摘要
A central issue in practical quantum computing is how to achieve a desired state or operator efficiently and reliably. An analogy in the classical world can be offered to describe this problem, namely, mountain climbing. Assume a climber would like to reach the top of a mountain from the base as quickly as possible. This is a typical gradient- based optimization task, and the process of finding path could be performed as follows. The climber measures the distance between the mountaintop and his/her current position. If there is a distance, the climber then figures out the gradient direction and keeps climbing along that direction until he/she arrives at the top satisfactorily.
A central issue in practical quantum computing is how to achieve a desired state or operator efficiently and reliably. An analogy in the classical world can be offered to describe this problem, namely, mountain climbing. Assume a climber would like to reach the top of a mountain from the base as quickly as possible. This is a typical gradient- based optimization task, and the process of finding path could be performed as follows. The climber measures the distance between the mountaintop and his/her current position. If there is a distance, the climber then figures out the gradient direction and keeps climbing along that direction until he/she arrives at the top satisfactorily.
