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Updated: May 11, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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

Published on: September 8, 2023

Micro-mobility dispatch optimization via quantum annealing incorporating historical data.

Takeru Goto1,2, Masayuki Ohzeki3,4,5

  • 1Graduate School of Information Sciences, Tohoku University, Miyagi, 980-8579, Japan. goto.takeru.s8@dc.tohoku.ac.jp.

Scientific Reports
|May 9, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for dispatching micro-mobility vehicles using quantum annealing (QA). This approach optimizes vehicle allocation by incorporating historical data, enhancing urban transportation efficiency.

Keywords:
Quantum annealingVehicle dispatchVehicle routing problem

Related Experiment Videos

Last Updated: May 11, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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

Published on: September 8, 2023

Area of Science:

  • Operations Research
  • Computer Science
  • Transportation Engineering

Background:

  • Micro-mobility services are crucial for sustainable urban transport.
  • Combinatorial optimization problems are common in logistics and transportation.
  • Quantum annealing (QA) is an emerging technology for solving complex optimization tasks.

Purpose of the Study:

  • To develop a novel dispatch formulation for micro-mobility vehicles.
  • To leverage quantum annealing (QA) for solving the micro-mobility dispatch problem.
  • To enhance operational efficiency by integrating historical usage data.

Main Methods:

  • Formulating the micro-mobility dispatch problem as a quadratic unconstrained binary optimization (QUBO) problem.
  • Utilizing quantum annealing (QA) as a solver for the QUBO formulation.
  • Incorporating historical customer arrival frequencies and destination distributions via a Bayesian approach.
  • Conducting simulation experiments to compare the proposed method with conventional approaches and classical solvers.

Main Results:

  • The proposed QUBO formulation effectively utilizes QA for micro-mobility dispatch.
  • Integration of historical data via Bayesian methods improves operational efficiency.
  • Quantum annealing demonstrates potential advantages over classical solvers for this specific dispatch problem.
  • Reverse annealing shows promise in enhancing solution quality.

Conclusions:

  • Quantum annealing offers a powerful approach for optimizing micro-mobility vehicle dispatch.
  • The proposed formulation, enhanced with historical data, provides an efficient solution for sustainable urban transportation.
  • Further investigation into QA, including reverse annealing, is warranted for advanced optimization in micro-mobility services.