Data identification method and device based on quantum computer and readable storage medium

Abstract

The invention discloses a data identification method and device based on a quantum computer and a computer readable storage medium. The method comprises: performing quantization on to-be-identified data to generate input quantum bits suitable for a quantum computer; performing quantum calculation on the input quantum bit by using a pre-trained quantum neural network to obtain a multi-dimensional output quantum bit; determining the total number of target quantum bits in the multi-dimensional output quantum bits according to the classification category numerical value of the to-be-identified data, and performing quantum state coding on each label according to the total number to obtain a quantum state coding label set; measuring each target quantum bit to obtain corresponding state information; and matching a corresponding target label in the quantum state coding label set based on the state information to serve as an identification result of the to-be-identified data, thereby realizinga full-linearization calculation task of data classification and identification in the quantum computer, and realizing a full-quantization calculation process without assistance of a traditional computer.

Description

technical field [0001] The present application relates to the technical field of quantum computing, in particular to a quantum computer-based data identification method, device, and computer-readable storage medium. Background technique [0002] With the deepening of quantum computing research, its integration with neural networks is gradually becoming a new research direction. The combination of neural network and quantum theory to construct a new neural computing model, that is, quantum neural computing, is an important frontier topic in artificial neural network research, and it is also a new hotspot in the development of artificial intelligence in the future. [0003] A quantum neural network includes qubits and quantum gates. The basic unit used in a quantum neural network is a qubit, which corresponds to the concept of a bit in a computer. Qubits have two possible states |0>, |1>, corresponding to the switch state of the bit, but the difference from the classica...

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Problems solved by technology

[0007] Although the current quantum neural network can directly use the quantum theory to design the neural network topology, the quantum neural network structure for performing classification tasks is not all realized on quantum computers, and it needs to rely on traditional computers for classification assistance. The reasoning process for performing classification tasks is in Realized in traditional computers, it is impossible to realize the fully quantized calculation process, and it is impossible to fully utilize the advantages of quantum computers

In addition, the classification functions used in the traditional calculation process are mostly nonlinear, such as the softmax function mentioned above, and there will be some unpredictable problems in the calculation process. The nonlinear calculation process can easily lead to poor generalization performance of quantum neural networks.

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