CN113657602A - Method and apparatus for quantum computing - Google Patents
Method and apparatus for quantum computing Download PDFInfo
- Publication number
- CN113657602A CN113657602A CN202110944292.4A CN202110944292A CN113657602A CN 113657602 A CN113657602 A CN 113657602A CN 202110944292 A CN202110944292 A CN 202110944292A CN 113657602 A CN113657602 A CN 113657602A
- Authority
- CN
- China
- Prior art keywords
- pulse signal
- noise
- quantum
- ideal
- evolution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
Abstract
The disclosure provides a method and a device for quantum computation, and relates to the field of quantum computation. The implementation scheme comprises the following steps: generating a noisy driving pulse signal; amplifying the noise part in the driving pulse signal at least once to obtain at least one noise amplification pulse signal; carrying out quantum evolution respectively by using each pulse signal in the driving pulse signal and the at least one noise amplification pulse signal to obtain a plurality of expected values containing noise; respectively calculating the distortion degree of each pulse signal in the driving pulse signal and the at least one noise amplification pulse signal relative to an ideal pulse signal; and calculating to obtain the ideal expected value according to the plurality of expected values containing noise and the distortion degree of the corresponding pulse signals. The method reduces or even eliminates the noise in the originally generated driving pulse signal, and improves the accuracy of the final quantum calculation result.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and apparatus for quantum computing, an electronic device, a computer storage medium, and a computer program product.
Background
The quantum computation is a novel computation mode for regulating and controlling quantum information units to perform computation according to a quantum mechanics law. The general theoretical model of quantum computer is a general turing machine which is re-interpreted by quantum mechanics laws. From the aspect of computational efficiency, due to the existence of quantum mechanical superposition, certain known quantum algorithms are faster than a traditional general-purpose computer in processing problems.
In the quantum computing process, an ideal pulse signal needs to be input into a quantum system of a quantum computer, so that the quantum system performs expected quantum evolution as expected, and an ideal expected value is obtained as a result of quantum computing. However, due to the accuracy of quantum computing devices, ideal pulse signals may not be generated, and the generated pulse signals generally contain certain noise.
The method for processing noise in the prior art mainly comprises the following steps: quantum error correction and quantum error mitigation. In the quantum error correction scheme, each logical qubit consists of a number of physical bits, and error correction is achieved by redundant physical qubit resources. However, as the number of physical bits increases, the types of errors that can occur in the system also increase, and meanwhile, the operation of multi-quantum bit coding requires non-local interaction between physical quantum bits, so that quantum error correction and quantum gates of logic bits are difficult to realize experimentally. The quantum error mitigation scheme does not require additional physical bits, but it puts requirements on the error type and error controllability of the quantum circuit, which makes the method difficult to implement on current quantum computers.
The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, unless otherwise indicated, the problems mentioned in this section should not be considered as having been acknowledged in any prior art.
Disclosure of Invention
The present disclosure provides a method for quantum computing, comprising: generating a driving pulse signal, wherein the driving pulse signal comprises an ideal pulse signal for enabling the quantum system to carry out expected quantum evolution and a noise part generated along with the ideal pulse signal; amplifying the noise part in the driving pulse signal at least once to obtain at least one noise amplification pulse signal; carrying out quantum evolution respectively by using each pulse signal in the driving pulse signal and the at least one noise amplification pulse signal to obtain a plurality of expected values containing noise; respectively calculating the distortion degree of each pulse signal in the driving pulse signal and the at least one noise amplification pulse signal relative to an ideal pulse signal; and calculating to obtain an ideal expected value according to the plurality of expected values containing the noise and the distortion degree of the pulse signals corresponding to the expected values, wherein the ideal expected value corresponds to an expected value obtained by performing expected quantum evolution by using the ideal pulse signals.
According to an aspect of the present disclosure, there is provided an apparatus for quantum computing, including: a pulse generating unit configured to generate a driving pulse signal including an ideal pulse signal for causing the quantum system to perform expected quantum evolution and a noise part generated along with the ideal pulse signal; the amplifying unit is configured to amplify the noise part in the driving pulse signal at least once to obtain at least one noise amplification pulse signal; the evolution unit is configured to perform quantum evolution respectively by using the driving pulse signal and each pulse signal in the at least one noise amplification pulse signal to obtain a plurality of expected values containing noise; a first calculation unit configured to calculate a distortion degree of each of the driving pulse signal and the at least one noise amplification pulse signal with respect to an ideal pulse signal, respectively; and the second calculation unit is configured to calculate an ideal expected value according to the plurality of expected values containing noise and the distortion degree of the pulse signals corresponding to the expected values, wherein the ideal expected value corresponds to an expected value obtained by performing expected quantum evolution by using the ideal pulse signals.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method described above.
According to another aspect of the present disclosure, there is also provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the above method.
According to another aspect of the present disclosure, there is also provided a computer program product comprising a computer program, wherein the computer program realizes the above method when executed by a processor.
The method according to one or more embodiments of the present disclosure reduces or even eliminates noise in the originally generated driving pulse signal, improving the accuracy of the final quantum computation result. The embodiment of the disclosure only improves the method of quantum computing, and does not change the hardware configuration of the quantum computing device, so the application range is wider. Whether the noise is caused by the insufficient precision of the signal generating equipment or the reason that the accurate mathematical expression of the driving pulse signal cannot be obtained, the method of the embodiment can reduce or even eliminate the noise. In addition, the method of the embodiment derives the ideal expected value according to the plurality of expected values containing noise and the corresponding distortion degrees thereof, so that the specific form of the noise part is not required to be determined in advance, and the calculation process is simpler.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.
Fig. 1 shows a flow chart diagram of a method of quantum computation of the related art;
FIG. 2 shows a flow diagram of a method for quantum computing according to one embodiment of the present disclosure;
FIG. 3 shows a flow diagram of a method for amplifying a noise portion in a drive pulse signal according to one embodiment of the present disclosure;
FIG. 4 is a graph schematically illustrating a comparison of a noise amplification pulse signal and an original driving pulse signal;
FIG. 5 shows a flow chart of a method of calculating a distortion degree of each of a driving pulse signal and at least one noise amplification pulse signal with respect to an ideal pulse signal according to one embodiment of the present disclosure;
FIG. 6 illustrates a flow diagram of a method for obtaining an ideal expected value using interpolative extrapolation, according to one embodiment of the present disclosure;
FIG. 7 shows a schematic diagram of fitting a mapping relationship using an interpolated data set according to an embodiment of the present disclosure;
FIG. 8 illustrates a schematic diagram of a method of updating a mapping relationship, according to one embodiment of the present disclosure;
FIG. 9 shows a comparison of an ideal expected value and a noisy expected value calculated using the method of the present disclosure;
FIG. 10 shows a schematic diagram of an apparatus for quantum computing, according to one embodiment of the present disclosure;
fig. 11 shows a schematic diagram of an apparatus for quantum computing, according to another embodiment of the present disclosure; and
FIG. 12 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, the timing relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, based on the context, they may also refer to different instances.
The terminology used in the description of the various examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the elements may be one or more. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.
Before describing the method of the present embodiment in detail, a brief description will be given of conventional quantum computation. As shown in fig. 1, fig. 1 shows a flow diagram of a method 100 of quantum computation of the related art.
and 103, after the evolution of the quantum system is finished, measuring the final state of the quantum system by using the measuring equipment to obtain an expected value.
The quantum system mentioned in all embodiments of the present disclosure is a part of the hardware of the quantum computer, which may be any physical system satisfying the principle of quantum mechanics so as to be applicable to the quantum computer. Such quantum systems include, but are not limited to, quantum systems, Nuclear Magnetic Resonance (NMR) systems, optical resonance systems, ion traps, superconducting isovolumetric systems. After the quantum system receives a driving pulse input from the outside, the quantum system evolves from an initial state quantum to a final state according to the quantum mechanics principle, and a final quantum calculation result can be obtained by measuring the final state of the quantum system.
In each quantum computation process, aiming at the computation content expected to be completed (for example, according to a quantum circuit which is predetermined), the vector subsystem inputs a corresponding driving pulse signal so as to expect the quantum system to evolve according to expected quantum evolution, and finally obtain an expected computation result. However, in the above step 101, the signal generating device for generating the driving pulse signal may not accurately generate the ideal pulse signal that enables the quantum system to proceed according to the expected quantum evolution, and thus may cause the final calculation result to be inaccurate. The reasons for the failure to accurately generate the ideal pulse signal may mainly include the following two points:
(1) although the expected quantum evolution of the quantum system can be determined, and even an ideal quantum gate matrix or quantum circuit equivalent to the expected quantum evolution can be obtained, an accurate mathematical expression of the driving pulse signal cannot be obtained, or does not exist at all;
(2) although there is an accurate mathematical representation of the ideal pulse signal, it may not be accurately modeled due to the accuracy of the signal generating equipment.
For the above reasons, the generated driving pulse signal has a deviation from the ideal pulse signal, and such a deviation is generally referred to as "noise". The noise generated due to the above-mentioned reason (2) can be reduced by improving the accuracy of the signal generating device but cannot be completely eliminated, whereas the noise generated due to the above-mentioned reason (1) cannot be reduced or eliminated by improving the hardware device.
Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
The present disclosure first provides a method for quantum computation, fig. 2 shows a flow diagram of a method 200 for quantum computation according to an embodiment of the present disclosure, the method 200 comprising:
and step 205, calculating to obtain an ideal expected value according to the plurality of expected values containing noise and the distortion degree of the corresponding pulse signals, wherein the ideal expected value corresponds to an expected value obtained by performing expected quantum evolution by using the ideal pulse signals.
In the quantum computing method of this embodiment, first, noise in a driving pulse signal generated by a quantum computing device is amplified to obtain a noise-amplified driving pulse signal. And then, respectively calculating the noise-containing expected values and the distortion degrees of the noise amplification pulse signals and the driving pulse signals, and finally calculating the ideal expected value when the driving pulse signals do not contain noise (namely, the ideal pulse signals) by using data containing the noise-containing expected values and the corresponding distortion degrees. The method of the embodiment reduces or even eliminates the noise in the originally generated driving pulse signal, and improves the accuracy of the final quantum calculation result.
The method of the embodiment improves the process of quantum computing without changing the hardware configuration of the quantum computing device, so the application range is wider. The method of the present embodiment is able to reduce or even eliminate noise, whether due to insufficient precision of the signal generating device or due to an inability to obtain an accurate mathematical representation of the drive pulse signal. In addition, the method of the embodiment derives the ideal expected value according to the plurality of expected values containing noise and the corresponding distortion degrees thereof, so that the specific form of the noise part does not need to be determined in advance, and the calculation process is simpler.
In step 201, the signal generating device may for example be a quantum control platform for generating a drive pulse signal, for example: baidu Quantum computing institute's quantum, IBM's Qiskit OpenPulse, and others. The specific operation process of this step is similar to that of step 101 described above, and is not described here again.
As described above, the driving pulse signal includes the ideal pulse signal and the noise portion, and in step 202, the noise portion of the driving pulse signal is amplified at least once while the ideal pulse signal is kept unchanged. In some embodiments, the noise part may be amplified multiple times according to different scaling factors, and for example, the noise part may be selected to be amplified twice, in the first amplification, the noise part is amplified to be 1.25 times of the original noise part (i.e., the noise part in the driving pulse signal generated in step 201), and in the second amplification, the noise part is amplified to be 1.5 times of the original noise part. The above-described noise-amplified drive pulse signal is referred to as a noise-amplified pulse signal, and for example, after amplifying a noise portion twice, a first noise-amplified pulse signal (i.e., a pulse signal after noise amplification by 1.25 times) and a second noise-amplified pulse signal (i.e., a pulse signal after noise amplification by 1.5 times) can be obtained. Of course, in other embodiments, the noise portion may be amplified more than twice, and each amplification may be amplified multiple times according to other scaling factors (e.g., 1.1, 1.2, 1.3, etc.). In summary, implementations of the present disclosure are not limited by the above-described magnification times and scaling factors.
In step 203, quantum evolution is performed by using the driving pulse signal and each pulse signal in the at least one noise amplification pulse signal obtained in step 202, so as to obtain a plurality of expected noise-containing values, and the obtaining process of the expected noise-containing values is similar to the processes of step 102 and step 103. Specifically, also taking the above example of amplifying the noise part twice (respectively amplifying by 1.25 times and 1.5 times), firstly, inputting the unamplified original driving pulse signal into the initial state quantum system, and after the quantum system has been evolved, measuring the final state of the quantum system by using the measuring device to obtain the first expected value E containing noise1Then, inputting the first noise amplification pulse signal into the quantum system, and after the quantum system evolution is finished, measuring the final state of the quantum system by using measuring equipment to obtain a second noise-containing expected value E2Finally, a third expected noise value E is obtained in the same manner3. It will be appreciated that the drive is due to the driving of the arteryBoth the impulse signal and the noise amplification impulse signal contain noise, so the evolution process by using the impulse signal is different from the expected quantum evolution process.
In step 204, a distortion degree of each of the driving pulse signal and the at least one noise amplification pulse signal with respect to the ideal pulse signal is calculated, respectively. The distortion degree indicates the magnitude of the difference between the driving pulse signal or the noise amplification pulse signal and the ideal pulse signal, and the larger the distortion degree, the larger the difference, and the larger the difference between the obtained expected value containing noise and the ideal expected value. Also taking the above example of amplifying the noise part twice (by 1.25 times and 1.5 times respectively), first a first distortion degree of the original driving pulse signal with respect to the ideal pulse signal is calculated, and then a second distortion degree and a third distortion degree of the two noise amplification pulse signals with respect to the ideal pulse signal are calculated. It can be understood that, since the noise portions are amplified in the first noise-amplified pulse signal and the second noise-amplified pulse signal respectively (by 1.25 and 1.5 times respectively), the second distortion degree and the third distortion degree should be larger than the first distortion degree, and the third distortion degree is larger than the second distortion degree.
In step 205, an ideal expected value corresponding to the case where the drive pulse signal does not contain noise (i.e., an ideal pulse signal) is calculated using the plurality of expected values containing noise obtained in step 203 and the plurality of distortion degrees obtained in step 204. In some embodiments, the plurality of expected noise values obtained in step 203 and the plurality of distortion degrees obtained in step 204 may be grouped into a plurality of data sets, and each data set includes expected noise values and distortion degrees corresponding to the driving pulse signal and one of the at least one noise amplifying pulse signal. And then carrying out numerical fitting on the plurality of data groups to obtain a mapping relation between the expected value containing noise and the distortion degree, and finally extrapolating through the mapping relation to obtain the expected value corresponding to the distortion degree equal to 0 to be used as the ideal expected value. Specific fitting and extrapolation methods are described in detail below.
How to amplify the noise portion in the driving pulse signal by the adjustment of the pulse signal will be described in detail below. Fig. 3 shows a flow diagram of a method 300 for amplifying a noise portion in a drive pulse signal, the method 300 comprising:
for each of the scaling factors, the scaling factor is,
Theoretically, the kinetic evolution of the noisy-quantum computation (T ∈ [0, T ]) can be described using the lindbold main equation, which is expressed as:
wherein the reciprocal sub-part [ K, rho ] containing time Hamiltonian K and system quantum state rho]Representing the expected quantum evolution process, and the lindbold operator
Representing the noise portion of the drive pulse signal that the quantum hardware generated from the expected quantum evolution is capable of implementing.
Although the operator Lindblad operator can not be determined
But it can be assumed that the term does not contain time and that the rate of the dissipation process of the noise section is characterized by a scalar noise coefficient lambda, i.e. the size of the noise section is proportional to the noise coefficient lambda. When the noise coefficient is lambda, the quantum state when the quantum system evolves to the termination time T is rhoλ(T). If the noise part is amplified by c times (i.e. the noise coefficient lambda is changed to c lambda), the quantum end state will change to rhocλ(T). As described above, because
The form of (A) does not contain time, so that the amplification process of the parameter lambda can be converted into the evolution of the systemThe extension of the time T, that is, the extension of the duration of the input drive pulse signal to cT. In other words, extending the time length of the input drive pulse signal by c times is equivalent to amplifying the noise coefficient λ by c times. Therefore, in step 301, the pulse duration of the driving pulse signal may be extended based on the scaling factor of the current amplification while keeping the waveform of the driving pulse signal unchanged. For example, if it is desired to amplify the noise portion by 1.25 times this time, the duration of the drive pulse signal is extended to 1.25 times the original duration.
Although the noise part is amplified in step 301, the non-noise term-i [ K, ρ ] is also amplified, so in step 302, the hamilton K needs to be reduced to ensure that the non-noise term-i [ K, ρ ] remains unchanged. The Hamiltonian K of the quantum hardware includes a time-free drive term and a time-dependent control term, which are typically applied to the quantum system in the form of drive pulses. Therefore, the integrated value of the hamiltonian K can be reduced by reducing the amplitude of the pulse signal. Specifically, in step 302, with the waveform of the drive pulse signal kept unchanged, the pulse amplitude thereof is reduced based on the scaling factor of the present amplification, so that the integral value of the non-noise term-i [ K, ρ ] is the same as the corresponding integral value of the original drive pulse signal. In this way, it is possible to surely amplify only a noise portion in the drive pulse signal. For example, if it is desired to amplify the noise portion by 1.25 times this time, the amplitude of the drive pulse signal is reduced to 4/5.
Fig. 4 exemplarily shows a comparison graph 400 of a noise amplification pulse signal and an original driving pulse signal, where the uppermost pulse signal in fig. 4 represents the driving pulse signal (i.e., the scaling factor is 1), the pulse signal in the middle of fig. 4 represents a first noise amplification pulse signal with a noise portion amplified by 1.25 times, and the pulse signal in the lower of fig. 4 represents a second noise amplification pulse signal with a noise portion amplified by 1.5 times. As can be seen from fig. 4, the pulse durations of the first noise amplification pulse signal and the second noise amplification pulse signal are extended by 1.25 times and 1.5 times, respectively, with respect to the drive pulse signal, while their pulse amplitudes are reduced by 1.25 times and 1.5 times, respectively, with respect to the drive pulse signal. The above-mentioned series of operations of extending the duration and reducing the amplitude of the pulse signal can be implemented by the relevant pulse signal modulation device, and the specific operation process thereof is well known to those skilled in the art and will not be described herein again.
How to calculate the distortion degree of the driving pulse signal and the noise amplification pulse signal will be described in detail below. In some embodiments, the distortion metric may be characterized by a throughput sub-gate distortion metric. Fig. 5 illustrates a method 500 of calculating a distortion degree of each of a driving pulse signal and at least one noise amplification pulse signal with respect to an ideal pulse signal according to an embodiment of the present disclosure, the method 500 including:
The various pulse signals to be input (driving pulse signals, noise amplification pulse signals, ideal pulse signals and the like) can be represented as an equivalent evolutionary gate matrix, and when the input pulse signals act on the quantum system, the equivalent evolutionary gate matrix can act on the initial state rho of the quantum system. Specifically, in step 501, an ideal evolved gate matrix equivalent to the ideal pulse signal may be determined, and in this embodiment, the ideal evolved gate matrix may be a Hadamard gate matrix. And in step 502, a plurality of noisy evolution gate matrices respectively equivalent to the driving pulse signal and each of the at least one noise amplification pulse signal are determined. For example, in the case of generating two noise amplification pulse signals (respectively amplified by 1.25 times and 1.5 times), a first noise-containing evolution gate matrix equivalent to the driving pulse signal, a second noise-containing evolution gate matrix equivalent to the first noise amplification pulse signal, and a third noise-containing evolution gate matrix equivalent to the second noise amplification pulse signal are respectively determined.
For each noisy evolution gate matrix, in step 503, a quantum gate distortion factor may be calculated according to the following formula,
wherein U represents an ideal evolutionary gate matrix; v represents a corresponding noisy evolution gate matrix, and n represents the dimension of the matrix; f (U, V) represents the quantum gate distortion degree of V relative to U.
Representation matrix
I.e. a matrix
The sum of the diagonal elements of (a).
For example, taking the calculation of the distortion of the driving pulse signal as an example, the quantum gate distortion may be calculated according to the following formula,
wherein U represents an ideal evolutionary gate matrix; v1Representing a first noise-containing evolution gate matrix corresponding to the driving pulse signal; f1And representing the quantum gate distortion degree of the first noise-containing evolution gate matrix relative to the ideal evolution gate matrix.
The quantum gate distortion factor F representing the second noisy evolution gate matrix relative to the ideal evolution gate matrix is obtained in a similar manner using the above formula2And a quantum gate distortion factor F representing the third noisy evolution gate matrix relative to the ideal evolution gate matrix3. It can be understood that F3>F2>F1。
How to obtain the ideal expected value through calculation of a plurality of expected values containing noise and their corresponding distortion degrees will be described in detail below. FIG. 6 shows a flow diagram of a method 600 for obtaining an ideal expected value using interpolative extrapolation, the method 600 comprising:
601, generating a first number of interpolation data sets, wherein each interpolation data set in the first number of interpolation data sets comprises a noise-containing expected value and a distortion degree corresponding to a corresponding pulse signal in a driving pulse signal and at least one noise amplification pulse signal;
and step 603, obtaining an ideal expected value based on the mapping relation.
In step 601, a first number of interpolated data sets may be formed by the plurality of distortion levels obtained in method 500 and the plurality of noisy expected values obtained in step 203 of method 200. Still taking the example of amplifying the noise portion in the driving pulse signal twice (by 1.25 times and 1.5 times, respectively), in this case, three sets of interpolation data sets can be generated accordingly. Specifically, the first data group includes F1,E1Two data, wherein F1Representing a first degree of distortion, E, of the drive pulse signal relative to the ideal pulse signal1And the first noise-containing expected value obtained by quantum evolution by using the driving pulse signal is shown. Similarly, the second data set includes F2,E2Two data, the third data group including F3,E3Two data.
In step 602, interpolation fitting may be performed using the three data sets to obtain a mapping relationship between expected values of noise and distortion. FIG. 7 shows a schematic diagram 700 of fitting a mapping relationship using an interpolated data set according to one embodiment of the present disclosure. As shown in fig. 7, the mapping relationship may be obtained by, for example, a linear fitting method, and specifically, the three data sets may be plotted in a cartesian coordinate system in which the abscissa and ordinate represent the distortion degree and the expected value of noise, respectively, and a straight line representing the mapping relationship may be obtained based on the data sets.
It is understood that when the degree of distortion of a certain pulse signal with respect to an ideal pulse signal is equal to zero, the pulse signal is equivalent to the ideal pulse signal, and therefore, the intersection point of the straight line representing the mapping relation and the y-axis, that is, the expected value E of noise, which corresponds to the degree of distortion being equal to 0, is the expected value of ideal. Therefore, in step 603, the expected value of the mapping relationship with the distortion degree equal to zero can be selected as the ideal expected value. Although in the above embodiments only three sets of data sets have been selected for fitting, it will be appreciated that in other embodiments more or fewer sets of data may be selected for fitting, and in general, the more sets of data that participate in the fitting, the more accurate the mapping that is obtained.
Besides the linear fitting mode, the mapping relation can be fitted in other modes according to the actual quantum computation requirement to obtain the ideal expected value. For example, in some embodiments, the desired value may also be obtained by Richardson extrapolation. In the field of numerical algorithms, Richardson extrapolation is an effective method for universally eliminating low-order estimation errors.
Assuming expected value E of noiseλWith respect to the ideal expected value E*＝E0Can be expressed as a distortion factor FλIn the form of a power series, i.e.
Wherein a is0＝E*And { a }k}kIs a set of parameters to be determined, λ is the different noise figure, d is the order in which the error is expected to be removed by extrapolation. Through the steps, a group of data groups { F) of distortion degrees and expected noise values corresponding to different parameter values of lambda can be obtainedλ,Eλ}λ. The data set { F } determined in step 601 is processedλ,Eλ}λSubstituting the equation set in the form of power series can determine the undetermined parameter { ak}k. A new estimator E of a linear combination is then iteratively constructed step by stepnCompared to the original noise expectancyValue EλFor E*With less estimation error.
Fig. 8 shows a schematic diagram of a method of updating a mapping relationship according to an embodiment of the present disclosure. The method may be implemented, for example, after step 602 of the method shown in fig. 6, the method comprising the steps of:
In step 801, a second number of data sets may be generated that is greater than the first number in step 601, which may include the first number of data sets. For example: the first number of data sets may include three sets of expected values of noise and corresponding distortion levels, and the second number of data sets may include more than four sets of expected values of noise and corresponding distortion levels. The second number of data sets may be generated based on the mapping obtained in step 602, and specifically, the data sets that conform to the variation trend of the mapping may be selected to form the second number of data sets. For example, if it is determined in step 602 that the trend of change of the mapping relationship is increasing, in step 801, only data sets of expected noise values and distortion degrees corresponding to the expected noise values that satisfy the increasing trend are generated as data in the second number of data sets.
In step 802, a second number of interpolated data sets are used to perform interpolation again, and this time, the interpolation uses more data sets, and these data sets satisfy the change trend of the mapping determined in step 602, so that the newly fitted mapping has higher accuracy than the original mapping.
To verify the effectiveness and advantages of the methods of the present disclosure, a benchmark test may be performed based on Clifford random quantum circuits. In particular, one can assume an n +1 long quantum circuit equivalent to an identity transform, where the first n quantum gates (C)j) Is a randomly generated single-bit Clifford unitary gate and the last quantumDoor C-1The equivalent inverse gate referring to the effect of the first n quantum gates on the continuum is such that the following equation holds:
C-1CnCn-1 … C1＝I.
applying the above quantum circuit to the initial state of |0>Qubit of (a), finally measure |0>Attitude projection operator
Is calculated from the expected value of (c).
Because the corresponding evolution operator of the circuit is equivalent to constant transformation, the expected value under ideal conditions<A>ideal1 [ identical to ] or; the final state output by the quantum circuit will deviate from |0 due to the existence of noise in the circuit>The expected value will also deviate from 1 (less than 1).
The quantum computing method of the present disclosure is implemented on a hundred-degree quanta platform and applied to the above computing task. Experimental data results are shown in fig. 9, which is a graph 900 comparing the expected ideal values and expected noise values calculated using the method of the present disclosure. In the figure, a curve 1 represents an ideal expected value obtained by theoretically performing expected quantum evolution, a curve 4 represents a noisy expected value obtained by performing quantum evolution by using an original driving pulse signal, a curve 5 represents a noisy expected value obtained by amplifying a noise part of the driving pulse signal according to a given scaling coefficient, and curves 2 and 3 represent ideal expected values obtained by applying the method disclosed by the invention, wherein the power series order selected by the curve 2 in the process of interpolation is greater than the curve 3. From the curves in the figures we can see that the accuracy of the ideal expected value obtained using the method of the present disclosure is significantly improved and approaches the theoretical ideal expected value with a rather high accuracy.
The present disclosure also provides an apparatus for quantum computing, fig. 10 shows a schematic diagram of an apparatus 1000 for quantum computing according to an embodiment of the present disclosure, the apparatus 1000 comprising: the pulse generation unit 1010 is configured to generate a driving pulse signal including an ideal pulse signal for causing the quantum system to perform expected quantum evolution and a noise part generated along with the ideal pulse signal; the amplifying unit 1020 is configured to amplify the noise part in the driving pulse signal at least once, resulting in at least one noise amplified pulse signal; the evolution unit 1030 is configured to perform quantum evolution respectively by using the driving pulse signal and each of the at least one noise amplification pulse signal to obtain a plurality of expected values containing noise; the first calculation unit 1040 is configured to calculate a distortion degree of each of the driving pulse signal and the at least one noise amplification pulse signal with respect to the ideal pulse signal, respectively; and the second calculation unit 1050 is configured to calculate an ideal expected value according to the plurality of expected values including noise and the distortion degree of the pulse signal corresponding to the expected values, wherein the ideal expected value corresponds to an expected value obtained by performing expected quantum evolution by using the ideal pulse signal.
In some embodiments, amplification unit 1020 is further configured to: the noise portions are amplified according to a plurality of different scaling factors, respectively.
Fig. 11 shows a schematic diagram of an apparatus 1100 for quantum computing according to another embodiment of the present disclosure, wherein, as shown in fig. 11, the amplifying unit 1120 includes: a duration amplification module 1121 configured to, for each scaling coefficient, amplify a pulse duration of the driving pulse signal based on the scaling coefficient; and an amplitude reduction module 1122 configured to reduce, for each scaling factor, the pulse amplitude of the driving pulse signal based on the scaling factor.
In some embodiments, the distortion measure comprises a quantum gate distortion measure, and the first calculation unit 1140 comprises: a first determining module 1141 configured to determine an ideal evolutionary gate matrix equivalent to the ideal pulse signal; a second determining module 1142 configured to determine a plurality of noisy evolution gate matrices respectively equivalent to the driving pulse signal and each of the at least one noise amplification pulse signal; and a first calculation module 1143 configured to calculate a quantum gate distortion factor of each of the plurality of noisy evolution gate matrices with respect to the ideal evolution gate matrix, respectively.
The first computing module 1143 is further configured to: for each noisy evolution gate matrix, the quantum gate distortion factor is calculated according to the following formula,
wherein U represents an ideal evolutionary gate matrix; v represents a corresponding noisy evolution gate matrix; f (U, V) represents the quantum gate distortion degree of V relative to U.
The second calculation unit 1150 includes: a first generation module 1151 configured to generate a first number of interpolated data sets, each interpolated data set of the first number of interpolated data sets including a noise-containing expected value and a distortion degree corresponding to one of the driving pulse signal and the at least one noise-amplified pulse signal; an interpolation fitting module 1152 configured to perform interpolation fitting using the first number of interpolation data sets to obtain a mapping relationship between a noise-containing expected value and a distortion factor; and an expected value acquisition module 1153 configured to obtain the ideal expected value based on the mapping relationship.
The second computing unit 1150 includes: a second generating module 1154 configured to generate a second number of sets of interpolated data based on the mapping, wherein the second number is greater than the first number; and an update module 1155 configured to perform interpolation fitting again using the second number of interpolated data sets to update the mapping.
In some embodiments, the interpolation fitting module 1152 is further configured to: the mapping relationship is fitted using Richardson extrapolation.
In some embodiments, the expected value acquisition module 1153 is further configured to: and selecting an expected value of which the distortion degree is equal to zero in the mapping relation as an ideal expected value.
The evolution unit 1130 further includes: an input module 1131, configured to input the pulse signal corresponding to the sub-quantum evolution into the quantum system in the initial state; a third determination module 1132 configured to determine that the quantum system evolution is over; and a measurement module 1133, configured to measure the final state of the quantum system, to obtain a noise-containing expected value corresponding to the sub-quantum evolution.
The respective units of the apparatuses 1100 and 1000 for quantum computing operate in a similar manner to the respective steps of the method for quantum computing described above, and are not described again here.
According to an embodiment of the present disclosure, there is also provided an electronic device, a readable storage medium, and a computer program product.
Referring to fig. 12, a block diagram of a structure of an electronic device 1200, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 12, the apparatus 1200 includes a computing unit 1201 which can perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)1202 or a computer program loaded from a storage unit 1208 into a Random Access Memory (RAM) 1203. In the RAM 1203, various programs and data required for the operation of the device 1200 may also be stored. The computing unit 1201, the ROM 1202, and the RAM 1203 are connected to each other by a bus 1204. An input/output (I/O) interface 1205 is also connected to bus 1204.
Various components in the device 1200 are connected to the I/O interface 1205 including: an input unit 1206, an output unit 1207, a storage unit 1208, and a communication unit 1209. The input unit 1206 may be any type of device capable of inputting information to the device 1200, and the input unit 1206 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote control. Output unit 1207 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. Storage unit 1208 may include, but is not limited to, magnetic or optical disks. The communication unit 1209 allows the device 1200 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices, and/or the like.
The computing unit 1201 may be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 1201 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 1201 performs the respective methods and processes described above, such as the above-described method for quantum calculation. For example, in some embodiments, the method for quantum computing may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 1208. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 1200 via the ROM 1202 and/or the communication unit 1209. When the computer program is loaded into RAM 1203 and executed by computing unit 1201, one or more steps described above for quantum computing may be performed. Alternatively, in other embodiments, the computing unit 1201 may be configured by any other suitable means (e.g., by means of firmware) to perform a method for quantum computing.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the methods, systems, and apparatus described above are merely exemplary embodiments or examples and that the scope of the present disclosure is not limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced with equivalent elements that appear after the present disclosure.
Claims (23)
1. A method for quantum computing, comprising:
generating a drive pulse signal comprising an ideal pulse signal for causing a quantum system to undergo a desired quantum evolution and a noise portion generated concomitantly with the ideal pulse signal;
amplifying the noise part in the driving pulse signal at least once to obtain at least one noise amplification pulse signal;
performing quantum evolution respectively by using the driving pulse signal and each pulse signal in the at least one noise amplification pulse signal to obtain a plurality of expected values containing noise;
respectively calculating the distortion degree of each pulse signal in the driving pulse signal and the at least one noise amplification pulse signal relative to the ideal pulse signal; and
and calculating to obtain an ideal expected value according to the plurality of expected values containing the noise and the distortion degree of the pulse signals corresponding to the expected values obtained by performing the expected quantum evolution by using the ideal pulse signals.
2. The method of claim 1, wherein amplifying the noise portion of the drive pulse signal at least once comprises:
the noise portions are amplified according to a plurality of different scaling factors, respectively.
3. The method of claim 2, wherein amplifying the noise portion by a plurality of different scaling factors, respectively, comprises:
for each of the scaling factors, the scaling factor is,
amplifying the pulse duration of the driving pulse signal based on the scaling factor; and
the pulse amplitude of the drive pulse signal is reduced based on the scaling factor.
4. The method of claim 1, wherein the distortion measure comprises a quantum gate distortion measure, and separately calculating the distortion measure for each of the drive pulse signal and the at least one noise-amplified pulse signal relative to the ideal pulse signal comprises:
determining an ideal evolutionary gate matrix equivalent to the ideal pulse signal;
determining a plurality of noisy evolvent matrices that are respectively equivalent to each of the drive pulse signal and the at least one noise amplified pulse signal; and
and respectively calculating the quantum gate distortion degree of each noisy evolution gate matrix in the plurality of noisy evolution gate matrices relative to the ideal evolution gate matrix.
5. The method of claim 4, wherein separately calculating a quantum gate distortion measure for each of the plurality of noisy evolution gate matrices relative to the ideal evolution gate matrix comprises:
for each noisy evolution gate matrix, the quantum gate distortion factor is calculated according to the following formula
Wherein U represents the ideal evolutionary gate matrix; v represents a corresponding noisy evolution gate matrix; f (U, V) represents the quantum gate distortion degree of V relative to U.
6. The method of any one of claims 1 to 5, wherein calculating the ideal expected value according to the plurality of expected values of noise and the distortion degree of the corresponding pulse signal comprises:
generating a first number of interpolated data sets, each interpolated data set of the first number of interpolated data sets comprising a noise-containing expected value and a distortion factor corresponding to a respective one of the drive pulse signal and the at least one noise-amplified pulse signal;
carrying out interpolation fitting by utilizing the first number of interpolation data groups to obtain a mapping relation between a noise-containing expected value and a distortion factor; and
and obtaining the ideal expected value based on the mapping relation.
7. The method of claim 6, wherein after interpolation fitting using the first number of interpolated data sets results in a mapping of noisy expectation values and distortion levels, the method further comprises:
generating a second number of interpolated data sets based on the mapping, wherein the second number is greater than the first number; and
and performing interpolation fitting again by using the second number of interpolation data groups to update the mapping relation.
8. The method of claim 6 or 7, wherein the fitting of the interpolation using the first number of interpolated data sets to obtain the mapping of the noisy expectation value to the distortion factor comprises:
the mapping was fitted using Richardson extrapolation.
9. The method of claim 6, wherein deriving an ideal expected value based on the mapping comprises:
and selecting the expected value with the distortion degree equal to zero in the mapping relation as the ideal expected value.
10. The method of any one of claims 1 to 5, wherein quantum evolving with each of the drive pulse signal and the at least one noise amplified pulse signal separately to obtain a plurality of noise-containing desired values comprises:
for each quantum evolution of the quantum, the quantum is,
inputting the pulse signal corresponding to the sub-quantum evolution into the quantum system in the initial state;
determining that the evolution of the quantum system is finished; and
and measuring the final state of the quantum system to obtain a noise-containing expected value corresponding to the sub-quantum evolution.
11. An apparatus for quantum computing, comprising:
a pulse generation unit configured to generate a drive pulse signal including an ideal pulse signal for causing the quantum system to perform the expected quantum evolution and a noise part generated along with the ideal pulse signal;
the amplifying unit is configured to amplify the noise part in the driving pulse signal at least once to obtain at least one noise amplification pulse signal;
the evolution unit is configured to perform quantum evolution respectively by using the driving pulse signal and each pulse signal in the at least one noise amplification pulse signal to obtain a plurality of expected values containing noise;
a first calculation unit configured to calculate a distortion degree of each of the driving pulse signal and the at least one noise amplification pulse signal with respect to the ideal pulse signal, respectively; and
and the second calculation unit is configured to calculate an ideal expected value according to the plurality of expected values containing noise and the distortion degree of the pulse signals corresponding to the expected values, wherein the ideal expected value corresponds to an expected value obtained by performing the expected quantum evolution by using the ideal pulse signals.
12. The apparatus of claim 11, wherein the amplification unit is further configured to:
the noise portions are amplified according to a plurality of different scaling factors, respectively.
13. The apparatus of claim 12, wherein the amplification unit comprises:
a time length amplification module configured to amplify, for each scaling coefficient, a pulse time length of the driving pulse signal based on the scaling coefficient; and
an amplitude reduction module configured to reduce, for each scaling factor, a pulse amplitude of the driving pulse signal based on the scaling factor.
14. The apparatus of claim 11, wherein the distortion measure comprises a quantum gate distortion measure, and the first computing unit comprises:
a first determination module configured to determine an ideal evolutionary gate matrix equivalent to the ideal pulse signal;
a second determining module configured to determine a plurality of noisy evolution gate matrices respectively equivalent to each of the driving pulse signal and the at least one noise amplification pulse signal; and
a first calculation module configured to calculate a quantum gate distortion factor of each of the plurality of noisy evolution gate matrices relative to the ideal evolution gate matrix, respectively.
15. The apparatus of claim 14, wherein the first computing module is further configured to:
for each noisy evolution gate matrix, calculating the quantum gate distortion degree according to the following formula,
wherein U represents the ideal evolutionary gate matrix; v represents a corresponding noisy evolution gate matrix; f (U, V) represents the quantum gate distortion degree of V relative to U.
16. The apparatus of any of claims 11 to 15, wherein the second computing unit comprises:
a first generation module configured to generate a first number of interpolated data sets, each interpolated data set of the first number of interpolated data sets including a desired noise-containing value and a distortion factor corresponding to the drive pulse signal and one of the at least one noise-amplified pulse signal;
the interpolation fitting module is configured to perform interpolation fitting by using the first number of interpolation data sets to obtain a mapping relation between the noise-containing expected value and the distortion degree; and
and the expected value acquisition module is configured to obtain an ideal expected value based on the mapping relation.
17. The apparatus of claim 16, wherein the second computing unit comprises further comprising:
a second generation module configured to generate a second number of sets of interpolated data based on the mapping, wherein the second number is greater than the first number; and
an updating module configured to perform interpolation fitting again using the second number of interpolation data sets to update the mapping relationship.
18. The apparatus of claim 16, wherein the interpolation fitting module is further configured to:
the mapping was fitted using Richardson extrapolation.
19. The apparatus of claim 16, wherein the expected value acquisition module is further configured to:
and selecting the expected value with the distortion degree equal to zero in the mapping relation as the ideal expected value.
20. The apparatus of any one of claims 11 to 15, wherein the evolution unit further comprises:
the input module is configured to input the pulse signal corresponding to the sub-quantum evolution into the quantum system in the initial state;
a third determination module configured to determine that the quantum system evolution is over; and
and the measurement module is configured to measure the final state of the quantum system to obtain a noise-containing expected value corresponding to the sub-quantum evolution.
21. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.
22. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-10.
23. A computer program product comprising a computer program, wherein the computer program realizes the method of any one of claims 1-10 when executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110944292.4A CN113657602A (en) | 2021-08-17 | 2021-08-17 | Method and apparatus for quantum computing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110944292.4A CN113657602A (en) | 2021-08-17 | 2021-08-17 | Method and apparatus for quantum computing |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113657602A true CN113657602A (en) | 2021-11-16 |
Family
ID=78480466
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110944292.4A Pending CN113657602A (en) | 2021-08-17 | 2021-08-17 | Method and apparatus for quantum computing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113657602A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114462613A (en) * | 2022-03-14 | 2022-05-10 | 北京百度网讯科技有限公司 | Quantum computer performance characterization method and device, electronic device and medium |
| CN115829046A (en) * | 2023-02-15 | 2023-03-21 | 苏州浪潮智能科技有限公司 | Quantum signal determination method and related device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080111653A1 (en) * | 2006-11-15 | 2008-05-15 | Qualcomm Incorporated | Delay line calibration |
| US20200175409A1 (en) * | 2018-12-03 | 2020-06-04 | International Business Machines Corporation | Implementation of error mitigation for quantum computing machines |
| CN112990472A (en) * | 2021-03-25 | 2021-06-18 | 北京百度网讯科技有限公司 | Method and apparatus for eliminating quantum noise, electronic device, and medium |
-
2021
- 2021-08-17 CN CN202110944292.4A patent/CN113657602A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080111653A1 (en) * | 2006-11-15 | 2008-05-15 | Qualcomm Incorporated | Delay line calibration |
| US20200175409A1 (en) * | 2018-12-03 | 2020-06-04 | International Business Machines Corporation | Implementation of error mitigation for quantum computing machines |
| CN112990472A (en) * | 2021-03-25 | 2021-06-18 | 北京百度网讯科技有限公司 | Method and apparatus for eliminating quantum noise, electronic device, and medium |
Non-Patent Citations (1)
| Title |
|---|
| 何志勇;朱忠奎;张茂青;: "基于小波包域噪声能量分布的脉冲噪声消除", 仪器仪表学报, no. 09 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114462613A (en) * | 2022-03-14 | 2022-05-10 | 北京百度网讯科技有限公司 | Quantum computer performance characterization method and device, electronic device and medium |
| CN115829046A (en) * | 2023-02-15 | 2023-03-21 | 苏州浪潮智能科技有限公司 | Quantum signal determination method and related device |
| CN115829046B (en) * | 2023-02-15 | 2023-05-05 | 苏州浪潮智能科技有限公司 | Quantum signal determination method and related device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113011593B (en) | Method and system for eliminating quantum measurement noise, electronic device and medium | |
| CN112990472B (en) | Method and apparatus for eliminating quantum noise, electronic device, and medium | |
| CN113298262B (en) | Quantum device denoising method and device, electronic device and computer readable medium | |
| CN113657602A (en) | Method and apparatus for quantum computing | |
| CN113098803B (en) | Inverse mapping decomposition method and device for quantum noise channel, electronic device, and medium | |
| CN114021728B (en) | Quantum data measuring method and system, electronic device, and medium | |
| CN113792880B (en) | Pulse-based quantum gate implementation method and device, electronic equipment and medium | |
| CN114374440B (en) | Quantum channel classical capacity estimation method and device, electronic equipment and medium | |
| CN114239840A (en) | Quantum channel noise coefficient estimation method and device, electronic device and medium | |
| AU2022241510B2 (en) | Method and apparatus for calibrating quantum measurement device, electronic device, and medium | |
| CN113516248B (en) | Quantum gate testing method and device and electronic equipment | |
| CN113723612B (en) | Method and device for operating quantum system of unidirectional quantum computer computation model | |
| CN114429218A (en) | Method and apparatus for eliminating quantum noise, electronic device and medium | |
| CN114580648A (en) | Method and device for eliminating generalized amplitude damping noise, electronic equipment and medium | |
| CN114462613A (en) | Quantum computer performance characterization method and device, electronic device and medium | |
| CN113098802B (en) | Inverse mapping decomposition method and device for quantum noise channel, electronic device, and medium | |
| US20210287126A1 (en) | Method for amplitude estimation with noisy intermediate-scale quantum computers | |
| CN113065659B (en) | Method and apparatus for eliminating quantum noise, electronic device, and medium | |
| CN114997407A (en) | Noise elimination method and device for quantum measurement equipment, electronic equipment and medium | |
| CN115577789A (en) | Quantum entanglement degree determining method, device, equipment and storage medium | |
| CN113517040B (en) | Quantum intrinsic solver implementation method and device and electronic equipment | |
| CN114462614A (en) | Quantum noise intensity determination method and device, electronic device and medium | |
| CN115310618A (en) | Quantum noise cancellation method and apparatus in quantum operation, electronic device, and medium | |
| CN114580649A (en) | Method and device for eliminating quantum Pagli noise, electronic equipment and medium | |
| CN114707358B (en) | Ion trap quantum gate fidelity optimization method and device, electronic equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |