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So, It might have some mistakes.
Contents：
1.1 Social background
1.2 Whai is Quantum Annealing ?
1.3 Purpose of Quantum Annealing
1.4 How to solve the optimization problem
transistor’s scale Moore's law : The number of transistors doubles every two years. However, It is becoming difficult to make the transistor small. ◦ Problem 2 : The increase in power consumption IT consumes 10% of the total electricity generation in the world. Especially the server uses huge amount of power. 4
small chips. Supercomputer K (京) 's power consumption is about 12 MW. On the other hand, the power consumption of the D-wave quantum annealing machine is about 20 kW. 5 Quantum annealing is an ecological computer.
Generic quantum computation ◦ Quantum Annealing Target issue : Combinatorial optimization problems 6 1.2 What is Quantum Annealing Both systems use Qbit.
bits are always coupled to each other. Because noise acts on the entire system, it is smaller than it works individually. 7 1.2 What is Quantum Annealing Quantum Annealing is easy to operate stably. http://obeidlab.blogspot.com/2011/06/ising-model-quantum-mechanics-and-very.html
problems. 8 1.3 Purpose of Quantum Annealing If Quantum Annealing is expanded, in principle it can be the same as the Quantum Gates and greatly speed up certain problems.
maximizing their single-valued cost function when there are many variables taking discrete values. Example : Ising model = − � < 𝑖𝑖 − � =1 ℎ = 1 , 2 , ⋯ , 9 1.4 How to solve the optimization problem
model belongs to the combinatorial optimization problem. Each Ising spin takes two values of ± 1. So, the total number of combinations is 2. 10 1.4 How to solve the optimization problem https://www.zaikei.co.jp/photo/237081.html
of each spin quantum mechanically uncertain. Initial setting to take two states at the same time in the sense of quantum mechanics. 11 1.4 How to solve the optimization problem
𝑖𝑖 and the local magnetic field ℎ . Each Ising spin selects autonomously confirmed state. 3. Block quantum fluctuation. The ground state of Hamiltonian is chosen. 12 1.4 How to solve the optimization problem