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.
Quantum circuit optimization using differentiable pro- gramming of tensor network states.arXiv preprint arXiv:2408.12583, 2024
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Gradient-based optimization of quantum photonic circuits is achieved via differentiable tensor networks that model nonlinear unitary gates and stochastic losses at low photon numbers.
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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.
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Optimizing Quantum Photonic Integrated Circuits using Differentiable Tensor Networks
Gradient-based optimization of quantum photonic circuits is achieved via differentiable tensor networks that model nonlinear unitary gates and stochastic losses at low photon numbers.