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Leveraging Wave Function from Quantum Mechanics in Machine Learning
Bridging Quantum Mechanics and Machine Learning for Unique Insights
Machine learning has witnessed numerous innovations inspired by nature and physics. Among these inspirations, the concept of wave functions, central to quantum mechanics, opens intriguing possibilities for modeling periodic, cyclic, and complex relationships. In this post, we explore a novel machine learning approach that uses wave function mechanics to model data relationships, complete with code and explanations.
What is a Wave Function?
In quantum mechanics, a wave function represents the state of a quantum system. It describes oscillatory behaviours and encodes probabilities. Two key components define a wave function:
- Amplitude: The strength or magnitude of the wave.
- Phase: The position within the wave cycle (like where you are on a sine curve).
Mathematically, a wave function can be expressed as:
- : Amplitude, controls the wave’s height.
- : Frequency, determines how fast the wave oscillates.
- : Phase, shifts the wave’s position.
