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Related Experiment Video

Updated: Apr 11, 2026

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

Experimental quantum annealing: case study involving the graph isomorphism problem.

Kenneth M Zick¹, Omar Shehab², Matthew French¹

¹University of Southern California Information Sciences Institute (USC ISI), Arlington, Virginia, USA.

Scientific Reports

|June 9, 2015

Summary

This summary is machine-generated.

This study explores quantum annealing for optimization problems using a D-Wave machine. Hybrid classical-quantum approaches improved graph isomorphism problem-solving, demonstrating advantages over baseline methods.

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Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

Published on: August 2, 2019

Area of Science:

Quantum Computing
Combinatorial Optimization
Computational Complexity

Background:

Quantum annealing leverages quantum mechanics for optimization.
Real-world solvers need integrated classical and quantum processing.
Optimizing hybrid solver components is an open research area.

Purpose of the Study:

Investigate quantum annealing performance on the graph isomorphism problem.
Evaluate the impact of classical pre- and post-processing on solver efficiency.
Analyze factors influencing real-world quantum annealing solver effectiveness.

Main Methods:

Utilized a 504-qubit D-Wave Two quantum annealer.
Employed a compact Hamiltonian for reduced Ising models.
Applied classical post-processing for error correction (majority voting).

Main Results:

Reduced median variables from N(2) to < N log2 N for random graphs.
Increased solvable graph sizes from N=5 to N=13.
Achieved correct classification for all mapped graph isomorphism problems.

Conclusions:

Hybrid classical-quantum solvers show promise for complex optimization.
Classical pre-processing significantly enhances quantum annealing efficiency.
Further research is needed to optimize hybrid solver components for practical applications.