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Qblox Integrates NVIDIA DGX Spark With Quantum System Controller

Insider Brief PRESS RELEASE — Qblox, a leader in scalable quantum control architecture, today announced the integration of NVIDIA DGX Spark with a quantum system controller using NVIDIA NVQLink, achieving measured roundtrip latency…

Mohib Ur Rehman

Publisher The Quantum Insider

Oct 22, 2026 at 1:40 PM UTC · 2 min de leitura

Qblox Integrates NVIDIA DGX Spark With Quantum System Controller
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Key Signal

<4.0 µs RTX PRO latency

Last Updated

há 2 dias

Traduzindo…

Insider Brief

  • Qblox has integrated NVIDIA DGX Spark with its quantum system controller using NVIDIA NVQLink, reporting round-trip latency below 4.0 microseconds on NVIDIA RTX PRO 6000 Blackwell and below 7.2 microseconds on DGX Spark.
  • The integration connects GPU computing with quantum control to support real-time hybrid quantum-classical workloads, including GPU-accelerated calibration and quantum error correction.
  • The system uses NVIDIA CUDA-Q to support development on desktop hardware, with the same code designed to transfer to GPU servers as quantum systems scale.

PRESS RELEASE — Qblox, a leader in scalable quantum control architecture, today announced the integration of NVIDIA DGX Spark with a quantum system controller using NVIDIA NVQLink, achieving measured roundtrip latency below 4.0 µs on NVIDIA RTX PRO 6000 Blackwell and below 7.2 µs on NVIDIA DGX Spark. This enables Qblox to support real-time hybrid quantum-classical applications, with a single desktop system that can host quantum, AI, and simulation workloads via NVIDIA CUDA-Q. The same code then carries over to data center graphics processing unit (GPU) servers as systems scale.

The integration brings GPU acceleration into the real-time control loop, a key building block for scaling quantum systems to support logical qubits. Qblox‘s Q1 sequence processors and LINQ deliver the low latency and high throughput the Qblox Cluster needs to work hand in hand with the GPU. GPU-accelerated calibration routines update qubit parameters while experiments run, instead of between them. For quantum error correction, syndrome data reaches GPU-based decoders with low latency, helping identify and correct errors in real time.

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