Sequence Alignment is the process of aligning biological sequences in order to identify similarities between multiple sequences. In this paper, a Quantum Algorithm for finding the optimal alignment between DNA sequences has been demonstrated which works by mapping the sequence alignment problem into a path-searching problem through a 2D graph. The transition, which converges to a fixed path on the graph, is based on a proposed oracle for profit calculation. By implementing Grover's search algorithm, our proposed approach is able to align a pair of sequences and figure out the optimal alignment within linear time, which hasn't been attained by any classical deterministic algorithm. In addition to that, the proposed algorithm is capable of quadratic speeding up to any unstructured search problem by finding out the optimal paths accurately in a deterministic manner, in contrast to existing randomized algorithms that frequently sort out the sub-optimal alignments, therefore, don't always guarantee of finding out the optimal solutions.
Sequence Alignment is the process of aligning biological sequences in order to identify similarities between multiple sequences. In this paper, a Quantum Algorithm for finding the optimal alignment between DNA sequences has been demonstrated which works by mapping the sequence alignment problem into a path-searching problem through a 2D graph. The transition, which converges to a fixed path on the graph, is based on a proposed oracle for profit calculation. By implementing Grover's search algorithm, our proposed approach is able to align a pair of sequences and figure out the optimal alignment within linear time, which hasn't been attained by any classical deterministic algorithm. In addition to that, the proposed algorithm is capable of quadratic speeding up to any unstructured search problem by finding out the optimal paths accurately in a deterministic manner, in contrast to existing randomized algorithms that frequently sort out the sub-optimal alignments, therefore, don't always guarantee of finding out the optimal solutions.
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.
Pairwise sequence alignment is central in bioinformatics for analyzing DNA, RNA, and proteins, revealing similarities that inform evolutionary relationships, gene or protein functions, and disease mechanisms. Classical dynamic programming guarantees optimal alignments but scales poorly, while heuristics reduce runtime at the cost of optimality and still require high resources. Quantum algorithms exploit superposition and entanglement to accelerate optimization or find exact solutions faster. We present a hybrid quantum reinforcement learning (RL) agent based on (Hamann & Wölk, 2022), optimized for NISQ hardware. The agent learns a policy mapping states to action sequences from feedback of an environment. Using a Grover-like subroutine, it evaluates all sequences in parallel, reinforcing those leading to a solution, which reduces environment interactions and can achieve a quadratic speedup over classical RL. To enable execution on current hardware, we introduce a novel Parameterized Quantum Policy embedding entire action sequences, combined with a simplified Grover routine using a black-box oracle. This reduces resource demands but requires prior knowledge and retraining per alignment, limiting generalization. We also outline design ideas for a more general Grover oracle and circuit with broader generalization, which, while not NISQ-compatible, suggest a promising direction for future work.
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.
Multiple sequence alignment (MSA) is a core operation for comparing genome sequences and is widely used in bio-informatics. MAFFT, a practical MSA tool, repeatedly shifts a pair of sequences and computes a distance. Because the number of sequence pairs grows quadratically with the number of sequences, this procedure can become a bottleneck.
We propose Quantum Shift-based Sequence Alignment (QShift-SA), which implements this ``shift-wise score computation'' as a gate-based quantum circuit and searches over shift amounts and sequence pairs using Grover algorithm. QShift-SA constructs an oracle circuit that compute the Hamming distance (the number of mismatches) between two sequences with data encoding, controlled shift, comparison, and addition. This oracle can search for candidates with small distances. QShift-SA does not aim to replace the full MSA workflow; instead, it targets the screening steps that often dominate the runtime in classical MAFFT as stated above.
We evaluate circuit resources (number of qubits, gate count, and depth) and benchmark simulation time across multiple quantum circuit simulators. We find that a decision diagram (DD)-based quantum circuit simulator runs more than 1,000× faster than state-vector and MPS simulators and can handle larger circuits.
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.
In this paper, it is demonstrated that the DNA-based algorithm [Ho et al. 2005] for solving an instance of the clique problem to any a graph G = (V, E) with n vertices and p edges and its complementary graph G1 = (V, E1) with n vertices and m = (((n*(n-1))/2)-p) edges can be implemented by Hadamard gates, NOT gates, CNOT gates, CCNOT gates, Grover's operators, and quantum measurements on a quantum computer. It is also demonstrated that if Grovers algorithm is employed to accomplish the readout step in the DNA-based algorithm, the quantum implementation of the DNA-based algorithm is equivalent to the oracle work (in the language of Grover's algorithm), that is, the target state labeling preceding Grover,s searching steps. It is shown that one oracle work can be completed with O((2 * n) * (n + 1) * (n + 2) / 3) NOT gates, one CNOT gate and O((4 * m) + (((2 * n) * (n + 1) * (n + 14)) / 6)) CCNOT gates. This is to say that for the quantum implementation of the DNA-based algorithm [Ho et al. 2005] a faster labeling of the target state is attained, which also implies a speedy solution to an instance of the clique problem.