CN111752630A - Method, equipment and medium for determining initialization parameters of quantum measurement and control system - Google Patents
Method, equipment and medium for determining initialization parameters of quantum measurement and control system Download PDFInfo
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Abstract
The embodiment of the application discloses an initialization parameter determination method, equipment and a medium of a quantum measurement and control system, wherein the initialization parameter determination method comprises the following steps: measuring the frequency of a first resonant cavity corresponding to a quantum bit in a quantum chip and first measurement data through a quantum measurement and control system; converting the frequency of the first resonant cavity and the first measurement data into a first vector and a second vector respectively through a vector conversion module; and inputting the first vector and the second vector into a pre-trained initialization parameter setting model to determine the initialization parameters of the quantum measurement and control system. In the embodiment of the description, the initialization parameter of the quantum measurement and control system is determined by the initialization parameter determination system, so that the superconducting quantum computer can normally operate.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, and a medium for determining an initialization parameter of a quantum measurement and control system.
Background
The core of the superconducting quantum computer is a quantum chip and a quantum measurement and control system, and a designed quantum circuit can be converted into a corresponding quantum control pulse signal through the quantum measurement and control system, so that the operation of the quantum computer is controlled. The initialization stage of the qubit is the basis of the application program of the quantum computing executed by the superconducting quantum computer, and the initialization parameters of the quantum measurement and control system need to be determined in the period. Because high-precision physical equipment such as a quantum measurement and control system, a quantum chip and the like is involved, and unpredictable factors such as quantum loss and the like are also considered, the prior art has difficulty in determining initialization parameters of the quantum measurement and control system.
Disclosure of Invention
In view of this, embodiments of the present application provide a method, a device, and a medium for determining an initialization parameter of a quantum measurement and control system, which are used to solve the problem in the prior art that it is difficult to determine the initialization parameter of the quantum measurement and control system.
The embodiment of the application adopts the following technical scheme:
the embodiment of the application provides an initialization parameter determination method of a quantum measurement and control system, which is characterized by comprising the following steps:
measuring the frequency of a first resonant cavity corresponding to a quantum bit in a quantum chip and first measurement data through a quantum measurement and control system;
converting the frequency of the first resonant cavity and the first measurement data into a first vector and a second vector respectively through a vector conversion module;
and inputting the first vector and the second vector into a pre-trained initialization parameter setting model to determine the initialization parameters of the quantum measurement and control system.
Further, the initialization parameter setting model comprises a first model and a second model;
before inputting the first vector and the second vector into a pre-trained initialization parameter setting model and determining initialization parameters of the quantum measurement and control system, the method further comprises:
measuring the frequency of a second resonant cavity corresponding to quantum bits in the quantum chip, second measurement data and initialization parameters in a real physical quantum computer environment through a quantum measurement and control system;
converting the frequency of the second resonant cavity into a third vector corresponding to the first model and a fourth vector corresponding to the second model through the vector conversion module, and converting the second measurement data into a fifth vector through the vector conversion module;
setting the proportion of the initial first model and the initial second model to generate initialization parameters according to the quantum bit number of the quantum chip;
training the initial first model according to the third vector, the fifth vector and the initialization parameters of the real physical quantum computer environment to obtain a qualified first model;
inputting the third vector and the fifth vector into the qualified first model to obtain a first initialization parameter;
and training the initial second model according to the fourth vector, the fifth vector, the first initialization parameter and the initialization parameter in the real physical quantum computer environment to obtain a qualified second model.
Further, the first model comprises a first generator and a first discriminator;
the training the initial first model according to the third vector, the fifth vector and the initialization parameter in the real physical quantum computer environment to obtain a qualified first model specifically includes:
fixing network parameters of the first generator, and training the first discriminator according to the third vector, the fifth vector and initialization parameters in the real physical quantum computer environment;
fixing the trained network parameters of the first discriminator and updating the network parameters of the first generator;
and training the first discriminator and the first generator for multiple times to obtain a qualified first model.
Further, the training the first discriminator according to the fourth vector, the fifth vector and the initialization parameter in the real physical quantum computer environment specifically includes:
inputting the fourth vector and the fifth vector to the first generator, and outputting a first initialization parameter;
and training the first discriminator through the first initialization parameter and the initialization parameter in the real physical quantum computer environment.
Further, the second model comprises a second generator and a second discriminator;
the training the initial second model according to the fourth vector, the fifth vector, the first initialization parameter and the initialization parameter in the real physical quantum computer environment to obtain a qualified second model specifically includes:
fixing network parameters of the second generator, and training the second discriminator according to the fourth vector, the fifth vector, the first initialization parameter and the initialization parameter in the real physical quantum computer environment;
fixing the trained network parameters of the second discriminator and updating the network parameters of the second generator;
and training the second discriminator and the second generator for multiple times to obtain a second model meeting the conditions.
Further, the training the second discriminator according to the fourth vector, the fifth vector, the first initialization parameter, and the initialization parameter in the real physical quantum computer environment specifically includes:
inputting the fourth vector, the fifth vector and the first initialization parameter into the second generator, and outputting a second initialization parameter;
and training the second discriminator through the second initialization parameter and the initialization parameter in the real physical quantum computer environment.
Further, the converting the frequency of the first resonant cavity and the first measurement data into a first vector and a second vector by a vector conversion module, specifically includes:
and converting the frequency of the first resonant cavity into a sixth vector corresponding to the first model and a seventh vector corresponding to the second model through the vector conversion module, and converting the first measurement data into the second vector through the vector conversion module.
Further, the first measurement data comprise waveforms and frequencies measured by the quantum measurement and control system.
The embodiment of the application also provides an initialization parameter determination method and device for a quantum measurement and control system, wherein the device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
measuring the frequency of a first resonant cavity corresponding to a quantum bit in a quantum chip and first measurement data through a quantum measurement and control system;
converting the frequency of the first resonant cavity and the first measurement data into a first vector and a second vector respectively through a vector conversion module;
and inputting the first vector and the second vector into a pre-trained initialization parameter setting model to determine the initialization parameters of the quantum measurement and control system.
An embodiment of the present application further provides a traffic accident handling medium, in which computer-executable instructions are stored, where the computer-executable instructions are configured to:
measuring the frequency of a first resonant cavity corresponding to a quantum bit in a quantum chip and first measurement data through a quantum measurement and control system;
converting the frequency of the first resonant cavity and the first measurement data into a first vector and a second vector respectively through a vector conversion module;
and inputting the first vector and the second vector into a pre-trained initialization parameter setting model to determine the initialization parameters of the quantum measurement and control system.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects: in the embodiment of the description, the initialization parameter of the quantum measurement and control system is determined by the initialization parameter determination system, so that the superconducting quantum computer can normally operate.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flowchart of an initialization parameter determination method for a quantum measurement and control system according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of an initialization parameter determination method for a quantum measurement and control system according to a second embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an initialization parameter determination system provided in the second embodiment of this specification.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of an initialization parameter determination method for a quantum measurement and control system according to an embodiment of the present specification, where the initialization parameter determination system may perform the following steps, which specifically include:
step S101, the initialization parameter determination system measures the frequency of a first resonant cavity corresponding to a quantum bit in a quantum chip and first measurement data through a quantum measurement and control system.
Step S102, the initialization parameter determination system converts the frequency of the first resonant cavity and the first measurement data into a first vector and a second vector respectively through a vector conversion module.
Step S103, the initialization parameter determination system inputs the first vector and the second vector into a pre-trained initialization parameter setting model to determine the initialization parameters of the quantum measurement and control system.
Corresponding to the first embodiment of the present specification, fig. 2 is a schematic flowchart of an initialization parameter determination method for a quantum measurement and control system provided in the second embodiment of the present specification, where the first embodiment of the present specification may include the following steps executed by an initialization parameter determination system, and specifically include:
step S201, the initialization parameter determining system measures, through the quantum measurement and control system, a frequency of a second resonant cavity corresponding to a quantum bit in the quantum chip, second measurement data, and an initialization parameter in a real physical quantum computer environment.
In step S201 of the embodiment of the present specification, the resonant cavity is a metal cavity in which a high-frequency electromagnetic field is continuously oscillated. The types of resonators are many, the most common being rectangular resonators and cylindrical resonators. Within the cavity, the magnitude of the frequency is related to the shape, geometry and mode of resonance of the cavity. The second measurement data comprises the waveform and the frequency measured by the quantum measurement and control system. The initialization parameters in the real physical quantum computer environment are the actual parameters of the quantum computer in the initialization stage.
Step S202, the initialization parameter determination system converts the frequency of the second resonant cavity into a third vector corresponding to the first model and a fourth vector corresponding to the second model through the vector conversion module, and converts the second measurement data into a fifth vector through the vector conversion module.
In step S202 of the embodiment of the present specification, the vector transformation module may be a coding neural network Ev model. For example, the third vector may be a vector of frequency translation for a 100bit cavity and the fourth vector may be a vector of frequency translation for a 400bit cavity.
Step S203, the initialization parameter determination system sets the ratio of the initial first model and the initial second model to the initialization parameter according to the quantum bit number of the quantum chip.
In step S203 of this embodiment, this step is to set a ratio of the first model and the second model to generate the initialization parameter, where the initialization parameter may be the qubit control data, for example, the qubit control data generated by the first model may be one fourth or one eighth of the qubit control data generated by the second model.
Step S204, the initialization parameter determination system trains the initial first model according to the third vector, the fifth vector and the initialization parameters under the real physical quantum computer environment to obtain a qualified first model.
In step S204 of an embodiment of the present specification, the first model includes a first generator and a first discriminator;
the training the initial first model according to the third vector, the fifth vector and the initialization parameter in the real physical quantum computer environment to obtain a qualified first model specifically includes:
initializing a first generator and a first discriminator;
fixing network parameters of the first generator, and training the first discriminator according to the third vector, the fifth vector and initialization parameters in the real physical quantum computer environment;
fixing the trained network parameters of the first discriminator, and updating the network parameters of the first generator, so that the first discriminator cannot distinguish the initialization parameters generated by the first generator from the initialization parameters from a real physical quantum computer, namely the larger the network output value formed by the first model is, the better the network output value is;
and training the first discriminator and the first generator for multiple times to obtain a qualified first model.
Further, training the first discriminator according to the third vector, the fifth vector and the initialization parameter in the real physical quantum computer environment specifically includes:
inputting the third vector and the fifth vector to the first generator, and outputting a first initialization parameter;
and training the first discriminator through the first initialization parameter and the initialization parameter in the real physical quantum computer environment.
It should be noted that the first initialization parameter generated must be close to the actual control data pattern, while the first initialization parameter generated must correspond to the input measurement data.
Step S205, the initialization parameter determination system inputs the third vector and the fifth vector to the qualified first model to obtain a first initialization parameter.
Step S206, the initialization parameter determination system trains the initial second model according to the fourth vector, the fifth vector, the first initialization parameter and the initialization parameter in the real physical quantum computer environment to obtain a qualified second model, wherein the first model and the second model form an initialization parameter setting model.
Further, in step S206 of the embodiment of the present specification, the second model includes a second generator and a second discriminator.
The training the initial second model according to the fourth vector, the fifth vector, the first initialization parameter and the initialization parameter in the real physical quantum computer environment to obtain a qualified second model specifically includes:
fixing network parameters of the second generator, and training the second discriminator according to the fourth vector, the fifth vector, the first initialization parameter and the initialization parameter in the real physical quantum computer environment;
fixing the trained network parameters of the second discriminator, and updating the network parameters of the second generator, so that the second discriminator cannot distinguish the initialization parameters generated by the second generator from the initialization parameters from a real physical quantum computer, namely, the larger the network output value formed by the second model is, the better the network output value is;
and training the second discriminator and the second generator for multiple times to obtain a second model meeting the conditions.
Further, training the second discriminator according to the fourth vector, the fifth vector, the first initialization parameter, and the initialization parameter in the real physical quantum computer environment specifically includes:
inputting the fourth vector, the fifth vector and the first initialization parameter into the second generator, and outputting a second initialization parameter;
and training the second discriminator through the second initialization parameter and the initialization parameter in the real physical quantum computer environment.
It should be noted that the generated second initialization parameter must be close to the actual control data pattern, while the generated second initialization parameter must correspond to the input measurement data.
Step S207, the initialization parameter determination system measures the frequency of the first resonant cavity corresponding to the quantum bit in the quantum chip and the first measurement data through the quantum measurement and control system.
In step S207 in the embodiment of this specification, the first measurement data includes a waveform and a frequency measured by the quantum measurement and control system.
Step S208, the initialization parameter determination system converts the frequency of the first resonant cavity into a sixth vector corresponding to the first model and a seventh vector corresponding to the second model through the vector conversion module, and converts the first measurement data into the second vector through the vector conversion module.
Step S209, the initialization parameter determination system inputs the second vector, the sixth vector and the seventh vector into a pre-trained initialization parameter setting model to determine the initialization parameters of the quantum measurement and control system.
In step S209 in the embodiment of this specification, the initialization parameter of the quantum measurement and control system is the sum of a first initialization parameter generated by the first model and a second initialization parameter generated by the initial second model.
Furthermore, after the initialization parameter of the quantum measurement and control system is determined, the generated initialization parameter can be fed back to the quantum measurement and control system, the quantum measurement and control system verifies the initialization effect according to the initialization parameter, continuously collects data fed back in the running process of a real quantum computer, and continuously optimizes the initialization parameter setting model, so that the initialization efficiency of the quantum computer is improved, and the execution accuracy of the quantum computing application program is improved.
It should be noted that, in the embodiments of the present disclosure, a Generic Adaptive Network (GAN) may be applied, and a main structure of the GAN network includes a generator g (generator) and a discriminator d (discriminator), and the two Networks compete with each other and continuously adjust parameters by using a game-based training process, so as to finally reach a nash equilibrium state. At present, GAN networks are widely applied to the field of generation, and GAN can produce impressive results and control smooth and reasonable semantic changes, and becomes the most important generation model framework for learning any complex data distribution.
It should be noted that Quantum computers (Quantum computers) in the embodiments of the present specification can surpass the best classical supercomputers at present, and almost all the basic problems have been solved theoretically. The superconducting quantum computing system works in an ultralow temperature environment, the core of the superconducting quantum computing system is a superconducting quantum chip, the quantum bit is controlled by applying pulse waveforms on microwave frequency, and the existing semiconductor micromachining process can be utilized in the manufacturing of microwave electronic devices, so that the superconducting quantum computing system becomes one of the current reliable physical systems for realizing quantum computing.
It should be noted that, the core of the superconducting quantum computer in the embodiment of the present specification is a quantum chip and a quantum measurement and control system, and a designed quantum circuit can be converted into a corresponding quantum control pulse signal by the quantum measurement and control system, so as to control the operation of the quantum computer. The initialization of the quantum bit is the basis for the superconducting quantum computer to execute the quantum computing application program, and because high-precision physical equipment such as a measurement and control system, a quantum chip and the like are involved, unpredictable factors such as quantum loss and the like are considered, some technical difficulties exist at present. In this case, in the embodiments of the present specification, the GAN and the deep learning technique are used to accurately set the initialization parameters of the quantum measurement and control system in the initialization stage, so as to implement the perfect docking between the quantum measurement and control system and the quantum chip. The superconducting quantum computer comprises a quantum chip, a quantum measurement and control system, a refrigeration system and a quantum service system, wherein the quantum chip works in an ultralow temperature environment, and interacts with the refrigeration equipment and the quantum chip through control modules such as a temperature control unit and a vacuum control unit of the quantum service system, and the control on a quantum bit is realized by applying a pulse waveform on microwave frequency through a microwave source of the quantum measurement and control system.
It should be noted that the initialization parameter determining system in the embodiment of the present specification is mainly composed of a vector converting module and an initialization parameter setting model, the initialization parameter determining system can generate a vector from frequency and waveform information measured by the quantum monitoring and control system through the vector converting module, the initialization parameter setting model includes two sub-network models, namely a first model and a second model, and can set a proportion occupied by generated qubits in the control of the first model and the initial second model according to an actual qubit number, the first model is composed of a first generator and a first discriminator, the first generator generates control data such as local oscillation frequency and amplitude of a microwave source of the quantum monitoring and control system according to the vector obtained through the vector converting module, the first discriminator can be a binary classifier network for distinguishing microwave source control data from a real quantum computer, or generated by the first generator, and simultaneously satisfies the correspondence between the microwave source control data and the measurement data obtained through the quantum chip operation; the second model is composed of a second generator and a second discriminator, the second generator takes the data generated by the first generator in the first model and the vector obtained by the vector conversion module as input, and control data related to more quantum bits of the quantum measurement and control system are generated; the second discriminator is a binary classifier network for discriminating between microwave source control data from a real quantum computer or generated by the second generator while satisfying the correspondence of the microwave source control data with the measurement data obtained through the quantum chip operation.
Referring to fig. 3, which shows a schematic structural diagram of the initialization parameter determining system, the resonant cavity frequency is converted into a vector fs corresponding to the first model by an encoder, the measurement data is generated into a vector v by an encoder Ev, the vector v and the vector fs are both input into a generator Gs, the generator Gs outputs generation control data (a first part of initialization parameters), the generation control data and the real quantum calculation control data are simultaneously input into a discriminator Ds, and a judgment result is output. In addition, the vector fs and the vector v are also input to the discriminator Ds. The resonant cavity frequency is converted into a vector fl corresponding to the second model through an encoder, the vector fl and the vector v are input into a generator Gl, the generator Gl outputs generated control data (second part initialization parameters), the generated control data and real quantum calculation control data are simultaneously input into a discriminator Dl, and a judgment result is output. In addition, the vector fl and the vector v are also input to the discriminator Dl.
The embodiment of the application also provides an initialization parameter determination method and device for a quantum measurement and control system, wherein the device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
measuring the frequency of a first resonant cavity corresponding to a quantum bit in a quantum chip and first measurement data through a quantum measurement and control system;
converting the frequency of the first resonant cavity and the first measurement data into a first vector and a second vector respectively through a vector conversion module;
and inputting the first vector and the second vector into a pre-trained initialization parameter setting model to determine the initialization parameters of the quantum measurement and control system.
An embodiment of the present application further provides a traffic accident handling medium, in which computer-executable instructions are stored, where the computer-executable instructions are configured to:
measuring the frequency of a first resonant cavity corresponding to a quantum bit in a quantum chip and first measurement data through a quantum measurement and control system;
converting the frequency of the first resonant cavity and the first measurement data into a first vector and a second vector respectively through a vector conversion module;
and inputting the first vector and the second vector into a pre-trained initialization parameter setting model to determine the initialization parameters of the quantum measurement and control system.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardsradware (Hardware Description Language), vhjhd (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. An initialization parameter determination method for a quantum measurement and control system is characterized by comprising the following steps:
measuring the frequency of a first resonant cavity corresponding to a quantum bit in a quantum chip and first measurement data through a quantum measurement and control system;
converting the frequency of the first resonant cavity and the first measurement data into a first vector and a second vector respectively through a vector conversion module;
and inputting the first vector and the second vector into a pre-trained initialization parameter setting model to determine the initialization parameters of the quantum measurement and control system.
2. The method for determining the initialization parameter of the quantum measurement and control system according to claim 1, wherein the initialization parameter setting model comprises a first model and a second model;
before inputting the first vector and the second vector into a pre-trained initialization parameter setting model and determining initialization parameters of the quantum measurement and control system, the method further comprises:
measuring the frequency of a second resonant cavity corresponding to quantum bits in the quantum chip, second measurement data and initialization parameters in a real physical quantum computer environment through a quantum measurement and control system;
converting the frequency of the second resonant cavity into a third vector corresponding to the first model and a fourth vector corresponding to the second model through the vector conversion module, and converting the second measurement data into a fifth vector through the vector conversion module;
setting the proportion of the initial first model and the initial second model to generate initialization parameters according to the quantum bit number of the quantum chip;
training the initial first model according to the third vector, the fifth vector and the initialization parameters of the real physical quantum computer environment to obtain a qualified first model;
inputting the third vector and the fifth vector into the qualified first model to obtain a first initialization parameter;
and training the initial second model according to the fourth vector, the fifth vector, the first initialization parameter and the initialization parameter in the real physical quantum computer environment to obtain a qualified second model.
3. The method for determining the initialization parameter of the quantum measurement and control system according to claim 2, wherein the first model comprises a first generator and a first discriminator;
the training the initial first model according to the third vector, the fifth vector and the initialization parameter in the real physical quantum computer environment to obtain a qualified first model specifically includes:
fixing network parameters of the first generator, and training the first discriminator according to the third vector, the fifth vector and initialization parameters in the real physical quantum computer environment;
fixing the trained network parameters of the first discriminator and updating the network parameters of the first generator;
and training the first discriminator and the first generator for multiple times to obtain a qualified first model.
4. The method for determining initialization parameters of a quantum measurement and control system according to claim 3, wherein the training of the first discriminator according to the fourth vector, the fifth vector and the initialization parameters in the real physical quantum computer environment specifically includes:
inputting the fourth vector and the fifth vector to the first generator, and outputting a first initialization parameter;
and training the first discriminator through the first initialization parameter and the initialization parameter in the real physical quantum computer environment.
5. The method for determining the initialization parameter of the quantum measurement and control system according to claim 2, wherein the second model comprises a second generator and a second discriminator;
the training the initial second model according to the fourth vector, the fifth vector, the first initialization parameter and the initialization parameter in the real physical quantum computer environment to obtain a qualified second model specifically includes:
fixing network parameters of the second generator, and training the second discriminator according to the fourth vector, the fifth vector, the first initialization parameter and the initialization parameter in the real physical quantum computer environment;
fixing the trained network parameters of the second discriminator and updating the network parameters of the second generator;
and training the second discriminator and the second generator for multiple times to obtain a second model meeting the conditions.
6. The method for determining initialization parameters of a quantum measurement and control system according to claim 5, wherein the training of the second discriminator according to the fourth vector, the fifth vector, the first initialization parameters, and the initialization parameters in the real physical quantum computer environment specifically includes:
inputting the fourth vector, the fifth vector and the first initialization parameter into the second generator, and outputting a second initialization parameter;
and training the second discriminator through the second initialization parameter and the initialization parameter in the real physical quantum computer environment.
7. The method for determining initialization parameters of a quantum measurement and control system according to claim 2, wherein the converting the frequency of the first resonant cavity and the first measurement data into a first vector and a second vector by a vector conversion module respectively comprises:
and converting the frequency of the first resonant cavity into a sixth vector corresponding to the first model and a seventh vector corresponding to the second model through the vector conversion module, and converting the first measurement data into the second vector through the vector conversion module.
8. The method for determining the initialization parameter of the quantum measurement and control system according to any one of claims 1 to 7, wherein the first measurement data comprises a waveform and a frequency measured by the quantum measurement and control system.
9. An initialization parameter determination apparatus of a quantum measurement and control system, characterized by comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
measuring the frequency of a first resonant cavity corresponding to a quantum bit in a quantum chip and first measurement data through a quantum measurement and control system;
converting the frequency of the first resonant cavity and the first measurement data into a first vector and a second vector respectively through a vector conversion module;
and inputting the first vector and the second vector into a pre-trained initialization parameter setting model to determine the initialization parameters of the quantum measurement and control system.
10. An initialization parameter determination medium of a quantum measurement and control system, storing computer executable instructions, characterized in that the computer executable instructions are configured to:
measuring the frequency of a first resonant cavity corresponding to a quantum bit in a quantum chip and first measurement data through a quantum measurement and control system;
converting the frequency of the first resonant cavity and the first measurement data into a first vector and a second vector respectively through a vector conversion module;
and inputting the first vector and the second vector into a pre-trained initialization parameter setting model to determine the initialization parameters of the quantum measurement and control system.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114065939A (en) * | 2021-11-22 | 2022-02-18 | 北京百度网讯科技有限公司 | Training method, device and equipment for quantum chip design model and storage medium |
| CN114326494A (en) * | 2021-12-21 | 2022-04-12 | 华东计算技术研究所（中国电子科技集团公司第三十二研究所） | Quantum measurement and control system and method of superconducting quantum computer |
| CN114362766A (en) * | 2021-12-30 | 2022-04-15 | 深圳量旋科技有限公司 | Radio frequency circuit board for superconducting quantum bit measurement and control |
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2020
- 2020-05-27 CN CN202010461057.7A patent/CN111752630A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114065939A (en) * | 2021-11-22 | 2022-02-18 | 北京百度网讯科技有限公司 | Training method, device and equipment for quantum chip design model and storage medium |
| CN114326494A (en) * | 2021-12-21 | 2022-04-12 | 华东计算技术研究所（中国电子科技集团公司第三十二研究所） | Quantum measurement and control system and method of superconducting quantum computer |
| CN114362766A (en) * | 2021-12-30 | 2022-04-15 | 深圳量旋科技有限公司 | Radio frequency circuit board for superconducting quantum bit measurement and control |
| CN114362766B (en) * | 2021-12-30 | 2024-02-23 | 深圳量旋科技有限公司 | Radio frequency circuit board for superconducting qubit measurement and control |
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