Seems a bit of conflation of notions going on here: Just because logic/quantum/probabilistic all end with "programming", it doesn't mean that they all are what is called a "programming paradigm" in the typical sense [0]. Where does a paradigm end and a library implementation begin?
For example, varying how a program arrives at the answer induces impertive/OOP vs. declarative paradigms [0].
On the other hand, quantum programming assumes a radically different type of "CPU" (i.e. instruction set) on which your program runs - which in turn obviously changes everything. But this can safely by implemented within existing paradigms, e.g. ProjectQ [1] is implemented in an OOP language: Python.
Thus, I would not call this a new programming paradigm (unfortunately [0] does that, but I think that is bad form).
> you can view differentiable programming as extending logic programming [...] that allows interpolation between data and code.
Reference? I have browsed one of the standard references [2] on automatic differentiation and googled a bit and could not find something that supports your statement. Even more so, is seems that even defining semantics for differential programming is barely in its starting stages [3].
> differentiable programming is at the level of syntax sugar for reverse mode differentiation, so I can't blame you for that conclusion.
By the same argument you could say that probabilistic programming is just syntax sugar for painless specification of statistical models.
Care to provide a reference where differential programming is presented as something more than "syntax sugar"?
[0] https://en.wikipedia.org/wiki/Programming_paradigm#Further_p...
[1] https://projectq.ch/
[2] Griewank A., Walther A., Evaluating Derivatives, SIAM 2008
[3] https://arxiv.org/pdf/1911.04523.pdf
For example, varying how a program arrives at the answer induces impertive/OOP vs. declarative paradigms [0]. On the other hand, quantum programming assumes a radically different type of "CPU" (i.e. instruction set) on which your program runs - which in turn obviously changes everything. But this can safely by implemented within existing paradigms, e.g. ProjectQ [1] is implemented in an OOP language: Python. Thus, I would not call this a new programming paradigm (unfortunately [0] does that, but I think that is bad form).
> you can view differentiable programming as extending logic programming [...] that allows interpolation between data and code.
Reference? I have browsed one of the standard references [2] on automatic differentiation and googled a bit and could not find something that supports your statement. Even more so, is seems that even defining semantics for differential programming is barely in its starting stages [3].
> differentiable programming is at the level of syntax sugar for reverse mode differentiation, so I can't blame you for that conclusion.
By the same argument you could say that probabilistic programming is just syntax sugar for painless specification of statistical models.
Care to provide a reference where differential programming is presented as something more than "syntax sugar"?
[0] https://en.wikipedia.org/wiki/Programming_paradigm#Further_p...
[1] https://projectq.ch/
[2] Griewank A., Walther A., Evaluating Derivatives, SIAM 2008
[3] https://arxiv.org/pdf/1911.04523.pdf