Added quick.circuit.Ansatz

[ACE07-Sev]

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[codacy-production]

Coverage summary from Codacy

See diff coverage on Codacy

Coverage variation	Diff coverage
✅ +0.08% (target: -1.00%)	✅ 96.00%

Coverage variation details

	Coverable lines	Covered lines	Coverage
Common ancestor commit (93bba85)	4160	3693	88.77%
Head commit (b49a3f2)	4209 (+49)	3740 (+47)	88.86% (+0.08%)

Coverage variation is the difference between the coverage for the head and common ancestor commits of the pull request branch: <coverage of head commit> - <coverage of common ancestor commit>

Diff coverage details

	Coverable lines	Covered lines	Diff coverage
Pull request (#48)	50	48	96.00%

Diff coverage is the percentage of lines that are covered by tests out of the coverable lines that the pull request added or modified: <covered lines added or modified>/<coverable lines added or modified> * 100%

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[codecov]

Codecov Report

Attention: Patch coverage is 96.00000% with 2 lines in your changes missing coverage. Please review.

Project coverage is 88.85%. Comparing base (93bba85) to head (b49a3f2).
Report is 9 commits behind head on 0.0.0-release.

Files with missing lines	Patch %	Lines
quick/circuit/ansatz.py	95.74%	2 Missing ⚠️

Additional details and impacted files

@@                Coverage Diff                @@
##           0.0.0-release      #48      +/-   ##
=================================================
+ Coverage          88.77%   88.85%   +0.08%     
=================================================
  Files                 61       62       +1     
  Lines               4160     4209      +49     
=================================================
+ Hits                3693     3740      +47     
- Misses               467      469       +2     

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[ACE07-Sev]

LGTM. I want to add a few other options for how thetas setter works. Right now it only does global optimization of the circuit which makes it prone to "orthogonality catastrophe". To tackle this, we need to at least allow for batched update of parameters. I will keep the PR open to add those.

For the time being as I mentioned in #44 I don't see a need for having a new optimizer module. Users can and should make their own depending on their usecase. However, for approximate compilation I may add a tensor network optimization approach using quimb which may be the first to come for this new module, or maybe put somewhere else. This is not a pure QML package, so I don't want to lean too much towards those features for now.