Summary:

This code allows one to do optimized parameter sweeps for Quantum Computing/Information experiments using systems integrated through Labber API

Use Cases:

graph LR
  B("class MeasurementOptimizer");
  D[Optimization Parameter];
  A[Input Parameter 1] ----> B;
  C[Input Parameter 2] ----> B;
  D ---> B;
  subgraph "MeasurementOptimizer.py"
  B--> E{Is the Optimization Parameter a Derived Quantity};
  E -->|Yes| F[Case 1];
  E -->|No| G[Case 2];
  F --> B;
  G --> B;
  end

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Usage

The program would require the following inputs:

• 2 parameters, their config. with Labber, and bounds to form the search space
• 1 parameter, its qualifier (isDerivedQuant: bool), and associated config. to optimize over
• hyper-parameters for optimization

Example:

from MeasurementOptimizer import *

MeasurementOptimizer()

To Do:

• study James' code
  • methods to deal with generalized output optimization
  • obj code verification
  • save data
  • implement SNR optimization
• test
  • toy
  • with system
• document
• 3 param opt.