A method for designing a flip-chip soldered superconducting quantum chip geometry optimized for radiation resistance

Abstract

The application discloses a kind of flip-chip type superconducting quantum chip anti-radiation optimized geometric structure design method, including the design bit structure's set of optimizable parameters, the multi-objective cost function model of specific frequency impedance etc.;Through the iterative optimization of multi-objective cost function model to the reverse design algorithm, output the optimal parameter set of bit structure.The application scheme is optimized to the bit structure parameter of superconducting quantum chip in flip-chip structure, by defining the set of optimizable parameters containing random structure parameters, the cost function with the minimum value of specific frequency impedance as the target is established, and the parameter iterative optimization is carried out.This method can effectively reduce the impedance mismatch of quantum bit under radiation environment through active anti-radiation design, reduce the influence of quasi-particle defects on bit, and enhance the anti-interference ability and calculation stability of superconducting quantum chip.

Description

Technical Field

[0001] This invention belongs to the field of quantum device technology, and in particular relates to a geometric structure design method for radiation-resistant optimization of flip-chip superconducting quantum chips. Background Technology

[0002] Superconducting quantum computing is considered one of the most promising technological pathways to achieve fault-tolerant quantum computing. The coherence time and computational fidelity of its core component, the superconducting quantum chip, directly determine the practical application prospects of quantum processors. However, in real-world operating environments, superconducting qubits are highly susceptible to external and internal radiation interference, leading to a significant increase in non-equilibrium quasi-particle density. This induces qubit flipping errors, energy relaxation, and coherence time decay, severely limiting the fidelity of quantum gates and the efficiency of algorithm execution.

[0003] In existing technologies, the main sources of radiation and interference mechanisms include secondary particle showers induced by cosmic rays and high-energy particles, as well as ionizing radiation such as gamma rays and X-rays in the environment. These generate high-energy electron-hole pairs in the chip substrate and diffuse to the Josephson junction to form quasi-particle poisoning. In addition, there is infrared thermal radiation and microwave radiation generated by the vibration of the chip packaging box, sample holder, cables and dilution refrigerator; as well as crosstalk radiation between adjacent bits and between the readout resonant cavity and the bit.

[0004] To mitigate these effects, existing technologies primarily rely on passive shielding and local optimization strategies. For example, Vepsäläinen et al., in "Impact of ionizing radiation on superconducting qubit coherence, Nature, 2020, 584: 551-556," systematically studied the impact of ionizing radiation on the coherence of superconducting qubits, confirming that cosmic ray events lead to a burst of quasi-particle growth, causing T1 to plummet to <10 μs. They proposed passive methods such as adding lead bricks and polyethylene composite shielding layers inside the refrigerator and operating in an underground laboratory, reducing the radiation flux by approximately 30 times and decreasing coherence time fluctuations from ~50% to ~15%. However, this approach still cannot completely eliminate sudden events, and lead shielding introduces additional thermal load and eddy current losses, making it difficult to engineer for large-scale surface processor deployment. Similarly, in "Correlated chargenoise and relaxation errors in superconducting qubits, Nature, 2021, 594: 369-373", Wilen et al. directly observed multi-bit correlated error events induced by cosmic rays through surface code experiments. They used a thick lead and copper structure to shield and reduce the event rate by about 5 times, but the correlated error rate was still present. Moreover, the scheme relied entirely on the passive shielding layer and could not actively suppress radiation emission and reception at the intrinsic structure level of the bit.

[0005] These representative works are all limited to a single passive strategy and lack active radiation-resistant design of the qubit intrinsic structure. This leads to frequent quasi-particle poisoning events and large fluctuations in coherence time in multi-qubit processors, making it difficult to meet the surface code error correction protocol's requirement of an error rate below 10⁻⁴. Therefore, a system solution is urgently needed to realize active radiation-resistant design of the qubit structure to reduce quasi-particle generation and improve qubit stability. Summary of the Invention

[0006] The purpose of this invention is to solve the problems mentioned in the background art and to propose a geometric structure design method for radiation-resistant superconducting quantum chips.

[0007] To achieve the objective of this invention, a method for designing a radiation-resistant geometric structure for a flip-chip superconducting quantum chip is disclosed, comprising the following steps:

[0008] Step 1: Determine the set of optimizable parameters for the bit structure of the flip-chip superconducting quantum chip;

[0009] Step 2: Construct a multi-objective cost function model based on minimizing the sum of impedances at a specific frequency. This model is optimized based on the minimum value of the sum of impedances at a specific frequency, and its mathematical formula is expressed as:

[0010]

[0011] Where F represents a specific frequency set {20 GHz, 80 GHz, 160 GHz, 320 GHz, 390 GHz}, Re(Z 11 (f)) and Im(Z) 11 (f) represents the real and imaginary parts of the impedance Z11 at frequency f, respectively;

[0012] Step 3: Iteratively optimize the multi-objective cost function model using a reverse design algorithm;

[0013] Step 4: Output the parameter set of the optimal flip-chip superconducting quantum chip bit structure.

[0014] Further, in step 1, the set of optimizable parameters for the flip-chip superconducting quantum chip qubit structure is determined; for the flip-chip superconducting quantum chip, its qubit structure is parametrically modeled, and key dimensional parameters include the arm length, arm width, width of the square at the top of the qubit, and width of the metal etching area; the radiation impedance at the qubit and Josephson junction is obtained by simulation calculation, and the optimization objective is to minimize the radiation impedance matching efficiency. The parameterized dimensions are used as optimization conditions, and the reverse design algorithm is used for iterative optimization to finally obtain a geometric structure with intrinsic radiation resistance.

[0015] Furthermore, in step 2, a multi-objective cost function model based on minimizing impedance at a specific frequency is constructed; the radiation reflection coefficient of the quantum bit's resistance to radiation is determined; the larger the radiation reflection coefficient, the better the radiation resistance, which can determine the sensitivity of the bit to radiation, thereby obtaining the capacitance parameter information corresponding to the maximum coupling efficiency, and determining the optimal size of the capacitor physical structure.

[0016] Furthermore, the radiation reflection coefficient is determined by the radiation impedance of the capacitor and the radiation impedance of the Josephson junction. The radiation reflection coefficient can quantify the matching degree between the radiation impedance of the capacitor and the radiation impedance of the Josephson junction, thereby achieving the best signal transmission or energy conversion efficiency.

[0017] The radiation reflection coefficient of the cross capacitor is determined by the following formula;

[0018]

[0019] Where Γ is the radiation reflection coefficient of the bit capacitor, Z L Z0 is the radiation impedance of the bit capacitor, Z0 is the radiation impedance of the Josephson junction, ... * 0 is the conjugate of Z0.

[0020] Furthermore, in step 3, the multi-objective cost function model is iteratively optimized using a reverse design algorithm. The reverse design algorithm uses the initial size information of the bit capacitor and the real and imaginary parts of the simulated impedance as target parameters to optimize the model, iterate, and find the minimum value of the impedance parameter, that is, the maximum radiation reflection coefficient.

[0021] Furthermore, the initial size information of the bit capacitor can be listed as the arm length and arm width of the bit, the width of the square at the top of the bit, the space between the bit and the plane, and the length at the Josephson junction;

[0022] The radiation impedance of a Josephson junction is determined by the functional relationship between the electromagnetic wave frequency, the quantum circuit time constant, and the tunneling resistance of the Josephson junction, as shown in the following formula:

[0023]

[0024] Where Z0 is the radiation impedance of the Josephson junction, ω is the frequency of the electromagnetic wave, τ is the time constant of the quantum circuit, and Rn is the tunneling resistance of the Josephson junction.

[0025] The time constant of a quantum circuit is determined by the electrical parameters of the Josephson junction, which characterize the speed of the quantum circuit response; the electrical parameters can be listed as the tunneling resistance and capacitance of the Josephson junction.

[0026] The time constant of a quantum circuit is determined by the following formula:

[0027] τ≡R0C0

[0028] Wherein, C0 is the capacitance of the Josephson junction; the value of ω ranges from 0 to 200 GHz and is set according to actual conditions; the value of R0 ranges from 6000 to 10000Ω, more preferably 8000Ω, and is set according to actual conditions; the range of C0 is from 0 to 10 fF, more preferably 2 fF, and is set according to actual conditions.

[0029] To achieve the objectives of this invention, this invention also discloses a radiation-resistant optimized geometric structure design system for flip-chip superconducting quantum chips, comprising the following modules:

[0030] Optimizable parameter determination module: used to determine the set of optimizable parameters for the bit structure of the flip-chip superconducting quantum chip;

[0031] Multi-objective cost function module: used to construct a multi-objective cost function model based on minimizing impedance at a specific frequency;

[0032] Iterative optimization module: Used to iteratively optimize the multi-objective cost function model using a genetic algorithm and output the optimal set of parameters for the flip-chip superconducting quantum chip bit structure.

[0033] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a method for optimizing the geometric structure of a flip-chip superconducting quantum chip for radiation resistance.

[0034] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a radiation-resistant optimized geometric design method for a flip-chip superconducting quantum chip.

[0035] Compared with existing technologies, the significant advancements of this invention are as follows: 1) The solution of this invention optimizes the bit structure in the flip-chip model of a quantum bit chip by defining an optimizable parameter set containing random structure parameters, establishing a multi-objective cost function with the goal of minimizing impedance at a specific frequency, and using an inverse optimization algorithm to optimize the parameters and output the optimal structure parameters. This method can effectively reduce electromagnetic coupling crosstalk between quantum bits, improve bit readout efficiency and coherence performance, and provide key technical support for the integration of high-performance multi-qubit chips. 2) For the bit structure of flip-chip superconducting quantum chips, this invention directly establishes the bit structure as the core design variable for controlling the impedance strength of quantum bits, achieving precise control of spatial radiation, thereby effectively suppressing the generation of quasiparticles while improving radiation resistance, and providing a physical basis for high-density quantum integration. 3) With the goal of improving the radiation resistance of quantum bits, this invention realizes the functional leap from passive shielding to active radiation resistance optimization in quantum packaging of superconducting quantum chips. This solution enables the superconducting quantum chip structure to work synergistically in three dimensions: spatial reconstruction, quantum control, and signal fidelity, providing an algorithm-driven deterministic design approach for high-performance flip-chip quantum chips.

[0036] To more clearly illustrate the functional characteristics and structural parameters of the present invention, further explanation is provided below in conjunction with the accompanying drawings and specific embodiments. Attached Figure Description

[0037] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0038] Figure 1 This is a sample design flowchart of the flip-chip type radiation-resistant superconducting quantum chip of the present invention;

[0039] Figure 2 This is an overall schematic diagram of the flip-chip type radiation-resistant superconducting quantum chip of the present invention;

[0040] Figure 3 This is a schematic diagram of the layer structure of the flip-chip type radiation-resistant superconducting quantum chip of the present invention;

[0041] Figure 4 This is a schematic diagram of the bit layer of the flip-chip type radiation-resistant superconducting quantum chip of the present invention;

[0042] Figure 5 This is a schematic diagram of the readout layer of the flip-chip type radiation-resistant superconducting quantum chip of the present invention;

[0043] Figure 6 This is a schematic diagram of the optimized radiation-resistant superconducting quantum chip bit structure in an embodiment of the present invention;

[0044] Figure 7 This is an impedance matching curve of the unoptimized radiation-resistant superconducting quantum chip in this embodiment of the invention;

[0045] Figure 8 This is an impedance matching curve of the optimized radiation-resistant superconducting quantum chip in the comparative example of this invention. Detailed Implementation

[0046] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] A method for radiation-resistant optimized geometric design of a flip-chip superconducting quantum chip includes the following steps:

[0048] Step 1: Determine the set of optimizable parameters for the bit structure of the flip-chip superconducting quantum chip;

[0049] Step 2: Construct a multi-objective cost function model based on minimizing impedance at a specific frequency;

[0050] Step 3: Iteratively optimize the multi-objective cost function model using a reverse design algorithm;

[0051] Step 4: Output the parameter set of the optimal flip-chip superconducting quantum chip bit structure.

[0052] This invention provides a method for optimizing the geometric structure of a flip-chip superconducting quantum chip for radiation resistance, wherein the bit is a flip-chip bit with a symmetrical π structure.

[0053] Step 1: Determine the set of optimizable parameters for the flip-chip superconducting quantum chip qubit structure. For the flip-chip superconducting quantum chip, parameterize the qubit structure. Key dimensional parameters include the arm length, arm width, width of the square at the top of the qubit, and width of the metal etching area. Use simulation calculations to obtain the radiation impedance at the qubit and Josephson junction. Minimize the radiation impedance matching efficiency as the optimization objective. Using the parameterized dimensions as optimization conditions, iterative optimization is performed using reverse design algorithms such as genetic algorithms to ultimately obtain a geometric structure with intrinsic radiation resistance.

[0054] Superconducting quantum chips mainly consist of functional modules such as qubits, resonant cavities, readout lines, control lines, and magnetic flux bias lines. Among them, the qubits, composed of Josephson junctions and capacitors, are crucial for the output of qubit states in quantum computing.

[0055] By controlling the capacitance of the qubit and the structure of the Josephson junction, the area of ​​the Josephson junction ring can be controlled without affecting its external radiation. The more external radiation it is affected by, the more likely it is to generate quasiparticles, which will affect the stability of the qubit itself.

[0056] Modulating the structure of a qubit does not change the area of ​​the superconducting quantum interference loop of the Josephson junction, and therefore does not affect the control of the qubit state by the magnetic flux bias line.

[0057] The invention is based on the Xmon capacitor structure. By simulating and analyzing the radiation impedance of the qubit and calculating its coupling efficiency with the junction impedance, the sensitivity of the qubit to radiation can be determined.

[0058] Step 2: Construct a multi-objective cost function model based on minimizing impedance at a specific frequency; determine the radiation reflection coefficient of the qubit's impedance resistance to radiation. A larger radiation reflection coefficient indicates better radiation resistance, allowing for assessment of the qubit's sensitivity to radiation. This, in turn, provides the capacitance parameter information corresponding to the maximum coupling efficiency, determining the optimal size of the capacitor's physical structure.

[0059] The radiation reflection coefficient of a capacitor is determined by the radiation impedance of the capacitor and the radiation impedance of the Josephson junction. The radiation reflection coefficient can quantify the matching degree between the radiation impedance of the capacitor and the radiation impedance of the Josephson junction, thereby achieving the best signal transmission or energy conversion efficiency.

[0060] The radiation reflection coefficient of the cross capacitor is determined by the following formula (1).

[0061] (1)

[0062] Where Γ is the radiation reflection coefficient of the bit capacitor, Z LZ0 is the radiation impedance of the bit capacitor, Z0 is the radiation impedance of the Josephson junction, ... * 0 is the conjugate of Z0.

[0063] The radiation impedance of the capacitor is determined through modeling and simulation. Specifically, a model is created based on the initial size information of the bit capacitor, and the model is simulated at the Josephson junction to obtain the radiation impedance of the capacitor. By simulating the radiation impedance of the bit capacitor and calculating its radiation reflection coefficient with the impedance of the Josephson junction, the sensitivity of the bit capacitor to radiation is determined, and thus the capacitor size corresponding to the insensitive state is determined.

[0064] Step 3: Iteratively optimize the multi-objective cost function model using a reverse design algorithm. The reverse design algorithm uses the initial size information of the bit capacitor and the real and imaginary parts of the simulated impedance as target parameters to optimize the model and iterate to find the minimum impedance parameter, i.e., the maximum radiation reflection coefficient.

[0065] The initial dimensions of a bit capacitor can be listed as the bit arm length and width, the width of the square at the top of the bit, the space between the bit and the plane, and the length of the Josephson junction, etc.

[0066] The radiation impedance of a Josephson junction is determined by the functional relationship between the electromagnetic wave frequency, the quantum circuit time constant, and the tunneling resistance of the Josephson junction.

[0067] The radiation impedance of the Josephson junction is determined by formula (2).

[0068] (2)

[0069] Where Z0 is the radiation impedance of the Josephson junction, ω is the frequency of the electromagnetic wave, τ is the time constant of the quantum circuit, and Rn is the tunneling resistance of the Josephson junction.

[0070] The time constant of a quantum circuit is determined by the electrical parameters of the Josephson junction, characterizing the speed of the quantum circuit's response. These electrical parameters can include, for example, the tunneling resistance and capacitance of the Josephson junction.

[0071] The time constant of the quantum circuit is determined by formula (3).

[0072] τ≡R0C0 (3)

[0073] Where C0 is the capacitance of the Josephson junction.

[0074] The value of ω ranges from 0 to 200 GHz and can be set according to actual conditions.

[0075] The value of R0 ranges from 6000 to 10000 Ω, and is more preferably 8000 Ω. It can be set according to actual conditions.

[0076] The range of C0 is 0 to 10 fF, more preferably 2 fF, and can be set according to actual conditions.

[0077] Step 4: Output the parameter set of the optimal flip-chip superconducting quantum chip bit structure; after iterative optimization using the reverse algorithm, obtain the parameter set of the optimal flip-chip superconducting quantum chip bit structure.

[0078] This invention transforms the bit structure of a flip-chip superconducting quantum chip into a radiation-resistant structure using a reverse design algorithm. Specifically, a genetic algorithm is first used to iteratively optimize a constructed multi-objective cost function model with the goal of minimizing the sum of impedances at a specific frequency. Simulation results are then used to determine the radiation impedance of the cross capacitor and the Josephson junction. The reflection coefficient of the cross capacitor is then determined using the functional relationship between the two. Furthermore, the coupling efficiency of the radiation impedance between the double-π bit and the Josephson junction is calculated to determine the sensitivity of the double-π bit to electromagnetic radiation. The parameter information of the bit structure corresponding to the lowest coupling efficiency is obtained, thus completing the structural design of the impedance-resistant bit.

[0079] Example

[0080] Combination Figure 1 A method for optimizing the geometric structure of a flip-chip superconducting quantum chip for radiation resistance, comprising four steps;

[0081] Step 1: Determine the set of optimizable parameters for the bit structure of the flip-chip superconducting quantum chip;

[0082] A schematic diagram of the flip-chip type radiation-resistant superconducting quantum chip of the present invention is shown below. Figure 2 As shown, it includes a bit structure layer 100 and a readout layer 200;

[0083] Combination Figure 3 The simulated structure of the flip-chip superconducting quantum chip in this embodiment includes a quantum bit structure layer 100 and a readout layer 200.

[0084] The parameters required for modeling also include: substrate height (e.g., 0.5 mm), cross capacitance and film thickness (e.g., 0.1 μm), and substrate material (e.g., sapphire).

[0085] This script uses Python to create an HFSS model, imports the initial size information of the cross capacitor, performs modeling, and then conducts simulations at the Josephson junction. Optional Python libraries used are listed below: Modeling: pywin32, numpy; Simulation calculations: numpy, scipy, matplotlib, pandas.

[0086] Both the quantum bit structure layer and the readout layer are 0.5 mm thick and made of sapphire.

[0087] Among them, bit structure layer 100 is located in the first layer, including bit structure and Josephson junction, and is the region where quantum state signals are generated;

[0088] Combination Figure 3 In this embodiment, the bit structure layer consists of two symmetrical π-type capacitors (first capacitor 101 and second capacitor 102) and a Josephson junction 103. The Josephson junction is an insulating structure, so the symmetrical capacitors do not intersect. Figure 4 As shown.

[0089] A simulation model of the Josephson junction was built, using a lumped port 104 to replace the structure covering and connecting the two symmetrical Josephson junctions, set to 50 ohms, and the boundary condition was a radial boundary condition.

[0090] In this embodiment, the bit structure is the optimization target. The arm length of the bit (105), the arm width of the bit (106), the width of the square at the top of the bit (107), and the width of the metal etching area (108) are optimizable parameters. The structural parameters are generated by random optimization.

[0091] The readout layer 200 is located below the bit structure layer, 0.008 mm away, and includes a resonant cavity and readout lines, serving to transmit quantum state signals.

[0092] Combination Figure 5 , Figure 6 In this embodiment, the bus structure 201 in the readout layer is a long straight microstrip line, and the resonant readout cavity 202 has a meandering microstrip line shape with a length of one-quarter wavelength.

[0093] In this embodiment, the bit structure is the optimization target, and the bit arm length (cross_lengths), bit arm width (cross_inside_widths), width of the top square of the bit (pad_sizes), and width of the metal etching area (gaps) are optimizable parameters. The structural parameters are generated by random optimization.

[0094] In addition, the optimizable parameters are subject to certain range constraints. In this embodiment, the structural parameters are constrained to be between 15-40 μm in the range of gaps, 900-1500 μm in the range of cross_lengths, 20-25 μm in the range of cross_inside_widths, and 35-70 μm in the range of pad_sizes.

[0095] Step 2: Iteratively optimize the multi-objective cost function model using a reverse design algorithm:

[0096]

[0097] Where, sum_{f in F}: summation over each frequency f in set F. F represents radiation-sensitive frequency points; in this embodiment, frequency optimization is performed within the 0-400 GHz range. |Re(Z 11 (f))| + |Im(Z 11 (f))| represents the cost of each frequency f, Z 11 (f) represents the input impedance at frequency f, and is a complexity number Re + j*Im, where Re represents the real part and Im represents the imaginary part.

[0098] Step 3: Iteratively optimize the multi-objective cost function model using a reverse design algorithm;

[0099] Algorithms include, but are not limited to, genetic algorithms, simulated annealing algorithms, and particle swarm optimization algorithms;

[0100] The iterative optimization in this embodiment is implemented using simulated annealing, i.e., minimizing the multi-objective cost function composite_score. A genetic algorithm is used to perform an efficient search in the parameter space. Each evaluation requires calling an electromagnetic simulator to calculate Z under that parameter combination. 11 The parameters are used to determine the performance and thus the cost, ultimately outputting a set of parameters that optimize overall performance. This process includes:

[0101] Step 3, iterative optimization, is implemented using a genetic algorithm, and its process includes:

[0102] Initialization: The algorithm starts by randomly generating an initial population. The population is a list of parameter sets, where each individual represents a combination of quantum chip geometry parameters within a constraint, including gap, cross_length, cross_inside_width, and pad_size. A population of POP_SIZE = 20 individuals is generated to form the initial population.

[0103] Evaluation: The fitness of each individual in the population is calculated, which involves setting parameters in HFSS, reconstructing the geometry, running the simulation, and extracting the impedance Z at five frequency points. 11 The real part (Re) and imaginary part (Im) are used to calculate the sum of |Re| + |Im| as the cost function.

[0104] Selection, crossover, and mutation: The process first randomly selects 3 individuals from the population using a tournament selection mechanism (tournsize = 3), compares their fitness, and prioritizes retaining individuals with low cost, thus biasing towards a superior solution. Subsequently, two-point crossover (cxTwoPoint) is applied to simulate reproduction, exchanging individual parameters with a crossover probability cxpb = 0.7 to increase population diversity. At the same time, random perturbation is introduced through index shuffle mutation (mutShuffleIndexes) with a mutation probability mutpb = 0.2 to prevent the algorithm from getting stuck in local optima, ensuring a globally optimal radiation-resistant configuration, and iteratively updating the population.

[0105] Iterative update: The new population is repeatedly evaluated, the old population is replaced, and evolution continues. The process is repeated for NGEN generations, and the population is updated each time.

[0106] Convergence: With NGEN = 20 fixed as the stopping condition, the optimal value is found through iteration.

[0107] Step 4: Output the optimal set of radiation-resistant bit modulation structure parameters.

[0108] In this embodiment, the initial population size (POP_SIZE) is set to 20, the maximum number of iterations (NGEN) is 20, the crossover probability (CXPB) is 0.7, the mutation probability (MUTPB) is 0.2, and the tournament size (tournsize) is 3 in the genetic algorithm.

[0109] Combination Figure 5 The optimized set of structural parameters output in this embodiment contains the four optimal structural parameters, which are used to guide the design of bits. The optimized set of structural parameters is determined by a subset of random structural parameters.

[0110] Combination Figure 7 The frequency and impedance curves of the radiation-resistant bit structure of the unoptimized bit structure in this embodiment are obtained at 20 GHz. L The value is .

[0111] In this embodiment, the optimal radiation-resistant bit structure obtained through genetic algorithm optimization has the following values: gap = 20.5, cross_lengths = 1018.5, cross_inside_width = 23.5, and pad_size = 46.5.

[0112] Figure 8 The graphs showing the frequency and impedance of the radiation-hardened qubit structure obtained through genetic algorithm optimization are shown, with an impedance of 49.9 μm at 20 GHz. J21.89 Ω, impedance is 64.3 at 40GHz. The impedance is 66.3 Ω at 60 GHz, with a j21.23 Ω value. J20.23Ω, impedance is 61.3Ω at 80GHz. The impedance is j16.7 Ω, and 53.9 - j6.5 Ω at 100 GHz.

[0113] The optimized radiation-hardened bit structure yielded a Josephson junction Z0 value at 20 GHz. Therefore, its reflectance coefficient is 0.988.

[0114] The Z-value of the bits obtained at 20 GHz from the original bit structure L The value is The obtained reflection coefficient is .

[0115] This invention also provides an optimization system for semiconductor quantum bit radiation-resistant qubit structures, comprising the following modules:

[0116] Optimizable parameter determination module: used to determine the set of optimizable parameters for the bit structure of flip-chip superconducting quantum chips;

[0117] Multi-objective cost function module: Constructs a multi-objective cost function model based on minimizing impedance at a specific frequency;

[0118] Iterative optimization module: Iteratively optimizes the multi-objective cost function model through a reverse design algorithm;

[0119] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0120] Step 1: Determine the set of optimizable parameters for the bit structure of the flip-chip superconducting quantum chip;

[0121] Step 2: Construct a multi-objective cost function model based on minimizing impedance at a specific frequency;

[0122] Step 3: Iteratively optimize the multi-objective cost function model using a reverse design algorithm;

[0123] Step 4: Output the parameter set of the optimal flip-chip superconducting quantum chip bit structure.

[0124] The present invention also provides a computer-storable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the following steps:

[0125] Step 1: Determine the set of optimizable parameters for the bit structure of the flip-chip superconducting quantum chip;

[0126] Step 2: Construct a multi-objective cost function model based on minimizing impedance at a specific frequency;

[0127] Step 3: Iteratively optimize the multi-objective cost function model using a reverse design algorithm;

[0128] Step 4: Output the parameter set of the optimal flip-chip superconducting quantum chip bit structure.

[0129] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0130] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for designing a radiation-resistant geometric structure for a flip-chip superconducting quantum chip, characterized in that, Includes the following steps: Step 1: Determine the set of optimizable parameters for the bit structure of the flip-chip superconducting quantum chip; Step 2: Construct a multi-objective cost function model based on minimizing the sum of impedances at a specific frequency. This model is optimized based on the minimum value of the sum of impedances at a specific frequency, and its mathematical formula is expressed as: Where F represents a specific frequency set {20 GHz, 80 GHz, 160 GHz, 320 GHz, 390 GHz}, Re(Z 11 (f)) and Im(Z) 11 (f) represents the real and imaginary parts of the impedance Z11 at frequency f, respectively; Step 3: Iteratively optimize the multi-objective cost function model using a reverse design algorithm; Step 4: Output the parameter set of the optimal flip-chip superconducting quantum chip bit structure.

2. The geometric structure design method for radiation-resistant optimization of a flip-chip superconducting quantum chip according to claim 1, characterized in that, In step 1, the set of optimizable parameters for the flip-chip superconducting quantum chip qubit structure is determined. For the flip-chip superconducting quantum chip, its qubit structure is parametrically modeled. Key dimensional parameters include the arm length, arm width, width of the square at the top of the qubit, and width of the metal etching area. The radiation impedance at the qubit and Josephson junction is obtained by simulation calculation. The optimization objective is to minimize the radiation impedance matching efficiency. The parameterized dimensions are used as optimization conditions. The reverse design algorithm is used for iterative optimization to finally obtain a geometric structure with intrinsic radiation resistance.

3. The geometric structure design method for radiation-resistant optimization of a flip-chip superconducting quantum chip according to claim 1, characterized in that, In step 2, a multi-objective cost function model based on minimizing impedance at a specific frequency is constructed; the radiation reflection coefficient of the quantum bit's resistance to radiation is determined; the larger the radiation reflection coefficient, the better the radiation resistance, which can determine the sensitivity of the bit to radiation, and thus obtain the capacitance parameter information corresponding to the maximum coupling efficiency, and determine the optimal size of the capacitor physical structure.

4. The geometric structure design method for radiation-resistant optimization of a flip-chip superconducting quantum chip according to claim 3, characterized in that, The radiation reflection coefficient is determined by the radiation impedance of the capacitor and the radiation impedance of the Josephson junction. The radiation reflection coefficient can quantify the matching degree between the radiation impedance of the capacitor and the radiation impedance of the Josephson junction, thereby achieving the best signal transmission or energy conversion efficiency. The radiation reflection coefficient of the cross capacitor is determined by the following formula; Where Γ is the radiation reflection coefficient of the bit capacitor, Z L Z0 is the radiation impedance of the bit capacitor, Z0 is the radiation impedance of the Josephson junction, ... * 0 is the conjugate of Z0.

5. The geometric structure design method for radiation-resistant optimization of a flip-chip superconducting quantum chip according to claim 1, characterized in that, In step 3, the multi-objective cost function model is iteratively optimized using a reverse design algorithm. The reverse design algorithm uses the initial size information of the bit capacitor and the real and imaginary parts of the simulated impedance as target parameters to optimize the model and iterate to find the minimum value of the impedance parameter, that is, the maximum radiation reflection coefficient.

6. The geometric structure design method for radiation-resistant optimization of a flip-chip superconducting quantum chip according to claim 5, characterized in that, The initial size information of the bit capacitor can be listed as the arm length and arm width of the bit, the width of the square at the top of the bit, the space between the bit and the plane, and the length at the Josephson junction; The radiation impedance of a Josephson junction is determined by the functional relationship between the electromagnetic wave frequency, the quantum circuit time constant, and the tunneling resistance of the Josephson junction, as shown in the following formula: Where Z0 is the radiation impedance of the Josephson junction, ω is the frequency of the electromagnetic wave, τ is the time constant of the quantum circuit, and Rn is the tunneling resistance of the Josephson junction. The time constant of a quantum circuit is determined by the electrical parameters of the Josephson junction, which characterize the speed of the quantum circuit response; the electrical parameters can be listed as the tunneling resistance and capacitance of the Josephson junction. The time constant of a quantum circuit is determined by the following formula: τ≡R0C0 Where C0 is the capacitance of the Josephson junction; ω ranges from 0 to 200 GHz; R0 ranges from 6000 to 10000 Ω; and C0 ranges from 0 to 10 fF.

7. A geometric structure design system for radiation-resistant optimization of a flip-chip superconducting quantum chip, the system being based on the method of any one of claims 1-6, characterized in that, Includes the following modules: Optimizable parameter determination module: used to determine the set of optimizable parameters for the bit structure of the flip-chip superconducting quantum chip; Multi-objective cost function module: used to construct a multi-objective cost function model based on minimizing impedance at a specific frequency; Iterative optimization module: Used to iteratively optimize the multi-objective cost function model using a genetic algorithm and output the optimal set of parameters for the flip-chip superconducting quantum chip bit structure.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

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