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NVIDIA presented QODA – Quantum-Optimized Device Architecture – a platform for hybrid quantum-classical computers that enables integration and programming of quantum processing units (QPUs), GPUs, and CPUs in one system. The tech is aimed at "the programmer who wants to do algorithm research and build hybrid applications for future quantum advantage."
"QODA enables GPU-accelerated system scalability and performance across heterogeneous QPU, CPU, GPU, and emulated quantum system elements."
QODA consists of a specification and a compiler NVQ++. According to NVIDIA, it delivers a unified programming model designed for quantum processors (actual or emulated) in a hybrid setting. It supports hybrid deployments via emulation on a GPU up to NVIDIA DGX SuperPOD and with multiple QPU partner backends.
The developers promise the tech's high performance, with "287X speedup in end-to-end Variational Quantum Eigensolver (VQE) performance with 20 qubits" and improved scaling compared to Pythonic frameworks.
QODA features:
- Kernel-based programming model extending C++ for hybrid quantum-classical systems (full Python support on the way)
- Native support for GPU hybrid compute, enabling GPU pre- and post-processing and classical optimizations
- System-level compiler toolchain featuring split compilation with NVQ++ compiler for quantum kernels, lowering to Multi-Level Intermediate Representation (MLIR) and Quantum Intermediate Representation (QIR)
- Standard library of quantum algorithmic primitives
- Interoperable with partner QPUs as well as simulated QPUs using the cuQuantum GPU platform; partnering with QPU builders across many different qubit types
You can find out more on NVIDIA's website and its blog.
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