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#### Issue description
When using with the `step_and_cost` function to optimize a cost function that involves computing probabilities with `qml.probs`, the output values of the QNode are stored as …
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could we support or do we plan to support more backends?
Theano can only support multiple GPU using OpenCL but not CUDA, tensorflow seems slower than other framework using GPU for now.
Could we suppo…
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I just run following commands
```
git clone https://github.com/RainerKuemmerle/g2o.git
mkdir build
cd build
cmake ../
make
```
and errors occur
```
[ 4%] Built target stuff
[ 16%] Built …
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For deep neural networks in particular, and optimization of complex functions in general, it would be very valuable to be able to automatically derive gradients for continuous functions. Doing this (o…
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I am working on a API that relies on minimizing a complicated equation using SciPy 1.6.3. On a working version, I was on NumPy 1.17.4 and there were no problems with running SciPy's minimize function.…
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Hello @TomLKoller ,
First of all Thank you for creating such wonderful research open source. I am going through code and your published paper as well and I had few queries.
1. I want to utilize Box…
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I was hoping that PennyLane could automatically compute higher order derivatives, especially the Hessian matrix (and I think I remember that this used to work in the past).
A minimal example that I…
cvjjm updated
3 years ago
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Submitting Author: Matthew Feickert (@matthewfeickert), Lukas Heinrich (@lukasheinrich), Giordon Stark (@kratsg)
Package Name: [`pyhf`](https://github.com/scikit-hep/pyhf)
One-Line Description of Pa…
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Hi, I am trying to use an autodifferentiation tool to abstract away the need to evaluate the derivatives of certain functions for use as coefficients in the declaration of the equations subsequently. …
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Currently, the MWE below:
``` julia
using JuMP, BARON
m = Model(BARON.Optimizer)
@variable(m, x)
f = x -> x
JuMP.register(m, :f, 1, f, autodiff=true)
@objective(m, Min, x)
@NLconstraint(m, f(…