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Automatic Differentiation for Complex Valued SVD

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arxiv 1909.02659 v3 pith:VNVDOKCW submitted 2019-09-04 math.NA cond-mat.stat-mechcond-mat.str-elcs.LGcs.NAquant-phstat.ML

classification math.NAcond-mat.stat-mechcond-mat.str-elcs.LGcs.NAquant-phstat.ML
keywords complexautomaticdifferentiationformulavaluedbackcompletedecompositions
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In this note, we report the back propagation formula for complex valued singular value decompositions (SVD). This formula is an important ingredient for a complete automatic differentiation(AD) infrastructure in terms of complex numbers, and it is also the key to understand and utilize AD in tensor networks.

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Cited by 3 Pith papers

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  1. Optimizing Quantum Photonic Integrated Circuits using Differentiable Tensor Networks

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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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  3. Enhancing Robotic System Robustness via Lyapunov Exponent-Based Optimization

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    A differentiable 'sum of Lyapunov exponents' score is used as a robustness objective to co-optimize robot hardware and control policies in simulation.

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