We propose QGENE, a quantum-enhanced framework for approximate string matching used to perform local DNA sequence alignment. QGENE is a dynamic programming model with recursive functions that emulates the Smith Waterman pairwise sequence alignment. QGENE facilitates overlapped computation of both the forward scan and traceback steps in the Smith-Waterman algorithm using quantum circuits within the Qiskit platform. The model integrates Quantum Fourier Transform (QFT)-based arithmetic operations and Grover's algorithm for score comparison. QGENE's traceback logic processes antidiagonals in the alignment matrix, while supporting precise nucleotide matching and efficient traceback operations. The results are comparatively evaluated with a Quadratic Unconstrained Binary Optimization (QUBO) model mapped to an Ising Hamiltonian, enabling advanced quantum optimization algorithms such as the Quantum Approximate Optimization Algorithm (QAOA).
We propose QGENE, a quantum-enhanced framework for approximate string matching used to perform local DNA sequence alignment. QGENE is a dynamic programming model with recursive functions that emulates the Smith Waterman pairwise sequence alignment. QGENE facilitates overlapped computation of both the forward scan and traceback steps in the Smith-Waterman algorithm using quantum circuits within the Qiskit platform. The model integrates Quantum Fourier Transform (QFT)-based arithmetic operations and Grover's algorithm for score comparison. QGENE's traceback logic processes antidiagonals in the alignment matrix, while supporting precise nucleotide matching and efficient traceback operations. The results are comparatively evaluated with a Quadratic Unconstrained Binary Optimization (QUBO) model mapped to an Ising Hamiltonian, enabling advanced quantum optimization algorithms such as the Quantum Approximate Optimization Algorithm (QAOA).
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.
A novel quantum algorithm for biological sequence alignment is presented and analyzed. The large volumes of data generated through genome sequencing, de novo assembly, resequencing, and transcriptome sequencing at the DNA and RNA levels foreshadow the growing demand for higher computational power and more sophisticated alignment methodologies. The rapid advancement of modern sequencing technologies in genomics has motivated the reconsideration of existing approaches for the design and implementation of alignment protocols. Emerging quantum computing accelerators may provide transformative solutions in this domain as quantum hardware progressively reaches higher levels of gate-operation maturity. This work proposes a computer-vision-based approach that exploits the unique properties of quantum entanglement within a dot-matrix representation to address the increasing demand for efficient processing of biological data. A quantum-accelerated protocol is developed and evaluated using the Qiskit software framework of IBM. Runtime experiments support the potential of the proposed methodology to provide advantageous sequence-alignment performance in terms of accuracy, completeness, and computational complexity. The system is evaluated under multiple operational conditions and demonstrates promising performance advantages.
We present a comprehensive quantum-inspired DNA computing framework implemented in IcsDNA using the InChroSil Virtual Machine [11], a production-ready DNA computing platform. Our approach extends the classical four-letter nucleotide alphabet {A, G, C, T} into a four-dimensional complex Hilbert space ℋ! , enabling superposition states, entanglement between nucleotide pairs, and quantum gate operations adapted to the biological domain. The framework introduces novel adaptations of fundamental quantum computing primitives, including Pauli gates, Hadamard transforms, rotation operators, and Bell-state generation, to a quaternary (base-4) nucleotide encoding. We further implement quantum-inspired algorithms for DNA sequence alignment, pattern matching via Grover-like amplitude amplification, Fourier analysis of genomic sequences, and quantum error correction for DNA storage integrity. The system provides both a high-level C++ object-oriented stack and a C API for integration into bioinformatics pipelines. Experimental evaluation demonstrates the applicability of quantum-inspired techniques to probabilistic genomic analysis, DNA-based cryptographic key generation, and fault-tolerant DNA data storage with decoherence simulation.
• Development of the quantum algorithm 'QuBio.py' for searching and analyzing genetic information. • Successful detection of a 50-nucleotide target sequence within a 3,022-nucleotide database using a 127-qubit simulator on the IBM quantum platform. • Securing flexibility that allows up to a 30% mismatch rate... Heralding a new horizon for synthetic biology and new drug development. [Quantum Biology Society] A "quantum computing"-based gene search algorithm has emerged, capable of analyzing exponentially increasing biological data at ultra-high speeds, surpassing the limitations of conventional computers. A study titled "GENETIC ENGINEERING THROUGH QUANTUM CIRCUITS: CONSTRUCTION OF CODES AND ANALYSIS OF GENETIC ELEMENTS BIOBLOQU" was recently published on bioRxiv, a preprint repository for biological sciences. The research team, including Patrícia Verdugo Pascoal, presented an innovative model that explores and manipulates genetic information utilizing quantum mechanics. ■ Translating the Four DNA Bases into Quantum Language... The Evolution of 'Grover's Algorithm' Modern biology generates approximately 1 Gb of sequencing data per hour, but analyzing this data using classical computing methods faces significant limitations. Existing database search methods require computation time proportional to the data size (O(N)), but the quantum-based 'Grover's algorithm' applied by the researchers can significantly reduce this to the O(√N}) level. In particular, a key feature of this study is that it adapted the algorithm to process the four nucleotides (A, T, G, C) that make up DNA, rather than simple binary symbols, thereby enabling it to operate efficiently on more complex datasets. ■ Detecting the Cas9 Target Sequence with 127 Qubits... Creation of Synthetic Genome (BioBloQu) Based on the 127-qubit simulator of the IBM cloud platform, the research team developed its own quantum algorithm code named 'QuBio.py'. Through this algorithm, they succeeded in finding a specific 50-nucleotide target DNA sequence within a genetic database consisting of up to 3,022 nucleotides. A conserved functional domain of the Cas9-like nucleotide sequence found in Brazilian biomes was used as the target sequence. Notably, this quantum algorithm permits a mismatch rate of up to 30% during the sequence comparison process. This enables flexible and rapid analysis in situations where sequence variations are expected, such as homologous gene analysis or evolutionary studies. Furthermore, based on these search results, the team succeeded in constructing a synthetic structure called a 'Quantum Biological Block (BioBloQu)' inside minimal cells (JCVI-syn3.0). The researchers stated that this algorithm "constitutes a unique starting point for developing a more sophisticated model for the manipulation of data". They also predicted that "the integration of quantum computing with artificial intelligence represents an attractive avenue... By optimizing genetic algorithms and facilitating real-time data analyses, quantum computing can revolutionize trait discovery processes in agriculture and biotechnology". This achievement is evaluated as an important example demonstrating how quantum computing can contribute to next-generation synthetic biology technologies that analyze large-scale genetic big data and design genomes from the bottom up.
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.
Reconstructing DNA sequences without a reference, known as de novo assembly, is a complex computational task involving the alignment of overlapping fragments. To address this problem, a usual strategy is to map the assembly to a Quadratic Unconstrained Binary Optimization (QUBO) formulation, which can be solved by different quantum algorithms. In this work, we focus on three versions of the Feedback-based Algorithm, a protocol that eliminates classical optimization loops via measurement feedback. We analyze long-read DNA fragments from SARS-CoV-2 and human mitochondrial DNA using standard FALQON, second-order FALQON (SO-FALQON), and time-rescaled FALQON (TR-FALQON). Numerical results show that both variants improve convergence to the ground state and increase success probabilities at reduced circuit depths. These findings indicate that enhanced feedback-driven dynamics are effective for solving combinatorial problems on near-term quantum hardware.