CN113159324B - Quantum device calibration method, device and medium - Google Patents
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Abstract
The application discloses a quantum equipment calibration method, a device, equipment and a medium, comprising the following steps: obtaining an actual output result of the quantum equipment; inputting the actual output result into a quantum neural network for training to obtain a corresponding network output result; updating the quantum neural network by utilizing the expected output result of the quantum equipment and the network output result until the quantum neural network converges, so as to obtain a trained quantum network line; and outputting a calculation result of the quantum equipment by using the trained quantum network circuit. That is, the application does not directly debug the hardware of the device, but trains by using a machine learning mode to obtain a trained quantum network line, and outputs the calculation result of the quantum device by using the trained quantum network line, thus, the quantum device can be effectively calibrated, the complexity of the quantum device calibration is reduced, and the debugging is convenient.
Description
Technical Field
The present application relates to the field of quantum information processing technologies, and in particular, to a method, an apparatus, a device, and a medium for calibrating quantum devices.
Background
Quantum computing is a novel computing which utilizes quantum mechanical properties to perform operation, and has far beyond the computing capability of classical computing in certain fields. In order to achieve quantum computing, several challenges in quantum architecture design and implementation must be overcome to control, program, and maintain quantum hardware devices. These challenges include improving qubit fidelity, ensuring quantum operation accuracy, extending coherence time, and the like.
At present, in order to perform quantum computation, the quantum equipment built each time needs to be adjusted and debugged manually and repeatedly so as to ensure reasonable precision, fidelity, coherence time and the like. Some existing calibration methods are mainly aimed at the control optimization of quantum hardware, the optimization method is determined by a physical model, the method is complex and complicated, and the bottom technical requirements on personnel are high.
Disclosure of Invention
Accordingly, the present application aims to provide a method, apparatus, device and medium for calibrating a quantum device, which can effectively calibrate the quantum device, reduce the complexity of the quantum device calibration, and facilitate debugging. The specific scheme is as follows:
in a first aspect, the application discloses a quantum device calibration method, comprising:
obtaining an actual output result of the quantum equipment;
inputting the actual output result into a quantum neural network for training to obtain a corresponding network output result;
updating the quantum neural network by utilizing the expected output result of the quantum equipment and the network output result until the quantum neural network converges, so as to obtain a trained quantum network line;
and outputting a calculation result of the quantum equipment by using the trained quantum network circuit.
Optionally, the method further comprises:
acquiring an initial state generated by the quantum equipment;
and generating a standard quantum state by using the initial state to obtain the expected output result.
Optionally, the updating the quantum neural network with the expected output result of the quantum device and the network output result includes:
performing exchange test on the expected output result and the network output result to obtain deviation between the expected output result and the network output result, and taking the deviation as the loss of the quantum neural network;
updating the quantum neural network using the loss back propagation.
Optionally, the updating the quantum neural network with the loss back propagation includes:
recording the loss into a preset auxiliary bit;
measuring the preset auxiliary bit to obtain a loss measurement value;
and updating the quantum neural network by using the back propagation of the loss measurement value.
Optionally, the measuring the preset auxiliary bit to obtain a loss measurement value includes:
and carrying out multiple measurements on the preset auxiliary bits, and carrying out average value calculation on the multiple measurement results to obtain the loss measurement value.
Optionally, the measuring the preset auxiliary bit to obtain the loss measurement value includes:
performing phase transformation on the preset auxiliary bit;
carrying out amplitude amplification on the preset auxiliary bits after phase conversion for a plurality of times by using a Grover algorithm;
determining a loss estimated value by using the amplitude amplification times and the preset auxiliary bit after the amplitude amplification, and recording the loss estimated value in the preset auxiliary bit;
and measuring the preset auxiliary bit to obtain the loss measurement value.
In a second aspect, the application discloses a quantum device calibration apparatus comprising:
the actual output result acquisition module is used for acquiring the actual output result of the quantum equipment;
the quantum network line training module is used for inputting the actual output result into a quantum neural network for training to obtain a corresponding network output result; updating the quantum neural network by utilizing the expected output result of the quantum equipment and the network output result until the quantum neural network converges, so as to obtain a trained quantum network line;
and the quantum calculation result output module is used for outputting the calculation result of the quantum equipment by utilizing the trained quantum network circuit.
Optionally, the quantum network line training module specifically includes:
the loss calculation unit is used for carrying out exchange test on the expected output result and the network output result to obtain deviation between the expected output result and the network output result, and taking the deviation as the loss of the quantum neural network;
and the network updating unit is used for updating the quantum neural network by using the loss back propagation.
In a third aspect, the present application discloses an electronic device, comprising:
a memory for storing a computer program;
and a processor for executing the computer program to implement the quantum device calibration method.
In a fourth aspect, the present application discloses a computer readable storage medium storing a computer program which, when executed by a processor, implements the quantum device calibration method described above.
Therefore, the application obtains the actual output result of the quantum equipment, and then inputs the actual output result into the quantum neural network for training to obtain the corresponding network output result; and updating the quantum neural network by utilizing the expected output result of the quantum equipment and the network output result until the quantum neural network converges to obtain a trained quantum network line, and then outputting the calculation result of the quantum equipment by utilizing the trained quantum network line. That is, the application does not directly debug the hardware of the device, but trains by using a machine learning mode to obtain a trained quantum network line, and outputs the calculation result of the quantum device by using the trained quantum network line, thus, the quantum device can be effectively calibrated, the complexity of the quantum device calibration is reduced, and the debugging is convenient.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for calibrating a quantum device according to the present disclosure;
FIG. 2 is a block diagram of a quantum neuron structure as disclosed in the prior art;
FIG. 3 is a schematic diagram of a particular quantum device calibration of the present disclosure;
FIG. 4 is a flow chart of a particular quantum device calibration method of the present disclosure;
FIG. 5 is a schematic diagram of a quantum device calibration apparatus according to the present disclosure;
fig. 6 is a block diagram of an electronic device according to the present disclosure.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
At present, in order to perform quantum computation, the quantum equipment built each time needs to be adjusted and debugged manually and repeatedly so as to ensure reasonable precision, fidelity, coherence time and the like. Some existing calibration methods are mainly aimed at the control optimization of quantum hardware, the optimization method is determined by a physical model, the method is complex and complicated, and the bottom technical requirements on personnel are high. Therefore, the application provides a quantum equipment calibration scheme which can effectively calibrate the quantum equipment, reduce the complexity of the quantum equipment calibration and is convenient for debugging.
Referring to fig. 1, an embodiment of the application discloses a quantum device calibration method, which includes:
step S11: and obtaining an actual output result of the quantum equipment.
In a specific embodiment, a large number of actual output results can be obtained as training data to perform training of the quantum neural network.
Step S12: and inputting the actual output result into a quantum neural network for training to obtain a corresponding network output result.
Step S13: and updating the quantum neural network by utilizing the expected output result of the quantum equipment and the network output result until the quantum neural network converges, so as to obtain a trained quantum network circuit.
In a specific embodiment, an initial state generated by the quantum device is obtained; and generating a standard quantum state by using the initial state to obtain an expected output result of the quantum device.
The initial state comprises a quantum state bit length, a quantum state characteristic and a quantum state waiting time length.
In the training process, carrying out exchange test on the expected output result and the network output result to obtain deviation between the expected output result and the network output result, and taking the deviation as the loss of the quantum neural network; updating the quantum neural network using the loss back propagation.
Specifically, the loss may be recorded in a preset auxiliary bit; measuring the preset auxiliary bit to obtain a loss measurement value; and updating the quantum neural network by using the back propagation of the loss measurement value.
In addition, in this embodiment, the preset auxiliary bit is measured for multiple times, and an average value of the results of the multiple measurements is calculated to obtain the loss measurement value.
That is, the resulting loss measurement is the average of the multiple measurements.
Step S14: and outputting a calculation result of the quantum equipment by using the trained quantum network circuit.
For example, referring to fig. 2, fig. 2 is a block diagram of a quantum neuron as disclosed in the prior art.
Wherein, |x i The sum |0 > is input qubit, i=1, 2 … n, |x i The method is characterized in that the method comprises the steps of (1) data bits, |0 > is an auxiliary bit, input quantum bits are input to a quantum neural network, R (theta) is quantum rotation gate operation, and rotation operation can be carried out on each quantum bit. The rotation angle theta is a parameter to be optimized in the neural network, and the optimization of theta is realized through back propagation.
Referring to fig. 3, fig. 3 is a schematic diagram of quantum device calibration according to an embodiment of the present application. It is noted that assuming that the quantum device produces an initial state of |00..0 >, it is expected that the output signal S0 is obtained after a number of operations:
wherein N is the number of bits, and due to the inherent problems of the quantum device and the problem of the quantum hardware stack, or the decoherence after a period of storage, the output signal may be quite inaccurate, and the actual signal Se is obtained:
whether it is the quantum state |x of the output signal>Or the amplitude c may be subject to error due to hardware equipment. The engineering personnel directly debug the quantum equipment, and the hardware parameter is time-consuming and labor-consuming to modify again, and is very difficult. The embodiment can be connected with original quantum hardware equipment, adjusts the output signal Se to obtain Sc, calculates the difference between Sc and S0, and counter-propagates the updated line parameters to enable the updated line parameters to be close to the target signal S0. And finally obtaining a convergence result through multiple iterations. First, standard quantum state |S0 is generated by the existing standard technology>I.e., the expected output, in particular embodiments, a series of initial states, including quantum state bit lengths, may be generatedA quantum state characteristic, a quantum state waiting period, etc. The quantum state |Se generated by the equipment to be calibrated>The actual output result is connected to an initialization QNN (Quantum Neural Network ) to obtain |Sc>For |Sc>And |S0>SWAP operation is performed and SWAP test is used, by a loss function l=tr (σ S0 σ Sc ) Calculating to obtain I Sc>And |S0>The deviation epsilon, namely the loss epsilon, is recorded to a preset auxiliary bit |0>Wherein the auxiliary bit |0 is preset>The auxiliary bit of the input quantum bit is different from the auxiliary bit of the quantum neural network. And carrying out Grover amplification on the preset auxiliary bit phase, measuring (M) for a plurality of times, and averaging to obtain a loss measurement value of Sc compared with S0. The turnstile angle parameter is updated by passing the loss measurement to the QNN by back propagation. The above steps are iterated until the loss epsilon reaches a convergence criterion (epsilon is less than a specific value). Wherein H is Hadamard operation. Thus, a large number of output signals are input into the quantum neural network, and a huge and stable quantum network line is obtained through multiple times of optimization. The network can realize automatic adjustment of initial input parameters to a certain extent, namely, the calibration of the quantum equipment is realized, the calibration of the quantum equipment is completed, and the deviation quantum state prepared by the quantum equipment can be obtained accurately enough by directly passing through the quantum network circuit.
Therefore, the embodiment of the application obtains the actual output result of the quantum equipment, and then inputs the actual output result into the quantum neural network for training to obtain the corresponding network output result; and updating the quantum neural network by utilizing the expected output result of the quantum equipment and the network output result until the quantum neural network converges to obtain a trained quantum network line, and then outputting the calculation result of the quantum equipment by utilizing the trained quantum network line. That is, the application does not directly debug the hardware of the device, but trains by using a machine learning mode to obtain a trained quantum network line, and outputs the calculation result of the quantum device by using the trained quantum network line, thus, the quantum device can be effectively calibrated, the complexity of the quantum device calibration is reduced, and the debugging is convenient.
It should be noted that the post-training quantum network circuit can be used for output calibration of the subsequent quantum device, whether it is internal or external.
Referring to fig. 4, an embodiment of the application discloses a specific quantum device calibration method flowchart.
Step S21: and obtaining an actual output result of the quantum equipment.
Step S22: and inputting the actual output result into a quantum neural network for training to obtain a corresponding network output result.
Step S23: and carrying out exchange test on the expected output result of the quantum equipment and the network output result to obtain deviation between the expected output result and the network output result, and taking the deviation as the loss of the quantum neural network.
Step S24: and recording the loss into a preset auxiliary bit.
Step S25: and carrying out phase transformation on the preset auxiliary bit, carrying out amplitude amplification on the preset auxiliary bit after phase transformation for a plurality of times by utilizing a Grover algorithm, determining a loss estimated value by utilizing the amplitude amplification times and the preset auxiliary bit after amplitude amplification, and recording the loss estimated value in the preset auxiliary bit.
Step S26: and measuring the preset auxiliary bit to obtain the loss measurement value.
In a specific embodiment, multiple measurements may be performed on the preset auxiliary bit, and an average value of the multiple measurement results may be calculated to obtain the loss measurement value.
It is to be noted that, through the processing of step S25, the number of measurements can be reduced with the accuracy of the loss measurement value ensured, relative to directly measuring the loss recorded in the preset auxiliary bit.
Step S27: and updating the quantum neural network by using the back propagation of the loss measurement value until the quantum neural network converges, so as to obtain a trained quantum network line.
Step S28: and outputting a calculation result of the quantum equipment by using the trained quantum network circuit.
In a specific embodiment, respective calibration circuits can be set for various working environments, for example, when the working temperature is 100K, a set of quantum network circuit parameters alpha 100 is optimized, and when the working temperature is 300K, a set of corresponding new parameters alpha 300 is optimized; or configuring corresponding parameter lines according to the working time, such as the working time is about a few seconds, the parameter αs is optimized, the working time is about a few minutes, the parameter αm is optimized, and the like.
Referring to fig. 5, an embodiment of the present application discloses a quantum device calibration apparatus, including:
an actual output result obtaining module 11, configured to obtain an actual output result of the quantum device;
the quantum network line training module 12 is configured to input the actual output result into a quantum neural network for training, so as to obtain a corresponding network output result; updating the quantum neural network by utilizing the expected output result of the quantum equipment and the network output result until the quantum neural network converges, so as to obtain a trained quantum network line;
and the quantum calculation result output module 13 is used for outputting the calculation result of the quantum equipment by utilizing the trained quantum network circuit.
Therefore, the embodiment of the application obtains the actual output result of the quantum equipment, and then inputs the actual output result into the quantum neural network for training to obtain the corresponding network output result; and updating the quantum neural network by utilizing the expected output result of the quantum equipment and the network output result until the quantum neural network converges to obtain a trained quantum network line, and then outputting the calculation result of the quantum equipment by utilizing the trained quantum network line. That is, the application does not directly debug the hardware of the device, but trains by using a machine learning mode to obtain a trained quantum network line, and outputs the calculation result of the quantum device by using the trained quantum network line, thus, the quantum device can be effectively calibrated, the complexity of the quantum device calibration is reduced, and the debugging is convenient.
The device also comprises an expected output result generation module, a quantum device generation module and a quantum device generation module, wherein the expected output result generation module is used for acquiring an initial state generated by the quantum device; and generating a standard quantum state by using the initial state to obtain the expected output result.
The quantum network circuit training module 12 specifically includes:
the loss calculation sub-module is used for carrying out exchange test on the expected output result and the network output result to obtain deviation between the expected output result and the network output result, and taking the deviation as the loss of the quantum neural network;
and a network updating sub-module for updating the quantum neural network by using the loss back propagation.
The network updating submodule specifically comprises:
a loss recording unit for recording the loss into a preset auxiliary bit;
the loss measurement unit is used for measuring the preset auxiliary bit to obtain a loss measurement value;
and the network updating unit is used for updating the quantum neural network by using the back propagation of the loss measured value.
In a specific embodiment, the loss measurement unit is specifically configured to perform multiple measurements on the preset auxiliary bit, and perform average calculation on the multiple measurement results to obtain the loss measurement value.
In some embodiments, the loss measurement unit is specifically configured to perform phase transformation on the preset auxiliary bit; carrying out amplitude amplification on the preset auxiliary bits after phase conversion for a plurality of times by using a Grover algorithm; determining a loss estimated value by using the amplitude amplification times and the preset auxiliary bit after the amplitude amplification, and recording the loss estimated value in the preset auxiliary bit; and measuring the preset auxiliary bit to obtain the loss measurement value.
Referring to fig. 6, an embodiment of the present application discloses an electronic device 20 comprising a processor 21 and a memory 22; wherein the memory 22 is used for storing a computer program; the processor 21 is configured to execute the computer program to implement the following steps:
obtaining an actual output result of the quantum equipment; inputting the actual output result into a quantum neural network for training to obtain a corresponding network output result; updating the quantum neural network by utilizing the expected output result of the quantum equipment and the network output result until the quantum neural network converges, so as to obtain a trained quantum network line; and outputting a calculation result of the quantum equipment by using the trained quantum network circuit.
Therefore, the embodiment of the application obtains the actual output result of the quantum equipment, and then inputs the actual output result into the quantum neural network for training to obtain the corresponding network output result; and updating the quantum neural network by utilizing the expected output result of the quantum equipment and the network output result until the quantum neural network converges to obtain a trained quantum network line, and then outputting the calculation result of the quantum equipment by utilizing the trained quantum network line. That is, the application does not directly debug the hardware of the device, but trains by using a machine learning mode to obtain a trained quantum network line, and outputs the calculation result of the quantum device by using the trained quantum network line, thus, the quantum device can be effectively calibrated, the complexity of the quantum device calibration is reduced, and the debugging is convenient.
For the specific procedure of the above steps, reference may be made to the corresponding content disclosed in the foregoing embodiment, and no further description is given here.
The memory 22 may be a carrier for storing resources, such as a read-only memory, a random access memory, a magnetic disk or an optical disk, and the storage mode may be transient storage or permanent storage.
In addition, the electronic device 20 further includes a power supply 23, a communication interface 24, an input-output interface 25, and a communication bus 26; wherein the power supply 23 is configured to provide an operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and the communication protocol to be followed is any communication protocol applicable to the technical solution of the present application, which is not specifically limited herein; the input/output interface 25 is used for acquiring external input data or outputting external output data, and the specific interface type thereof may be selected according to the specific application requirement, which is not limited herein.
Further, the embodiment of the application also discloses a computer readable storage medium for storing a computer program, wherein the computer program is executed by a processor to realize the quantum device calibration method disclosed in the previous embodiment.
For the specific process of the quantum device calibration method, reference may be made to the corresponding content disclosed in the foregoing embodiment, and no further description is given here.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above detailed description of a quantum device calibration method, device and medium provided by the present application applies specific examples to illustrate the principles and embodiments of the present application, and the above examples are only used to help understand the method and core ideas of the present application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (7)
1. A method of quantum device calibration, comprising:
obtaining an actual output result of the quantum equipment;
inputting the actual output result into a quantum neural network for training to obtain a corresponding network output result;
updating the quantum neural network by utilizing the expected output result of the quantum equipment and the network output result until the quantum neural network converges, so as to obtain a trained quantum network line;
outputting a calculation result of the quantum equipment by using the trained quantum network circuit;
wherein the method further comprises: acquiring an initial state generated by the quantum equipment; generating a standard quantum state by using the initial state to obtain the expected output result; the initial state comprises a quantum state bit length, a quantum state characteristic and a quantum state waiting time length;
the updating the quantum neural network with the expected output result of the quantum device and the network output result comprises:
performing exchange test on the expected output result and the network output result to obtain deviation between the expected output result and the network output result, and taking the deviation as the loss of the quantum neural network;
updating the quantum neural network using the loss back propagation.
2. The quantum device calibration method of claim 1, wherein the updating the quantum neural network with the loss back propagation comprises:
recording the loss into a preset auxiliary bit;
measuring the preset auxiliary bit to obtain a loss measurement value;
and updating the quantum neural network by using the back propagation of the loss measurement value.
3. The quantum device calibration method of claim 2, wherein the measuring the preset auxiliary bit to obtain the loss measurement value comprises:
and carrying out multiple measurements on the preset auxiliary bits, and carrying out average value calculation on the multiple measurement results to obtain the loss measurement value.
4. The quantum device calibration method of claim 2, wherein the measuring the preset auxiliary bit to obtain the loss measurement value comprises:
performing phase transformation on the preset auxiliary bit;
carrying out amplitude amplification on the preset auxiliary bits after phase conversion for a plurality of times by using a Grover algorithm;
determining a loss estimated value by using the amplitude amplification times and the preset auxiliary bit after the amplitude amplification, and recording the loss estimated value in the preset auxiliary bit;
and measuring the preset auxiliary bit to obtain the loss measurement value.
5. A quantum device calibration apparatus, comprising:
the actual output result acquisition module is used for acquiring the actual output result of the quantum equipment;
the quantum network line training module is used for inputting the actual output result into a quantum neural network for training to obtain a corresponding network output result; updating the quantum neural network by utilizing the expected output result of the quantum equipment and the network output result until the quantum neural network converges, so as to obtain a trained quantum network line;
the quantum calculation result output module is used for outputting the calculation result of the quantum equipment by utilizing the trained quantum network circuit;
the device also comprises an expected output result generation module, a quantum device generation module and a quantum device generation module, wherein the expected output result generation module is used for acquiring an initial state generated by the quantum device; generating a standard quantum state by using the initial state to obtain the expected output result; the initial state comprises a quantum state bit length, a quantum state characteristic and a quantum state waiting time length;
the quantum network line training module specifically comprises:
the loss calculation unit is used for carrying out exchange test on the expected output result and the network output result to obtain deviation between the expected output result and the network output result, and taking the deviation as the loss of the quantum neural network;
and the network updating unit is used for updating the quantum neural network by using the loss back propagation.
6. An electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the quantum device calibration method of any one of claims 1 to 4.
7. A computer-readable storage medium, for storing a computer program which, when executed by a processor, implements the quantum device calibration method of any one of claims 1 to 4.
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| CN114567418B (en) * | 2022-03-04 | 2023-01-20 | 中国人民解放军战略支援部队信息工程大学 | Quantum error correction method of matrix-based quantum platform |
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