Quantum Mechanics is linear and look very similar to equations of linear regression in ML. Let me give you an example,
y = t0x0 + t1x1 + t2x2 where at least one of t0, t1 or t2 isn’t zero.
This is a linear equation and one such is used in linear regression to fit supervised learning problems like predicting house prices.
In quantum mechanics, Schrödinger equation is a linear partial differential equation. This similarity can be exploited to fit parameters of equations to predict variety of outcomes which human mind wouldn’t be able to predict. One such prediction we can make is regarding photon with following equation
y = a |p1) + b |p2)
That is, when we take observation, with ‘a’ probability property p1 is observed and vice versa. After many observations, with a large dataset, properties of photon can be predicted. Like patterns the photon shows which mind couldn’t capture and open new chapters.
Disclaimer: This is just a random thought with very limited knowledge of quantum mechanics. So, if this make no sense or if it does also, please feel free to comment and connect.
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