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QOBLIB gains quantum optimization data from JIJ

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August 21, 2026 by Ivy Delaney

JIJ is now contributing benchmark results to QOBLIB, an open library evaluating both quantum and classical optimization methods, signaling a push for standardized comparisons across the field, the company says. The company benchmarked its quantum optimization method using a subset of problems from QOBLIB and shared the data with the IBM Quantum team, who then referenced JIJ in a recent technical blog. “Shared benchmarks such as QOBLIB provide a common basis for comparing optimization methods,” JIJ stated, emphasizing the importance of transparent evaluation as it continues developing its technology within the broader quantum optimization community.

JIJ Benchmarks Quantum Optimization Method with QOBLIB Datasets

IBM Quantum recently highlighted JIJ’s contribution in a technical blog post detailing QOBLIB, publicly acknowledging the new benchmark submissions. This recognition indicates IBM is actively monitoring JIJ’s progress in quantum optimization and values the transparency offered by shared benchmarking datasets. JIJ intends to continue refining its technology and participating in open benchmarking initiatives within the quantum optimization community, according to QOBLIB.

The OMMX Quantum Benchmarks repository currently includes a subset of problems sourced from QOBLIB, furthering the availability of standardized datasets for researchers. Utilizing these benchmarks is an important step in understanding the performance characteristics of its method through transparent, comparable evaluation, according to the company.

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Avatar of Ivy Delaney

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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