Abstract
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.
Highlights
The idea of using the fundamental physical building blocks of nature for computation [1] laid the foundation for the second quantum revolution, focusing on controlling quantum systems and engineering them for arbitrary computation, instead of a passive understanding of quantum mechanical phenomena
We construct a heuristic-free quantum algorithm primitive to achieve a high performance global alignment algorithm. This is explored in this research, where we present the quantum algorithm corresponding to a naive sub-sequence alignment, which can currently be implemented as a proof-of-concept using simulators
This research is motivated by the bottleneck of DNA sequence reconstruction in genomics, and explores how quantum acceleration can be applied in this domain
Summary
The idea of using the fundamental physical building blocks of nature for computation [1] laid the foundation for the second quantum revolution, focusing on controlling quantum systems and engineering them for arbitrary computation, instead of a passive understanding of quantum mechanical phenomena. The development of quantum algorithms for various use-cases is a very active field of research These proof-of-concept implementations can be tested [3] for small (up to around 50 qubits [4]) problem instances on quantum simulators. Our work [7] in this article falls under the umbrella of quantum search-based algorithms, where the high dimensional state space of the qubits are harnessed to explore/search an optimization landscape faster and better While these generic algorithms are ubiquitous in computer science and data-structures, in this research an exemplary case of DNA sequencing application is considered in depth. Quantum approaches to DNA sequencing have not been explored in much depth before This is the first time [7] a gate-based quantum algorithm has been presented where the DNA index of the best matching sub-string is retrieved with high probability.
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