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the claim

D-Wave quantum computers solve optimization problems via quantum annealing

the verdict

SUPPORTED

the evidence backs this

Recorded sources

6 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

D-Wave quantum computers are specifically designed to solve combinatorial optimization problems using quantum annealing by mapping problems into QUBO or Ising formulations.

The analysis

The claim is that D-Wave quantum computers solve optimization problems via quantum annealing. Multiple retrieved papers explicitly discuss using D-Wave hardware to solve combinatorial optimization problems through quantum annealing and QUBO formulations. There are no papers refuting this core functionality.

Evidence for · 6

Comparing three generations of D-Wave quantum annealers for minor embedded combinatorial optimization problems

Recorded source metadata

Elijah Pelofske. Comparing three generations of D-Wave quantum annealers for minor embedded combinatorial optimization problems. 2023. https://doi.org/10.1088/2058-9565/adb029

Compares multiple generations of D-Wave quantum annealers for solving discrete combinatorial optimization problems.

Source citation format

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More for · 5

Optimal Solving of a Scheduling Problem Using Quantum Annealing Metaheuristics on the D-Wave Quantum Solver

Recorded source metadata

W. Bożejko, Ryszard Klempous, J. Pempera, Jerzy W. Rozenblit, 　Czesław Smutnicki, Mariusz Uchroński, M. Wodecki. Optimal Solving of a Scheduling Problem Using Quantum Annealing Metaheuristics on the D-Wave Quantum Solver. 2025. https://doi.org/10.1109/TSMC.2024.3458873

Examines task scheduling problems solved using quantum annealing metaheuristics on the D-Wave quantum solver.

Source citation format

Quantum optimization algorithm for solving elliptic boundary value problems on D-Wave quantum annealing device

Recorded source metadata

Rebecca Conley, Deokkyu Choi, Gregory Medwig, E. Mroczko, David Wan, Paulo Castillo, Kwangmin Yu. Quantum optimization algorithm for solving elliptic boundary value problems on D-Wave quantum annealing device. 2023. https://doi.org/10.1117/12.2649076

Discusses how D-Wave quantum annealing devices solve optimization problems formulated as QUBO models.

Source citation format

Unraveling reverse annealing: A study of D-wave quantum annealers

Recorded source metadata

Vrinda Mehta, H. Raedt, K. Michielsen, Fengping Jin. Unraveling reverse annealing: A study of D-wave quantum annealers. 2025. https://doi.org/10.1103/vfxh-gjgy

Explores D-Wave quantum annealers and their features like reverse annealing for refining optimization solutions.

Source citation format

Using Quantum Annealing to Solve Large-Scale Optimization Problems in Logistics and Scheduling

Recorded source metadata

Sandeep Kumar. Using Quantum Annealing to Solve Large-Scale Optimization Problems in Logistics and Scheduling. 2025. https://doi.org/10.1109/ESCI63694.2025.10988208

Demonstrates the use of the D-Wave quantum annealer to solve large-scale logistics and scheduling optimization problems via QUBO.

Source citation format

Quantum Optimization Realized: Quantum Annealing for Combinatorial Spatial Problems in Practice

Recorded source metadata

Amr Magdy. Quantum Optimization Realized: Quantum Annealing for Combinatorial Spatial Problems in Practice. 2025. https://doi.org/10.1145/3764923.3777407

Summarizes D-Wave's quantum annealing technology specializing in solving combinatorial optimization problems in practice.

Source citation format

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first checked01 Aug 2026

judged → SUPPORTED · 8501 Aug 2026

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