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Adjoint Differentiation

See original GitHub issue

While not really viable for NiSQ circuits (outside of simulation), the request has been made a few times to implement the adjoint gradient calculation method for differentiation. This would roughly entail:

Writing a new C++ op to calculate gradients via the adjoint method
Writing a new differentiator that uses this op inside of it and only defines this operation for differentiate_analytic.

Going to leave this open for discussion for anyone who’s interested.

Issue Analytics

State:
Created 3 years ago
Reactions:2
Comments:10 (1 by maintainers)

Top GitHub Comments

1reaction

MichaelBroughtoncommented, Aug 26, 2020

Oops, that was an initial misunderstanding of mine. Thank you for pointing it out @refraction-ray . We are now well on our way to finishing the adjoint differentiator 😃

1reaction

wangleiphycommented, Jul 9, 2020

You can also refer to Section 3 “Reversible Computing and Automatic Differentiation” of the Yao.jl paper . This is the key technique that allows Yao.jl to train a, say, 10000 layer variational circuit on a laptop.

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