Method, device and medium for constructing optical quantum computing system, and optical quantum computer

Abstract

The application provides a construction method and device of a light quantum computing system, a medium and a light quantum computer, and relates to the technical field of quantum computing. The method comprises the following steps: determining a target transformation matrix for solving a target quantum computing problem; constructing an initial light quantum computing system based on the complexity of the target quantum computing problem; determining an equivalent transformation matrix corresponding to the initial light quantum computing system; and adjusting system parameters of the initial light quantum computing system based on the difference between the equivalent transformation matrix and the target transformation matrix to obtain the light quantum computing system. The embodiments of the application can realize accurate parameter mapping, thereby accurately constructing the system.

Description

Construction methods, devices, media, and optical quantum computers of optical quantum computing systems Technical Field

[0001] This application relates to the field of quantum computing technology, specifically to a method, apparatus, medium, and optical quantum computer for constructing an optical quantum computing system. Background Technology

[0002] With the development of quantum computing technology, quantum computing has demonstrated a potential far exceeding that of classical computers in solving specific computational problems. Currently, there are various physical systems for realizing quantum computing, with mainstream technological paths including superconducting quantum computing, ion trap quantum computing, and optical quantum computing. Compared to other physical systems, optical quantum computing has advantages such as long coherence time, room temperature operation, and high-speed transmission.

[0003] To build a general-purpose quantum computer capable of executing complex algorithms, it is essential to fabricate and manipulate large-scale qubit arrays. In the field of optical quantum computing, existing technologies primarily employ two approaches: purely spatial parallelism and purely time-multiplexing. However, the classic Clements or Reck decomposition schemes are only applicable to fully connected spatial parallel architectures. They fail for spatial parallel architectures with specific sparse topologies or complex architectures involving the time dimension, thus lacking generality and hindering accurate system construction. Summary of the Invention

[0004] In view of this, the embodiments of this application aim to provide a method, apparatus, medium and optical quantum computer for constructing an optical quantum computing system, so as to solve the problem that the existing technology cannot accurately construct the system due to the lack of a universal analytical parameter decomposition method.

[0005] In a first aspect, one embodiment of this application provides a method for constructing an optical quantum computing system. The method includes: determining a target transformation matrix for solving a target quantum computing problem; constructing an initial optical quantum computing system based on the complexity of the target quantum computing problem; determining an equivalent transformation matrix corresponding to the initial optical quantum computing system; and adjusting the system parameters of the initial optical quantum computing system based on the difference between the equivalent transformation matrix and the target transformation matrix to obtain the optical quantum computing system.

[0006] In conjunction with the first aspect, in some implementations of the first aspect, determining the equivalent transformation matrix corresponding to the initial optical quantum computing system includes: expanding the system model corresponding to the initial optical quantum computing system in the spatial dimension to obtain an equivalent system model; and determining the equivalent transformation matrix based on the transformation matrix corresponding to the equivalent system model.

[0007] In conjunction with the first aspect, in some implementations of the first aspect, adjusting the system parameters of the initial optical quantum computing system based on the difference between the equivalent transformation matrix and the target transformation matrix to obtain the optical quantum computing system includes: constructing a cost function based on the difference between the equivalent transformation matrix and the target transformation matrix; determining the parameter optimization result corresponding to the initial optical quantum computing system using a preset optimization algorithm with the objective of minimizing the function value of the cost function; and adjusting the system parameters of the initial optical quantum computing system based on the parameter optimization result to obtain the optical quantum computing system.

[0008] In conjunction with the first aspect, in some implementations of the first aspect, the system parameters include system architecture parameters and component configuration parameters; constructing an initial optical quantum computing system based on the complexity of the target quantum computing problem includes: constructing a target optical quantum computing system based on the complexity of the target quantum computing problem; adjusting the system architecture parameters of the target optical quantum computing system to obtain multiple initial optical quantum computing systems; adjusting the system parameters of the initial optical quantum computing system based on the parameter optimization results to obtain an optical quantum computing system includes: determining, from the parameter optimization results corresponding to the multiple initial optical quantum computing systems, a parameter optimization result that satisfies a preset computational accuracy condition, as the target parameter optimization result; adjusting the system architecture parameters and component configuration parameters of the initial optical quantum computing system based on the target parameter optimization result to obtain the optical quantum computing system.

[0009] In conjunction with the first aspect, in some implementations of the first aspect, when there are multiple optimization results for the target parameters, adjusting the system architecture parameters and component configuration parameters of the initial optical quantum computing system based on the optimization results of the target parameters to obtain the optical quantum computing system includes: determining evaluation index values ​​corresponding to the multiple optimization results of the target parameters respectively; determining the parameter optimization result with the highest evaluation index value from the multiple optimization results of the target parameters as the optimal parameter optimization result; and adjusting the system architecture parameters and component configuration parameters of the initial optical quantum computing system based on the optimal parameter optimization result to obtain the optical quantum computing system.

[0010] In conjunction with the first aspect, in some implementations of the first aspect, determining the evaluation index values ​​corresponding to the optimization results of the plurality of target parameters respectively includes: performing the following steps for each of the optimization results of the plurality of target parameters to obtain the evaluation index values ​​corresponding to the optimization results of the plurality of target parameters respectively: determining the index values ​​corresponding to the optimization results of the target parameters under a plurality of parameter evaluation indicators, wherein the plurality of parameter evaluation indicators include at least two of the following: computational accuracy index, hardware adaptation index, and resource consumption index; and determining the evaluation index value corresponding to the optimization results of the target parameters based on the index values ​​corresponding to the optimization results of the target parameters under the plurality of parameter evaluation indicators.

[0011] In conjunction with the first aspect, in some implementations of the first aspect, the system architecture parameters include at least one of the component connection structure, photonic signal step size, and number of optical waveguides; the component configuration parameters include control parameters for tunable components.

[0012] Secondly, one embodiment of this application provides a device for constructing an optical quantum computing system. The device includes: a matrix determination module for determining a target transformation matrix for solving a target quantum computing problem; a system construction module for constructing an initial optical quantum computing system based on the complexity of the target quantum computing problem; a matrix equivalence module for determining an equivalent transformation matrix corresponding to the initial optical quantum computing system; and a parameter adjustment module for adjusting the system parameters of the initial optical quantum computing system based on the difference between the equivalent transformation matrix and the target transformation matrix, thereby obtaining the optical quantum computing system.

[0013] In conjunction with the second aspect, in some implementations of the second aspect, the matrix equivalence module is further used to: expand the system model corresponding to the initial optical quantum computing system in the spatial dimension to obtain a system equivalent model; and determine the equivalent transformation matrix based on the transformation matrix corresponding to the system equivalent model.

[0014] In conjunction with the second aspect, in some implementations of the second aspect, the parameter adjustment module is further configured to: construct a cost function based on the difference between the equivalent transformation matrix and the target transformation matrix; determine the parameter optimization result corresponding to the initial optical quantum computing system using a preset optimization algorithm with the objective of minimizing the function value of the cost function; and adjust the system parameters of the initial optical quantum computing system based on the parameter optimization result to obtain the optical quantum computing system.

[0015] In conjunction with the second aspect, in some implementations of the second aspect, the system parameters include system architecture parameters and component configuration parameters; the system construction module is further configured to: construct a target optical quantum computing system based on the complexity of the target quantum computing problem; and adjust the system architecture parameters of the target optical quantum computing system to obtain multiple initial optical quantum computing systems. Correspondingly, the parameter adjustment module is further configured to: determine, from the parameter optimization results corresponding to the multiple initial optical quantum computing systems, a parameter optimization result that satisfies a preset computational accuracy condition, as the target parameter optimization result; and adjust the system architecture parameters and component configuration parameters of the initial optical quantum computing system based on the target parameter optimization result to obtain the optical quantum computing system.

[0016] In conjunction with the second aspect, in some implementations of the second aspect, when there are multiple optimization results for the target parameters, the parameter adjustment module is further configured to: determine the evaluation index values ​​corresponding to the multiple optimization results for the target parameters respectively; determine the parameter optimization result with the highest evaluation index value from the multiple optimization results for the target parameters as the optimal parameter optimization result; and adjust the system architecture parameters and component configuration parameters of the initial optical quantum computing system based on the optimal parameter optimization result to obtain the optical quantum computing system.

[0017] In conjunction with the second aspect, in some implementations of the second aspect, the parameter adjustment module is further configured to: perform the following steps for each of the multiple target parameter optimization results to obtain evaluation index values ​​corresponding to the multiple target parameter optimization results respectively: determine the index values ​​corresponding to the target parameter optimization results under multiple parameter evaluation indicators, wherein the multiple parameter evaluation indicators include at least two of the following: computational accuracy index, hardware adaptation index, and resource consumption index; and determine the evaluation index value corresponding to the target parameter optimization results based on the index values ​​corresponding to the target parameter optimization results under the multiple parameter evaluation indicators.

[0018] In conjunction with the second aspect, in some implementations of the second aspect, the system architecture parameters include at least one of the component connection structure, photonic signal step size, and number of optical waveguides; the component configuration parameters include control parameters for tunable components.

[0019] Thirdly, one embodiment of this application provides a computer-readable storage medium storing a computer program for executing the method for constructing the optical quantum computing system described in the second aspect.

[0020] Fourthly, one embodiment of this application provides an optical quantum computer, which includes: a processor; a memory for storing processor-executable instructions; and the processor for executing the method for constructing the optical quantum computing system described in the second aspect.

[0021] Fifthly, one embodiment of this application provides a computer program product including instructions that, when executed on an electronic device, cause the electronic device to implement the method for constructing the optical quantum computing system described in the second aspect.

[0022] In this application, by determining the equivalent transformation matrix corresponding to the initial optical quantum computing system constructed for the target quantum computing problem, and based on the difference between the equivalent transformation matrix and the target transformation matrix used to solve the target quantum computing problem, the system parameters of the initial optical quantum computing system are adjusted, thereby obtaining the optical quantum computing system. This realizes the mapping from arbitrary target transformation matrix to system parameters, so that optical quantum computers are no longer limited to specific hardware topologies or dedicated algorithms. Whether it is a pure time-multiplexed, non-fully connected sparse space architecture, or other complex hybrid architectures, this application can achieve accurate parameter mapping, thereby accurately constructing the system. Attached Figure Description

[0023] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0024] Figure 1 shows a schematic diagram of the structure of an optical quantum computing system provided in an embodiment of this application.

[0025] Figure 2 shows a schematic diagram of the structure of a space processing module provided in an embodiment of this application.

[0026] Figure 3 shows a schematic diagram of the structure of a space processing module provided in another embodiment of this application.

[0027] Figure 4 shows a schematic diagram of the structure of a spatial interference unit provided in an embodiment of this application.

[0028] Figure 5 shows a schematic diagram of the structure of a space processing module provided in another embodiment of this application.

[0029] Figure 6 shows a schematic diagram of the time processing module provided in an embodiment of this application.

[0030] Figure 7 shows a schematic diagram of the structure of a time delay unit provided in an embodiment of this application.

[0031] Figure 8 shows a schematic diagram of the structure of a space processing module provided in another embodiment of this application.

[0032] Figure 9 shows a schematic diagram of the structure of a space processing module provided in another embodiment of this application.

[0033] Figure 10 shows a schematic diagram of the structure of an optical quantum computing system provided in another embodiment of this application.

[0034] Figure 11 shows a schematic diagram of the structure of an optical quantum computer provided in an embodiment of this application.

[0035] Figure 12 is a flowchart illustrating a method for constructing an optical quantum computing system according to an embodiment of this application.

[0036] Figure 13 shows a schematic diagram of the structure of a construction device for an optical quantum computing system provided in an embodiment of this application.

[0037] Figure 14 shows a schematic diagram of the structure of an optical quantum computer provided in another embodiment of this application. Detailed Implementation

[0038] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] Furthermore, to better illustrate this application, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that this application can be implemented even without certain specific details. In some instances, methods and means well-known to those skilled in the art have not been described in detail in order to highlight the main points of this application.

[0040] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0041] Furthermore, the terms “first,” “second,” “third,” and “fourth” are used only for distinguishing descriptions and should not be interpreted as indicating or implying relative importance.

[0042] Quantum computing is a novel computing paradigm that manipulates quantum information units according to the laws of quantum mechanics. Utilizing the superposition and entanglement properties of qubits, it demonstrates potential far exceeding that of classical computers in tackling specific computationally challenging problems. Currently, there are various physical systems for realizing quantum computing, with mainstream technological paths including superconducting quantum computing, ion trap quantum computing, and optical quantum computing. Compared to other physical systems, optical quantum computing offers advantages such as long coherence time, room temperature operation, and high-speed transmission. Optical quantum computing qubits are typically encoded in the physical degrees of freedom of photons, such as polarization, path, time, frequency, or orbital angular momentum.

[0043] To build a general-purpose quantum computer capable of executing complex algorithms, it is essential to fabricate and manipulate large-scale qubit arrays. In the field of optical quantum computing, existing technologies primarily employ two approaches. One is a purely spatial parallel approach, which involves constructing optical networks containing a large number of parallel processing units on a photonic integrated chip using optical components such as phase shifters, as exemplified by the Clements and Reck architectures. However, the number of required optical components increases exponentially with the scale of the problem, leading to a sharp deterioration in chip area, wiring density, and electromagnetic crosstalk between components, making scaling to the megabit level physically extremely difficult. The other approach is a purely time-multiplexing approach, which generates a long sequence of photon pulses in the time dimension. By using optical components such as high-speed optical switches, photon pulses at different points in time can interact, thus weaving a one-dimensional (1D) or quasi-two-dimensional (2D) entangled state along the time axis. The problem with this approach lies in computational latency and depth limitations. Time-multiplexing schemes are essentially serial processing. For the first photon in the sequence to interact with the thousandth photon, the first photon must cycle through a delay loop, introducing a huge computational delay. Each cycle of a photon in the optical fiber incurs unavoidable losses, which accumulate and severely limit the depth of executable quantum algorithms.

[0044] In summary, existing technologies exhibit limitations: pure spatial parallelism is constrained by the polynomial growth bottlenecks in physical integration density, manufacturing processes, and control complexity, hindering effective scaling of computational scale and thus creating a "width" bottleneck; while pure time-multiplexing schemes are limited by computational latency, accumulated photon loss, and topology limitations resulting from serial processing, making it difficult to support highly complex quantum algorithms and thus creating a "depth" bottleneck. The inherent defects of these two schemes severely impede the ultimate realization of scalable, high-performance, and fault-tolerant optical quantum computers. Therefore, there is an urgent need in this field for a novel computing system that can integrate the strengths of both while mitigating their weaknesses, enabling effective scaling of both quantum computing scale and computational depth.

[0045] Based on this, this application aims to provide a novel scalable spatiotemporal hybrid optical quantum computing system and introduces a hardware-software co-design concept to systematically overcome the dual limitations of "width" and "depth" mentioned above. Specifically, the optical quantum computing system provided in this application's embodiments achieves dual reuse of quantum computing in both spatial and temporal dimensions by alternately deploying spatial processing modules and temporal processing modules on multiple parallel optical waveguides. Furthermore, the system construction method provided in this application's embodiments automatically searches for optimal system parameters in the optical quantum computing system based on the target transformation matrix capable of solving the target quantum computing problem, thereby efficiently and accurately mapping the target transformation matrix in the optical quantum computing system. This hardware-software co-design approach not only significantly reduces photon loss and computational latency but also provides unprecedented flexibility and programmability for constructing high-dimensional entanglement and executing complex quantum algorithms, ultimately opening a new technical path for realizing truly scalable, high-performance, fault-tolerant optical quantum computers.

[0046] The optical quantum computing system provided in this application will be described in detail below with reference to Figures 1 to 10.

[0047] Figure 1 shows a schematic diagram of the structure of an optical quantum computing system provided in an embodiment of this application. This optical quantum computing system can be integrated onto a chip, and each module and circuit of the system can be manufactured in one go on a single substrate using a monolithic integration process. The substrate material may include, for example, silicon, lithium niobate, silicon nitride, etc.

[0048] As shown in Figure 1, the optical quantum computing system includes multiple optical waveguides 11, on which multiple spatial processing modules 12 and multiple temporal processing modules 13 are arranged. The spatial processing modules 12 are used to perform quantum state transformation on the input optical quantum information to achieve spatial parallel processing; the temporal processing modules 13 are used to perform time adjustment processing on the input optical quantum information to achieve time multiplexing. The spatial processing modules 12 and temporal processing modules 13 are arranged alternately and are connected to each other through multiple optical waveguides 11.

[0049] In some examples, the optical waveguide 11 can be a line capable of transmitting optical quantum information. The material of this line can be, for example, a low-loss optical fiber waveguide, a high-density integrated silicon-based optical waveguide, or a thin-film lithium niobate waveguide. The optical quantum computing system provided in this application embodiment may include multiple parallel optical waveguides 11. By setting alternating spatial processing modules 12 and temporal processing modules 13 on the optical waveguides 11, the purpose of multiplexing in both spatial and temporal dimensions is achieved.

[0050] In some examples, the spatial processing module 12 can be a reconfigurable linear optical interference network, whose core function is to perform parallel processing of the input in space to achieve a feature transformation, such as a unitary transform. This spatial processing module 12 can perform quantum state transformation processing on the optical quantum information input to the module, wherein the quantum state transformation processing includes, but is not limited to, phase modulation, multimode interference, and other processing.

[0051] In some specific examples, the spatial processing module 12 may include multiple optical elements, which can be tunable elements, such as tunable phase modulators, tunable beam splitters, or Mach-Zehnder interferometers (MZIs). These optical elements can be arranged in a specific topology, such as a single-layer arrangement or a multi-layer arrangement. The element network architecture in the spatial processing module 12 theoretically allows for feature transformation processing corresponding to a specific dimension transformation matrix by precisely setting the parameter values ​​within each element. The specific dimension transformation matrix could, for example, be N... An N-unitary matrix. Based on this, the component setup network architecture in the spatial processing module 12 can be designed as a single-layer arrangement to achieve basic transformations, or as a multi-layer cascaded structure to achieve more complex and higher-fidelity transformations.

[0052] It should be noted that all tunable elements within the aforementioned space processing module 12 can employ active modulation. Specifically, for example, a thermo-optic phase modulator can be used, which integrates a miniature resistive heater above or to the side of the waveguide, utilizing the thermo-optic effect of the material to change the effective refractive index of the waveguide, thereby achieving phase modulation. Alternatively, an electro-optic phase modulator can be used, utilizing the electro-optic effect of the material, applying an external electric field to rapidly change the refractive index, thus achieving phase modulation. Electro-optic modulation offers a faster response speed compared to thermo-optic modulation.

[0053] In some examples, the timing processing module 13 may be a module capable of timing-adjusting the quantum information input to it, where the timing adjustment can be, for example, time delay processing. The timing processing module 13 may include, for example, a set of parallel tunable delay structures, thereby allowing programmable time delay processing to be introduced independently for each optical waveguide corresponding to a line. The tunable delay structure may be an active, programmable optical switching delay structure based on a tunable element, such as an MZI.

[0054] Based on the optical quantum computing system shown in Figure 1, the system may include multiple spatial processing modules 12 and multiple temporal processing modules 13 monolithically integrated on the same substrate. These modules are arranged alternately to form a processing core. The number of alternations between the spatial and temporal processing modules 12 and 13 can be determined based on the complexity of the quantum computing problem to be solved; higher complexity allows for more alternations. Furthermore, it should be noted that the component network architecture used within each spatial processing module 12 can be different, and the control parameters corresponding to the tunable components can also be different. Similarly, the tunable delay structures used within each temporal processing module 13 can have different delay durations, which are not limited here.

[0055] For example, the photon signal emitted from the light source can be input to the system from the left signal input terminal of the optical waveguide 11, and then pass through the spatial processing module 12, the time processing module 13, the spatial processing module 12, the time processing module 13, etc., and finally be output from the right signal output terminal after passing through the spatial processing module 12. The output information can be the optical quantum information obtained by the optical quantum computing system after processing the input photon signal in a specified order.

[0056] In addition to the arrangement shown in Figure 1, the alternating arrangement of the spatial processing module 12 and the time processing module 13 can also, in some other examples, begin with the time processing module 13 and end with the spatial processing module 12. Furthermore, in some other examples, it can begin with the spatial processing module 12 and end with the time processing module 13. Moreover, in some other examples, it can begin with the time processing module 13, proceed through the alternating structure of the spatial processing module 12 and the time processing module 13, and then end with the time processing module 13.

[0057] Thus, by setting up multiple alternating spatial processing modules and temporal processing modules on multiple optical waveguides, the spatial processing modules are used to perform quantum state transformations on the optical quantum information to achieve spatial parallel processing, and the temporal processing modules are used to perform temporal adjustment processing on the optical quantum information to achieve temporal multiplexing. This allows spatial parallelism and temporal multiplexing to be organically combined, thereby integrating the advantages of both and avoiding their disadvantages, and achieving a dual effective expansion of the scale and depth of quantum computing.

[0058] Based on this, in some embodiments, as shown in Figures 2 and 3, the spatial processing module 12 includes at least one spatial interference unit 121, wherein the spatial interference unit 121 is connected to at least two of the multiple optical waveguides and is used to perform interference processing on the optical quantum information in the at least two optical waveguides.

[0059] In some examples, the spatial interference unit 121 can be a unit capable of multimode interference processing of multiple optical quantum information. This spatial interference unit 121 can be, for example, an MZI, a tunable beam splitter, etc. For example, taking an MZI as an example, the connection between the MZI and the two optical waveguides can be as shown in Figure 4. For instance, the upper input and upper output of the MZI 1211 are connected to the first optical waveguide 111, and the lower input and lower output of the MZI 1211 are connected to the second optical waveguide 112. After the optical quantum information in the first optical waveguide 111 and the optical quantum information in the second optical waveguide 112 are input into the MZI 1211, interference processing between the two optical quantum information can be achieved, and the optical quantum information obtained after interference processing is output to the first optical waveguide 111 and the second optical waveguide 112, respectively.

[0060] Based on this, the arrangement of the spatial interference units 121 in the spatial processing module 12 can be, for example, as shown in Figure 2. That is, each pair of adjacent optical waveguides can be connected to a spatial interference unit 121. For example, the first optical waveguide 111 and the second optical waveguide 112 can be connected to a spatial interference unit 121, the third optical waveguide 113 and the fourth optical waveguide 114 can be connected to a spatial interference unit 121, and so on.

[0061] In other embodiments, in order to improve the efficiency of quantum computing, the spatial interference unit 121 in the spatial processing module 12 can also be arranged in an odd-even alternating topology, that is, at least one spatial interference unit 121 is connected between any two adjacent optical waveguides in the multiple optical waveguides.

[0062] Based on this, the arrangement of the spatial interference unit 121 in the spatial processing module 12 can also be as shown in Figure 3, that is, any two adjacent optical waveguides are connected to a spatial interference unit 121. For example, the first optical waveguide 111 and the second optical waveguide 112 can be connected to a spatial interference unit 121, the second optical waveguide 112 and the third optical waveguide 113 can be connected to a spatial interference unit 121, the third optical waveguide 113 and the fourth optical waveguide 114 can be connected to a spatial interference unit 121, and so on.

[0063] It should be noted that, in addition to the single-layer arrangement shown in Figures 2 and 3 above, the arrangement can be repeated multiple times along the optical waveguide to form a multi-layer cascaded structure, thereby achieving more complex and higher-fidelity feature transformations.

[0064] In other embodiments, as shown in FIG5, the above-mentioned spatial processing module includes multiple phase adjustment units 122, wherein: at least one phase adjustment unit 122 is correspondingly provided on an optical waveguide 11; the phase adjustment unit 122 is used to perform phase adjustment processing on the optical quantum information in the optical waveguide 11.

[0065] In some examples, the phase adjustment unit 122 may be a unit capable of adjusting the phase of optical quantum information, such as an adjustable phase modulator.

[0066] For example, as shown in FIG5, a phase adjustment unit 122 may be provided on each optical waveguide 11, and each phase adjustment unit 122 may perform phase adjustment processing on the optical quantum information in the optical waveguide.

[0067] It should be noted that the spatial interference unit described in the foregoing embodiments and the phase adjustment unit described in this embodiment can exist separately in the optical quantum computing system, or they can exist simultaneously in the optical quantum computing system, which is not limited here.

[0068] In addition, in some embodiments, as shown in FIG6, the time processing module 13 includes a plurality of time delay units 131, wherein: at least one time delay unit 131 is correspondingly provided on an optical waveguide 11; the time delay unit 131 is used to perform time delay processing on the optical quantum information in the optical waveguide 11.

[0069] In some examples, the aforementioned time delay unit can be a unit capable of time-delaying optical quantum information, such as a tunable delay line, which can specifically be an active, programmable optical switch delay unit based on MZI. The delay duration corresponding to the time delay unit can be zτ, where τ represents the time interval between adjacent photon pulses when a photon signal is input into the optical waveguide, and z can be an integer greater than or equal to 0.

[0070] For example, as shown in FIG6, a time delay unit 131 can be provided on each optical waveguide 11. Each time delay unit 131 can perform time delay processing on the optical quantum information in the optical waveguide. The delay duration corresponding to the time delay unit 131 provided on different optical waveguides can be different.

[0071] Based on this, in some specific embodiments, the time delay unit includes an input terminal and an output terminal. The input terminal includes a first input terminal and a second input terminal, and the output terminal includes a first output terminal and a second output terminal. Specifically: the first input terminal of the time delay unit is connected to an optical waveguide and is used to receive target quantum information input to the time delay unit from the optical waveguide; the second input terminal and the second output terminal of the time delay unit are connected via a delay line to form a delay path, so as to realize time delay processing of the target quantum information when it passes through the delay path; the first output terminal of the time delay unit is connected to the optical waveguide and is used to output the time-delayed target quantum information to the optical waveguide.

[0072] In some examples, the time delay unit can be an active, programmable optical switch delay unit including a tunable element. This tunable element may have two inputs and two outputs, with one input connected to one output to form a delay path. Specifically, a delay line can be used, such as a low-loss fiber waveguide, a high-density integrated silicon-based optical waveguide, or a spatial optical path. Furthermore, the target optical quantum information can be any optical quantum information transmitted in the optical waveguide.

[0073] For example, taking an active, programmable optical switch delay unit based on MZI as an example of a time delay unit, as shown in Figure 7, in the time delay unit 131, the first input terminal of MZI is the lower input terminal 1311, the first output terminal of MZI is the lower output terminal 1312, the second input terminal of MZI is the upper input terminal 1313, and the second output terminal of MZI is the upper output terminal 1314. The upper input terminal 1313 and the upper output terminal 1314 of MZI are connected by a low-loss optical fiber waveguide to form a delay path, and their optical path difference can be the time interval of one or more adjacent input photon pulses. The lower input terminal 1311 of MZI is used to receive the photon information input from the optical waveguide 11, and the lower output terminal 1312 of MZI is used to output the time-delayed photon information to the optical waveguide 11. Furthermore, under the above connection method, the MZI, as a time delay unit, can also perform interference processing on the quantum information input from the second input terminal and the quantum information present in the delay line. In this way, the two internal phase shifters integrated on the MZI interferometer arm can precisely control the interference ratio and phase of the input pulse signal by applying control voltage or current, thereby realizing the control of the interference result.

[0074] In addition, in some embodiments, as shown in FIG8, the above-mentioned spatial processing module 12 further includes multiple phase initialization units 123, wherein: at least one phase initialization unit 123 is correspondingly provided on an optical waveguide 11 and is connected to the signal input terminal 115 through the phase initialization unit 123; the phase initialization unit 123 is used to perform initialization phase modulation processing on the photon signal input to the signal input terminal 115, and to output the photon information obtained by the initialization phase modulation processing to the optical waveguide 11.

[0075] In some examples, the signal source can emit photon signals to the signal input terminals 115 of each optical waveguide 11, for example, emitting photon pulses with a time interval of τ to each optical waveguide 11, for a total of k photon pulses. A phase initialization unit 123 can be located at the beginning of the optical waveguide 11 to perform initial, independently tunable initial phase modulation on the photon pulses carrying quantum information, thereby preparing a specific input quantum state. This phase initialization unit 123 can, for example, be a tunable phase modulator.

[0076] For example, as shown in FIG8, a phase initialization unit 123 can be set after the signal input terminal 115 corresponding to each optical waveguide 11. Each phase initialization unit 123 can perform independently adjustable initialization phase modulation processing on the optical quantum information in the optical waveguide to prepare a specific input quantum state.

[0077] In addition, in some embodiments, as shown in FIG9, the above-mentioned spatial processing module 12 further includes multiple phase compensation units 124, wherein: at least one phase compensation unit 124 is correspondingly provided on an optical waveguide 11 and is connected to the signal output terminal 116 through the phase compensation unit 124; the phase compensation unit 124 is used to perform phase compensation processing on the optical quantum information output by the optical waveguide 11, and to output the optical quantum information obtained by the phase compensation processing to the signal output terminal 116.

[0078] In some examples, the phase compensation unit 124 may be located at the end of the optical waveguide 11 to perform phase compensation on the final output quantum state of the optical quantum information in the optical waveguide 11, so as to meet the needs of subsequent single-photon detection or further processing. The phase compensation unit 124 may be, for example, an tunable phase modulator.

[0079] For example, as shown in FIG9, a phase compensation unit 124 can be set before the signal output terminal 116 corresponding to each optical waveguide 11. Each phase compensation unit 124 can perform independently adjustable phase compensation processing on the final output photon information in the optical waveguide to meet the needs of subsequent single photon detection or further processing.

[0080] Based on this, and building upon the various embodiments described above, a specific example is given below in conjunction with Figure 10 to better illustrate the structure of the optical quantum computing system designed in this application.

[0081] The optical quantum computing system shown in Figure 10 may include n optical waveguides (n is an integer greater than 2), with the signal input end on the left and the signal output end on the right. Starting from the signal input end of the n optical waveguides, an input phase modulation layer 91, an initial time delay layer 92, a spatial unitary transform layer 93, a time delay layer 94, and an output phase modulation layer 95 are respectively set.

[0082] Here, the input phase modulation layer 91 belongs to the spatial processing module, and the first phase modulators included in the input phase modulation layer 91 are the phase initialization units mentioned in the above embodiments. The input phase modulation layer 91 can directly act on the input n photon pulse signals to perform initial, independently adjustable phase modulation on the photon pulses input to the n optical waveguides in order to prepare a specific input quantum state.

[0083] The initial time delay layer 92 belongs to the time processing module. The tunable delay lines included in this initial time delay layer 92 are the time delay units mentioned in the above embodiments. The delay duration corresponding to each tunable delay line in the initial time delay layer 92 can be independently and precisely set to the time interval τ of one or more adjacent input photon pulses. The function of the initial time delay layer 92 is to initially map the purely spatially encoded qubits onto a spatiotemporally mixed degree of freedom, preparing for subsequent cross-time-slice interactions.

[0084] The spatial unitary transform layer 93 belongs to the spatial processing module. The MZI included in the spatial unitary transform layer 93 is the spatial interference unit mentioned in the above embodiments. The spatial unitary transform layer 93 may include a tunable MZI network. The MZI network is arranged in an odd-even alternating topology, which can realize n-level quantum information transmission between n parallel optical waveguides. n-unitary matrix transformation. This is the core layer for realizing quantum interference and information exchange between spatial modes.

[0085] The time delay layer 94 belongs to the time processing module. The tunable delay lines included in this time delay layer 94 are the time delay units mentioned in the above embodiments. The delay duration corresponding to each tunable delay line in the time delay layer 94 can also be independently and precisely set to the time interval τ of one or more adjacent input photon pulses. The function of the time delay layer 94 is to perform a time "shift" on the photon pulses in each spatial mode after each spatial transformation, thereby ensuring that when entering the spatial unitary transform layer in the next cycle, photon pulses from different time slices can enter the same MZI for interference, realizing cross-time quantum interaction.

[0086] The output phase modulation layer 95 belongs to the spatial processing module. The various second phase modulators included in this output phase modulation layer 95 are the phase compensation units mentioned in the above embodiments. The function of the output phase modulation layer 95 is to perform phase compensation on the quantum state of the final output photon information to meet the needs of subsequent single-photon detection or further processing.

[0087] It should be noted that the core computing module of the optical quantum computing system may include the aforementioned spatial unitary transformation layer 93 and time delay layer 94. The spatial unitary transformation layer 93 and time delay layer 94 can be cyclically set in the optical waveguide. That is, the initialized optical quantum information can pass through the spatial unitary transformation layer 93 and time delay layer 94 t times in sequence (t is an integer greater than or equal to 1), so as to achieve a dual expansion of quantum computing scale and computing depth.

[0088] The core design idea of ​​the aforementioned optical quantum computing system lies in the organic combination of time multiplexing and spatial parallelism. Using hardware resources of n physical spatial modes—that is, n optical waveguide lines—it efficiently achieves a unitary transformation equivalent to N spatial modes, where N is greater than n. This design is expected to significantly reduce the number of physical components required to realize a linear optical network from O(N²), thus fundamentally solving the scalability problem of purely spatial solutions. Simultaneously, it shortens the line depth, solving the computational delay and accumulated photon loss problems of purely time multiplexing, and is expected to achieve sufficiently good approximate unitary matrix mapping and problem solving with O(N) complexity.

[0089] In summary, the optical quantum computing system provided in this application achieves an equivalent N-dimensional quantum computing task by employing a spatiotemporal hybrid architecture combining n physical spatial modes with tunable delay lines and inputting k consecutive photon pulse signals. Compared to a purely spatial scheme requiring O(N²) optical elements, the physical resource consumption of this application is only O(N). This significantly reduces the requirements for chip area, manufacturing process, and control system complexity, clearing the most significant physical obstacle to building ultra-large-scale optical quantum computers.

[0090] Furthermore, in the hybrid spatiotemporal architecture of the optical quantum computing system provided in this application, each optical quantum information only needs to pass through one tunable delay line once through the core computing module. With a total of t such computing modules, this means that each input pulse only needs to pass through t tunable delay lines. In contrast, in a pure time-multiplexing scheme, to achieve N-mode interaction, photons may need to cycle through the delay loop O(N) times, resulting in huge computational delays and unacceptable photon losses. This application significantly reduces the "temporal depth" of the computation from O(N), which greatly shortens the total transit time of photons in the system and effectively controls accumulated losses, thus making it possible to execute highly complex quantum algorithms requiring numerous sequential gate operations.

[0091] The embodiments of the optical quantum computing system provided in this application have been described in detail above with reference to Figures 1 to 10. In addition, embodiments of this application also provide an optical quantum chip, which can integrate the optical quantum computing system provided in any embodiment of this application. This optical quantum chip can be an optical quantum integrated circuit, which can be configured in an optical quantum computer as the core carrier of quantum information processing in the optical quantum computer. This optical quantum chip can be used to programmably manipulate the quantum state of a single photon, realizing core functions such as quantum logic operations, quantum entanglement generation, and quantum state storage.

[0092] The optical quantum computer according to an embodiment of this application will now be described with reference to FIG11. FIG11 is a schematic diagram of the structure of an optical quantum computer provided in an exemplary embodiment of this application.

[0093] As shown in Figure 11, in the optical quantum computer 1100, the core components of the quantum information processing link can include a quantum light source 1101, an optical quantum chip 1102, and a quantum detector 1103. The quantum light source 1101 is the "source of quantum information," the optical quantum chip 1102 is the "processor for quantum computing," and the quantum detector 1103 is the "readout device for quantum results." These three components form a closed loop through high-precision optical connections, jointly completing the generation, manipulation, and measurement of quantum states.

[0094] For example, the core function of the quantum light source 1101 is to generate quantum states with defined quantum properties, such as single photons and compressed light. These quantum states are the basic information carriers (qubits) for optical quantum computing. The types of quantum light sources 1101 can include: quantum dot single-photon sources (based on spontaneous emission from semiconductor quantum dots), diamond color center sources (such as NV centers), and spontaneous parametric down-conversion (SPDC) sources (generating entangled photon pairs or multiphoton states through nonlinear optical processes). For instance, a quantum dot single-photon source can emit single photons with over 99% purity under pulsed laser excitation, and the polarization state can be switched on demand through external field manipulation.

[0095] For example, the optical quantum chip 1102 is the core carrier of quantum information processing. Its function is to programmably manipulate the quantum state of a single photon to realize core functions such as quantum logic operation, quantum entanglement generation, and quantum state storage.

[0096] For example, the core function of the quantum detector 1103 is to accurately measure the presence, time, polarization, frequency, or phase amplitude of a photon or the light field, converting the "quantum information" of the photon quantum state into a readable electrical signal, and ultimately outputting the quantum computing result. The quantum detector 1103 can be of various types, including superconducting nanowire quantum detectors (SNSPDs, with a time resolution <10 ps), single-photon avalanche diodes (SPADs, with high integration), and balanced zero-beat detectors for continuous variable measurements. For instance, SNSPDs can achieve a detection efficiency of up to 95% for 1550 nm photons and a dark count rate of less than 0.1 count / s, making them a core device for high-precision quantum measurements.

[0097] Furthermore, it should be noted that in practical applications, the optical quantum computing system and / or optical quantum chip provided in this application can be implemented based on basic materials such as lithium niobate (LiNbO3) and then integrated into an optical quantum computer. For details regarding the specific processing procedures of an optical quantum computer, please refer to the specific descriptions of related technologies; these will not be elaborated upon here.

[0098] The above is a detailed description of the optical quantum computing system based on a spatiotemporal hybrid architecture provided in the embodiments of this application. The following describes the construction method of the optical quantum computing system provided in the embodiments of this application, comparing it with existing optical quantum computing systems and the optical quantum computing system based on a spatiotemporal hybrid architecture provided in the embodiments of this application.

[0099] In existing technologies, for purely spatial parallel schemes, although analytical methods exist to decompose the target unitary matrix into physical parameters corresponding to each optical element for fully connected general architectures (such as the Clements or Reck architectures), in practical applications, non-fully connected, sparse, or specific topological architectures are often used to reduce optical losses, decrease the number of elements, or adapt to specific algorithms. For these more general spatial parallel architectures, existing technologies lack a universal analytical parameter decomposition method, making it difficult to quickly and accurately map the target quantum logic gates (i.e., the target transformation matrix, such as the target unitary transform) to hardware parameters. Furthermore, for purely time-multiplexed schemes, for any given target computation task (i.e., the target transformation matrix, such as the target unitary transform), there is also a lack of an analytical parameter decomposition method to quickly determine the on / off state of the optical switches and the dynamic phase modulation parameters at each moment, which limits its ability to execute complex general algorithms.

[0100] In summary, existing technologies, especially for optical quantum computing systems with purely spatial parallel or purely temporal multiplexing architectures, particularly those with non-fully connected spatial parallel, purely temporal multiplexing, and more complex spatiotemporal hybrid architectures, lack a universal analytical parameter decomposition method. This prevents existing technologies from quickly and accurately mapping the target quantum logic gate (i.e., the target transformation matrix, such as the target unitary transformation) to the physical parameters of the underlying optical components. Consequently, these technologies lack universality and thus cannot accurately construct the system.

[0101] Based on this, this application aims to provide a general analytical parameter decomposition method and introduce a hardware-software co-design concept to solve the problems existing in the prior art. Specifically, the method for constructing an optical quantum computing system provided in this application determines the equivalent transformation matrix corresponding to the initial optical quantum computing system constructed for the target quantum computing problem. Based on the difference between the equivalent transformation matrix and the target transformation matrix used to solve the target quantum computing problem, the system parameters of the initial optical quantum computing system are adjusted to obtain the optical quantum computing system. This realizes the mapping from arbitrary target transformation matrix to system parameters, so that optical quantum computers are no longer limited to specific hardware topologies or dedicated algorithms. Whether it is a pure time-multiplexed, non-fully connected sparse spatial parallel architecture, or other complex hybrid architectures, this application can achieve accurate parameter mapping, thereby accurately constructing the system.

[0102] The construction method of the optical quantum computing system provided in this application will be described in detail below with reference to Figures 12 to 14.

[0103] Figure 12 is a flowchart illustrating a method for constructing an optical quantum computing system according to an embodiment of this application. This method can be applied to electronic devices; exemplarily, the electronic device may include devices such as optical quantum computers. As shown in Figure 12, the method may include the following steps.

[0104] S1210, determine the target transformation matrix used to solve the target quantum computing problem.

[0105] In some examples, the target quantum computing problem can be a specific quantum computing problem to be solved. The target transformation matrix can be a matrix capable of solving the target quantum computing problem, such as an N-dimensional target unitary matrix. The dimension N of this matrix represents the total number of quantum modes required to solve the target quantum computing problem. It should be noted that N has different physical mapping meanings for different system hardware architectures: in a purely spatial parallel architecture, N corresponds to the number of optical waveguides; in a purely temporal or spatiotemporal hybrid architecture, N is less than or equal to the product of the number of optical waveguides and the time-bins.

[0106] For example, a target N-dimensional unitary matrix can be determined by extracting or defining a mathematical description of the specific quantum computing problem to be solved.

[0107] S1220, based on the complexity of the target quantum computing problem, constructs an initial optical quantum computing system.

[0108] In some examples, the initial optical quantum computing system can include optical quantum computing systems of any architecture type, such as optical quantum computing systems with a purely spatial parallel architecture, optical quantum computing systems with a purely time-multiplexed architecture, or optical quantum computing systems with a spatiotemporal hybrid architecture.

[0109] For example, the initial optical quantum computing system may include a plurality of adjustable system parameters, which may specifically include at least one of system architecture parameters and component configuration parameters, wherein the system architecture parameters may be parameters for defining the outer architecture of the system, and the component configuration parameters may be parameters for configuring tunable components in the system.

[0110] In some embodiments, system architecture parameters may include at least one of the following: component connection structure, photon signal step size, and the number of optical waveguides. Examples include the unit connection structure of the spatial processing module, the unit connection structure of the temporal processing module, the unit connection structure between the spatial and temporal processing modules in an initial optical quantum computing system, the number of repetitions of the core computing unit, the photon signal step size, and the number of optical waveguides. The unit connection structure can be the arrangement and connection method of the various units within the module, and the photon signal step size can be the number of photon pulses emitted by the signal source to the optical waveguide.

[0111] In other embodiments, the component configuration parameters may include control parameters of the tunable component, such as the unit delay duration of the time processing module and the unit phase adjustment degree of the spatial processing module in the initial optical quantum computing system. The unit delay duration of the time processing module can be the delay duration corresponding to each unit in the time processing module, and the unit phase adjustment degree of the spatial processing module can be the phase adjustment degree corresponding to each unit in the spatial processing module, such as the control voltage or control current corresponding to a Mach-Zehnder interferometer (MZI).

[0112] For example, based on the complexity of the target quantum computing problem, an architecture search algorithm can be used to dynamically explore and determine system architecture parameters such as the optimal topology of the optical quantum circuit, thereby constructing an initial optical quantum computing architecture as an initial optical quantum computing system.

[0113] In some specific examples, for purely spatial parallel architectures, a search algorithm can be used to determine the spatial topology connectivity graph. This can include determining the number of physical waveguide layers, the specific arrangement of MZI or beam splitters (such as rectangular mesh, triangular mesh, or sparse random connectivity), and which parameters are fixed and which are adjustable, thereby achieving a balance between hardware complexity and expressive power.

[0114] In other specific examples, for purely temporal architectures, the time multiplexing sequence parameters can be determined. Specifically, this may include determining the required time step, the number of fiber delay loops, and the delay time corresponding to each delay loop (e.g., 1τ, 2τ, ...), thereby constructing a temporal interferometric network that meets the dimension N requirement.

[0115] In other specific examples, for spatiotemporal hybrid architectures, architecture search algorithms can be used to collaboratively consider spatial and temporal resources and dynamically adjust the circuit structure to determine the outer parameters of the spatiotemporal hybrid circuit, such as the number of physical modes n in the spatiotemporal hybrid optical quantum computing architecture, the topology of the spatial processing module, the number of layers in the spatial processing module, and the delay time nτ of each temporal processing module.

[0116] In addition to using the architecture search algorithm, any combination of one or more of the following algorithms can be used to construct the initial optical quantum computing system: the fully connected pruning algorithm, the constructive generation algorithm, and the differentiable architecture search algorithm.

[0117] S1230, determine the equivalent transformation matrix corresponding to the initial optical quantum computing system.

[0118] In some examples, the equivalent transformation matrix may be, for instance, the physical transformation matrix corresponding to the physical model of the initial optical quantum computing system. Exemplarily, the equivalent transformation matrix can be determined by constructing a system equivalent model corresponding to the initial optical quantum computing system, and then based on the transformation matrix corresponding to that system equivalent model.

[0119] Different methods can be used to construct the system equivalent model and determine the equivalent transformation matrix for different types of system architectures.

[0120] In some embodiments, for an initial optical quantum computing system with a purely spatial parallel architecture, the equivalent transformation matrix can be determined by directly constructing a cascaded transmission model as the system's equivalent model. Specifically, since photons only propagate in space and do not involve time loops, each optical component (such as a beam splitter or phase shifter) in the initial optical quantum computing system circuit can be directly represented as a corresponding fundamental unitary matrix. Following the order of the photon transmission path, all fundamental unitary matrices are multiplied in an ordered manner to obtain a global equivalent unitary matrix U(θ) describing the input-output relationship of the entire optical quantum network. This global equivalent unitary matrix U(θ) is the equivalent transformation matrix corresponding to the initial optical quantum computing system. Here, θ can be the set of phase parameters for all spatial processing elements (or units).

[0121] In other embodiments, for an initial optical quantum computing system with a pure temporal architecture or a spatiotemporal hybrid architecture, an equivalent spatial dimension unfolding technique is used to unfold the initial optical quantum computing system in a spatial dimension, generating a pure spatial dimension equivalent model of the system, and then determining the equivalent transformation matrix. Based on this, step S1230 above may specifically include: unfolding the system model corresponding to the initial optical quantum computing system in a spatial dimension to obtain the system equivalent model; and determining the equivalent transformation matrix based on the transformation matrix corresponding to the system equivalent model.

[0122] In some examples, the equivalent model of the system obtained after unfolding in the spatial dimension can be a static, fully spatialized quantum optical network model.

[0123] For example, taking the initial optical quantum computing system with a spatiotemporal hybrid architecture as an example, in order to achieve N-dimensional feature transformation, a time step k can be set (that is, the number k of photon pulses emitted by the signal source to the optical waveguide), and k can be optimized as an adjustable system parameter during the parameter optimization process. The equivalent spatial dimension unfolding technique can be used to unfold the system model corresponding to the initial optical quantum computing system in the equivalent spatial dimension; that is, the spatiotemporal hybrid architecture system model with n modes and a time step of k is unfolded in the spatial dimension. This unfolding operation equivalently transforms the spatiotemporal hybrid system model into a static, fully spatialized optical quantum network model, i.e., the system equivalent model. This system equivalent model has… Each input and output mode may contain [number] input and output modes, which can include [number] input and output modes. Individual delay line modes and One physical model (here) The delay line mode can be the mode corresponding to the photonic quantum information temporarily stored in the delay line during quantum state detection, and the physical mode can be the mode corresponding to the photonic quantum information that can be directly detected during quantum state detection. Thus, the equivalent model of this system can correspond to one... The dimensional physical evolution matrix (i.e., the transformation matrix). Furthermore, due to the dimensionality of the expanded physical evolution matrix... Typically much larger than the dimension N required to solve the target quantum computing problem, therefore, it is possible to expand it into... From the N×N dimensional physical evolution matrix, one or more N×N dimensional submatrices are selected using the sliding window method or other random methods. This serves as the equivalent transformation matrix, preparing for subsequent comparison with the target transformation matrix. The submatrix... The selection process corresponds to physically determining the injection and measurement time windows of the photon pulse. Thus, by using these extracted N-dimensional sub-matrices as the objects of subsequent co-optimization, the interference from the invalid parameter space can be significantly reduced, optimization efficiency improved, and preparation made for achieving high-precision parameter mapping.

[0124] S1240, based on the difference between the equivalent transformation matrix and the target transformation matrix, adjust the system parameters of the initial optical quantum computing system to obtain the optical quantum computing system.

[0125] For example, the equivalent transformation matrix corresponding to the initial optical quantum computing system can be quantized using a preset difference quantization algorithm. Transformation matrix with target The system parameters of the initial optical quantum computing system are continuously adjusted to minimize the differences between the parameters, and then converted into physical drive signals recognizable by the optical quantum chip controller. Through a high-speed digital-to-analog converter and a multi-channel logic controller, the generated voltage and sequence signals are precisely applied to the corresponding tunable phase shifters, variable beam splitters, and high-speed optical switches on the chip housing the optical quantum computing system. This ultimately yields an optical quantum computing system suitable for solving the target quantum computing problem.

[0126] Furthermore, when using this optical quantum computing system to complete the computational task corresponding to the target quantum computing problem, a photon pulse sequence encoded with initial quantum information can be input into the chip according to an optimized time step k, allowing it to evolve within the optical quantum computing system. The final output quantum state of the optical quantum information is a high-fidelity simulation of the initial quantum state after the target unitary matrix transformation is applied, thereby completing the computational task corresponding to the target quantum computing problem.

[0127] Thus, this application embodiment determines the equivalent transformation matrix corresponding to the initial optical quantum computing system constructed for the target quantum computing problem. Based on the difference between the equivalent transformation matrix and the target transformation matrix used to solve the target quantum computing problem, the system parameters of the initial optical quantum computing system are adjusted to obtain the optical quantum computing system. This realizes the mapping from arbitrary target transformation matrix to system parameters, so that optical quantum computers are no longer limited to specific hardware topologies or dedicated algorithms. Whether it is a pure time-multiplexed, non-fully connected sparse space architecture, or other complex hybrid architecture, this application can achieve accurate parameter mapping, thereby accurately constructing the system.

[0128] Based on this, in some embodiments, the above S1240 may specifically include: constructing a cost function based on the difference between the equivalent transformation matrix and the target transformation matrix; using a preset optimization algorithm to determine the parameter optimization result corresponding to the initial optical quantum computing system with the objective of minimizing the function value of the cost function; and adjusting the system parameters of the initial optical quantum computing system based on the parameter optimization result to obtain the optical quantum computing system.

[0129] In some examples, a cost function can be established to quantify the difference between the equivalent transformation matrix and the target transformation matrix. This cost function can be the fidelity loss function shown in formula (1).

[0130]

[0131] Where P represents the set of adjustable parameters (i.e., system parameters) in the initial optical quantum computing system, and Tr() represents the trace operation of the matrix. The equivalent transformation matrix corresponding to the optical quantum computing system under the system parameter set P is represented by the following matrix. Represents the target unitary matrix.

[0132] For example, to minimize the function value of the cost function With the goal of finding a set of system parameters, an iterative search can be performed in a multidimensional parameter space containing the system parameter set P, using a pre-defined optimization algorithm, until a set of parameters that can be found is found. Approximating to the maximum extent Optimal parameter set As a result of parameter optimization, this optimal parameter set can then be used as a basis for further optimization. Adjust the system parameters of the initial optical quantum computing system.

[0133] In some examples, the aforementioned preset optimization algorithm can be a gradient optimization algorithm, such as stochastic gradient descent. This gradient optimization algorithm is suitable for cases where the parameter space is continuously differentiable. For example, the gradient of the cost function with respect to each phase parameter is calculated using automatic differentiation techniques, and the parameters are updated using optimizers such as Adam, SGD (stochastic gradient descent), and L-BFGS. This approach has fast convergence speed and is suitable for large-scale parameter arrays.

[0134] In other examples, the aforementioned preset optimization algorithm can also be a non-gradient heuristic algorithm, such as particle swarm optimization (PSO) or simulated annealing. This non-gradient heuristic algorithm is suitable for situations where the parameter space contains discrete variables (such as switch states) or where gradients are difficult to compute. For example, algorithms such as genetic algorithms, particle swarm optimization, simulated annealing, or differential evolution can be used to search for optimal parameter solutions globally by simulating natural evolution or group behavior, effectively avoiding getting trapped in local optima.

[0135] In other examples, optimal parameter prediction based on machine learning models can also be employed. A predictive model is built using deep neural networks (such as Long Short-Term Memory networks, Transformers, or graph neural networks). The model is pre-trained using a large number of "target matrix-optimal parameters" data samples; during the inference phase, the target transformation matrix is ​​directly input into the model, which outputs a set of predicted parameters. These predicted parameters can be used directly as the result of parameter optimization or as initial values ​​for gradient optimization to accelerate convergence.

[0136] Therefore, in order to find the most efficient mapping scheme, the parameter optimization results under different parameter configurations can be evaluated and screened. In some embodiments, the above step S1220 may specifically include: constructing a target optical quantum computing system based on the complexity of the target quantum computing problem; adjusting the system architecture parameters of the target optical quantum computing system to obtain multiple initial optical quantum computing systems.

[0137] Accordingly, the above-mentioned adjustment of the system parameters of the initial optical quantum computing system based on the parameter optimization results to obtain the optical quantum computing system may specifically include: determining the parameter optimization results that meet the preset computing accuracy conditions from the parameter optimization results corresponding to multiple initial optical quantum computing systems, and using them as the target parameter optimization results; adjusting the system architecture parameters and component configuration parameters of the initial optical quantum computing system based on the target parameter optimization results to obtain the optical quantum computing system.

[0138] In some examples, the target quantum computing system can be an initial quantum computing system constructed according to the aforementioned methods, based on the complexity of the target quantum computing problem. The preset computational accuracy condition can be that the fidelity of the quantum computing system constructed based on parameter optimization results is greater than a preset threshold, such as 95%. Fidelity can be used to characterize the similarity between the transformation matrix finally achieved by the quantum computing system and the target transformation matrix.

[0139] For example, a target optical quantum computing system can be constructed first, and then multiple rounds of parallel or serial parameter optimization can be performed by adjusting the system architecture parameters of the target optical quantum computing system (such as trying different time steps k and trying different spatial topological sparsity).

[0140] Specifically, multiple initial quantum computing systems with various system architecture parameters can be constructed by repeatedly adjusting the system architecture parameters of the target quantum computing system or iteratively adjusting the system architecture parameters of the previously adjusted quantum computing system. System parameters are then optimized for each initial quantum computing system, resulting in optimized parameter results for each system. These optimized results are stored in a candidate result pool. Parameter optimization results with a fidelity greater than 95% are selected from this pool and used as the target parameter optimization results. Based on these target parameter optimization results, the system architecture parameters and component configuration parameters of the initial quantum computing systems are then adjusted to obtain the final quantum computing system.

[0141] Based on this, in some embodiments, when there are multiple target parameter optimization results, the above-mentioned adjustment of the system architecture parameters and component configuration parameters of the initial optical quantum computing system based on the target parameter optimization results to obtain the optical quantum computing system may specifically include: determining the evaluation index values ​​corresponding to the multiple target parameter optimization results respectively; determining the parameter optimization result with the highest evaluation index value from the multiple target parameter optimization results as the optimal parameter optimization result; and adjusting the system architecture parameters and component configuration parameters of the initial optical quantum computing system based on the optimal parameter optimization result to obtain the optical quantum computing system.

[0142] For example, for optimization results of multiple target parameters that meet the conditions, at least one parameter evaluation index can be introduced to evaluate and filter the optimization results of multiple target parameters. This parameter evaluation index can be an indicator used to characterize the key performance of the optical quantum computing system built based on the optimization results of specific parameters, such as computational accuracy index, hardware adaptability index, resource consumption index, etc.

[0143] Specifically, for each of the at least one parameter evaluation indicators, the optimization results of each target parameter can be scored and evaluated, thereby obtaining the evaluation indicator value corresponding to each target parameter optimization result. Based on the evaluation indicator values ​​corresponding to each target parameter optimization result, the optimization results of multiple target parameters are sorted, and the target parameter optimization result with the highest evaluation indicator value is determined as the optimal parameter optimization result. Adjusting the system parameters of the initial optical quantum computing system based on this optimal parameter optimization result yields an optical quantum computing system that is best suited to the target quantum computing problem and has the highest computational efficiency.

[0144] Furthermore, in some embodiments, to improve the accuracy of evaluating the parameter optimization results, the aforementioned evaluation index values ​​can be scores obtained based on a multi-dimensional comprehensive judgment of multiple parameter evaluation indicators. Therefore, in some embodiments, the determination of the evaluation index values ​​corresponding to the optimization results of multiple target parameters may specifically include: performing the following steps for each of the multiple target parameter optimization results to obtain the evaluation index values ​​corresponding to the optimization results of multiple target parameters: determining the index values ​​corresponding to the target parameter optimization results under multiple parameter evaluation indicators; and determining the evaluation index value corresponding to the target parameter optimization results based on the index values ​​corresponding to the target parameter optimization results under multiple parameter evaluation indicators.

[0145] In some examples, multiple parameter evaluation metrics may include at least two of the following: computational accuracy metrics, hardware compatibility metrics, and resource consumption metrics. The computational accuracy metric can characterize the fidelity of the optical quantum computing system built based on the parameter optimization results. For example, the metric value can be determined by the similarity between the transformation matrix finally implemented by the optical quantum computing system and the target transformation matrix. The hardware compatibility metric can characterize the compatibility between the parameter optimization results and the hardware implementing the optical quantum computing system. For example, the metric value can be determined by assessing whether the parameter optimization results fall within the linear operating region of the hardware and whether they are sensitive to manufacturing errors (i.e., robustness). The resource consumption metric can characterize the resource consumption of the optical quantum computing system built based on the parameter optimization results. For example, the metric value can be determined by assessing the total photon transmission path length required to complete the computation (corresponding to photon loss), the total number of time steps required (corresponding to computation delay), and the number of active components required (corresponding to energy consumption).

[0146] For example, a corresponding weight can be set for each parameter evaluation index, and the optimization result of the target parameter can be evaluated and scored for each parameter evaluation index to obtain the index value corresponding to the optimization result of the target parameter under each parameter evaluation index. Based on the weights corresponding to the multiple parameter evaluation indexes, the index values ​​corresponding to the optimization result of the target parameter under the multiple parameter evaluation indexes are weighted and summed to obtain the final evaluation index value corresponding to the optimization result of the target parameter.

[0147] In this way, through the collaborative optimization of the above embodiments of this application, it is ensured that the hardware system is adapted while meeting the requirements of computational accuracy, and at the same time, the fewest possible computational steps are used, thereby reducing the overall computational latency and photon loss of the optical quantum computing system.

[0148] In addition, in some embodiments, the method may further include: if there is no parameter optimization result that meets the preset calculation accuracy condition among multiple parameter optimization results, returning to the execution of step S1220 above to adjust the parameters based on the reconstructed initial optical quantum computing system.

[0149] For example, if the calculated optimization results of multiple parameters do not meet the preset calculation accuracy conditions, that is, there are no parameter optimization results that meet the preset calculation accuracy conditions, the process can return to the execution of step S1220 above, reconstruct the initial optical quantum computing system, and then perform subsequent parameter optimization steps based on the redesigned initial optical quantum computing system.

[0150] Thus, by transforming the complex problem of quantum circuit design into a well-defined mathematical optimization problem, automation from algorithm to hardware execution is achieved. Designers no longer need to manually design complex system architectures; they only need to provide the target transformation matrix, and the system can automatically generate the most efficient and accurate physical implementation. This co-optimization ensures that quantum computing tasks can run at optimal performance given hardware resources, maximizing hardware utilization efficiency.

[0151] In summary, on the one hand, the embodiments of this application establish a spatiotemporal equivalent expansion model to determine the equivalent transformation matrix, and incorporate system architecture parameters and component configuration parameters into a unified collaborative optimization framework, enabling optical quantum computers to no longer be limited to specific hardware topologies or dedicated algorithms. Whether it's a pure time-multiplexed, non-fully connected sparse spatial architecture, or a complex spatiotemporal hybrid architecture, this invention can provide an accurate parameter mapping scheme. This greatly expands the ability of optical quantum hardware to execute arbitrary quantum logic gates (i.e., arbitrary target transformation matrices), significantly improving the system's versatility and programmability.

[0152] On the other hand, this application's embodiments employ a hierarchical collaborative design strategy that combines architecture search with parameter fine-tuning. During the optimization process, system architecture parameters such as the topological connections of the spatially parallel architecture and the time step k of the temporal architecture are dynamically adjusted as variable parameters. Compared to traditional blind global search, this collaborative strategy effectively reduces the ineffective search space and avoids the optimization algorithm getting trapped in local optima. By dynamically adjusting the architecture scale (e.g., automatically finding the minimum necessary number of time loops k), this application's embodiments can quickly converge to the optimal parameter solution while maintaining high fidelity, making online compilation and rapid configuration of large-scale optical quantum chips possible.

[0153] Furthermore, this application's embodiments introduce a multi-dimensional comprehensive evaluation mechanism in the final decision-making stage. When selecting the optimal solution, it considers not only mathematical approximation errors but also physical indicators such as photon loss, computational latency (number of time steps), and hardware power consumption, thereby automatically selecting the "lowest-cost" execution path. For example, under the same computational accuracy, it prioritizes architecture configurations with fewer time loops or fewer optical components. This not only directly reduces photon transmission loss within the chip (crucial for optical quantum computing) and lowers the overall system latency but also indirectly improves the success rate and robustness of computational tasks running on imperfect hardware.

[0154] The above description, with reference to Figure 1, details an embodiment of the method for constructing the optical quantum computing system of this application. The following description, with reference to Figure 2, details an embodiment of the apparatus for constructing the optical quantum computing system of this application. It should be understood that the descriptions of the method embodiments for constructing the optical quantum computing system correspond to the descriptions of the apparatus embodiments for constructing the optical quantum computing system; therefore, any parts not described in detail can be referred to the preceding method embodiments.

[0155] Figure 13 is a schematic diagram of the structure of a construction device for an optical quantum computing system provided in an embodiment of this application. As shown in Figure 13, the construction device 1300 for an optical quantum computing system provided in this embodiment includes:

[0156] The matrix determination module 1301 is used to determine the target transformation matrix for solving the target quantum computing problem;

[0157] System building module 1302 is used to build an initial optical quantum computing system based on the complexity of the target quantum computing problem;

[0158] Matrix equivalence module 1303 is used to determine the equivalent transformation matrix corresponding to the initial optical quantum computing system;

[0159] The parameter adjustment module 1304 is used to adjust the system parameters of the initial optical quantum computing system based on the difference between the equivalent transformation matrix and the target transformation matrix, so as to obtain the optical quantum computing system.

[0160] In some embodiments of this application, the matrix equivalence module 1303 is further configured to: expand the system model corresponding to the initial optical quantum computing system in the spatial dimension to obtain the system equivalent model; and determine the equivalent transformation matrix based on the transformation matrix corresponding to the system equivalent model.

[0161] In some embodiments of this application, the parameter adjustment module 1304 is further configured to: construct a cost function based on the difference between the equivalent transformation matrix and the target transformation matrix; determine the parameter optimization result corresponding to the initial optical quantum computing system using a preset optimization algorithm with the goal of minimizing the function value of the cost function; and adjust the system parameters of the initial optical quantum computing system based on the parameter optimization result to obtain the optical quantum computing system.

[0162] In some embodiments of this application, the system parameters include system architecture parameters and component configuration parameters. The system construction module 1302 is further configured to: construct a target optical quantum computing system based on the complexity of the target quantum computing problem; and adjust the system architecture parameters of the target optical quantum computing system to obtain multiple initial optical quantum computing systems. Correspondingly, the parameter adjustment module 1304 is further configured to: determine the parameter optimization results that satisfy the preset computational accuracy conditions from the parameter optimization results corresponding to the multiple initial optical quantum computing systems, and use them as the target parameter optimization results; and adjust the system architecture parameters and component configuration parameters of the initial optical quantum computing system based on the target parameter optimization results to obtain the optical quantum computing system.

[0163] In some embodiments of this application, when there are multiple target parameter optimization results, the parameter adjustment module 1304 is further configured to: determine the evaluation index values ​​corresponding to the multiple target parameter optimization results respectively; determine the parameter optimization result with the highest evaluation index value from the multiple target parameter optimization results as the optimal parameter optimization result; and adjust the system architecture parameters and component configuration parameters of the initial optical quantum computing system based on the optimal parameter optimization result to obtain the optical quantum computing system.

[0164] In some embodiments of this application, the parameter adjustment module 1304 is further configured to: perform the following steps for the optimization results of multiple target parameters respectively to obtain the evaluation index values ​​corresponding to the optimization results of multiple target parameters respectively: determine the index values ​​corresponding to the optimization results of target parameters under multiple parameter evaluation indicators, wherein the multiple parameter evaluation indicators include at least two of the following: calculation accuracy index, hardware adaptation index, and resource consumption index; and determine the evaluation index value corresponding to the optimization results of target parameters based on the index values ​​corresponding to the optimization results of target parameters under multiple parameter evaluation indicators.

[0165] In some embodiments of this application, the system architecture parameters include at least one of the component connection structure, photonic signal step size, and number of optical waveguides; the component configuration parameters include control parameters for tunable components.

[0166] The optical quantum computer according to an embodiment of this application will now be described with reference to FIG14. FIG14 is a schematic diagram of the structure of an optical quantum computer provided in another exemplary embodiment of this application.

[0167] As shown in Figure 14, the optical quantum computer 1400 includes one or more processors 1401 and memory 1402.

[0168] The processor 1401 may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the optical quantum computer 1400 to perform desired functions.

[0169] The memory 1402 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 1401 may execute the program instructions to implement the data acquisition methods of the various embodiments of this application described above and / or other desired functions. The computer-readable storage medium may also store various contents such as data acquisition task templates, configuration information, instance tasks, etc.

[0170] In one example, the optical quantum computer 1400 may also include an input device 1403 and an output device 1404, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0171] The input device 1403 may include, for example, a keyboard, a mouse, etc.

[0172] The output device 1404 can output various information to the outside, including data acquisition task templates, configuration information, instance tasks, etc. The output device 1404 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0173] Of course, for simplicity, Figure 14 only shows some of the components of the optical quantum computer 1400 relevant to this application, omitting components such as buses, input / output interfaces, etc. In addition, the optical quantum computer 1400 may include any other suitable components depending on the specific application.

[0174] In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the data acquisition methods according to various embodiments of this application described above.

[0175] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0176] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the data acquisition methods according to various embodiments of this application described above.

[0177] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0178] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0179] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0180] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0181] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0182] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for constructing an optical quantum computing system, characterized in that, include: Determine the target transformation matrix used to solve the target quantum computing problem; Based on the complexity of the target quantum computing problem, an initial optical quantum computing system is constructed. Determine the equivalent transformation matrix corresponding to the initial optical quantum computing system; based on the difference between the equivalent transformation matrix and the target transformation matrix, adjust the system parameters of the initial optical quantum computing system to minimize the difference, wherein the system parameters include system architecture parameters and component configuration parameters; And the adjusted system parameters are mapped onto the optical quantum chip device to obtain an optical quantum computing system that solves the target quantum computing problem.

2. The method according to claim 1, characterized in that, The step of determining the equivalent transformation matrix corresponding to the initial optical quantum computing system includes: expanding the system model corresponding to the initial optical quantum computing system in the spatial dimension to obtain the equivalent system model; and determining the equivalent transformation matrix based on the transformation matrix corresponding to the equivalent system model.

3. The method according to claim 1 or 2, characterized in that, The step of adjusting the system parameters of the initial optical quantum computing system based on the difference between the equivalent transformation matrix and the target transformation matrix to minimize the difference includes: constructing a cost function based on the difference between the equivalent transformation matrix and the target transformation matrix; determining the parameter optimization result corresponding to the initial optical quantum computing system using a preset optimization algorithm with the objective of minimizing the function value of the cost function; and adjusting the system parameters of the initial optical quantum computing system based on the parameter optimization result.

4. The method according to claim 3, characterized in that, The step of constructing an initial optical quantum computing system based on the complexity of the target quantum computing problem includes: constructing a target optical quantum computing system based on the complexity of the target quantum computing problem; adjusting the system architecture parameters of the target optical quantum computing system to obtain multiple initial optical quantum computing systems; and adjusting the system parameters of the initial optical quantum computing systems based on the parameter optimization results, including: determining the parameter optimization results that satisfy the preset computational accuracy conditions from the parameter optimization results corresponding to the multiple initial optical quantum computing systems, and using them as the target parameter optimization results; and adjusting the system architecture parameters and component configuration parameters of the initial optical quantum computing systems based on the target parameter optimization results.

5. The method according to claim 4, characterized in that, When there are multiple optimization results for the target parameters, adjusting the system architecture parameters and component configuration parameters of the initial optical quantum computing system based on the optimization results for the target parameters includes: determining the evaluation index values ​​corresponding to each of the multiple optimization results for the target parameters; determining the parameter optimization result with the highest evaluation index value from the multiple optimization results for the target parameters as the optimal parameter optimization result; and adjusting the system architecture parameters and component configuration parameters of the initial optical quantum computing system based on the optimal parameter optimization result.

6. The method according to claim 5, characterized in that, The step of determining the evaluation index values ​​corresponding to the optimization results of the multiple target parameters includes: performing the following steps for each of the optimization results of the multiple target parameters to obtain the evaluation index values ​​corresponding to the optimization results of the multiple target parameters respectively: determining the index values ​​corresponding to the optimization results of the target parameters under multiple parameter evaluation indicators, wherein the multiple parameter evaluation indicators include at least two of the following: computational accuracy index, hardware adaptation index, and resource consumption index; and determining the evaluation index value corresponding to the optimization results of the target parameters based on the index values ​​corresponding to the optimization results of the target parameters under the multiple parameter evaluation indicators.

7. The method according to claim 1, characterized in that, The system architecture parameters include at least one of the following: component connection structure, photonic signal step size, and number of optical waveguides; the component configuration parameters include control parameters for tunable components.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for executing the method for constructing the optical quantum computing system according to any one of claims 1 to 7.

9. A quantum optical computer, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is used to execute the method for constructing the optical quantum computing system according to any one of claims 1 to 7.

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