Understanding the Tensor Product Space: What is the Motivation Behind It?

In summary, the total Hilbert space of a system composed of two independent subsystems is the tensor product of the Hilbert spaces of the subsystems because it gives the correct equations for joint probability distributions and is, in a sense, the natural way to represent a joint wavefunction.
  • #1
Yoran91
37
0
Hi everyone,

I'm reading through tensor product spaces and one question really bogs me. Why is it that the total Hilbert space of a system composed of two independent subsystems is the tensor product of the Hilbert spaces of the subsystems?

It is always posed, but I've never seen a proof or a single argument for this. Sure, it works, but what is the motivation?

Thanks for any help
 
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  • #2
I think it is a postulate. It also gives the correct equations for joint probability distributions and is, in a sense, the natural way to represent a joint wavefunction.

This might not be the best explanation (it is certainly not he most well put together), but here is another argument for it.
Say you have two disconnected systems, and have observables A on the first and B on the second. It is natural to require that measuring both A and B "simultaneously", the joint observable AB is linear in both arguments: [tex]AB|\psi,\phi\rangle =A|\psi\rangle B|\phi\rangle[/tex] or in a more suggestive notation, [tex]A\otimes B(|\psi\rangle \otimes |\phi\rangle) =(A|\psi\rangle)\otimes (B|\phi\rangle)[/tex] where A and B are linear operators. In short, an observable on each of two disconnected systems gives rise to a unique observable on their tensor product.
 
  • #3
It's basically espen180's argument. That argument shows that all states of the form:
[tex]\Psi \otimes \Phi[/tex] should be states of the total system.

Then, under the normal Rules of Quantum Mechanics, any linear combination of such states should
also be a state (principle of superposition) this gives you a space [tex]\mathcal{H}[/tex] Finally all Cauchy sequences in [tex]\mathcal{H}[/tex] should converge, since the limit of any Cauchy sequence will always correspond to the action of some unitary operator on a state already in [tex]\mathcal{H}[/tex] So adding in all such limits gives [tex]\overline{\mathcal{H}}[/tex] which is the tensor product of the two original spaces.
 
  • #4
This isn't a very rigorous argument, but at least it's an argument...

Consider two systems that aren't interacting with each other. If system 1 is in state ##|\psi\rangle## when we measure A, the probability of result ##a## is
$$P(a)=\left|\langle a|\psi\rangle\right|^2.$$ If system 2 is in state ##|\phi\rangle## when we measure B, the probability of result ##b## is
$$P(b)=\left|\langle b|\phi\rangle\right|^2.$$ I'm not sure what the notational conventions are for tensor products of bras, so maybe a and b should be swapped on the right-hand side below... The standard rules for probabilities tell us that the probability of getting both of these results is
$$P(a\ \&\ b)=P(a)P(b)=\left|\langle a|\psi\rangle\right|^2\left|\langle b|\phi\rangle\right|^2=\left|\langle a|\otimes\langle b|\ |\psi\rangle\otimes|\phi\rangle\right|^2.$$ So if we use the tensor product space to represent the states of the combined system, the Born rule will hold for that space too.
 
  • #5
The only actual proof that I'm aware of uses the GNS theorem, where the use of the tensor product is just the noncommutative generalisation of the use of [tex]A \times B[/tex] as the sample space for a system composed of two independent subsystems in normal Kolmogorov probability.

Many things that seem mysterious in quantum mechanics are really just noncommutative versions of things you already know from normal probability. A good book on this is Statistical Dynamics: A Stochastic Approach to Nonequilibrium Thermodynamics by Raymond Streater, see chapter 8.
 
  • #6
A small LaTeX tip for DarMM. You can use ## the way you would use a single dollar sign in a LaTeX document (i.e. instead of itex tags). You can also use $$ instead of tex tags. Hit the quote button next to post #4 to see examples.

Regarding an actual proof...I think the 1978 article by Aerts & Daubechies gets the job done, but I haven't studied it in detail and it's been a couple of years since I looked at it, so I'm not sure I would be able to answer any questions about it.

https://web.math.princeton.edu/~ingrid/publications/AD78.pdf
 
  • #7
Wow, thanks for the comments! I'll be sure to look into these arguments, thanks a lot!
 

FAQ: Understanding the Tensor Product Space: What is the Motivation Behind It?

1. Why is the tensor product space important in mathematics?

The tensor product space is important in mathematics because it allows for the combining of two vector spaces to create a new vector space. This is useful in many areas of mathematics, such as linear algebra, functional analysis, and differential geometry. It also has applications in physics and engineering.

2. What is the difference between the tensor product and the direct product?

The tensor product and the direct product are both ways of combining vector spaces, but they have different properties. The tensor product is a more general construction that takes into account the specific structure of the vector spaces being combined, while the direct product is a more basic operation that simply combines the elements of the vector spaces.

3. How is the tensor product space related to the concept of multilinear maps?

The tensor product space is closely related to multilinear maps, as it provides a way to represent these maps as elements of a vector space. Multilinear maps are functions that take in multiple vectors and produce a scalar value, and the tensor product space allows for the manipulation and calculation of these maps.

4. Can the tensor product space be generalized to more than two vector spaces?

Yes, the tensor product space can be generalized to any number of vector spaces. This is known as the n-fold tensor product and is denoted by V1 ⊗ V2 ⊗ ... ⊗ Vn. This allows for the combination of more than two vector spaces and has applications in higher dimensional mathematics.

5. How is the tensor product space used in quantum mechanics?

In quantum mechanics, the tensor product space is used to describe the state of a system with multiple particles. This is known as the many-body state and is represented by the tensor product of the individual particle states. The tensor product space is also used in tensor networks, a powerful tool for analyzing and simulating quantum systems.

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