Parameterised Quantum Circuits Reveal Redundancy-Driven Spectral Bias
In a 2026 preprint, Callum Duffy analyzes spectral bias in parameterised quantum circuits (PQCs), proving it arises from redundancy among Fourier coefficients and linking redundancy to training gradient magnitudes. He empirically validates the effect across three encoding schemes and shows higher redundancy yields robustness to parameter perturbations. The paper also finds that large parameter initializations and low-entanglement circuit designs slow convergence, informing PQC architecture choices.
Key Points
- 1Prove redundancy drives spectral bias in PQCs using Fourier-series formulation
- 2Demonstrate gradient magnitudes correlate with coefficient redundancy across three encoding schemes
- 3Show redundancy increases robustness to parameter perturbations; initialization and entanglement affect convergence
Scoring Rationale
Strong theoretical and empirical contribution with practical design guidance; limited by niche quantum-ML scope and single preprint source
Sources
Public references used for this report.
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