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##### Activity
Harshit Garg
@githg22_gitlab
Its not against the rules btw, in warmup you're free to discuss hints
Sagar Mishra
@achieveordie
like the expression?
Harshit Garg
@githg22_gitlab
(register!)
Sagar Mishra
@achieveordie
And if that is so, did you solve problem C?
Harshit Garg
@githg22_gitlab
Length(register!)
For example
Yeah I solved C
Sagar Mishra
@achieveordie
and if I wanted to access some element, how would do that in LittleEndian?
Any hints?
Harshit Garg
@githg22_gitlab
register![1]
C is actually similar to a problem in Quantum Katas
It's kind of a hack-y method
Sagar Mishra
@achieveordie
In which notebook is it?
Harshit Garg
@githg22_gitlab
Superposition
Sagar Mishra
@achieveordie
X(LittleEndian![0]); is this a valid expression?
nevermind, I'm stupid.
wasted 24 hours on something this stupid
Harshit Garg
@githg22_gitlab
hehe it happens
Were you writing LittleEndian instead of register?
Sagar Mishra
@achieveordie
yep, not the first time when I've doubted my sanity. Thanks for the help
Harshit Garg
@githg22_gitlab
Thank you sir Increment problem was so simple I was thinking wayy too much
@githg22_gitlab: If you allocate an extra qubit, then it has to be returned to |0⟩ at the end of the using block. As you note, you can't use measurement to do that in an adjointable operation; given that resetting is a special case of measurement, the Reset operation will also fail to preserve adjointability. The trick is that you need to coherently reset the qubit to |0⟩ without measurement. You can do that because you know exactly what state it's in at the end of the using, and can unprepare it using only unitary operations. The within/apply feature of Q# or the ApplyWithCA operation will be very handy here.

By the way, @crazy4pi314 will be streaming with @bettinaheim about the Q# compiler today in a little under three hours if anyone is interested to join at https://twitch.tv/crazy4pi314. More details at https://twitter.com/crazy4pi314/status/1271837229221101569.

Stream starting in just a few moments at https://www.twitch.tv/crazy4pi314!
vashisth malik
I know the difference between Identity and X gate and there adjoint are they themselves. But i did not understand coding part like this part "operation Solve (unitary : (Qubit => Unit is Adj+Ctl)) : Int " is in this part we are calling qubit to unitary ? Any resource to understand this will be helpful?
@VashisthMalik_twitter: The type of an operation that takes an input of type 'T and that returns an output of type 'U is written as 'T => 'U; thus, unitary : (Qubit = > Unit is Adj + Ctl) indicates that unitary takes a Qubit input and returns Unit. The is Adj + Ctl tells you that unitary is adjointable and controllable (very important for some of the contest problems!). In this case, we can read the signature of Solve as telling us that it takes an adjointable and controllable single-qubit operation, and returns an Int.
Stream's now live, by the way, if you want to learn more about the internals of the Q# compiler! 💕
vashisth malik
on which i perform functor like Adjoint Unitary (?) . what is the name of qubit ? is it qubit itself?
vashisth malik
is it qs?
@VashisthMalik_twitter: Applying the Adjoint functor to an adjointable operation doesn't change the type of that operation. For example, Microsoft.Quantum.Intrinsic.X has type Qubit => Unit is Adj + Ctl, such that Adjoint X also has type Qubit => Unit is Adj + Ctl. In the example from the contest problem, Adjoint unitary is an operation with type (Qubit => Unit is Adj + Ctl). By contrast, for a controllable operation op, Controlled op does modify the type to add the new control register. For more detail, you can checkout my book with @crazy4pi314 at bit.ly/qsharp-book, or the Microsoft Quantum docs at https://docs.microsoft.com/quantum/user-guide/using-qsharp/operations-functions#controlled-and-adjoint-operations.
Amir Ebrahimi
@amirebrahimi
Hi there - I assume most people here are working on the Q# Challenge. When I try to run https://github.com/microsoft/MLADS2020-QuantumClassification/blob/master/QuantumClassification/ExploringQuantumClassificationLibrary.ipynb locally I get the following errors from jupyter notebook:
IQSharpError: The Q# kernel raised the following errors:
C:/Users/AmirEbrahimi/dev/qc/CodeForces2020/Warmup/notebook/Backend.qs(6,10): error QS6104: No namespace with the name "Microsoft.Quantum.Intrinsic" exists.
C:/Users/AmirEbrahimi/dev/qc/CodeForces2020/Warmup/notebook/Backend.qs(9,10): error QS6104: No namespace with the name "Microsoft.Quantum.MachineLearning" exists.
C:/Users/AmirEbrahimi/dev/qc/CodeForces2020/Warmup/notebook/Backend.qs(19,38): error QS6005: No type with the name "ControlledRotation" exists in any of the open namespaces.
C:/Users/AmirEbrahimi/dev/qc/CodeForces2020/Warmup/notebook/Backend.qs(12,54): error QS6005: No type with the name "SamplingSchedule" exists in any of the open namespaces.
C:/Users/AmirEbrahimi/dev/qc/CodeForces2020/Warmup/notebook/Backend.qs(33,23): error QS5022: No identifier with the name "Mapped" exists.
...
It looks like when qsharp.reload happens and it finds a .qs file in the directory that it somehow doesn't have the full qsharp environment ?
Mariia Mykhailova
@amirebrahimi Check that the IQ# version you have installed is the same as the version used by the tutorial, and either install older IQ# or update the tutorial to newer version (if you do that, make sure you update both the Jupyter notebook and the csproj file).
Harshit Garg
@githg22_gitlab
How do you people use prettier w/ Q#?
Considering there isn't a formatter yet
Gopal Ramesh Dahale
@Gopal-Dahale
I am not able to think about how to start with problem A5 (Z vs -Z). Can anyone give me a starting Hint? Global phase makes all my measurements same.
Harshit Garg
@githg22_gitlab
Well, it didn't work out w/ just one qubit for me
And you can't differentiate b/w them with simple |0> and |1>
Think some clever superposition
Harshit Garg
@githg22_gitlab
People who completed the machine learning tasks,
How did you decide upon which design to use?
How do I generally decide in what way to design a circuit
Mariia Mykhailova
@Gopal-Dahale Does the "official" hint from the contest help you? "Z and -Z gates differ by a global phase they introduce, so you cannot distinguish them by applying them to a single qubit. Take advantage of the fact that the given unitary has controlled variant defined."
Mariia Mykhailova
@githg22_gitlab In this particular case your data is encoded in just 1 qubit, so you have a choice of Rx, Ry and Rz gates :-) In general that's the same kind of challenge that classical machine learning faces - you need to figure out the feature engineering and the model structure before you can train it
Amir Ebrahimi
@amirebrahimi

@amirebrahimi I think it's the same issue as discussed at https://quantumcomputing.stackexchange.com/questions/12466/qsharp-reload-throws-error-in-python and in comments at https://codeforces.com/blog/entry/77614

This fixed it for me. Thank you @tcNickolas_twitter!

vashisth malik
hey there i need some help in increment problem. every term in this Question is new to me. can you please explain the Question?
Amir Ebrahimi
@amirebrahimi
@VashisthMalik_twitter - can you share a bit more about what you don't understand?

Re: D1 - I'm trying to modify the ClassifierStructure() to simply add an additional controlled rotation:
ControlledRotation((1, new Int[0]), PauliY, 1)
but get the following error:
Unhandled exception. System.ArgumentOutOfRangeException: Specified argument was out of the range of valid values. ---> Microsoft.Quantum.MachineLearning.EstimateGradient on D:\a\1\s\submodules\QuantumLibraries\MachineLearning\src\GradientEstimation.qs:line 78 at Microsoft.Quantum.MachineLearning._RunSingleTrainingStep on D:\a\1\s\submodules\QuantumLibraries\MachineLearning\src\Training.qs:line 148 at Microsoft.Quantum.MachineLearning._RunSingleTrainingEpoch on D:\a\1\s\submodules\QuantumLibraries\MachineLearning\src\Training.qs:line 229 at Microsoft.Quantum.MachineLearning._TrainSequentialClassifierAtModel on D:\a\1\s\submodules\QuantumLibraries\MachineLearning\src\Training.qs:line 388 at Microsoft.Quantum.MachineLearning.TrainSequentialClassifierAtModel on D:\a\1\s\submodules\QuantumLibraries\MachineLearning\src\Training.qs:line 322 at Microsoft.Quantum.MachineLearning.TrainSequentialClassifier on D:\a\1\s\submodules\QuantumLibraries\MachineLearning\src\Training.qs:line 98 at Microsoft.Quantum.Kata.QuantumClassification.TrainLinearlySeparableModel on C:\Users\AmirEbrahimi\dev\qc\CodeForces2020\Warmup\notebook\Backend.qs:line 0

I've dug through the source on GitHub a bit and it seems it is failing at estimating the number of qubits needed. Any ideas of how to resolve this? I would think simply by adding that ControlledRotation with another qubit referenced that it would expand the number of qubits.

vashisth malik