Quantum Applications, High Performance Computing

Alice & Bob says quantum’s success depends on full stack

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September 9, 2026 by Ivy Delaney

The future of quantum computing in high-performance computing depends not on delivering qubits, but on building a complete software ecosystem, according to a new report from Alice & Bob and Hyperion Research. Informed by fifteen interviews with practitioners at leading supercomputing institutions across Europe, APAC, and the US, including Argonne National Laboratory and RIKEN, the report reveals a shift in focus within the HPC community.

“As quantum hardware has progressed, the conversation in HPC is shifting from whether qubits will be delivered to how to get the most from them,” says Juliette Peyronnet, GM US, Alice & Bob, highlighting concerns that an immature software stack could erode trust in quantum computing for years to come.

HPC-Quantum-AI Readiness: Insights from Supercomputing Institutions

Supercomputing institutions are increasingly focused on maximizing the utility of delivered qubits, rather than simply achieving higher qubit counts. A key finding centers on latency tolerance; how tightly quantum compute must couple with classical and AI systems depends directly on a workload’s ability to withstand delays, a factor that can only be determined through measurement, not theoretical modeling.

Hyperion Research’s SVP of Research, Bob Sorensen, noted the report’s timely release, stating HPC end users are actively seeking guidance on integrating quantum power into existing and future computing ecosystems. The report urges quantum vendors and HPC centers to establish open standards for device management and job submission, while allowing competition in areas like compilers and programming models.

Successful integration, the report argues, will require co-design efforts between HPC, AI experts, and quantum vendors, citing a quantum chemistry workflow with the potential for a 10,000-fold speed-up as an example. The first fault-tolerant machines, according to the analysis, will likely be deployed while institutions are already building the necessary operational readiness and expertise. “HPC-Quantum-AI: Shaping the Next Compute Era” is the second in a series.

The report cites an example of a quantum chemistry workflow targeting a 10,000-fold speed-up.

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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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