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NVIDIA launches Ising, the first open-source AI model suite dedicated to quantum computing, addressing the two major bottlenecks hindering the field's development. What problems does quantum computing face? Ising comprises two independent but complementary AI models. The first model, Ising Calibration, acts as an autonomous, continuous calibration system for quantum processors. Instead of taking days as today, calibration can be shortened to a few hours. This model is about 15 times smaller than comparable solutions currently on the market, making practical deployment much more feasible. The second model, Ising Decoding, specializes in real-time detection and correction of errors, a mandatory step to ensure the results of quantum computations are reliable. This model comes in two variants: one optimized for speed and one optimized for accuracy, depending on deployment needs. Superior performance relative to current standards. Compared with pyMatching, the most widely used open-source toolkit in the industry for quantum error correction, Ising Decoding is 2.5 times faster and 3 times more accurate. Additionally, this model requires 10 times less training data, significantly simplifying training and fine-tuning for specific systems. Both Ising models operate within the CUDA-Q ecosystem, NVIDIA's open-source platform for quantum development, compatible with various quantum processors. Currently, the models have been put into practical use by researchers, universities, and leading companies.
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