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Qblox says it connected NVIDIA DGX Spark to its quantum system controller using NVIDIA NVQLink, measuring round-trip latency below 7.2 microseconds on DGX Spark. The company says the setup is intended to support real-time hybrid quantum-classical tasks, including calibration and quantum error correction; broader performance and deployment details have not been disclosed.
Qblox has integrated NVIDIA’s DGX Spark computing system with its quantum system controller using NVIDIA NVQLink, reporting round-trip latency below 7.2 microseconds on DGX Spark. The announcement matters because it describes a measured connection between GPU computing and quantum control, intended to support hybrid workloads that need classical processing while quantum experiments are running.
In its announcement, Qblox reported round-trip latency below 4.0 microseconds using an NVIDIA RTX PRO 6000 Blackwell GPU and below 7.2 microseconds using DGX Spark. The company did not provide further test conditions in the supplied material, such as the workload, measurement method or whether the figures represent typical performance. The two figures are results on different hardware and should not be read as a direct comparison of otherwise identical systems.
Qblox says its Q1 sequence processors and LINQ connect the company’s Cluster quantum control system with the GPU. The stated use cases include GPU-accelerated calibration, in which qubit parameters could be updated while experiments run, and quantum error correction, where syndrome data can be sent to GPU-based decoders. These are described as capabilities the integration is intended to support, rather than evidence that a particular quantum application has achieved improved results.
The development uses NVIDIA CUDA-Q for programming and is presented as a desktop development path: researchers can run quantum, AI and simulation workloads on one system, then carry code to GPU servers as hardware needs grow. Qblox and NVIDIA describe that portability as a design goal; the announcement does not report a large-scale deployment or a specific customer using the system.
Connecting GPUs to Quantum Control
Quantum processors depend on classical systems to control operations, interpret measurements and respond to errors. If those steps take too long, they can constrain experiments that require fast feedback. Qblox’s reported latency figures are relevant because they offer a measurement of the link between its control hardware and GPU computing, rather than describing only a proposed architecture.
The potential benefit is a tighter loop between a quantum device and the computational resources used to calibrate or decode it. Qblox says GPU-accelerated calibration could update qubit settings during experiments, while GPU-based error-correction decoders could process syndrome data with low latency. That could help researchers test approaches to scaling quantum systems, but the announcement does not establish that the integration improves qubit quality, delivers fault-tolerant computing or reaches a particular scale.
For researchers, a desktop system that supports development before moving workloads to larger GPU servers could make it easier to iterate across hardware sizes, if the claimed code portability works in practice. The reported latency and stated workflow are early infrastructure details, not a demonstration of a complete quantum application or a commercial performance guarantee.
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From NVQLink Plans to Hardware Results
The announcement builds on earlier Qblox work with NVIDIA. According to the companies’ account, they announced work involving NVIDIA NVQLink in October 2025, followed by Qblox support for the CUDA-Q real-time API in March 2026. The October 2026 announcement presents the new step as a move from architecture and software support to measured hardware performance.
NVQLink is the connection used in this integration to link GPU computing with quantum control. CUDA-Q provides the development environment referenced by the companies. Qblox supplies the quantum control equipment, while NVIDIA provides the GPU platforms cited in the latency results. The announcement frames the setup as a foundation for hybrid quantum-classical workloads, not as a standalone quantum computer.
“Quantum computers will scale only as fast as the classical control systems around them.”
— Niels Bultink, Qblox CEO and co-founder
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Performance Details Still Missing
The reported latency figures are company-provided, and the announcement does not specify the test setup, workload, measurement methodology or variability. It is also unclear whether the figures include the full path from quantum measurement through GPU processing and back to the controller, or which operations were used to characterize round-trip time.
Qblox and NVIDIA did not provide customer deployment details, system availability, pricing or independent verification in the supplied material. Nor does the announcement report measured improvements in calibration time, error-correction performance, logical-qubit operation or overall quantum system scale. The proposed uses remain intended applications until such results are reported.
GPU-accelerated quantum calibration tools
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Further Tests and Scaling Results
The next evidence to watch for is a fuller description of the latency testing and demonstrations of the system running calibration or error-correction workloads with a quantum device. Results across different GPUs, workloads and operating conditions would help researchers assess how the reported measurements translate to practical experiments.
The companies have not announced a schedule for additional benchmarks, general availability or customer deployments. Qblox’s stated direction is to let researchers develop on DGX Spark and move the same code to GPU servers as quantum systems grow; whether that workflow scales reliably remains to be shown.
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Key Questions
What did Qblox announce?
Qblox said it integrated NVIDIA DGX Spark with its quantum system controller through NVQLink, reporting measured round-trip latency below 7.2 microseconds on DGX Spark.
What latency did the companies report?
Qblox reported latency below 7.2 microseconds on DGX Spark and below 4.0 microseconds on an NVIDIA RTX PRO 6000 Blackwell GPU. The release does not provide enough test details to compare the results beyond the stated hardware and figures.
What could the integration be used for?
Qblox identifies GPU-accelerated calibration and processing data for quantum error-correction decoders as intended uses. The announcement does not report that these applications have delivered specific performance gains.
Can researchers move code from DGX Spark to larger systems?
Qblox and NVIDIA say the setup uses CUDA-Q and is designed so code developed on desktop hardware can carry over to GPU servers. The release does not include independent testing or details about how broadly that portability has been demonstrated.
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