Tracking states of randomness with three states

In summary, quantum mechanics allows for an infinite number of states over which a system can exist, as long as the system is measured in a specific way.
  • #1
I know that matter can only exist in one state at a time; however at the quantum level knowing what state it is in at a set time is impossible to know for sure until you look at the system. Like with how Schrodinger cat is in a state of randomness between the two states of dead and alive until you open the box.That leads me to the question, how does it work when it comes to three states over two, because I've only been able to find how it works with two. A system to think about would be the color charge of quarks.
 
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  • #2
Let's put Schrodinger's cat aside, because neither Schrodinger nor anyone else has seriously suggested that the cat is both alive and dead at the same time; the cat is either dead or alive just as a coin sitting on the floor is either heads-up or heads-down even if you're not close enough to see which it is. Schrodinger proposed this paradox to point out a flaw in the then-current (75 years ago) understanding of quantum mechanics: although there was no doubt that the cat would always be either dead or alive, quantum mechanics did not satisfactorily explain why. Much progress has been made in this area since then (google for "quantum decoherence", but be warned that some of the math is heavy going).

So with that said: there are plenty of states in which there are more than two possible measurement outcomes. Put a system into one of those states and make a measurement, and you might get any of those outcomes. An example would be an electron in free space: there is an infinite number of positions (mostly fairly close to one another) where you might find it if you make a sufficiently precise measurement of its position.
 
  • #3
Thank You this has really helped me understand a few things, but is there anyway you could explain it in math terms. I'm trying to do a science fair project and I need to explain the math portion. If you can't can you direct me to a starting point.
 
  • #4
Zachary Nichols said:
Thank You this has really helped me understand a few things, but is there anyway you could explain it in math terms. I'm trying to do a science fair project and I need to explain the math portion. If you can't can you direct me to a starting point.

What is your math background?
 
  • #5
ap calc with a little calc 2 and 3 (self study)
 

1. What is the significance of tracking states of randomness with three states?

Tracking states of randomness with three states allows scientists to better understand and predict patterns in complex systems, such as weather patterns or stock market fluctuations. It also helps in developing more accurate and efficient algorithms for data analysis and machine learning.

2. How do scientists track states of randomness with three states?

Scientists use mathematical models and statistical techniques to track states of randomness with three states. This involves collecting and analyzing large amounts of data to identify patterns and trends.

3. What are the three states used in tracking randomness?

The three states used in tracking randomness are chaotic, ordered, and critical. Chaotic states are highly unpredictable and turbulent, while ordered states have clear patterns and regularity. Critical states are in between chaotic and ordered states and are characterized by self-organized criticality.

4. How does tracking states of randomness with three states impact real-world applications?

The ability to track randomness with three states has numerous real-world applications, such as predicting natural disasters, understanding the behavior of financial markets, and improving the performance of artificial intelligence systems. It also has implications in fields such as biology, psychology, and neuroscience.

5. What are some challenges in tracking states of randomness with three states?

One of the main challenges in tracking states of randomness with three states is the complexity of the systems being studied. It can be difficult to accurately model and analyze these systems, and there is always a level of uncertainty involved. Additionally, the data used for tracking randomness may be incomplete or noisy, making it harder to identify and understand patterns.

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