Deploy World’s First Tightly Coupled Hybrid Quantum Computing Commercial Data Center and Drive Quantum-AI Applications with Anyon

March, 2026 — Anyon Technologies (“Anyon”) today announced two milestones that expand the practical frontier of tightly coupled hybrid quantum-classical computing. SDT Inc. has launched the first commercial tightly coupled hybrid quantum-classical data center, connecting Anyon’s quantum computing system and its proprietary quantum processing unit (QPU) to NVIDIA accelerated computing via NVIDIA NVQLink. In parallel, Anyon is collaborating with AI researchers to develop hybrid quantum reinforcement learning (QRL) models for real-world applications, designed to run on such NVQLink hybrid quantum-classical computing systems.

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As AI supercomputing becomes the default engine of modern computing infrastructure, scaling quantum computing beyond laboratory demonstrations requires a seamless way to integrate quantum processors with accelerated classical computing. NVQLink provides the missing interface between QPUs and AI supercomputing, enabling real-time orchestration of QPUs alongside CPUs and GPUs, supporting both the computationally demanding workflows needed to build logical qubits and the execution of useful hybrid quantum-classical applications.

Figure 1 World's first commercial deployment of tightly coupled hybrid quantum-classical computer.

“Practical hybrid quantum computing is about closing the loop between QPUs and GPUs in real time”, added Dr. Roger Luo, co-founder & CEO, Anyon Technologies, “This world’s first commercial tightly coupled quantum-classical computing system deployment in Korea and our work with applied AI researchers reflect our focus on tightly coupled infrastructure that can support both scaling workflows and application pipelines.”

The Korea deployment brings a production-oriented hybrid architecture into a commercial data center environment, engineered for real-time and high-throughput interaction across quantum and classical resources. In tightly coupled systems, classical compute performs real-time tasks such as calibration, decoding, networking, and hybrid control flows that are essential for scaling.

By linking Anyon’s QPU with NVIDIA accelerated computing via NVQLink, the deployment supports real-time hybrid orchestration across CPU/GPU/QPU resources, reducing overhead between quantum execution and classical processing. Developers can leverage NVIDIA CUDA-Q to program hybrid workflows for high-impact applications in AI research, chemical engineering, and financial services by interlacing QPU execution with accelerated classical compute with real-time callbacks across NVQLink.

“SDT is proud to partner with Anyon Technologies to deliver this integrated quantum solution”, said Jiwon Yune, Founder and CEO of SDT, “By pairing Anyon’s quantum hardware with SDT’s custom RF electronics, we’ve enabled NVQLink connectivity for a high-performance hybrid environment. This deployment serves as a vital first step for future large-scale projects, and we are thrilled to be part of a tight-knit tech ecosystem that translates complex hardware into scalable, real-world impact.”

Commercial hybrid deployment is the foundation required to run applications that inherently depend on rapid quantum-classical interaction. Many emerging workloads require tight feedback loops where a classical program decides what quantum computation to run next, and the quantum outputs are processed by a classical program. These are precisely the workflows where low-latency QPU-GPU coupling can shift hybrid quantum computing from conceptual integration to measurable system-level performance.

Figure 1 Tight-Hybrid-Quantum-Classical Computing unlocks opportunities in fintech, chemistry, and AI

“NVQLink was built to allow the performant, robust and scalable integration of quantum processors into supercomputer environments”, said Sam Stanwyck, Director of Quantum Product at NVIDIA, “Anyon’s work to deploy their quantum hardware in a real data center is a huge step towards quantum-GPU supercomputers running useful quantum applications.”

Hybrid quantum reinforcement learning (RL) is an example where RL agents learn through repeated cycles of state evaluation, action selection, and reward feedback. Implemented as a hybrid quantum-classical workflow, RL can benefit from accelerated classical training and inference while incorporating quantum components where they can add expressive power in the stochastic inference process, making it a natural target for tightly coupled hybrid systems.

Anyon and industry end-users are collaborating further to explore and build quantum reinforcement learning methods aimed at autonomous workflows. The program focuses on practical hybrid quantum-AI architectures that combine GPU training throughput and QPU-enhanced stochastic inference, where quantum circuits could contribute expressive representations in sampled distribution that are difficult to reproduce classically. A core technical objective is addressing the hybrid training bottleneck that often limits quantum-enhanced learning. The collaboration will explore training flows that preserve high-throughput GPU pipelines while using the quantum path where it matters most in the end-to-end system.