Why use adiabatic evolution instead of cooling?

In summary, the groundstate of a particular Hamiltonian is more likely to encode the desired solution to a satisfiability problem if the evolution takes place slowly.
  • #1
mupsi
32
1
Hi everyone,

For adibatic quantum computation one prepares the groundstate of a particular Hamiltonian and than adiabatically evolves the system to a problem Hamiltonian Hp which groundstate encodes the desired solution to a satisfiability problem. If the evolution takes place slowly the system will be in a groundstate Hp with a high probability. My question: why can't I start with Hp to begin with, cool down the system such that the wacefunction relaxes into the goundstate?
 
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  • #2
How do you cool the system if your Hamiltonian requires that you don't have decoherent interactions with the environment?
 
  • #3
mfb said:
How do you cool the system if your Hamiltonian requires that you don't have decoherent interactions with the environment?
Why? I could couple the Hamiltonian to a bath for a short time, cool it down and separate the system from the environment. I don't see any problem with that. Decoherence just causes the system to have a Boltzmann-Distribution
 
  • #4
I think as soon as you do that coupling, your Hamiltonian does something, but not solving the problem. Which means you are doing what you described in post 1: cool to the ground state of one Hamiltonian, then go to a different one.
 
  • #5
mfb said:
I think as soon as you do that coupling, your Hamiltonian does something, but not solving the problem. Which means you are doing what you described in post 1: cool to the ground state of one Hamiltonian, then go to a different one.

I agree, but when you separate the system from its environment you get the original hamiltonian again and the wave function should be in the groundstate.
 
  • #6
If you do it adiabatically.
Now we have everything you asked about.
 
  • #7
mfb said:
If you do it adiabatically.
Now we have everything you asked about.
but that approach is fundamentally different. I think that the argument is time complexity. In general it takes more time for the system to relax into the groundstate (as a function of the number of qubits) than if you perform an adiabatic evolution. The computation time is related to the number of qubits via the inverse of the spectral gap between groundstate and the first excited state (which itself is a function of # qubits) and the spectral gap might (in some cases) only increases polynomially depeding on the computational problem whereas the relaxation time increases exponentially (this is speculative). A further assumption is that the groundstate of the initial hamiltonian is easy to prepare. I could be wrong though.
 

1. What is adiabatic evolution?

Adiabatic evolution is a process used in quantum computing to manipulate the state of a quantum system without any energy exchange with the environment.

2. How does adiabatic evolution differ from cooling?

Adiabatic evolution involves gradually changing the parameters of a quantum system, while maintaining its energy level, in order to reach a desired state. Cooling, on the other hand, involves reducing the temperature of a system through energy exchange with the environment.

3. Why is adiabatic evolution preferred over cooling in quantum computing?

Adiabatic evolution is preferred over cooling in quantum computing because it is a more precise and efficient method for manipulating quantum states. It also minimizes the risk of errors due to energy exchange with the environment.

4. What are the limitations of using adiabatic evolution?

The main limitation of adiabatic evolution is the time it takes to achieve the desired state. This can be a significant factor in large and complex quantum systems. Additionally, adiabatic evolution relies on the system remaining isolated from the environment, which can be challenging to maintain in practice.

5. In what applications is adiabatic evolution most commonly used?

Adiabatic evolution is commonly used in quantum computing for tasks such as quantum annealing, finding the ground state of a physical system, and solving optimization problems. It is also used in the creation of quantum gates for quantum information processing.

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