AI and GPUs Accelerate Silicon Quantum Computing with 3.3µs Real-Time Control

Diraq and QM Technologies Inc. have demonstrated a significant advance in silicon quantum computing by integrating quantum processing units (QPUs) with NVIDIA’s DGX Quantum architecture. Engineers at Diraq’s Sydney laboratory achieved real-time communication – with a latency of 3.3 microseconds – between their silicon quantum processor and NVIDIA Grace-Hopper superchips, facilitated by QM’s OPX1000 hybrid controller. This integration enabled the implementation of accelerated readout, automated calibration via machine learning, and faster quantum state initialisation, representing a substantial step towards scalable quantum computation.

Real-time Communication Achieved

Engineers at Diraq’s Sydney laboratories achieved real-time communication between a silicon quantum processor and NVIDIA Grace-Hopper superchips with a measured round-trip time of 3.3 microseconds. This integration utilised the NVIDIA DGX Quantum architecture and QM Technologies’ OPX1000 hybrid controller, enabling correlated readout, a computationally intensive signal processing task previously limited by Field Programmable Gate Arrays (FPGAs). The system also facilitates real-time feedback loops crucial for maintaining quantum coherence and preventing information loss.

Machine learning algorithms, executed on the GPU, automate calibration procedures, reducing manual tuning time. Furthermore, Diraq’s established algorithmic initialization protocols benefit from GPU acceleration, enabling faster quantum state preparation. This rapid deployment – three distinct applications operational within one week of DGX Quantum installation – underscores the viability of GPU-based control systems for future quantum computers and allows direct compatibility with NVIDIA’s CUDA-Q, an open-source quantum development platform.

GPU Acceleration of Key Processes

GPU acceleration addresses critical bottlenecks in silicon-based quantum computing through real-time processing of complex tasks. Diraq and QM Technologies demonstrated this capability by integrating NVIDIA DGX Quantum architecture with Diraq’s silicon quantum processor, achieving a 3.3-microsecond round-trip communication time. This integration enables real-time correlated readout and automates calibration procedures, reducing manual tuning time from hours to an automated process.

GPU acceleration also facilitates faster quantum state initialization, leveraging Diraq’s established algorithmic protocols. This rapid state preparation mitigates quantum decoherence – the loss of quantum information – before computations commence. The low latency of 3.3 microseconds is fundamental to these advancements, enabling real-time feedback loops essential for maintaining quantum information integrity and practical computation. DGX Quantum also provides direct integration with NVIDIA’s CUDA-Q platform, supporting the development of GPU-based quantum control systems.

Rapid System Integration and Results

Engineers at Diraq’s Sydney laboratories rapidly integrated NVIDIA’s DGX Quantum architecture to achieve real-time communication between a silicon quantum processor and NVIDIA Grace-Hopper superchips, with a 3.3-microsecond round-trip time. This integration supports real-time readout enhancement, automated calibration, and accelerated state initialisation, all within one week of installation.

The system’s architecture allows for immediate feedback loops, enabling rapid adjustments to quantum states before information degrades. Furthermore, Diraq’s hardware is now directly compatible with NVIDIA’s CUDA-Q, an open-source quantum development platform, demonstrating the viability of GPU-based quantum control. This integration paves the way for implementing real-time error-correction protocols within a hybrid quantum-classical architecture.

Advancing Error Correction and Open-Source Integration

Diraq and QM Technologies have integrated NVIDIA’s DGX Quantum architecture to facilitate real-time error correction and open-source development within a hybrid quantum-classical system. This integration achieves a 3.3-microsecond round-trip communication time between the silicon quantum processor and NVIDIA Grace-Hopper superchips, orchestrated via QM’s OPX1000 hybrid controller.

This low latency is critical for implementing real-time error-correction protocols, essential for stabilising quantum computations and mitigating decoherence. The system’s architecture allows for immediate feedback loops, enabling rapid adjustments to quantum states before information degrades. Furthermore, the collaboration directly integrates Diraq’s hardware with NVIDIA’s open-source quantum development platform, CUDA-Q, providing researchers with access to a comprehensive software ecosystem for developing and deploying quantum algorithms, leveraging the computational power of NVIDIA GPUs.

Future Hybrid Algorithm Development

Future development will focus on implementing real-time error-correction protocols within a hybrid quantum-classical architecture, necessitating the low-latency communication – currently demonstrated at 3.3 microseconds – between the quantum processor and the GPU.

The NVIDIA DGX Quantum system enables direct integration of Diraq’s hardware with NVIDIA’s CUDA-Q platform, an open-source development platform designed to accelerate the creation of quantum-classical algorithms. This integration facilitates the development of algorithms that leverage the strengths of both quantum and classical computation, with the GPU handling computationally intensive tasks and the quantum processor performing calculations suited to its unique capabilities. This collaborative approach is expected to pioneer new areas of computing by overcoming limitations inherent in either system operating in isolation.