Quantum feedback algorithms for DNA assembly using FALQON variants

TL;DR

This paper explores quantum feedback algorithms, specifically variants of FALQON, to improve DNA assembly processes by solving QUBO formulations more efficiently on near-term quantum hardware.

Contribution

It introduces and analyzes three FALQON variants, demonstrating improved convergence and success probabilities for DNA assembly problems.

Findings

FALQON variants enhance convergence to ground state

Success probabilities increase with reduced circuit depth

Effective for combinatorial problems on near-term quantum hardware

Abstract

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.