# Quantum Computing Optimizer

Quantum Computing Optimizer (QCOptimizer) is an implementation of Step 2 of our information design technology. It is CAE tool for modelling and optimization of complex control systems which use multiple knowledge bases and quantum generalization. - User Manual.

## QCOptimizer main features are:

- Free control system structure, model of control system can be constructed from various blocks (including but not limited to fuzzyfication, fuzzy knowledge bases, quantum generalization).
- Optimization support:
- Genetic optimization:
- Classical single-fitness GA
- NSGA - multiple-fitness function optimization
- Binary chromosomes
- Real-valued chromosomes

- Non-genetical algorithms:
- Gradient descent
- Simplex descent method

- Fitness-function calculation:
- Based on learning signal files
- Based on external signal
- Calculated in external program (typically Matlab/Simulink)

- Optimization target selection: can select any subset of available sybsystems for optimization
- Optimization speed-up options:
- Parallel execution of several optimization algorithms, operating over different parts of model
- Optional duplicate chromosome detection

- Import of SCOptimizer knowledge bases
- Support data exchange link with external programs

## Screenshorts

**Main program window**

Main program window with model of complex control system inluding three fuzzy controllers and one quantum generalization module.

**Quantum generalization module parameters**

Dialog window used to set parameters of quantum generalization block.

**Fuzzy knowledge base module parameters**

Dialog window used to set parameters of fuzzy inference knowledge base. Table representation is shown, graphical representation is also avialable.

**Fuzzyfication module parameters**

Dialog window used to set parameters of fuzzification block. Membership functions can be edited either by entereing parameters in numerical form or dragging active points on graph by the mouse.

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