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arXiv:2107.11346

A Quantum Dot Plot Generation Algorithm for Pairwise Sequence Alignment

Joseph Clapis

Published Jul 23, 2021• 5 citations
arXivHTMLPDF

Abstract

The Quantum Pairwise Sequence Alignment (QPSA) algorithm offers exponential speedups in data alignment tasks. However, it relies on an open problem of efficiently encoding the classical data being aligned into quantum superposition. Previous works suggest the use of specialized nonlinear Kerr media to implement a black-box oracle that achieves the superposition. We provide an alternative, explicit construction of this oracle called the Quantum Dot Plot (QDP), which is compatible with universal gate machines. We evaluate QDP's operational complexity via analysis of the quantum machine instructions generated by the Q# and Qiskit software frameworks for various sample circuits. Our analysis confirms that the data encoding scheme is exponentially difficult, precluding QDP's (and thus, QPSA's) employment on general-purpose quantum computers. Nevertheless, we find utility in estimating the circuit depth and run time of both QDP and QPSA for IBM's superconducting architecture and AQT's trapped ion architecture for direct comparison and overall practicality purposes.

DOI
10.48550/arxiv.2107.11346

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The Quantum Pairwise Sequence Alignment (QPSA) algorithm offers exponential speedups in data alignment tasks. However, it relies on an open problem of efficiently encoding the classical data being aligned into quantum superposition. Previous works suggest the use of specialized nonlinear Kerr media to implement a black-box oracle that achieves the superposition. We provide an alternative, explicit construction of this oracle called the Quantum Dot Plot (QDP), which is compatible with universal gate machines. We evaluate QDP's operational complexity via analysis of the quantum machine instructions generated by the Q# and Qiskit software frameworks for various sample circuits. Our analysis confirms that the data encoding scheme is exponentially difficult, precluding QDP's (and thus, QPSA's) employment on general-purpose quantum computers. Nevertheless, we find utility in estimating the circuit depth and run time of both QDP and QPSA for IBM's superconducting architecture and AQT's trapped ion architecture for direct comparison and overall practicality purposes.
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.
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×\times× faster than state-vector and MPS simulators and can handle larger circuits.
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.
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Quantum pattern recognition techniques have recently raised attention as potential candidates in analyzing vast amount of data. The necessity to obtain faster ways to process data is imperative where data generation is rapid. The ever-growing size of sequence databases caused by the development of high throughput sequencing is unprecedented. Current alignment methods have blossomed overnight but there is still the need for more efficient methods that preserve accuracy in high levels. In this work, a complex method is proposed to treat the alignment problem better than its classical counterparts by means of quantum computation. The basic principal of the standard dot-plot method is combined with a quantum algorithm, giving insight into the effect of quantum pattern recognition on pairwise alignment. The central feature of quantum algorithmic -quantum parallelism- and the diffraction patterns of x-rays are synthesized to provide a clever array indexing structure on the growing sequence databases. A completely different approach is considered in contrast to contemporary conventional aligners and a variety of competitive classical counterparts are classified and organized in order to compare with the quantum setting. The proposed method seems to exhibit high alignment quality and prevail among the others in terms of time and space complexity.
Abstract A novel quantum algorithm for use in Biological Sequence Alignment is presented and analyzed. The large amounts of data extracted from genome sequencing, de novo assembly sequencing, resequencing, and transcriptome sequencing at the DNA or RNA level, foreshadow the need for higher computing power as well as more sophisticated alignment methods. Modern and faster sequencing techniques in genomics have led to the reconsideration of current methods of designing or implementing alignment protocols. Novel quantum computing accelerators may provide drastic solutions in this field reaching the desired levels of gate operation maturity. This paper proposes a computer vision-based approach, using the unprecedented power of entanglement in a dot-matrix, to address the high demand for fast harnessing of biological data. A quantum accelerated protocol is demonstrated and tested using the Qiskit software framework of IBM. Runtime tests verify the expectations to obtain a potentially advantageous sequence alignment algorithmic process in terms of accuracy, completeness and computing complexity. The performance has been tested under various conditions and promises a clear and viable advantage.