Quantum Simulation, High Performance Computing

Aegiq runs 100-qubit tests on NVIDIA’s DGX Spark server

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October 22, 2026 by Ivy Delaney

Aegiq has been using an NVIDIA DGX Spark, equipped with a GB10 Grace Blackwell Superchip, to run tests of quantum computing tasks for fundamental condensed matter models with approximately 100 qubits. The NVIDIA DGX Spark allowed Aegiq to develop code for larger GPU clusters without altering existing Python workflows, the company says. This ease of access, facilitated through a one-click connection, differs from typically complex quantum development setups.

Qubit Tests Advance Aegiq’s Quantum CFD Algorithms

These tests used acceleration from NVIDIA’s cuTensorNet alongside Aegiq’s proprietary libraries, enabling rapid cycles of testing and debugging that will inform larger implementations with around 1000 qubits on cloud-based NVIDIA accelerated computing. Dr. Matthew Horner, Quantum Applications Scientist at Aegiq, emphasized the server’s role in bridging the gap between theoretical quantum computing and practical engineering challenges.

Horner noted that maintaining a familiar coding experience was a key benefit, allowing polished live demonstrations accessible to clients and smooth remote access through a VPN. He said the integration with NVIDIA CUDA and cuQuantum was smooth, adding, “I could easily forget I was even connected to it, as it didn’t change my coding experience at all.” Access to the DGX Spark is streamlined through the NVIDIA Sync program, which features a simple graphical user interface that allows connection with a single click.

According to Horner, connecting to the server is as simple as clicking the NVIDIA Sync icon in the Windows system tray and selecting “connect.” The server’s compact size also proved advantageous, fitting comfortably on a desk and encouraging frequent use. “The box looks cool too – it fits on my desk nicely and makes me want to use it!” Horner added. Aegiq’s work with tensor networks, a method for efficiently representing quantum states, is central to their approach to CFD.

These networks decompose complex systems into smaller, interconnected tensors, enabling powerful analysis of entanglement and physical systems. The DGX Spark’s capabilities are expected to support further exploration of quantum-inspired methods for transient turbulent problems, potentially offering both speedups and memory compression on conventional computers.

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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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