σ-VQE uses low-depth circuits and an energy-selective cost function to preferentially prepare quantum many-body scar states on NISQ devices.
da Silva Fonseca, C
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Loss-aware natural gradient variants are introduced by embedding the loss hypersurface in a statistical manifold or using quantum state overlaps, yielding conformal updates that adjust effective step size.
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$\sigma$-VQE: Excited-state preparation of quantum many-body scars with shallow circuits
σ-VQE uses low-depth circuits and an energy-selective cost function to preferentially prepare quantum many-body scar states on NISQ devices.
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Loss-aware state space geometry for quantum variational algorithms
Loss-aware natural gradient variants are introduced by embedding the loss hypersurface in a statistical manifold or using quantum state overlaps, yielding conformal updates that adjust effective step size.