Method and device for predicting stability of new crown vaccine through quantum gating recurrent neural network

Abstract

The invention provides a method for predicting stability of a new crown vaccine based on a quantum gating recurrent neural network, and belongs to the technical field of quantum computing. According to the method, a quantum gating circulating unit is used for resetting and updating a basic group in an RNA sequence and a corresponding hidden state, a new hidden state is generated, and the reaction rate and degradation rate of the RNA sequence at different positions are output, so that the stability of the new crown vaccine is predicted, and therefore, the stability of the new crown vaccine is predicted. The method meets the requirement for establishing the long-term and short-term dependency relationship of the sequence by a gated recurrent neural network algorithm, and is low in computing resource consumption, so that the method is widely applied to cooperative work of a quantum chip and an electronic chip.

Description

technical field [0001] The invention relates to the field of quantum computing, in particular to a method and device for predicting the stability of a new crown vaccine based on a quantum-gated recurrent neural network. Background technique [0002] Although mRNA vaccines are safer than other vaccines, they are also easy to make. However, they can be very fragile. Currently, the mRNA COVID-19 vaccine faces severe limitations of having to be stored in cold storage, which poses a major obstacle to mass vaccination. [0003] The classic gated recurrent neural network algorithm is a research hotspot in the field of artificial intelligence and is used in a variety of application scenarios, such as biomedicine, materials, neuroscience, etc. However, the operation of these models requires a lot of computing resources. In the past, the computing resources for algorithm operation were mainly provided by chips manufactured by electronic integrated circuits. However, as the electroni...

AI Technical Summary

Problems solved by technology

However, they can be very fragile

Currently, mRNA COVID-19 vaccines face severe limitations of having to be stored in cold storage, which poses a major obstacle to mass vaccination

[0003] The classic gated recurrent neural network algorithm is a research hotspot in the field of artificial intelligence and is used in various application scenarios, such as biomedicine, materials, neuroscience, etc. However, the operation of these models requires a lot of computing resources

In the past, the computing resources for algorithm operation were mainly provided by chips manufactured by electronic integrated circuits. However, as the electronic tunneling effect restricts the process approaching the nanometer limit, it is difficult to continue to increase the computing power.

Quantum computing chips are complementary to the way electronic chips compute, but classical gated recurrent neural network algorithms cannot be processed on quantum chips in the same way as they are on electronic chips

[0004] Although the problem of predicting the stability of the new crown vaccine has achieved good performance based on the gated recurrent neural network model, the gated recurrent neural network algorithm needs to meet the needs of establishing long-term and short-term dependencies of sequences, and consumes a lot of computing resources

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