New symmetric five-pulse and longer composite sequences are constructed that compensate amplitude, detuning, and duration errors for X and Hadamard gates via derivative cancellation in the Cayley-Klein parametrization and numerical infidelity minimization.
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Spectral Born machines are Fourier-phase quantum generative models over Z_d^n that train classically via graph-spectral MMD and show reduced parameters plus apparent overfitting resistance on integer data.
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Composite quantum gates simultaneously compensated for multiple errors
New symmetric five-pulse and longer composite sequences are constructed that compensate amplitude, detuning, and duration errors for X and Hadamard gates via derivative cancellation in the Cayley-Klein parametrization and numerical infidelity minimization.
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Spectral Born machines: classically trainable quantum generative models for discrete data
Spectral Born machines are Fourier-phase quantum generative models over Z_d^n that train classically via graph-spectral MMD and show reduced parameters plus apparent overfitting resistance on integer data.