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Researchers have developed an amplitude-inspired encoding method for Quantum Support Vector Machines that sustains perfect classification accuracy even when hardware error rates reach 10%. This approach represents a ten-fold improvement over existing entangled alternatives, which suffer significant performance drops under similar noisy conditions. The findings provide a critical pathway for deploying practical quantum machine learning on current noisy intermediate-scale quantum devices by establishing robust standards for data encoding.
Researchers Find Resilient Feature Map Maintains Accuracy at 10% Noise
Maintaining perfect classification accuracy despite increasing errors has long eluded practical quantum machine learning systems. Now, an amplitude-inspired encoding within Quantum Support Vector Machines sustains 100% test accuracy even with error probabilities reaching 0. 10, a ten-fold improvement over entangled alternatives which dropped to between 65 and 90%.
Muhammad Ahsan Shakeel from Management Sciences and colleagues have shown that an amplitude-inspired quantum feature map maintains one hundred per cent test accuracy up to an error probability of 0.10 across depolarizing, bit-flip and phase-flip noise channels. Other tested feature maps experienced performance drops between sixty-five and ninety percent under similar conditions. Maintaining high classification accuracy in Quantum Support Vector Machines was previously challenging…
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