Preface

On December 4, 2020, China’s quantum computer prototype, the Nine Chapter Quantum Computer, came out. The Nine Chapter Quantum Computer realized an entangled state of 76 qubits. The calculation speed is 100 trillion times that of the fastest supercomputer in the world. ! In this issue, I will follow the craze of Chapter Nine Quantum Computers and talk about why the Nine Chapter Quantum Computer is so fast, and whether we can use the quantum entangled state of the Nine Chapter Computer in the intelligent optimization algorithm!

1. Why is the "Nine Chapter" quantum computer so powerful?

Why is the Nine Chapter Computer so powerful? First of all, everyone should clearly know that the calculation of the computer is to use 0,1 to express the target, and it is not 0 or 1. But this is not the case with quantum computers. The calculation process is: 0 and 1 again, that is, 0 and 1 can exist at the same time. Quantum computers perform calculations through quantum entanglement and use the principle of superposition state of quantum mechanics.
For example: a 2 quantum entangled qubit can be expressed as the superposition of four states (0,0), (0,1), (1,0), (1,1), and a 3 quantum entangled qubit Qubits can be expressed as (0,0,0), (0,0,1), (0,1,0), (0,1,1), (1,0,0), (1,0, 1), (1,1,0), (1,1,1) these 8 quantum superposition states; and the number of quantum entanglement of the nine chapter computer prototype reaches 76 bits, which is $2^{76}$ quantum superposition states. This means that traditional supercomputers need $2^{76}$ A target that can be represented by $^{6}$ electronic bits of 0 and 1, and only one of the nine chapters quantum computer is needed!

2. Common points between "Nine Chapters" and intelligent optimization algorithms

1. Representation

Like Chapter Nine quantum computers, there are individual "representations" in the swarm intelligence optimization algorithm-individual information. For example, in the particle swarm algorithm, a single particle at The representation in $t$ iterations is $x_j^t$ , If there are 50 population sizes, it can only represent 50 location information. If so, the position information can be re-represented by the quantum superposition state in quantum mechanics, which means that 50 population individuals can represent $2^50$ position information! ! However, swarm intelligence optimization algorithms often do not use such a huge amount of information. Therefore, we can appropriately reduce the number of individuals in the population to 6. While reducing the complexity of the algorithm, not only does it not reduce the performance of the algorithm, but because of the quantum superposition representation $2^6=64>50$ , there will be some improvement on the performance of the algorithm. This improvement is not only reflected in the greatly reduced search time of the algorithm, but also in the accuracy of the algorithm!

2. Specific operation

Regarding the application of quantum in the swarm intelligence optimization algorithm, it has been demonstrated in the previous article, and students who are interested can browse it by themselves.
Intelligent optimization algorithm (3): quantum-based pigeon flock optimization algorithm

to sum up

The craze of quantum computers in the nine chapters will inevitably drive the development of a series of related scientific research, and the submissions in related directions will also make the reviewers shine. I hope you all hurry up!

"Nine Chapters" Quantum Computers and Intelligent Optimization Algorithms

Chapter 9 Quantum Computer-Intelligent Optimization Algorithm