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Shortcut into Quantum Hardware Engineering.md

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Shortcut into Quantum Hardware Engineering: By Onri Jay Benally

Topic Details
Start with a 3D modeling & linguistics framework May involve a custom keywords glossary.
Spectroscopy or "scatterometry" Involves probing something without physical contact.
Topics covered Materials science & engineering, quantum metrology, quantum transport, quantum optics, quantum electronic design automation.
Electronic filters Know how they are configured, designed, & what they look like.
Filter components Understand what they are made of.
Passive vs active filters Know the difference.
Optical, microwave, & RF isolators Understand the differences among isolators, circulators, & mixers.
Amplifiers Be aware of room temperature & cryogenic amplifiers & their materials.
Amplifier noise types Thermal, shot, external, quantum.
Signal manipulation Know how a signal curve or response is manipulated.
Signal triggering Know how signals are triggered.
Impedance matching Understand how many ohms are required.
Smith chart Know how it works.
Resistors Understand their many purposes.
Multiphase power Know what it means.
Resonators Understand what a resonator & resonator cavity is.
Equipment Vector network & spectrum analyzers, arbitrary waveform generators, signal generators.
Oscillator circuit Know its role in voltage fluctuation or AC.
Inverter circuit Understand DC to AC conversion.
Rectifier circuit Understand AC to DC conversion.
Signal filtering High-pass, low-pass, band-pass, band-stop filter circuits/crossover networks.
Comparator circuit Understand threshold indicators.
Logic gates Know their basic functions (e.g., calculator).
PID controller Understand closed-loop control for self-balancing.
Feed forward controller Understand open-loop control as a self-balancing alternative.
Bonus project Build a simple electronic audio amplifier device (similar components to quantum systems).

Most Useful Coding Topics for Hardware Engineers

Topic Description
Library installation Understand how to install & manage libraries.
Syntax & commenting Proper code structuring & documentation.
Curve fitting Learn direct parameterization & mesh parameterization.
Automation scripting Write scripts for automation tasks.
Data management Work with data structures efficiently.
Parallel processing Leverage accelerated computing techniques.
Interpolation & extrapolation Learn data estimation techniques.
Linear regression Perform linear modeling & prediction.
Signal processing Process & analyze signals effectively.
Noise plots Visualize & interpret noise in data.
Manual debugging Practice troubleshooting & resolving errors.