Quantum Computing: Superpositon of states problem

In summary, quantum computing allows for the superposition of all possible states, which can be processed in one operation. However, to extract the desired information, the wavefunction must be collapsed through measurement, resulting in a probabilistic answer. This process can be repeated multiple times to increase the probability of obtaining the correct answer. Constructing quantum algorithms can be challenging due to the need for cleverly extracting information from the superposition.
  • #1
joelio36
22
1
Hey, I'm learning about quantum computing for a project and I'm a bit stumped about a concept:

They say in quantum computing you can have the superpostion of all possible states, then perform an operation on that wavefunction, and thus have all possible states processed in one operation.

That sits fine with me, I get that, but what stumps me is how do you extract all that information without collapsing the wavefunction? Yes you have a superpostion of all the states you need, but as soon as you extract (read: observe) a state, don't all the others in the superpostion disappear?

By the way, I'm writing this to explain quantum computing to my peers, 3rd year Physics B.Sc students, so we aren't the brightest bunch!

Thanks
 
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  • #2
Bump. Any chance of an input? I can't find anything online. Cheers.
 
  • #3
Still nothing hey...
 
  • #4
Yes, at the end you do measure the wavefunction and all the other states in the superposition disappear. But this measurement gives you the answer you want, so you don't care about the rest.
Remember, with quantum computing you want an answer to a specific problem like in classical computing. The last measurement that you do, which collapses the wavefunction, gives you this answer.
That's all.
 
  • #5
I guess this is one of the reasons why constructing quantum algorithms is tough. It has to be clever enough to extract the information from the superposition.

Algorithms/operations might also collapse the wave function to the particular state that we desire with higher probability but there might be a chance of getting some other output. Then based on the probabilities we have to repeat the computation till we are satisfied.
 
  • #6
Also, since you collapse the wave function at the end to get the answer, the answer is usually probabilistic like in Grover's algorithm, so you have to run it a couple of times (collapse the wave function of several systems) to get the right answer with high probability.
 
Last edited:

Related to Quantum Computing: Superpositon of states problem

1. What is superposition in quantum computing?

Superposition is a principle in quantum computing where a quantum system can exist in multiple states simultaneously. This is different from classical computing where a system can only exist in one state at a time.

2. How is superposition used in quantum computing?

In quantum computing, superposition is used to represent and manipulate information in a quantum system. By harnessing the power of superposition, quantum computers can perform certain calculations and solve problems much faster than classical computers.

3. What is the superposition of states problem?

The superposition of states problem refers to the challenge of maintaining and controlling the delicate balance of multiple states within a quantum system. This is a key issue in quantum computing, as any external interference or measurement can cause the system to collapse into a single state, losing the benefits of superposition.

4. How do scientists address the superposition of states problem?

Scientists are constantly researching and developing new techniques and technologies to address the superposition of states problem. Some approaches include using error correction codes, developing more stable quantum systems, and implementing advanced control methods to maintain superposition.

5. What are the potential applications of quantum computing's superposition of states?

The use of superposition in quantum computing has the potential to revolutionize various fields such as cryptography, machine learning, and drug discovery. It could also lead to more efficient and accurate simulations of complex systems and processes, impacting industries such as finance and materials science.

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