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Updated: Aug 8, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Data on the solution and processing time reached when constructing a phylogenetic tree using a quantum-inspired

Wataru Onodera¹, Nobuyuki Hara², Shiho Aoki¹

¹Faculty of Science and Engineering, Waseda University, TWIns, 2-2 Wakamatsu, Shinjuku, Tokyo 162-8480, Japan.

|March 6, 2023

Summary

Constructing phylogenetic trees is computationally intensive. This study introduces a novel method using a quantum-inspired computer to rapidly generate optimal phylogenetic trees by solving graph-cut problems, offering a faster alternative for evolutionary analysis.

Keywords:

Distance-matrix method Graph cut Phylogenetic reconstruction Quantum-inspired computing

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Last Updated: Aug 8, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Area of Science:

Computational Biology
Phylogenetics
Quantum Computing Applications

Background:

Phylogenetic trees are crucial for understanding species and molecular evolution.
Traditional phylogenetic tree construction methods face combinatorial explosion challenges, limiting scalability.
Brute-force approaches to determine optimal trees are computationally infeasible for large datasets.

Purpose of the Study:

To develop a high-speed method for constructing optimal phylogenetic trees.
To leverage quantum-inspired computing for solving complex combinatorial optimization problems in phylogenetics.
To compare the performance of the novel method against existing phylogenetic tree reconstruction techniques.

Main Methods:

Developed a phylogenetic tree construction method utilizing the Fujitsu Digital Annealer, a quantum-inspired computer.
The method involves partitioning sequence sets, framed as a graph-cut problem.
Optimality was assessed using the normalized cut value and compared with existing methods on simulated and real data.

Main Results:

The quantum-inspired approach successfully generated phylogenetic trees by efficiently solving graph-cut problems.
Performance was evaluated on diverse simulated datasets (32-3200 sequences) with varying evolutionary parameters.
The method demonstrated a viable alternative for constructing phylogenetic trees, addressing computational limitations.

Conclusions:

A novel, high-speed phylogenetic tree construction method using quantum-inspired computing has been successfully developed.
This approach offers a significant advancement over traditional methods, particularly for large-scale evolutionary analyses.
The study provides a valuable dataset and methodology for future research in phylogenetic tree reconstruction.