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quantum-computing
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Here are some examples:
- Root README is missing machine learning category of samples
- README in algorithms is missing
repeat-until-success
andsudoku-grover
- [README in diagnostics](https://github.com/microsoft/Quantum/blob/main/samples/diagnostics/RE
What is the expected behavior?
While there is still a distinction between Gate
(strictly unitary) and Instruction
(not necessarily unitary) we should not only have a to_instruction
but also a to_gate
on the o
Is your feature request related to a use case or problem? Please describe.
The use case is documentation.
Describe the solution you'd like
Basically something like the docstring used here: https://github.com/quantumlib/Cirq/blob/d58423a3f06dd0a40fd8acfdd006d9a876274a7c/cirq/circuits/qasm_output.py#L86
**What is the urgency from your perspective for this issue? Is it blocking impo
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Did you find a doc bug or broken link that needs to be fixed?
English Qiskit Docs: https://qiskit.org/documentation/release_notes.html#id187
Korean Qiskit Docs: https://qiskit.org/documentation/locale/ko_KR/release_notes.html#id155
We probably might want to generate the version history table in all docs.
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Adding type hints
To get this codebase up to standard, we need to add type hints. At the very least, perhaps we should start insisting that all new code has type hints added, but moreover we need to add typing to the code that currently exists.
This is a pretty large project, but luckily it can be done incrementally - I believe at this point it's a stretch goal for v1, if anyone wants to help it would be much ap
Running travis lint
gives a bunch of warning. Like for instance, sudo : required
is no longer needed, and Travis migrated to a new architecture. So the travis.yml
file needs to be updated.
https://blog.travis-ci.com/2018-10-04-combining-linux-infrastructures
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For example, pylint would have caught ProjectQ-Framework/ProjectQ#19 via an undefined variable warning.
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What is the expected enhancement?
>>> from qiskit.aqua.operators import I, X
>>> (0 * X + I) == I
False # should be True
A possible solution would be to add a ``SummedOp.chop_summands(threshold=0)
It would be nice to allow users to set a custom value for the tensorflow dtype if they are really pushing their simulations (and aware of the memory tradeoffs). This would be a very straightforward feature to add, most likely by adjusting backend_options
to accept a dtype
argument or something similar
See, e.g., the discussion below:
You could change this line https://github.com/XanaduAI
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There seems to be confusion between 'schedule' and 'scheduler' throughout sources and doc. They seem to be used interchangeably, which seems confusing. Is a "schedule" the same as a "scheduleR"?
For example, consider the following code snippet in src/compressor/compressor.lisp
:
(let ((lschedule (make-lscheduler)))
;; load up the logical schedule
The variable lschedule
and
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Several katas could have better error messaging:
DumpMachine
toAssertEqualOnZeroState
)