Quantum Computing and Superposition of states

In summary: So what you get is that you could be in one of two states and don't know which, with 50% probability for either.And then you'll have to understand what "a state" is. "A state" is not "the state of a system", it's a particular way to decompose a system into parts.In summary, the professor discussed the behavior of a single photon in a beam splitter and the concept of superposition states. The outgoing paths of the photon can be viewed as a superposition state of |0> and |1>, and the behavior of the photon can be better understood by thinking of it as a classical wave. The actual state of the photon is a superposition, but it is affected differently by
  • #1
eprparadox
138
2
I'm watching a lecture on the intro to quantum computing.

See the attached image which will be useful as I describe my question.

So the professor says that we have this single photon and it's in this state, ## | 0 > ##.

He states that when we send this photon through a beam splitter that it will (assuming we're not doing any sort of measurement), end up in a superposition state of

[tex] \frac{1}{\sqrt{2}}( |0> + |1>) [/tex]

If you look at beam splitter 1 (BS1) in the image, you'll see the incoming photon in state ## |0> ## and two outgoing states, ## |0> ## to the right and ## |1>## up.

My question is this: considering this superposition state, does it even make sense to think about these two different paths? Or is EACH outgoing path through BS1 this same superposition state of

[tex] \frac{1}{\sqrt{2}}( |0> + |1>) [/tex]

Put another way, should I basically think about the beamsplitter as this black box that takes an input and just outputs a single superposition state into the next beam splitter?

Thanks a lot.
 

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  • #2
eprparadox said:
I'm watching a lecture on the intro to quantum computing.

[]

My question is this: considering this superposition state, does it even make sense to think about these two different paths? Or is EACH outgoing path through BS1 this same superposition state of

[tex] \frac{1}{\sqrt{2}}( |0> + |1>) [/tex]

Put another way, should I basically think about the beamsplitter as this black box that takes an input and just outputs a single superposition state into the next beam splitter?

Thanks a lot.
A quantum beam splitter has two input channels and two output channels. The diagram looks wrong. One of the inputs is being assumed and you need to know what it is.

See the 'Quantum Description' section here https://en.wikipedia.org/wiki/Beam_splitter#Quantum_mechanical_description
 
  • #3
It may help here if you think of a photon as of a classical wave. Then a superposition of the states is just a sum of two waves - one going right and another going up. So the superposition applies to the "global" state, considering both paths, but if you want to consider somehow the state along one path, it is not a superposition (in this particular experiment), but either |0> or |1>.
 
  • #4
MichPod said:
It may help here if you think of a photon as of a classical wave. Then a superposition of the states is just a sum of two waves - one going right and another going up. So the superposition applies to the "global" state, considering both paths, but if you want to consider somehow the state along one path, it is not a superposition (in this particular experiment), but either |0> or |1>.

Thanks @MichPod. I still have some confusion about these outbound paths that he labled |0> and |1>. Those paths are only really defined if we actually MAKE a measurement correct? That is, if we setup a detector that tries to detect which outbound path the photon is coming from, THEN we will have these paths as |0> and |1>. Is that correct?

And then second question: in the case where we don't try and detect the path, then as you said it's this superposition. But my confusion there lies in the fact that there are still TWO inputs into the second beam splitter. What do those two inputs represent?

Thanks again for the help.
 
  • #5
These "paths" are |0> and |1> by themselves, unrelated to whether any measurement is done or not. Again, taking a classical pucture, you can say that you have some wave propagating right and some wave propagating up. Or you can talk of them as of one wave propagating both up and right (a superposition).

The actual state of the photon is a superposition, but if you put a mirror on some path, that mirror will affect only one part of the superposition - the same way as a mirror put on the way of the classical wave will affect only the part of the wave which collided with a mirror.

Before you even talk about "detection" which is a quantum phenomena, try to fully understand how a classical light would propagate through the system. This system is known as https://en.m.wikipedia.org/wiki/Mach–Zehnder_interferometer and must be described in many places on internet, including some very basic layman level.
 
  • #6
eprparadox said:
does it even make sense to think about these two different paths? Or is EACH outgoing path through BS1 this same superposition state

You will have a very hard time if you try to think of each outgoing path's state on its own.

Basically, in order to do so, you'll have to understand mixed states, partial traces, and second quantization. You'll have to start thinking of the superpositions as this:

##\frac{1}{\sqrt{2}}|\text{left}_{\text{photon=yes}}\rangle |\text{right}_{\text{photon=no}}\rangle + \frac{1}{\sqrt{2}}|\text{left}_{\text{photon=no}}\rangle |\text{right}_{\text{photon=yes}}\rangle##

then, in order to focus on one path, you'll have to "trace out" the other path. This only works if you convert to a density matrix first. If you trace out ##|\text{right}_*\rangle## you get:

##0.5 |\text{left}_{\text{photon=no}}\rangle\langle\text{left}_{\text{photon=no}}| + 0.5 |\text{left}_{\text{photon=yes}}\rangle\langle\text{left}_{\text{photon=yes}}|##

Which is equivalent to a classical probability distribution. Not because the state is actually random instead of under superposition, but because the process we used throws away the information necessary to understand in what way the state is quantum instead of probabilistic.
 

FAQ: Quantum Computing and Superposition of states

What is quantum computing and how does it differ from traditional computing?

Quantum computing is a type of computing technology that uses quantum-mechanical phenomena, such as superposition and entanglement, to perform operations on data. This differs from traditional computing, which uses classical bits to represent and process information. In quantum computing, quantum bits (qubits) are used, allowing for a much larger and more complex range of calculations and operations.

What is superposition of states in quantum computing?

Superposition of states refers to the ability of a qubit to exist in multiple states at the same time. In traditional computing, a bit can only exist in one state (0 or 1) at a time. In quantum computing, a qubit can exist in a combination of both states simultaneously, allowing for much more complex calculations to be performed.

How is superposition of states used in quantum computing?

In quantum computing, superposition of states can be used to perform calculations much more efficiently than traditional computing. By allowing a qubit to exist in multiple states at once, it can perform multiple calculations simultaneously, greatly increasing the speed and power of the computer.

What are the challenges in utilizing superposition of states in quantum computing?

One of the main challenges in utilizing superposition of states in quantum computing is the delicate nature of qubits. They are highly sensitive to environmental factors, and even the slightest interference can cause them to lose their superposition state. This makes it difficult to maintain the stability and accuracy of calculations in a quantum computer.

What are the potential applications of quantum computing and superposition of states?

Quantum computing and superposition of states have the potential to revolutionize many industries, such as finance, healthcare, and logistics. They can be used to solve complex optimization and simulation problems, as well as improve data security and encryption. They may also have applications in artificial intelligence and machine learning.

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