Adaptive Symmetry-Preserving Quantum Ansätze via Machine Learning

D'Cunha et al. (2022) showed that hardware-efficient ansätze often break symmetries (e.g., spin or particle number), causing nondifferentiable energy curves. Meanwhile, Shao et al. (2023) demonstrated ML can predict density matrices with quantum-level accuracy. This idea proposes training an ML model to detect and correct symmetry violations in real-time during VQE optimization. The model would take the ansatz's output state, predict symmetry errors, and apply corrective unitaries. Unlike static ansätze like UCCSD (Shen et al. 2015), this creates a self-healing ansatz that adapts to hardware noise. It directly addresses D'Cunha’s pitfalls while leveraging quantum-native ML, potentially enabling accurate VQE for d-orbital systems like TiH (Clary et al. 2022).

References:

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