Quantum Hardware, High Performance Computing

Qblox Advances Hybrid Quantum Computing with NVIDIA Integration

Avatar of Ivy Delaney
October 22, 2026 by Ivy Delaney

Qblox reports achieving roundtrip latency below 4.0 microseconds on an NVIDIA RTX PRO 6000 Blackwell when integrated with the Qblox Cluster, a key threshold for real-time hybrid quantum-classical computing. The company integrated NVIDIA DGX Spark with a quantum system controller using NVIDIA NVQLink, also demonstrating 7.2 µs latency and bringing GPU acceleration directly into the real-time control loop, the company says.

“Quantum computers will scale only as fast as the classical control systems around them,” said Niels Bultink, CEO and Co-founder of Qblox, adding that these developments allow GPUs to become part of that control loop.

Qblox Cluster Achieves Sub-4µs Latency with NVIDIA RTX PRO 6000 Blackwell

The Qblox Cluster has been integrated with multiple NVIDIA GPUs, enabling real-time control for hybrid quantum-classical computing with roundtrip latency below 4.0 µs on NVIDIA RTX PRO 6000 Blackwell and below 7.2 µs on NVIDIA DGX Spark. This benchmark demonstrates a significant reduction in the delay between sending control signals to a quantum processor and receiving a response, a longstanding bottleneck in scaling quantum systems.

The integration uses NVIDIA’s NVQLink to facilitate this speed, allowing for GPU acceleration directly within the quantum system’s control loop. This means calibration routines can now adjust qubit parameters during experiments, rather than requiring pauses between them for updates. Qblox also measured roundtrip latency below 7.2 microseconds on NVIDIA DGX Spark, indicating the scalability of this low-latency control system beyond a single desktop GPU.

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The integration enables support for real-time hybrid quantum-classical applications, with a single desktop system that can host quantum, AI, and simulation workloads via NVIDIA CUDA-Q . According to Qblox, the company’s Q1 sequence processors and LINQ architecture deliver the necessary throughput and low latency for smooth interaction with the GPU. GPU-accelerated decoders can now process syndrome data with minimal delay, improving the speed and accuracy of real-time error correction.

This achievement builds upon previous collaborations between Qblox and NVIDIA, including work announced in October 2025 and March 2026, and represents a transition from architectural design to verified hardware performance, according to the company. Niels Bultink, CEO and Co-founder of Qblox, said, “With these developments, the GPUs become part of that control loop, giving quantum systems access to the computing power they need in real time.”

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Sam Stanwyck, director of quantum product at NVIDIA
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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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