CN-122021534-A - Quantum chip layout design method, device, equipment and storage medium

CN122021534ACN 122021534 ACN122021534 ACN 122021534ACN-122021534-A

Abstract

The invention discloses a quantum chip layout design method, device, equipment and storage medium, which relate to the technical field of chip design and comprise the steps of carrying out data cleaning and normalization processing on collected layout data of a quantum chip to obtain processed data, constructing an initial model frame based on a deep learning algorithm, defining model parameters corresponding to the initial model frame based on device information of the quantum chip to obtain an initial layout optimization model, training the initial layout optimization model by utilizing the processed data and an experience playback mechanism, obtaining a target layout optimization model after the initial layout optimization model reaches a preset training stop condition, inputting acquired quantum chip design requirements into the target layout optimization model, and completing the layout of quantum components based on the quantum chip design requirements on a preset blank layout of a layout design tool to obtain a corresponding target quantum chip layout. Thus, the design efficiency of the quantum chip can be improved.

Inventors

YU HONGZHEN
LI YANZHEN
XUE CHANGQING

Assignees

山东云海国创云计算装备产业创新中心有限公司

Dates

Publication Date: 20260512
Application Date: 20260228

Claims (10)

1. The quantum chip layout design method is characterized by comprising the following steps of: Performing data cleaning and normalization processing on the collected layout data of the quantum chip to obtain processed data; constructing an initial model frame based on a deep learning algorithm, and defining model parameters corresponding to the initial model frame based on device information of the quantum chip to obtain an initial layout optimization model; Training the initial layout optimization model by using the processed data and an experience playback mechanism, and obtaining a target layout optimization model after the initial layout optimization model reaches a preset training stop condition, wherein the preset training stop condition is that the initial layout optimization model converges or the model performance of the initial layout optimization model meets a preset model performance requirement; Inputting the obtained quantum chip design requirements to the target layout optimization model, and completing the layout of quantum components on the basis of the quantum chip design requirements on the preset blank layout of a layout design tool by utilizing the target layout optimization model so as to obtain a target quantum chip layout corresponding to the quantum chip design requirements.
2. The quantum chip layout design method according to claim 1, wherein the performing data cleaning and normalization processing on the collected layout data of the quantum chip to obtain processed data includes: collecting layout data of quantum chips of different types, wherein the layout data comprises types, positions, connection relations and chip performance parameters of quantum components corresponding to the quantum chips; Performing data cleaning on the layout data to remove noise data and data with marking errors in the layout data; and unifying the physical quantity in the layout data after the data cleaning to a preset numerical range to obtain the processed data.
3. The quantum chip layout design method according to claim 1, wherein the constructing an initial model frame based on a deep learning algorithm, and defining model parameters corresponding to the initial model frame based on device information of the quantum chip, to obtain an initial layout optimization model, comprises: constructing an initial model frame based on a deep learning algorithm, wherein the network structure of the initial model frame is a multi-layer perceptron; The model input of the initial model frame is the current state of layout data of the quantum chip, and the output is the layout action of the quantum components; Encoding preset layout information into a target multidimensional vector, and defining a state space based on the target multidimensional vector; The preset layout information comprises quantum component position information and chip type of the chip layout completed on the quantum chip, and quantum component information which is not completed on the quantum chip; defining an action space based on a preset placement position and a preset placement direction pair corresponding to the quantum components; Defining a target rewarding function based on the coupling relation among the quantum components, the position distance and the chip area utilization rate of the quantum chip; an initial layout optimization model is determined based on the initial model framework, the state space, the action space, and the target rewards function.
4. A quantum chip layout design method according to claim 3, wherein the defining the target bonus function based on the coupling relation between the quantum components, the position distance and the chip area utilization of the quantum chip comprises: determining a current coupling coefficient between the quantum components, and defining a first rewarding function based on a difference value between the current coupling coefficient and a target coupling coefficient; Determining the position distance between the quantum components, and defining a second prize function based on the signal transmission delay result determined by the position distance; Determining the chip area utilization rate of the quantum chip based on the first area of the quantum components which complete the chip layout on the quantum chip and the total area of the quantum chip, and defining a third prize function by utilizing the chip area utilization rate; A target bonus function is defined based on the first bonus function, the second bonus function, and the third bonus function.
5. The quantum chip layout design method of claim 4, wherein the defining a target prize function based on the first prize function, the second prize function, and the third prize function comprises: Acquiring design requirements and chip performance indexes aiming at the quantum chip, and respectively determining weight values corresponding to the first rewarding function, the second rewarding function and the third rewarding function; and defining a target bonus function based on the weight value, the first bonus function, the second bonus function, and the third bonus function.
6. The quantum chip layout design method according to claim 1, wherein the training the initial layout optimization model by using the processed data and the experience playback mechanism, and obtaining the target layout optimization model after the initial layout optimization model reaches a preset training stop condition, comprises: Inputting the processed data to the initial layout optimization model to start training, and storing model training state data of single-round training of the initial layout optimization model to a preset experience playback buffer zone by combining an experience playback mechanism; determining the model training state data and the processed data stored in the preset experience playback buffer area as new processed data, and jumping to the step of inputting the processed data into the initial layout optimization model to start training until the initial layout optimization model reaches a preset training stop condition, and obtaining a target layout optimization model.
7. The quantum chip layout design method according to any one of claims 1 to 6, wherein inputting the obtained quantum chip design requirement to the target layout optimization model, and completing layout of quantum components based on the quantum chip design requirement on a preset blank layout of a layout design tool by using the target layout optimization model comprises: the method comprises the steps of obtaining a quantum chip design requirement, wherein the quantum chip design requirement comprises a design requirement, type information, quantity information and performance constraint conditions of quantum components; Inputting the quantum chip design requirement into the target layout optimization model, and executing a quantum component layout action corresponding to the quantum chip design requirement on a preset blank layout of a layout design tool by utilizing the target layout optimization model; And displaying a layout result corresponding to the deployment action of the quantum components on a preset visual interface in real time, and generating a quantum component layout scheme corresponding to the design requirement of the quantum chip after completing the layout of the quantum components.
8. The quantum chip layout design device is characterized by comprising: the data collection module is used for carrying out data cleaning and normalization processing on the collected layout data of the quantum chip so as to obtain processed data; The model definition module is used for constructing an initial model frame based on a deep learning algorithm, and defining model parameters corresponding to the initial model frame based on the device information of the quantum chip so as to obtain an initial layout optimization model; the model training module is used for training the initial layout optimization model by using the processed data and an experience playback mechanism, and obtaining a target layout optimization model after the initial layout optimization model reaches a preset training stop condition, wherein the preset training stop condition is that the initial layout optimization model converges or the model performance of the initial layout optimization model meets a preset model performance requirement; The chip design module is used for inputting the obtained quantum chip design requirements to the target layout optimization model, and completing the layout of the quantum components on the basis of the quantum chip design requirements on the preset blank layout of the layout design tool by utilizing the target layout optimization model so as to obtain a target quantum chip layout corresponding to the quantum chip design requirements.
9. An electronic device, comprising: A memory for storing a computer program; a processor for executing the computer program to implement the steps of the quantum chip layout design method of any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the quantum chip layout design method according to any one of claims 1 to 7.

Description

Quantum chip layout design method, device, equipment and storage medium Technical Field The present invention relates to the field of chip design technologies, and in particular, to a method, an apparatus, a device, and a storage medium for quantum chip layout design. Background EDA (Electronic Design Automation ) technology is a new technology special for an electronic system, which takes a computer as a core tool and fuses multi-disciplinary knowledge such as a database, computational mathematics, graph theory, graphics, topological logic, optimization theory and the like. The simulation, debugging and error correction of each level of electronic engineering can be effectively supported, the design efficiency is remarkably improved, and the workload is reduced. As a software tool indispensable for chip and circuit board design, EDA covers the whole flow from design, wiring to verification and test, and no other technology is replaced at present. Along with the development of quantum computing, the design complexity of quantum chips is increased, wherein the layout of quantum components directly affects key indexes such as chip performance, reliability, quantum bit coherence time and the like. The traditional layout method relies on manual experience, is time-consuming and labor-consuming, is difficult to realize optimal solution, and is low in efficiency especially when complex problems such as quantum bit coupling and interaction between different components are handled. Although layout schemes based in part on rules and algorithms exist, they have insufficient adaptive capabilities. In order to solve the problems of ever-increasing scale and ever-complex functions of quantum chips, a method for improving the layout and design efficiency of quantum chips is needed. Disclosure of Invention The embodiment of the invention aims to provide a method, a device, equipment and a storage medium for designing a quantum chip layout, which can improve the design efficiency of the quantum chip. The specific scheme is as follows: In a first aspect, the application discloses a quantum chip layout design method, which comprises the following steps: Performing data cleaning and normalization processing on the collected layout data of the quantum chip to obtain processed data; constructing an initial model frame based on a deep learning algorithm, and defining model parameters corresponding to the initial model frame based on device information of the quantum chip to obtain an initial layout optimization model; Training the initial layout optimization model by using the processed data and an experience playback mechanism, and obtaining a target layout optimization model after the initial layout optimization model reaches a preset training stop condition, wherein the preset training stop condition is that the initial layout optimization model converges or the model performance of the initial layout optimization model meets the preset model performance requirement; Inputting the obtained quantum chip design requirements to a target layout optimization model, and completing the layout of the quantum components on the basis of the quantum chip design requirements on a preset blank layout of a layout design tool by utilizing the target layout optimization model so as to obtain a target quantum chip layout corresponding to the quantum chip design requirements. Optionally, performing data cleaning and normalization processing on the collected layout data of the quantum chip to obtain processed data, including: collecting layout data of different types of quantum chips, wherein the layout data comprises types, positions, connection relations and chip performance parameters of quantum components corresponding to the quantum chips; performing data cleaning on the layout data to remove noise data and data with marking errors in the layout data; and unifying the physical quantity in the layout data after data cleaning to a preset numerical range to obtain the processed data. Optionally, constructing an initial model frame based on a deep learning algorithm, and defining model parameters corresponding to the initial model frame based on device information of the quantum chip to obtain an initial layout optimization model, including: constructing an initial model frame based on a deep learning algorithm, wherein the network structure of the initial model frame is a multi-layer perceptron; The model input of the initial model frame is the current state of layout data of the quantum chip, and the output is the layout action of the quantum components; encoding the preset layout information into a target multidimensional vector, and defining a state space based on the target multidimensional vector; The preset layout information comprises quantum component position information and chip type of the chip layout completed on the quantum chip and quantum component information which is not completed on the quantum chip; defining an action space