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# Title: An algorithm for DNA read alignment on quantum accelerators

Abstract: 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 this https URL
 Comments: Keywords: quantum algorithms, quantum search, DNA read alignment, genomics, associative memory, accelerators, in-memory computing Subjects: Quantum Physics (quant-ph); Emerging Technologies (cs.ET); Genomics (q-bio.GN) Cite as: arXiv:1909.05563 [quant-ph] (or arXiv:1909.05563v1 [quant-ph] for this version)

## Submission history

From: Aritra Sarkar [view email]
[v1] Thu, 12 Sep 2019 11:01:55 GMT (472kb,D)
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