Automatic Differentiation
Yao currently contains builtin automatic differentiation engine (an operator overloading based) especially for quantum circuits. It uses the reversible context of quantum computation to optimize the performance during simulation, thus you may find this is way faster than any other AD engine at the moment.
Builtin Reverse mode AD engine for simulation
As for expectation, the usage is pretty simple, since the evluation of expectations are just
expect(H, rand_state(10)=>circuit)
to get the gradients, simply add an adjoint
expect'(H, rand_state(10)=>circuit)
which will return the pair of gradients, one is the gradient of input register and the other is the gradient of circuit parameters.
Integration with General purpose AD engine
The builtin AD engine for Yao only provides the differentiation of quantum circuits, but you can plug it into a general AD engine, such as Zygote, since we have ported these rules to ChainRules.
APIs
YaoBlocks.AD.apply_back
— Methodapply_back(st::Tuple{<:AbstractArrayReg, <:AbstractArrayReg}, block::AbstractBlock; kwargs...) -> (out, outδ), paramsδ
The backward function of apply!
. Returns a tuple of ((input register, gradient of input register), parameter gradients)
YaoBlocks.AD.generator
— Methodgenerator(rot::Rotor) -> AbstractBlock
Return the generator of rotation block.
YaoBlocks.AD.mat_back
— Methodmat_back([::Type{T}, ]block::AbstractBlock, adjm::AbstractMatrix) -> Vector
The backward function of mat
. Returns the gradients of parameters.
YaoBlocks.AD.rotgrad
— MethodThe matrix gradient of a rotation block.