Wave Function Collapse and Bayesian Probabilty

In summary, there is a potential connection between wave function collapse and Bayesian inference, as discussed in the context of the Monty Hall problem. However, this topic is complex and there is no simple answer. The issue of context and observer dependence in quantum mechanics is also raised, as well as the potential role of non-local influences in generating entanglement. This is a fascinating and ongoing area of study in the field of quantum mechanics.
  • #1
unchained1978
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I'm curious as to whether or not there is a connection to be drawn between the phenomenon of wave function collapse and the idea of Bayesian inference. I began thinking about this within the context of one of the variants of the Monty Hall problem. If you have one kid, what's the probability that you will have a girl, given that the first is a boy. Before you learned that the first was a boy, the probability that the kid would be a girl is just .5. We could treat that probability as a sort of probabilistic wavefunction (loosely speaking). Now when we learn that one child is a boy, the probability of the next being a girl changes to 2/3. (BB BG GB GG) The last option is now excluded given the new information, giving rise to a new "wavefunction" which effectively collapses the previous wavefunction. It seems strikingly similar to a particle in a box, in which the observation of a rightward moving particle collapses the wavefunction and vice versa. Any thoughts?
 
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  • #2
There is indeed an interpretation of quantum mechanics in which uncertainty just reflects our ordinary classical ignorance of the system. It is known as the Bohmian interpretation, and it says that particles have well-defined positions and momenta at all times, we just may not know what they are. The only price you have to pay is nonlocality: particles are able to influence each other faster than the speed of light.
 
  • #3
I remember reading about this some time ago, but I don't seem to recall how non locality arises in such a system. Could you elaborate?
 
  • #5
unchained1978 said:
I remember reading about this some time ago, but I don't seem to recall how non locality arises in such a system. Could you elaborate?
It has to do with quantum entanglement and Bell's theorem, which is an absolutely fascinating topic. I suggest you read the excellent, yet easy to understand explanation "quantumtantra.com/bell2.html" .
 
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  • #6
unchained1978 said:
I'm curious as to whether or not there is a connection to be drawn between the phenomenon of wave function collapse and the idea of Bayesian inference. I began thinking about this within the context of one of the variants of the Monty Hall problem. If you have one kid, what's the probability that you will have a girl, given that the first is a boy. Before you learned that the first was a boy, the probability that the kid would be a girl is just .5. We could treat that probability as a sort of probabilistic wavefunction (loosely speaking). Now when we learn that one child is a boy, the probability of the next being a girl changes to 2/3. (BB BG GB GG) The last option is now excluded given the new information, giving rise to a new "wavefunction" which effectively collapses the previous wavefunction. It seems strikingly similar to a particle in a box, in which the observation of a rightward moving particle collapses the wavefunction and vice versa. Any thoughts?

There has been discussion of this by a number of authors, as has been mentioned. You should be aware that this is no simple answer for anything. The issue is that the context of a measurement controls the resulting statistical correlations. In other words, we live in an observer dependent reality. Either that, or as mentioned, there are non-local influences (or both).

For example: There are a lot of different ways to generate entanglement, and it is possible to entangle particles AFTER the fact. You would have to admit that it gets pretty tricky to explain (using Bayesian probabilities) how you can entangle particles that have never even been in causal contact.

http://arxiv.org/abs/quant-ph/0201134
 

Related to Wave Function Collapse and Bayesian Probabilty

1. What is wave function collapse?

Wave function collapse is a phenomenon that occurs in quantum mechanics, where a particle's wave function collapses from a superposition of multiple states to a single state when it is observed or measured. This is a fundamental aspect of quantum mechanics and is often referred to as the collapse of the wave function.

2. How does wave function collapse relate to Bayesian probability?

Bayesian probability is a way of assigning probabilities to different hypotheses based on evidence and prior knowledge. In the case of wave function collapse, Bayesian probability is used to describe the probability of a particle being in a certain state after observation, based on prior knowledge about the particle's wave function and the measurement being performed.

3. Can wave function collapse be predicted using Bayesian probability?

No, wave function collapse cannot be predicted using Bayesian probability. This is because wave function collapse is a random and unpredictable event, and Bayesian probability is based on known probabilities and evidence. However, Bayesian probability can be used to describe and analyze the probabilities of different outcomes after wave function collapse has occurred.

4. What is the significance of wave function collapse in quantum computing?

Wave function collapse is a crucial aspect of quantum computing, as it allows for the manipulation and measurement of quantum bits (qubits). Qubits can exist in a superposition of multiple states, and wave function collapse is what allows them to be observed and used for calculations. The ability to control and measure qubits is what makes quantum computing so powerful and promising for solving complex problems.

5. Are there any applications of wave function collapse and Bayesian probability outside of quantum mechanics?

Yes, the principles of wave function collapse and Bayesian probability can be applied to other areas such as machine learning and decision making. In machine learning, Bayesian probability is used to update the probabilities of different outcomes as new data is observed. In decision making, Bayesian probability can be used to make optimal decisions based on available information and prior knowledge.

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