Visualizing Quantum Mechanics with Mathematical Physics Models for use in Complex Systems


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Visualizing Quantum Mechanics with Mathematical Physics Models for use in Complex Systems

Visualizing Quantum Mechanics with Mathematical Physics Models for use in Complex Systems

In this video I talk about how the seemingly recalcitrant behavior of quantum systems can be seen through the lens of complex adaptive systems, modeled using mathematical physics, with analogies to classical mechanics. This is necessary in order for us to realize that quantum systems contain structure which is a balance between precise exchange rules and probabilistic dynamics.

The balance between exploitation of momentum exchange and exploration of the paths of probabilities results in the quantum particles forming the complex behavior that we see in systems such as Bose-Einstein Condensates and Superconducting Materials.

Moreover, it is no surprise therefore that we can model the complex systems we see in the macroscopic world using such systems, as many of the complex adaptive behavior in the macroscopic world is itself analogous to what occurs when quantum systems are under examination and steered towards particle phase transitions, for example in simulated annealing.

Using analogies with classical mechanics allows us to create mechanistic models to create a linked between classical and quantum mechanics, with the Quantum Newton’s Cradle model being a way to describe momentum exchange with a condensate that is linked together in a quantum network, where all the atoms are synchronized in the same ground state.

Modern areas of research such as creating neuromorphic computers using networks of linked oscillators could very well be accomplished using systems that emulate quantum behavior and, conversely, quantum systems can themselves form networks that emulate complex dynamics such as those seen in emergent systems arising from couplings between multiple nodes in a network.
From models of the brain, through genetic models, to the electrical power grids; being able to predict and even control the behavior of such networks is crucial in the modern world. It all suggests a tantalizing pathway to understand and offer high levels of control of these networks across a huge range of applications in natural physical, chemical, biological, and technological systems.


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