Quantum system noise risk early warning method and device and electronic equipment

Abstract

The invention provides a quantum system noise risk early warning method and apparatus, and an electronic device. The method comprises the following steps: obtaining noise data in real time; classifying the noise data based on the type of the uncertainty risk factor; determining risk measurement values of system noise for different types of noise data based on entropy measurement; and when the risk measurement value exceeds a set risk threshold value, carrying out noise risk early warning. The system noise of the quantum system is quantized through an entropy measurement method, the system noise is accurately measured in multiple aspects of microcosmic, macroscopic and operation means, and early warning is performed on the system noise exceeding a threshold value, so that feedback control on the system noise is realized.

Description

Technical Field

[0001] The present invention belongs to the field of quantum computer technology, and specifically relates to a quantum system noise risk warning method, device and electronic equipment. Background Art

[0002] The core functionality of a quantum computer relies on the performance of qubits, the key to which lies in creating a near-ideal two-level quantum state operating environment. Quantum state preparation and manipulation achieve quantum computing through the manipulation of microwave photon transitions, superposition, and entanglement. However, the extreme fragility of quantum information makes it highly susceptible to noise interference. Noise not only undermines the accuracy of quantum state preparation and operational reliability, but also affects measurement results and may even completely undermine the potential advantages of quantum computing. Therefore, analyzing and suppressing all noise factors that cause quantum energy dissipation and keeping noise at extremely low and stable levels is a core challenge in building a highly stable quantum computer. Summary of the Invention

[0003] The purpose of the present invention is to propose a quantum system noise risk warning method, device and electronic equipment to solve the problem of lack of real-time analysis, judgment, warning and control of noise uncertainty risk factors.

[0004] To this end, in a first aspect, the present invention provides a quantum system noise risk early warning method, comprising the following steps:

[0005] Obtain noise data in real time;

[0006] classifying the noise data based on the type of uncertainty risk factor;

[0007] Determining a risk measure value of system noise based on entropy measurement for different types of noise data;

[0008] When the risk measurement value exceeds a set risk threshold, a noise risk warning is issued.

[0009] Optionally, the types of uncertainty risk factors include: microscopic noise interference uncertainty risk factors, macroscopic noise interference uncertainty risk factors or quantum bit preparation operation means uncertainty risk factors.

[0010] Optionally, determining the risk measure value of system noise based on entropy measurement for different types of noise data includes:

[0011] For each type of noise data, determining corresponding noise characteristic parameters according to the type of uncertainty risk factor to which the noise data belongs;

[0012] An entropy value is calculated according to the probability distribution of the noise characteristic parameters as a risk measurement value.

[0013] Optionally, risk measurement values ​​of system noise are determined by parallel calculation for different types of noise data.

[0014] Optionally, the risk threshold is determined according to the type of uncertainty risk factor corresponding to the noise data.

[0015] Optionally, for different types of noise data, it is determined by parallel criteria whether the risk measure value exceeds a set risk threshold.

[0016] Optionally, it also includes feedback control of the quantum state preparation and manipulation process based on the control strategy according to the risk measurement value.

[0017] Optionally, for different types of noise data, risk factors and control strategies are output in parallel according to risk measurement values.

[0018] Optionally, different types of noise data may be displayed in parallel to provide a visual output of noise risk warning.

[0019] A noise risk criterion and early warning device for a quantum state preparation and manipulation process system includes: a data acquisition module for acquiring noise data in real time;

[0020] A data classification module, configured to classify the noise data based on the type of uncertainty risk factors;

[0021] A risk measurement module, configured to determine a risk measurement value of system noise based on entropy measurement for different types of noise data;

[0022] The risk criterion module is used to issue a noise risk warning when the risk measurement value exceeds a set risk threshold.

[0023] In a third aspect, an electronic device is provided, including a memory and a processor;

[0024] The memory stores computer-executable instructions;

[0025] The processor executes the computer-executable instructions stored in the memory, so that the processor performs the quantum system noise risk early warning method.

[0026] Beneficial effects:

[0027] (1) The present disclosure provides a quantum system noise risk warning method, device, and electronic device, which quantify the system noise of the quantum system by using the entropy measurement method, so that the system noise can be accurately measured in multiple aspects of microscopic, macroscopic, and operational means, and a warning can be issued for system noise exceeding the threshold, so as to achieve feedback control of the system noise.

[0028] (2) After classifying the noise data in this disclosure, parallel computing, parallel judgment, parallel output, and parallel control are implemented for different types of noise data, and the risk factors of noise uncertainty are analyzed, judged, warned, and controlled in real time to complete the closed-loop control of noise risks, optimize system resources and robustness, and improve the response speed of the quantum system.

[0029] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 This is a flow chart of a quantum system noise risk early warning method disclosed herein;

[0032] Figure 2 A schematic diagram of risk factor classification for a quantum system noise risk early warning method disclosed herein;

[0033] Figure 3 This is a method flow chart of S300 of a quantum system noise risk early warning method in the present disclosure;

[0034] Figure 4 This is a method flow chart of S400 of a quantum system noise risk early warning method in the present disclosure;

[0035] Figure 5 This is a schematic structural diagram of a quantum system noise risk warning device disclosed in the present invention;

[0036] Figure 6 This is a system structure diagram of an electronic device in the present disclosure.

[0037] In the figure, 101-data acquisition module, 102-data classification module, 103-risk measurement module, 104-risk judgment module, 105-risk control module, 200-electronic device, 201-processor, 202-memory, 203 communication component, 204-bus. DETAILED DESCRIPTION

[0038] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0039] In the specification and claims of this application, as well as in the accompanying drawings, the terms "first," "second," "third," "fourth," and the like are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that such terms are interchangeable where appropriate. For example, first information could be referred to as second information, and similarly, second information could be referred to as first information without departing from the scope of this disclosure.

[0040] The word "if" as used herein may be interpreted as "when" or "when" or "in response to determining," depending on the context.

[0041] Furthermore, as used herein, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise.

[0042] It should be further understood that the terms “comprises” and “includes” indicate the existence of features, steps, operations, elements, components, items, types, and / or groups, but do not preclude the existence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, types, and / or groups.

[0043] The terms "or" and "and / or" as used herein are to be interpreted as inclusive, or mean any one or any combination. Thus, "A, B, or C" or "A, B, and / or C" means "any one of the following: A; B; C; A and B; A and C; B and C; A, B, and C." An exception to this definition occurs only when a combination of elements, functions, steps, or operations are inherently mutually exclusive in some manner.

[0044] The present disclosure provides Figure 1 A quantum system noise risk early warning method shown includes the following steps:

[0045] S100, acquiring noise data in real time;

[0046] Among them, noise data is obtained in real time by configuring embedded sensors in the low-temperature environment subsystem, measurement and control subsystem, chip subsystem and test laboratory environment of the superconducting quantum computer.

[0047] The sensors are connected in parallel to obtain noise data in parallel.

[0048] S200, classifying the noise data based on the type of uncertainty risk factors;

[0049] like Figure 2 As shown, the types of uncertainty risk factors include: microscopic noise interference uncertainty risk factors, macroscopic noise interference uncertainty risk factors or quantum bit preparation operation means uncertainty risk factors.

[0050] The risk factor of uncertainty in microscopic noise interference is the indirect effect on various factors affecting the performance of quantum bits during the construction of quantum computing; the uncertainty in macroscopic noise interference is the direct impact of physical equipment on the quantum state during the preparation and manipulation of the quantum state; the risk factor of uncertainty in the means of quantum bit preparation operation is the impact on the quantum state caused by the operating means used in the preparation of the quantum bit.

[0051] Specifically, microscopic noise interference uncertainty risk factors include those affecting qubit fidelity and quantum vector transport. These include excitation source noise, quasiparticle noise, charge fluctuation noise, magnetic field fluctuation flux noise, superthermal excitation, and Purcell effect agent uncertainty. Quantum vector transport uncertainty risk factors include superconducting current fluctuation noise and superconducting Josephson junction information uncertainty.

[0052] Macro noise interference uncertainty risk factors include environmental noise uncertainty risk factors affecting the test laboratory, including vibration noise uncertainty risk factors, electromagnetic field noise uncertainty risk factors, thermal noise uncertainty risk factors, acoustic noise uncertainty risk factors and ground clutter noise uncertainty risk factors.

[0053] Uncertainty risk factors affecting qubit preparation and manipulation include microwave pulse noise uncertainty risk factors affecting quantum state preparation and manipulation, and quantum state characteristic noise uncertainty risk factors. These include microwave pulse stability uncertainty risk factors, microwave pulse resolution uncertainty risk factors, microwave pulse signal-to-noise ratio uncertainty risk factors, and microwave pulse delay uncertainty risk factors. Quantum state characteristic noise uncertainty risk factors include electromagnetic field quantum uncertainty risk factors and quantum state coherence uncertainty risk factors.

[0054] S300, determining a risk measurement value of system noise based on entropy measurement for different types of noise data;

[0055] like Figure 3 shown, including:

[0056] S301. For each type of noise data, determine corresponding noise characteristic parameters according to the type of uncertainty risk factor to which the noise data belongs;

[0057] The noise data is processed to establish a mapping relationship between each type of noise data and noise characteristic parameters. The noise characteristic parameters represent the impact of this type of system noise. Noise characteristic parameters are observable or calculable physical quantities that characterize the intensity, characteristics, or impact.

[0058] S302: Calculate an entropy value as a risk measurement value according to the probability distribution of the noise characteristic parameters.

[0059] According to the noise characteristic parameters, the uncertainty risk brought by the noise is quantified by calculating the entropy value.

[0060] In one embodiment, the risk measurement value of the noise characteristic parameter is calculated using the Shannon entropy formula, which is as follows:

[0061] R=-plnp

[0062] Among them, R is the risk measurement value of the noise characteristic parameter, and p is the probability that the noise characteristic parameter brings risk.

[0063] The larger the risk measurement value, the more chaotic and unstable the noise characteristic parameters are, the greater the uncertainty of the noise is, and the higher the risk is; the smaller the risk measurement value, the more concentrated and stable the noise characteristic parameters are, the smaller the uncertainty of the noise is, and the lower the risk is.

[0064] The noise risk of the abstract quantum system is converted into noise characteristic parameters, and the risk of the noise characteristic parameters is quantified. The uncertainty of the noise characteristic parameters is used to calculate the risk measurement value, and the risk measurement value is used as a numerical indicator to measure the risk brought by the noise source to judge the risk brought by the noise.

[0065] For different types of noise data, the risk measurement value of system noise is determined through parallel calculation.

[0066] Quantum systems are highly complex, involving numerous noise sources. Noise data is collected from different parts, sources, or mechanisms within a superconducting quantum computer system. This noise information, with varying characteristics, represents the various uncertainties that interfere with the superconducting computer system. Serially calculating the risk measure for each noise source would be extremely time-consuming and unable to meet the system's requirements for real-time risk monitoring, early warning, and control. Parallel computing involves the simultaneous utilization of multiple computing resources, such as multi-core CPUs, GPUs, distributed computing nodes, or hardware accelerators, to process different computing tasks. For different types of noise data, steps S301 and S302 can be executed simultaneously, enabling risk measure values ​​to be calculated for each type of noise data separately through parallel computing.

[0067] In one embodiment, the types of uncertainty risk factors include: microscopic noise interference uncertainty risk factors, macroscopic noise interference uncertainty risk factors, and quantum bit preparation operation means uncertainty risk factors.

[0068] The uncertainty risk factors of microscopic noise interference include uncertainty risk factors affecting quantum bit fidelity and uncertainty risk factors affecting quantum vector transport.

[0069] Methods for determining risk measurement values ​​for risk factors affecting quantum bit fidelity uncertainty include:

[0070] Determination of risk measurement value of uncertainty risk factor caused by excitation source noise interference.

[0071] The risk of uncertainty from excitation noise interference refers to defects in amorphous materials and loss of TLS in dielectric / insulator layers, which directly leads to reduced qubit lifetime (qubit decay). The underlying cause in the manufacturing process is material defects. Excitation noise interference can lead to some poorly coherent two-level systems (TLS), which, in turn, couple with the bits and reduce their lifetime.

[0072] Define the calculation formula for the dielectric loss caused by the excitation element to the bit lifetime, and use the dielectric loss as the noise characteristic parameter.

[0073]

[0074] in, is the transition probability amplitude between energy levels, tanδ c is the loss coefficient (the loss coefficient can be decomposed into the sum of contributions from different interface losses); ω ij is the frequency of the angle between bits i and j; E c is the energy corresponding to the capacitor C; h is the vibration amplitude; T is the coherence time; is Planck's constant, B is the bandwidth (B = 1 / pulse width); k B The resonant cavity with bandwidth B reads the cavity line width.

[0075] The dielectric loss is used as the noise characteristic parameter to measure the entropy and determine the risk assessment value, which is recorded as R 111 ,Right now

[0076]

[0077] Where, l is the number of measurements, n is the maximum number of measurements, is the probability of dielectric loss occurring (statistical value).

[0078] Determination of risk measure value of uncertainty risk factor interfered by quasiparticle noise.

[0079] The risk of quasiparticle noise interference uncertainty refers to unpaired Cooper pairs of quasiparticles. This is primarily due to the absorption of high-frequency infrared radiation by the qubit, which causes the Cooper pairs to achieve transition energies exceeding the superconducting energy gap. This directly impacts the qubit lifetime. The underlying cause is that quasiparticles tunneling near the junction introduce additional bit decay mechanisms, resulting in an increased concentration of quasiparticles in the superconducting device.

[0080] Define the calculation formula of quasiparticle loss noise, using quasiparticle loss as the noise characteristic parameter.

[0081]

[0082] Among them, x qp is the concentration of Cooper pairs qp quasiparticles; E J Cooper pair superconducting gap transition energy; △ is the detuning coefficient; ω is the frequency; is Planck's constant.

[0083] The risk assessment value is determined by entropy measurement using quasiparticle loss noise as the noise characteristic parameter, denoted as R 112 ,Right now

[0084]

[0085] Where q and p are arbitrary Cooper pairs; is the probability of the occurrence of any Cooper pair noise.

[0086] Determination of risk measure value of charge fluctuation noise uncertainty risk factor.

[0087] The risk of charge fluctuation noise interfering with uncertainty arises from charge fluctuations in interface dielectrics, junction tunnel barriers, and defects in the substrate itself, or from charge traps. The qubit frequency is also sensitive to broadband charge fluctuations, and low-frequency charge noise can longitudinally couple to the qubit and affect decoherence time. Therefore, charge fluctuation noise directly reduces qubit lifetime and increases decoherence time.

[0088] Define the energy loss noise calculation formula and use energy loss as the noise characteristic parameter.

[0089] S qp =E J

[0090] Where q and p are arbitrary Cooper pairs; E J Cooper pair superconducting gap transition energy; E c is the energy of the capacitance change corresponding to the charge fluctuation.

[0091] Define energy loss as the noise characteristic parameter to measure entropy and determine the risk assessment value, denoted as R 113 ,Right now

[0092]

[0093] Where q and p are arbitrary Cooper pairs; is the probability of energy loss occurring for any Cooper pair q and p.

[0094] Determination of risk measure value for uncertainty risk factor of magnetic field fluctuation flux noise.

[0095] The risk of flux fluctuation noise uncertainty arises from the random flipping of spins (magnetic dipoles) on the surface of the superconducting metal that makes up the qubit. This leads to effective magnetic field noise, which can bias the flux-tunable qubit. Flux noise can be converted into frequency noise in the bit, causing changes in the bit dephasing rate and affecting the decoherence time.

[0096] The calculation formula of magnetic flux noise is defined, and magnetic flux noise is used as the noise characteristic parameter.

[0097]

[0098] Where A is quantum flux; f is the frequency of the bit; ω is the frequency of the acoustic spectrum; Φ is the magnetic flux; S is the frequency-sensitive position of the magnetic flux; the first-order rate Γ Φ .

[0099] The first-order rate Γ caused by dephasing Φ Participate in the calculation, and:

[0100]

[0101] Define the risk assessment value by taking the magnetic flux noise as the noise characteristic parameter and measuring the entropy, which is denoted as R 114 ,Right now

[0102]

[0103] Where ω is the frequency of the sound spectrum at any moment; is the probability of the sound spectrum frequency noise appearing in the time period t.

[0104] Determination of risk measurement value for uncertainty risk factors brought about by epithermal excitation.

[0105] The uncertainty risk of superthermal excitation refers to the fact that in the circuit QED architecture, there are photon number fluctuations in the residual microwave field in the cavity, which affect the quantum bits through interactions in the dispersion state, directly resulting in an increase in the decoherence time.

[0106] The calculation formula of epithermal excitation noise is defined, and epithermal excitation noise is used as the noise characteristic parameter.

[0107]

[0108] Γ Φ is the decoherence rate caused at the bit, and:

[0109]

[0110] Where κ is the read cavity linewidth, n th is the number of thermal photons, and χ is the dispersion coupling strength between the bit and the read resonant cavity.

[0111] The risk assessment value is determined by entropy measurement using epithermal excitation noise as the noise characteristic parameter, denoted as R 115 ,Right now

[0112]

[0113] in, is the probability of superthermal excitation noise occurring in time period t.

[0114] The Purcell effect brings about the determination of risk measurement values ​​for uncertain risk factors.

[0115] The Purcell effect introduces uncertainty risks, which arise when low-lifetime systems coupled to a qubit reduce the lifetime of the qubit itself. The most common scenario involves the coupling between the qubit and the read cavity directly impacting the qubit's lifetime. Furthermore, qubits can also couple with other low-lifetime components, such as various driver transmission lines and couplers between qubits.

[0116] A calculation formula for the bit-limited noise due to the Purcell effect is defined, and the bit-limited noise due to the Purcell effect is used as a noise characteristic parameter.

[0117]

[0118] where g is the coupling strength between the bit and the cavity, Δ is the detuning coefficient, and κ is the read cavity linewidth.

[0119] Define the Purcell effect on bit-limited noise as the noise characteristic parameter to measure the entropy and determine the risk assessment value, denoted as R 116 ,Right now

[0120]

[0121] in, is the probability of the bit-limited noise due to the Purcell effect in time period t.

[0122] The method for determining the risk measurement value of the uncertainty risk factors affecting quantum vector transportation includes:

[0123] Determination of risk measure value of uncertainty risk factor of superconducting current fluctuation noise interference.

[0124] The circuit of a superconducting qubit is a resonant circuit formed by a Josephson junction and a capacitor in parallel. The capacitance and inductance of the bit resonant circuit together determine the qubit's resonant frequency and anharmonicity, and thus influence the performance of the superconducting qubit. Factors affecting the performance of superconducting qubits include the noise of the Josephson junction's critical current, the thermal noise of the capacitor in the bit resonant circuit, and the charge noise caused by nonequilibrium single electron tunneling.

[0125] Current noise is calculated using the voltage signal-to-noise ratio of the equivalent noise current pulse, and the calculation formula is defined as:

[0126]

[0127] Where Q is the input signal charge, V OM is the output voltage amplitude, V n is the RMS noise voltage, η V is the voltage signal-to-noise ratio.

[0128] Define the capacitor thermal noise calculation formula:

[0129]

[0130] Where Q is the charge carried by the capacitor, κ is the Boltzmann constant, and T e is the effective noise temperature expressed in κ, and U is the potential difference between the two plates of the capacitor.

[0131] The charge noise caused by electron tunneling is calculated using the signal-to-noise ratio of the equivalent noise charge, defined by the calculation formula:

[0132]

[0133] Where, ENC is the charge of the input current pulse, Q is the charge of the input signal, η Q is the charge signal-to-noise ratio.

[0134] Define the risk assessment value by measuring the entropy using the superconducting quantum bit performance noise as the noise characteristic parameter, denoted as R 121 ,Right now:

[0135]

[0136] in, are the probabilities of the corresponding variable noise occurring in time period t.

[0137] Determination of risk measure value for information uncertainty risk factor of superconducting Josephson junction.

[0138] In the process of constructing quantum computing at the mathematical operation layer, the superposition effect of quantum bits is used to obtain the state information of quantum bits. Two bits are super-controlled twice or multiple times to realize multi-dimensional vector operations. At the same time, multiple bits can be used to perform matrix operations simultaneously. Therefore, it will be affected by the uncertainty of information such as the frequency and phase of the output signal of the superconducting Josephson junction.

[0139] The frequency and phase information of the output signal of the superconducting Josephson junction are used as noise characteristic parameters.

[0140] Define the output signal frequency:

[0141] f out =1 / 2πRC

[0142] Where R is the resistance value and C is the capacitance value.

[0143] Define the output signal phase difference:

[0144]

[0145] Among them, λ1 and λ2 are the wavelengths of the two waves, and Δx is the difference in propagation distance.

[0146] Define the frequency and phase information of the output signal of the superconducting Josephson junction as the noise characteristic parameters to measure the entropy and determine the risk assessment value, denoted as R 122 ,Right now:

[0147]

[0148] in, are the frequency of the output signal and the probability of phase noise occurring in time period t respectively.

[0149] The uncertainty risk factors for macro noise interference include the uncertainty risk factors of noise affecting the test experiment environment.

[0150] The methods for determining the risk measurement value of the uncertainty risk factors affecting the test experiment environment noise include:

[0151] Method for determining risk measurement value of vibration and noise uncertainty risk factors.

[0152] The risk of vibration noise uncertainty refers to the uncertainty caused by various types of vibration noise in the experimental environment acting on the microwave measurement and control system on the preparation and manipulation of quantum states.

[0153] Assume that the vibration noise value measured by the sensor at any time t is recorded as V te (t), the vibration amplitude value is used as the noise characteristic value to perform entropy measurement to determine the risk measurement value, which is recorded as R 21 ,Right now:

[0154]

[0155] in, is the probability of the variable noise occurring in the time period t.

[0156] Method for determining risk measurement value of uncertainty risk factor of electromagnetic field noise.

[0157] The uncertainty risk of magnetic field noise refers to the uncertainty caused by various magnetic field-induced noises in the experimental environment acting on the superconducting quantum computer system in the preparation and manipulation of quantum states.

[0158] Assume that the electromagnetic field noise frequency value measured by the sensor at any time t is E ic (t), the frequency value of electromagnetic field noise is used as the noise characteristic value to perform entropy measurement to determine the risk measurement value, which is recorded as R 22 ,Right now:

[0159]

[0160] in, is the probability of the variable noise occurring in the time period t.

[0161] Method for determining risk measurement value of thermal noise uncertainty risk factor.

[0162] Thermal noise uncertainty risk refers to the uncertainty caused by the thermal noise of the chip subsystem caused by the microwave signal in the experimental environment on the preparation and manipulation of quantum states.

[0163] Assume that the power spectrum density value measured by the sensor at any time t is G psd (t) is used as the thermal noise measurement value, and the thermal noise measurement value is used as the noise measurement value to perform entropy measurement to determine the risk measurement value, which is recorded as R 23 ,Right now:

[0164]

[0165] Power spectral density G psd (t) is the Rieter transform of the signal autocorrelation function:

[0166] G psd (t) = F[R x (τ)]

[0167] G psd (t)The unit is W / Hz (watt per hertz).

[0168] [R x (τ)] is the signal autocorrelation function:

[0169] R x (τ) = E(x(t)·x(t+τ))

[0170] Where E represents the expected value and F is the Fourier transform; is the probability of the variable noise occurring in the time period t.

[0171] Method for determining risk measurement value of acoustic noise uncertainty risk factor.

[0172] The acoustic noise uncertainty risk refers to the uncertainty caused by the acoustic noise of the dilution refrigerator pulsating system in the experimental environment acting on the superconducting quantum computer in the preparation and manipulation of quantum states.

[0173] Assume that the acoustic noise value measured by the sensor at any time t is N se (t), the acoustic noise value is used as the noise characteristic value to perform entropy measurement to determine the risk measurement value, which is recorded as R 24 ,Right now:

[0174]

[0175] in, is the probability of the variable noise occurring in the time period t.

[0176] Method for determining risk measurement value of uncertainty risk factor of ground clutter noise.

[0177] The risk of ground clutter noise uncertainty refers to the uncertainty caused by the echoes generated by the periodic reflection of ground clutter in the external environment to the quantum state preparation and manipulation of the microwave measurement and control system in the experimental environment.

[0178] Assume that the ground clutter power spectrum density value measured by the sensor at any time t is G er (t) is used as the ground clutter noise measurement value. The ground clutter noise measurement value is used as the noise characteristic value to perform entropy measurement to determine the risk measurement value, which is recorded as R 25 ,Right now:

[0179]

[0180] in, is the probability of the variable noise occurring in the time period t.

[0181] The uncertainty risk factors of quantum bit preparation and operation methods include the uncertainty risk factors of microwave pulse noise that affects quantum state preparation and manipulation, and the uncertainty risk factors of noise that affects quantum state characteristics.

[0182] The method for determining the risk measurement value of the uncertainty risk factor of quantum state preparation and manipulation microwave pulse noise includes:

[0183] Method for determining risk measurement value of uncertainty risk factor of microwave pulse stability.

[0184] The uncertainty risk of microwave pulse stability refers to the uncertainty of quantum noise generated by the amplitude, frequency and phase of microwave pulse initialization.

[0185] Assume the initial microwave pulse amplitude is A V (0), definition:

[0186] A V (0)=[V max -V min ] / V max

[0187] Among them, V max is the maximum voltage, V min The lowest voltage value.

[0188] Assume that the initial microwave pulse frequency is f RC (0), definition:

[0189] f vλ (0)=[v max / λ max -v min / λ min ] / v max / λ max

[0190] Among them, v is the wave velocity value and λ is the wavelength.

[0191] Assume that the delay rate of the initial microwave pulse phase is definition:

[0192]

[0193] Where Δx is the propagation distance difference and λ is the wavelength of the microwave signal.

[0194] The microwave pulse stability is used as the noise characteristic value to measure the entropy and determine the risk measurement value, which is recorded as R 311 ,Right now:

[0195]

[0196] in, is the pulse amplitude A V (0) Stability, is the microwave pulse frequency accuracy; is the microwave pulse phase / delay stability; α1, α2, and α3 are the corresponding weight coefficients.

[0197] Method for determining risk measure value of uncertainty risk factor of microwave pulse resolution.

[0198] The uncertainty risk of microwave pulse resolution refers to the uncertainty of quantum noise generated by the combined effect of the microwave signal wavelength and beam width at any time αt of the microwave pulse.

[0199] Assume that the microwave pulse resolution at any time t is MAS, and define:

[0200] MAS(t)=λ(t) / 2θ

[0201] Where λ is the wavelength of the microwave signal and θ is the beam width.

[0202] Assume that the microwave pulse resolution measured at any time t is MAS, and use the microwave pulse resolution as the noise characteristic value to perform entropy measurement to determine the risk measurement value, which is recorded as R 312 ,Right now:

[0203]

[0204] in, is the probability of noise occurring in time period t.

[0205] Method for determining risk measurement value of uncertainty risk factor of microwave pulse signal-to-noise ratio.

[0206] The uncertainty risk of microwave pulse signal-to-noise ratio refers to the uncertainty of quantum noise generated by the combined effect of the microwave out-band signal intensity, signal frequency, noise bandwidth, and original signal bandwidth at any time t of the microwave pulse.

[0207] The initial microwave pulse phase noise is used as the noise characteristic value to perform entropy measurement to determine the risk measurement value, which is recorded as R 313 ,definition:

[0208] R 313 =P N0 +10log(f / f0)+10log(Δf / BW)

[0209] Among them, P N0 Out-of-band signal strength, f is the signal frequency, f0 is the out-of-band signal frequency level, Δf is the noise bandwidth, and BW is the original signal bandwidth.

[0210] A method for determining the risk measurement value of microwave pulse delay uncertainty risk factors.

[0211] The risk of microwave pulse delay uncertainty refers to the uncertainty of the time changes caused by the microwave propagation from the sending to the receiving process in the channel.

[0212] Assume that the total delay noise of microwave pulse at any time t is recorded as T z (t), definition:

[0213] T z (t)(total delay) = T SV (t)(propagation delay)+T SW (t)(sending delay)+T Q (Queuing delay - waiting time)

[0214] Among them, T SV (t) = S sp / V sp , S is the channel length, V is the propagation speed of microwave in the channel; T SW (t) = S DB / CW,S DB Data block length, CWWEI channel width (the maximum number of bits that can be transmitted per unit time (bit / second)).

[0215] The total microwave pulse delay noise is used as the noise characteristic parameter to measure the entropy and determine the risk assessment value, which is recorded as R 314 ,Right now:

[0216]

[0217] in, are the probabilities of microwave pulse propagation delay, transmission delay, and queuing delay noise occurring at any time t; β1, β2, and β3 are the corresponding weight coefficients.

[0218] Methods for determining the risk measurement value of the uncertainty risk factor affecting quantum state characteristic noise include:

[0219] Method for determining risk measurement value of quantum uncertainty risk factors in micro-electromagnetic fields.

[0220] The quantum uncertainty risk of micro-electromagnetic fields refers to the uncertainty of quantum noise generated by the signal bandwidth, center frequency, and weak signal signal-to-noise ratio.

[0221] To better demonstrate the quantum nature of electromagnetic fields, it is necessary to select a weak signal-to-noise ratio with strong signal bandwidth / center frequency coherence. Therefore, the uncertainty affecting these two sub-indicators is selected as the noise characteristic parameter.

[0222] Let the quantum noise of micro-electromagnetic field be DB co (t), definition:

[0223] DB co (t) = min [signal bandwidth / center frequency]

[0224] Assume that the weak signal noise ratio is DB ws (t), definition:

[0225] DB ws (t) = max[signal amplitude / noise RMS]

[0226] The quantum noise of the micro-electromagnetic field is used as the risk characteristic value to perform entropy measurement to determine the risk measurement value, which is recorded as R 321 ,Right now:

[0227]

[0228] Among them, ∑DB co (t),∑DB ws (t) is the sum of the quantum variables of the micro-electromagnetic field at any time t.

[0229] A method for determining the risk measure value of the uncertainty risk factor of quantum state coherence.

[0230] The quantum state coherence uncertainty risk refers to the uncertainty of quantum noise generated by the frequency and capacitance of the bit.

[0231] Improving quantum state coherence requires increasing coupling strength. Similar to bandwidth in the electromagnetic spectrum, bits are coupled via capacitance, and the coupling strength depends on the bit's frequency and capacitance. The frequency width of the quantum state's qubit 1 energy level is chosen relative to the bit's center frequency; the maximum / minimum adjustable range (ratio) of the coupling between the bit and the environment or other bits is chosen. Therefore, metrics influencing these two sub-items of uncertainty are selected as noise characteristic parameters.

[0232] Let the quantum state coherence noise be denoted as Q co (t), definition:

[0233] Q co (t) = min [frequency width / bit center frequency]

[0234] Let the bit coupling noise be recorded as Q cc (t), definition:

[0235]

[0236] The entropy of the quantum state coherence (or purity) noise uncertainty is defined as the risk assessment value, denoted as R 322 ,Right now:

[0237]

[0238] in, are the probability of occurrence of quantum state coherence noise at any time t and the coupling strength between bits.

[0239] S400: issuing a noise risk warning when the risk measurement value exceeds a set risk threshold.

[0240] like Figure 4 shown, including,

[0241] S401: Perform a threshold judgment on the risk measurement value to determine whether the risk measurement value exceeds a set risk threshold.

[0242] The risk threshold is determined based on the type of uncertainty risk factor to which the noise data corresponds. The value of the risk threshold is not fixed or uniform, but is highly dependent on the type of uncertainty risk factor to which the noise data belongs, and is set based on a safety margin or critical value.

[0243] S402: If the risk measurement value exceeds the set risk threshold, a noise risk warning is issued.

[0244] For different types of noise data, it is determined by parallel criteria whether the risk measure value exceeds a set risk threshold.

[0245] Each type of noise data corresponds to a previously calculated risk measure, representing a quantitative measure of the uncertainty risk associated with that type of noise. Each risk measure is compared with its corresponding risk threshold to determine whether it exceeds the threshold. The parallel criterion compares the risk measures of different types of noise data against the risk threshold in parallel, meeting real-time and computational speed requirements.

[0246] Different types of noise data are displayed in parallel to provide visual output of noise risk warning.

[0247] The parallel display is used to visualize the risk warning output of different types of noise data, meeting the requirements of real-time performance and calculation speed.

[0248] In one embodiment, a threshold criterion method for the uncertainty risk factor of microscopic noise interference includes:

[0249] Excitation element noise risk threshold judgment method.

[0250] The dielectric loss caused by the excitation element to T1 requires the vibration amplitude h≤1μm; the average decoherence time T≥15μs. increases; h decreases, E C / h increases. Therefore,

[0251] The term increases. According to the monotonically decreasing characteristic of -plnp, it can be seen that for h≤1μm, T≥15μs, the risk measurement value ≤ the bound value. This constraint is used as R 111 Threshold value:

[0252]

[0253] in, l is the number of measurements, n is the maximum number of measurements, where is the transition probability amplitude between energy levels, tanδ c is the loss coefficient, which can be decomposed into the sum of contributions from different interface losses; ω ij is the frequency of the angle between bits i and j; E c is the energy corresponding to the capacitor C; h is the vibration amplitude; T is the coherence time; is Planck's constant, B is the bandwidth, B = 1 / pulse width; k B The resonant cavity with bandwidth B reads the cavity line width.

[0254] Quasiparticle noise risk threshold criterion method.

[0255] Quasiparticle noise requires the Cooper pair quasiparticle concentration x qp To be low, x qp ≤2e -7 , the T1 limit for 5GHz Transmon is about 50us. qp Reduce, The term decreases. According to the monotonically decreasing characteristic of -plnp, we know that for x qp ≤2e -7 , the risk measurement value ≥ the limit value, and this constraint is used as R 112 The threshold value is:

[0256]

[0257] Where q and p are arbitrary Cooper pairs, x qp is the concentration of Cooper pairs qp quasiparticles; E JCooper pair qp superconducting gap transition energy; △ is the detuning coefficient; ω is the frequency; is Planck's constant.

[0258] Charge fluctuation noise risk threshold judgment method:

[0259] Charge fluctuation requires that the EJ / EC ratio of the quantum bit is not large enough and is less than about 60, and this constraint is used as R 113 The threshold value is:

[0260]

[0261] Where ξ is the interval charge fluctuation coefficient.

[0262] Flux noise risk threshold criterion method.

[0263] The flux change requires the bit frequency to be constrained to 4-6GHz, and the decoherence time has an impact on the first order because, Similarly, Take this constraint as R 14 Threshold, that is:

[0264]

[0265] Among them, ω is the sound spectrum frequency at any time; α is the interval frequency fluctuation coefficient.

[0266] Epithermal excitation noise risk threshold criterion method.

[0267] The super-thermal excitation change requires that the coupling strength between the bit and the read resonant cavity be κ = χ = 2π·1MHz, then This constraint is R 14 Threshold, that is:

[0268]

[0269] Among them, n th is the number of thermal photons per us, and χ is the dispersion coupling strength between the bit and the read resonant cavity.

[0270] Purcell effect on bit-limited noise risk threshold criterion method.

[0271] The Purcell effect requires the coupling strength between the chip bit and the reading cavity to be g = 80 ± 5 MHz, which is used as the constraint for R 16 The threshold value is:

[0272]

[0273] Where Δ is the detuning coefficient and κ is the read cavity linewidth.

[0274] The uncertainty risk threshold judgment methods for macro noise interference include:

[0275] Vibration and noise risk threshold judgment method.

[0276] To reduce the vibration noise in the experimental environment, it is necessary to reduce the environmental vibration and control the vibration amplitude V te (t)≤1μm, and this constraint is used as R 21 The threshold value is:

[0277]

[0278] Among them, ∫V te (t)dt is the sum of the vibration amplitude values ​​corresponding to the time period t.

[0279] Electromagnetic field noise risk threshold judgment method.

[0280] To reduce the various magnetic field induced noises in the experimental environment, it is necessary to reduce the electromagnetic intensity E acting on the superconducting quantum computer system for quantum state preparation and manipulation. ic (t)≤100nT, and use this constraint as R 22 The threshold value is:

[0281]

[0282] Among them, ∫E ic (t)dt is the sum of the electromagnetic field strength corresponding to the time period t.

[0283] Thermal noise risk threshold criterion method.

[0284] To reduce the thermal noise of the chip subsystem caused by microwave signals in the experimental environment, it is necessary to reduce the effects on the quantum state preparation and manipulation of the superconducting quantum computer system, requiring a power spectral density value G psd (t) ≥ 12 μW / Hz (unit: W / Hz), and use this constraint as R 23 The threshold value is:

[0285]

[0286] Among them, ∫G psd (t)dt is the sum of the noise amount of the variable corresponding to the time period t, E is the expected value, and F is the Fourier transform.

[0287] The uncertainty risk threshold judgment methods for quantum bit preparation and operation means include: quantum state preparation and manipulation microwave pulse noise risk threshold judgment and quantum state characteristic risk threshold judgment.

[0288] Quantum state preparation and manipulation microwave pulse noise risk threshold judgment methods include:

[0289] Microwave pulse stability risk threshold criterion method.

[0290] To improve the stability of microwave pulses, it is necessary to ensure that the pulse waveform is stable within a unit time, so the initialization pulse amplitude stability must be ensured. Microwave pulse frequency accuracy Microwave pulse phase / delay stability Take this constraint as R 311 The threshold value is:

[0291]

[0292] Microwave pulse resolution risk threshold criterion method.

[0293] Improving the microwave pulse resolution requires a vertical resolution of λ(t) / 2θ≥16 bits, and this constraint is used as the R 312 The threshold value is:

[0294]

[0295] Where λ is the wavelength of the microwave signal and θ is the beam width.

[0296] Microwave pulse signal-to-noise ratio risk threshold judgment method.

[0297] Improve the microwave pulse signal-to-noise ratio and reduce noise. The microwave pulse signal-to-noise ratio R 313 ≥50dB, use this constraint as R 313 The threshold value is:

[0298] R 313 =P N0 +10log(f / f0)+10log(Δf / BW)≥50dB

[0299] Among them, P N0 Out-of-band signal strength, f is the signal frequency, f0 is the out-of-band signal frequency level, Δf is the noise bandwidth, and BW is the original signal bandwidth.

[0300] Microwave pulse delay risk threshold judgment method.

[0301] Limiting the total microwave pulse delay noise means controlling the total microwave pulse delay to T z (t)≤10 -12 (unit s), using this constraint as R 314 The threshold value is:

[0302]

[0303] Among them, T z (t) Total bit delay, TSV (t) is the propagation delay, T SW (t) is the sending delay, T Q is the queuing delay minus the waiting time; β1, β2, and β3 are the corresponding weight coefficients.

[0304] The quantum state characteristic risk threshold judgment method includes:

[0305] A risk threshold criterion method for quantum properties of micro-electromagnetic fields.

[0306] To reduce the influence of quantum noise of micro-electromagnetic field on quantum state characteristics, DB co (t) = signal bandwidth / center frequency ≤ 1×10-5, magnetic field noise DB ws (t)≤1nT, dynamic weak magnetic field noise DB ws (t)≤10pT / Hz ∧ 0.5, using this constraint as R 321 The threshold value is:

[0307]

[0308] Quantum state coherence risk threshold judgment method.

[0309] To reduce quantum state coherence noise and improve quantum state stability, the minimum operating temperature of the superconducting quantum computer is ≤10mK, so that the fluctuation is less than 10%. co (t)≤1×10-5, the coupling strength between bits (Unit: MHz), using this constraint as R 322 The threshold value is:

[0310]

[0311] S500. Feedback control is performed on the quantum state preparation and manipulation process based on the control strategy according to the risk measurement value.

[0312] For different types of noise data, risk factors and control strategies are output in parallel according to the risk measurement values.

[0313] Parallel output is a parallel output of risk factors and control strategies for risk warnings of different types of noise data, meeting the requirements of real-time performance and calculation speed. Different control strategies are set for different types of noise data to achieve feedback control of system noise.

[0314] The control strategies for the uncertainty risk factors of microscopic noise interference include:

[0315] Control strategies for stimulating meta-noise risk factors.

[0316] Excitation noise mainly affects the lifetime of quantum bits, and the main noise is related to the processing technology. Therefore, the control strategies are: first, to select a better material Ta; second, to stabilize the production line of process parameters; and third, to recruit experienced processing personnel.

[0317] Control strategies for quasiparticle noise risk factors.

[0318] Improve infrared radiation shielding, reduce the antenna pattern of the bit, and shield the different infrared radiation sources in the refrigerator. Reduce the transition energy of the superconducting energy gap, increase the lifetime of the quantum bit, reduce the loss of quasiparticles to the bit lifetime T1, and reduce the concentration of quasiparticles in the superconducting device.

[0319] Control strategies for charge fluctuation noise risks.

[0320] Electric field noise is transverse to the quantization axis of the transmon qubit. The underlying reason is that the qubit frequency is very sensitive to broadband charge fluctuations. Low-frequency charge noise will couple longitudinally to the bit and affect the decoherence time. Therefore, it is necessary to reduce charge fluctuations caused by temperature fluctuations and charge fluctuations caused by the coupling of mechanical vibrations with electrical signals.

[0321] Control strategies for flux noise risk factors.

[0322] The direct impact of magnetic flux noise is an increase in decoherence time. The underlying reason is that for frequency-tunable qubits, the use of magnetic flux to adjust the bit frequency causes magnetic flux noise to convert into frequency noise, causing the bit to dephase. Therefore, to reduce noise intensity in frequency-tunable qubits, magnetic shielding measures should be implemented in the magnetic shielding process, such as the packaging box, refrigerator shielding tube, and other noise-related components to avoid the impact of low-temperature components.

[0323] Control strategies for epithermal excitation noise risk factors.

[0324] Paying attention to the uncertainty of changes in the number of thermal photons and sample parameters means that when selecting the number of thermal photons and sample parameters, the method to reduce the superthermal excitation effect is mainly to make a good heat sink to make the device temperature as low as possible.

[0325] Control strategy of the Purcell effect on bit-limited noise risk factors.

[0326] When measuring a superconducting qubit coupled to a read cavity, the lifetime of the bit is limited by the short-lived cavity to ensure read speed. The read cavity requires a relatively long lifetime to allow light to escape quickly. Focus on chip design, the coupling strength between the qubit and the read cavity, and use Purcell filters to suppress the Purcell effect.

[0327] The control strategies for the uncertainty risk factors of macro noise interference include:

[0328] Control strategies for vibration and noise risk factors.

[0329] Reduce the vibration noise of the experimental environment and reduce the environmental vibration that affects the preparation and manipulation of quantum states in superconducting quantum computer systems.

[0330] Control strategies for electromagnetic field noise risk factors.

[0331] The way to eliminate the risk is to reduce various magnetic field induced noises in the experimental environment and reduce the electromagnetic intensity acting on the superconducting quantum computer system for quantum state preparation and manipulation.

[0332] Control strategies for thermal noise risk factors.

[0333] Quantum chips require an extremely low-temperature environment, with a minimum operating temperature of ≤10mK, a maximum cooling power of ≥400μW at 100mK, and a required ultra-low-temperature cooling power of ≥12μW at 20mK. The key to mitigating these risks is maintaining environmental stability within the ultra-low-temperature environment. This involves a low-temperature monitoring subsystem that consistently maintains an extremely low temperature for the quantum chip, ensuring the stability of the physical processing unit and preserving its superconducting properties.

[0334] The control strategies for the uncertainty risk factors of quantum bit operation methods include:

[0335] Control strategies for risk factors of microwave pulse stability.

[0336] Provide stable initial microwave pulses and stable microwave pulses throughout the entire process for quantum state preparation and manipulation.

[0337] Control strategies for risk factors of microwave pulse resolution.

[0338] Provide stable initial microwave pulses for quantum state preparation and manipulation processes, and improve microwave pulse resolution.

[0339] Control strategies for microwave pulse signal-to-noise ratio risk factors.

[0340] Provide stable initial microwave pulses for quantum state preparation and manipulation processes, enhance signal strength, increase signal frequency, and improve microwave pulse signal-to-noise ratio.

[0341] Control strategies for microwave pulse delay risk factors.

[0342] Control the total delay of microwave pulses, that is, control the delay of each link such as propagation, transmission, and queuing.

[0343] Control strategies for quantum risk factors in micro-electromagnetic fields.

[0344] To reduce the impact of quantum noise in the micro-electromagnetic field on the characteristics of the quantum state, the refrigerator temperature needs to be ≤10mK and the dynamic weak magnetic field noise needs to be low enough to improve the quantum properties. The 0-state and 1-state test points in the experimental measurement are more concentrated, and the accuracy used for calculation is higher.

[0345] Control strategies for quantum state coherence risk factors.

[0346] The quantum state expressed using a two-level qubit structure requires a narrower energy-level frequency bandwidth, resulting in better qubit coherence and, equivalently, a longer lifetime. Therefore, it is important to select qubits that meet the required capacitance and high survival rate.

[0347] Second, as Figure 5 As shown, a quantum system noise risk early warning device is provided, comprising:

[0348] A data acquisition module 101 is used to acquire noise data in real time;

[0349] A data classification module 102 is configured to classify the noise data based on the type of uncertainty risk factors;

[0350] A risk measurement module 103 is configured to determine a risk measurement value of system noise based on entropy measurement for different types of noise data;

[0351] The risk criterion module 104 is configured to issue a noise risk warning when the risk measurement value exceeds a set risk threshold.

[0352] The risk control module 105 is used to perform feedback control on the quantum state preparation and manipulation process based on the control strategy according to the risk measurement value.

[0353] Thirdly, as Figure 6 As shown, an electronic device is provided, characterized in that it includes: a memory, a processor;

[0354] Memory stores computer-executable instructions;

[0355] The processor executes the computer-executable instructions stored in the memory, so that the processor performs the above method.

[0356] In one embodiment, the electronic device 200 includes: at least one processor 201 and a memory 202. Optionally, the electronic device 200 further includes a communication component 203. The processor 201, the memory 202 and the communication component 203 are connected via a bus 204.

[0357] During the specific implementation process, at least one processor 201 executes the computer-executable instructions stored in the memory 202, so that the at least one processor 201 performs the above method.

[0358] The specific implementation process of the processor 201 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0359] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented by a hardware processor or implemented by a combination of hardware and software modules in the processor.

[0360] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory.

[0361] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified into address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0362] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A quantum system noise risk early warning method, characterized in that: The steps include: Obtain noise data in real time; classifying the noise data based on the type of uncertainty risk factor; Determining a risk measure value of system noise based on entropy measurement for different types of noise data; When the risk measurement value exceeds a set risk threshold, a noise risk warning is issued.

2. The method according to claim 1, characterized in that The types of uncertainty risk factors include: microscopic noise interference uncertainty risk factors, macroscopic noise interference uncertainty risk factors or quantum bit preparation operation means uncertainty risk factors.

3. The method according to claim 1, characterized in that Determining the risk measure value of system noise based on entropy measurement for different types of noise data includes: For each type of noise data, determining corresponding noise characteristic parameters according to the type of uncertainty risk factor to which the noise data belongs; An entropy value is calculated according to the probability distribution of the noise characteristic parameters as a risk measurement value.

4. The method according to claim 3, characterized in that For different types of noise data, the risk measurement value of system noise is determined through parallel calculation.

5. The method according to claim 3, characterized in that The risk threshold is determined according to the type of uncertainty risk factor corresponding to the noise data.

6. The method according to claim 5, characterized in that For different types of noise data, it is determined by parallel criteria whether the risk measure value exceeds a set risk threshold.

7. The method according to claim 1, characterized in that It also includes feedback control of the quantum state preparation and manipulation process based on the control strategy according to the risk measurement value.

8. The method according to claim 7, characterized in that For different types of noise data, risk factors and control strategies are output in parallel according to the risk measurement values.

9. The method according to claim 1, characterized in that Different types of noise data are displayed in parallel to provide visual output of noise risk warning.

10. A quantum system noise risk warning device, characterized in that: include: A data acquisition module is used to acquire noise data in real time; A data classification module, configured to classify the noise data based on the type of uncertainty risk factors; A risk measurement module, configured to determine a risk measurement value of system noise based on entropy measurement for different types of noise data; The risk criterion module is used to issue a noise risk warning when the risk measurement value exceeds a set risk threshold.

11. An electronic device, characterized in that: Including memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 9.

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