Quantum Algorithms, Quantum Applications

D-Wave launches quantum training to speed up optimization use

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November 4, 2026 by Ivy Delaney

D-Wave Quantum Inc. reports its enhanced Stride hybrid solver now finds better solutions to complex optimization problems in 15 seconds, a task that previously took 120 seconds. Benchmarks show the updated solver achieved solutions within 0.02% of the best possible result for traveling salesperson problems with up to 10,000 locations. “Developers need proven technology that finds better answers, faster,” said Alex Condello, senior vice president of algorithms, performance and tools at D-Wave.

Stride Hybrid Solver Performance Gains on Optimization Benchmarks

The enhanced Stride hybrid solver now delivers solutions to traveling salesperson problems with over 10,000 locations within 3.8% of the optimal result in 120 seconds, a substantial improvement over the previous version’s performance. Benchmarks reveal the updated solver achieves solutions more than 500 times closer to the best possible outcome using the same runtime, demonstrating a significant leap in optimization capability for complex logistical challenges.

This speed and accuracy are enabled by algorithmic enhancements extending performance across problems already supported by the system, D-Wave says. Select courses within the path provide access to the Leap service and dedicated office hours, supporting hands-on learning for developers with varying levels of experience.

The focus on developer training reflects a broader strategy to accelerate the adoption of quantum optimization techniques. “In a highly disruptive world, the ability to adapt plans, reallocate resources and act quickly is critical. We’re giving developers the technology and practical expertise to make hybrid-quantum optimization part of how their businesses respond in real time.” The company’s efforts aim to equip businesses with tools to respond to rapidly changing conditions through optimized resource allocation and planning.

Developers need proven technology that finds better answers, faster, so they can tackle computationally complex problems at the speed their businesses demand.

Alex Condello, senior vice president of algorithms, performance and tools at D-Wave
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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