomena, specifically focusing on quantum tunneling, superposition, and entanglement, to address complex challenges in biological data analysis. The core mechanism involves simulating quantum effects during processes like protein folding and DNA sequence recognition, and utilizing quantum algorithms – such as Grover's algorithm and quantum annealing – to optimize sequence data analysis. We present a conceptual approach to formulating biological problems as quantum computing problems, offering a fundamentally new perspective for bioinformatics. The potential impact of this approach lies in developing more efficient and accurate algorithms for tasks including genomic sequence alignment, protein structure prediction, and drug discovery. This work aims to bridge the gap between quantum mechanics and biology, paving the way for a new era of bioinformatics driven by quantum principles. ---
This paper explores the potential of quantum computing to revolutionize bioinformatics, specifically focusing on enhanced gene sequence analysis and protein structure prediction. The core concept centers around utilizing quantum error correction and quantum computation to accelerate these computationally intensive tasks. We propose a novel algorithm architecture leveraging quantum entanglement and superposition to overcome limitations inherent in classical approaches. The paper details the design of a quantum algorithm for sequence alignment and protein folding, emphasizing the key quantum mechanisms that underpin its enhanced performance. We present a preliminary analysis of the algorithm's potential advantages, focusing on reduced computational complexity and improved accuracy compared to existing classical methods. The research highlights the significance of quantum algorithms in addressing critical challenges within the field of bioinformatics.
Genetic information is encoded as linear sequences of nucleotides, represented by letters ranging from thousands to billions. Differences between sequences are identified through comparative approaches like sequence analysis, where variations can occur at the individual nucleotide level or collectively due to various phenomena such as recombination or deletion. Detecting these sequence differences is vital for understanding biology and medicine, but the complexity and size of genomic data require substantial classical computing power. Inspired by human visual perception and pixel representation on quantum computers, we leverage these techniques to implement pairwise sequence analysis. Our method utilizes the Flexible Representation of Quantum Images (FRQI) framework, enabling comparisons at a fine granularity to single letters or amino acids within gene sequences. This novel approach enhances accuracy and resolution, surpassing traditional methods by capturing subtle genetic variations with precision. In summary, our approach offers algorithmic advantages, including reduced time complexity, improved space efficiency, and accurate sequence comparisons. The novelty lies in applying the FRQI algorithm to compare quantum images in genome sequencing, allowing for examination at the individual letter or amino acid level. This breakthrough holds promise for advancing biological data analysis and enables a more comprehensive understanding of genetic information.
Multiple sequence alignment (MSA) reveals homology in biological sequences, which is crucial for phylogenetics, medicine, and molecular biology. Many heuristic MSA algorithms use guide trees to determine sequence alignment order, but finding an optimal guide tree is an NP-hard problem. Conventional guide tree algorithms are greedy heuristics designed for better scalability at the cost of accuracy. Due to the NP-hardness of guide tree construction, it is desirable to explore the utility of quantum algorithms such as quantum annealing. We propose a guide tree construction algorithm that utilizes quantum annealing. The theoretical foundations of our method are both minimum evolution and molecular clock. Unlike classical greedy approaches, we reformulate the minimum evolution problem as a traveling salesperson problem (TSP), which quantum annealing can target. The TSP solution enables the guide tree to be constructed with linear time overhead using the molecular clock. We used the Concorde TSP solver and D-Wave’s quantum-classical hybrid solvers to demonstrate the results instead of native QPU annealing alone, due to limitations in current quantum hardware. Benchmarking on BAliBASE 3.0 showed no statistically significant differences in alignment scores between our method and classical guide trees. When Concorde was used, the empirical wall-clock time scaling of our method was better than that of the textbook UPGMA implementation. Classical TSP solvers allow practical implementation of our method for small-scale guide trees, while larger problems motivate the use of quantum annealing. Further performance analysis should be conducted on larger-scale benchmark tests. Fortunately, rapid advances in quantum hardware may soon enable these tests. This study highlights the potential of quantum algorithms for targeting computational bottlenecks in MSA.
The task of Multiple Sequence Alignment (MSA) is a constrained combinatorial optimization problem that is generally considered a complex computational problem. In this paper, we first present a binary encoding of MSA and devise a corresponding soft-constrained cost-function that enables a Hamiltonian formulation and implementation of the MSA problem with the variational Quantum Approximate Optimization Algorithm (QAOA). Through theoretical analysis, a bound on the ratio of the number of feasible states to the size of the Hilbert space is determined. Furthermore, we consider a small instance of our QAOA-MSA algorithm in both a quantum simulator and its performance on an actual quantum computer. While the ideal solution to the instance of MSA investigated is shown to be the most probable state sampled for a shallow p<5 quantum circuit in the simulation, the level of noise in current devices is still a formidable challenge for the kind of MSA-QAOA algorithm developed here. In turn, we are not able to distinguish the feasible solutions from other states in the quantum hardware output data at this point. This indicates a need for further investigation into both the strategy utilized for compiling the quantum circuit, but also the possibility of devising a more compact ansatz, as one might achieve through constraint-preserving mixers for QAOA.