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A quantum machine learning startup reports that a pre-trained model, fine-tuned on a 21-qubit superconducting processor, shortened single-qubit gates by 33% without measured fidelity loss, and improved some two-qubit Bell-state fidelities. The results are company-reported, though randomized benchmarking at a third-party facility is a stronger form of evidence than simulation alone. For readers, the key question is whether AI-driven pulse optimization can generalize across devices and scale beyond this demonstration.
FirstQFM Foundation Model Cuts Quantum Gate Times by 33% on QuantWare QPU via NVIDIA Computing
FirstQFM AB has achieved a 33% reduction in single-qubit gate execution times on QuantWare’s 21-qubit Contralto-D21 superconducting quantum processor, without compromising gate fidelity. This was accomplished using their patented quantum foundation model technology, which integrates with Quantum Machines’ OPX control hardware and QUA calibration stack. The model, developed with NVIDIA’s accelerated computing infrastructure, also improved two-qubit Bell-state fidelities. The post FirstQFM Foundation Model Cuts Quantum Gate Times by 33% on QuantWare QPU via NVIDIA Computing appeared first on Quantum Computing Report.
Quantum machine learning developer FirstQFM AB has announced hardware-validated benchmarking results demonstrating a 33% reduction in single-qubit gate execution times on QuantWare’s 21-qubit Contralto-D21 superconducting quantum processor (QPU), executed without degradation…
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