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New feature map boosts quantum neural network accuracy by up to 44%
Quantum Neural Networks (QNNs) depend on input encoding into quantum states, and while amplitude encoding uses O( log_2d) qubits for d features, conventional methods have a representational cost due to normalization causing invariance to rescaling. The authors introduce a feature map that distinguishes all real inputs under subsequent amplitude encoding, improving classification accuracy by 21–44 % across multiple benchmarks.
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