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
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A differentiable tensor-network method optimizes nonlinear quantum photonic circuits, including losses, and finds that moderate nonlinearity can beat linear circuits for phase sensing.
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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
A differentiable tensor-network method optimizes nonlinear quantum photonic circuits, including losses, and finds that moderate nonlinearity can beat linear circuits for phase sensing.