Information processing system and information processing method

Abstract

To provide an information processing system and an information processing method, which enable knowledge database collation by a quantum computer.SOLUTION: In an information processing system, an information processor includes a sentence acquisition section, a conversion section, an instruction section, and a collation section. The sentence acquisition section acquires a plurality of sentences, and the conversion section acquires a graph Laplacian obtained by converting logical relationships between propositions on the basis of the propositions of the sentences. The instruction section outputs quantum encoding instructions for applying the graph Laplacian to a unitary matrix to a quantum computer. The collation section collates the sentence to be collated with information necessary for the quantum encoding instruction including graph data or the graph Laplacian and outputs the quantum encoding instruction using information necessary for the quantum encoding that is a collation result.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information processing system and an information processing method for constructing a knowledge database suitable for processing by a quantum computer. [Background technology]

[0002] A knowledge database is a database that is constructed for the purpose of improving the searchability and usability of data by consolidating business experience, know-how, etc. In a knowledge database, knowledge is organized based on the relationships between knowledge, and various technologies have been proposed to search for these relationships using computer processing.

[0003] Patent Document 1 discloses a fuzzy inference technique that learns inference rules necessary for fuzzy inference and performs fuzzy inference based on the learning results. Patent Document 1 discloses that by learning the causal relationships between knowledge information as fuzzy inference rules, it becomes possible to acquire inference rules from knowledge information that includes ambiguity in a few minutes or less, and that fuzzy inference can be performed using the truth values ​​and validity of the rules. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 06-202870 [Non-patent literature]

[0005] [Non-Patent Document 1] Kubra Yeter-Aydeniz et al (2019). “Quantum topological data analysis with continuous variables” [Non-Patent Document 2] Ankit Khandelwal and M Girish Chandra (2023). “Quantum-Enhanced Topological Data Analysis: A Peep from an Implementation Perspective” Summary of the Invention [Problem to be solved by the invention]

[0006] As mentioned above, a problem with database searches that include connections between knowledge is that computer processing takes time. In particular, the amount of information has increased in recent years, and the relationships between these information tend to become more complex.

[0007] Quantum computers can demonstrate superior computing power compared to classical computers by using algorithms that utilize the characteristics of quantum computing. In order for quantum computers to demonstrate superior computing power, the challenge has been to quantum-encode and convert the information to be calculated.

[0008] The present invention has been made in view of the above problems, and aims to provide a technique that enables knowledge database matching using quantum encoding that can be advantageously calculated by a quantum computer. Another aim of the present invention is to provide a technique for generating a quantum circuit that is advantageous in searching a knowledge database using quantum encoding. [Means for solving the problem]

[0009] In order to solve the above-mentioned problems, the present invention comprises a text acquisition unit, a conversion unit, and an instruction unit, wherein the text acquisition unit acquires a plurality of texts, the conversion unit acquires a graph Laplacian by converting a logical relationship between propositions based on the propositions of the texts, and the instruction unit outputs a quantum encoding instruction to the quantum computer for applying the graph Laplacian to a unitary matrix.

[0010] With this configuration, text can be converted into information that can be calculated by a quantum computer.

[0011] In a preferred embodiment of the present invention, the quantum computer further includes a comparison unit that acquires eigenvalues ​​from the quantum computer as quantum computation results for the quantum encoding instruction, and determines whether or not a logical relationship exists from the eigenvalues. With this configuration, it is possible to quickly calculate eigenvalues ​​using a quantum computer, and based on the results, it is possible to determine whether or not a logical relationship exists in the target sentence.

[0012] In a preferred embodiment of the invention, The system includes a knowledge database that stores information necessary for the quantum encoding instruction, and the matching unit matches the sentence to be matched with information necessary for the quantum encoding instruction, which includes graph data or a graph Laplacian, and outputs a quantum encoding instruction using the information necessary for quantum encoding, which is the matching result. With this configuration, it is possible to issue a quantum encoding instruction using information necessary for quantum encoding corresponding to the sentence to be matched.

[0013] In a preferred embodiment of the present invention, the conversion unit includes an analysis unit that maps text vectors obtained by vectorizing text as point cloud data and outputs, as an analysis result, identity between text vectors that connect each point by changing the radius of each point, and a graph conversion unit that generates the graph Laplacian based on the text vectors and the analysis result. In a preferred embodiment of the present invention, a comparison unit is provided that acquires an eigenvalue as a quantum computation result for the quantum encoding instruction, and determines that there is no logical connection when the Betti number of a zero-dimensional homology group from the eigenvalue is not 1. This configuration makes it possible to adjust the range of ambiguity in the meaning of propositions accumulated through geometric analysis. Also, if it is found that there is no geometric connection from the Betti number, it is possible to determine that there is no logical connection in the sentences, which eliminates the need for further logical relationship calculations and reduces calculation costs.

[0014] In a preferred embodiment of the present invention, a circuit generation unit is provided that generates a quantum circuit for processing the quantum encoded information based on a gene tree which is a multi-way tree, and the circuit generation unit generates the quantum circuit based on constraints that generate a gene tree consisting of a parent node having a unitary gate and a child node which is either a quantum bit, a unitary gate for a single quantum bit, or a unitary gate having one or more control bits. In a preferred embodiment of the present invention, the circuit generation unit generates a quantum circuit based on a constraint for generating a gene tree in which nodes other than terminal nodes are unitary gates. In a preferred embodiment of the present invention, the circuit generation section evaluates the generated quantum circuit using an evaluation function that highly evaluates a quantum circuit having a small number of nodes. Such a configuration makes it possible to generate quantum circuits suitable for processing quantum-encoded sentences. Effect of the Invention

[0015] According to the present invention, it is possible to provide a technique that enables knowledge database matching using a quantum computer. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is a block diagram of a system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram showing the hardware configuration of the present embodiment. [Diagram 3] 3 shows a process sequence for constructing a knowledge database according to the present embodiment. [Figure 4] 3 shows a processing sequence relating to knowledge database matching according to the present embodiment. [Diagram 5] FIG. 2 is a schematic diagram of a logical relationship of a sentence according to the embodiment. [Figure 6] Schematic diagram of persistent homology. [Figure 7] 4 is a process flowchart for constructing a knowledge database according to the present embodiment. [Figure 8] 4 shows an example of the configuration of graph data according to the present embodiment. [Figure 9] A schematic diagram of graph data with nodes and edges. [Figure 10] 4 is a process flowchart relating to knowledge database matching according to the embodiment. [Figure 11] 1 shows an example of the configuration of a quantum circuit including a controlled unitary gate according to this embodiment. [Figure 12] 4 is a process flowchart for generating a quantum circuit according to the present embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, an information processing system according to an embodiment of the present invention will be described with reference to the drawings. Note that the embodiment described below is an example of the present invention, and the present invention is not limited to the embodiment described below, and various configurations can be adopted.

[0018] In this embodiment, the configuration, operation, etc. of an information processing system, an information processing device, and an information processing program are described, but a method with a similar configuration, a computer program, and a program recording medium on which the program is recorded also have the same effect. By using a program recording medium, for example, the program can be installed in a computer. A series of processes according to this embodiment described below is provided as a computer-executable program, and can be provided via a non-transitory computer-readable recording medium such as a CD-ROM or a flexible disk, or even via a communication line.

[0019] The information processing system is composed of a computer device. The computer device has an arithmetic unit such as a CPU (Central Processing Unit) and a storage device. The computer device can function as an information processing device by executing an information processing program stored in the storage device with the arithmetic unit. The information processing method is realized by processing of the computer device including the information processing device.

[0020] In this embodiment, sentences are interpreted as propositions. Sentences have a logical relationship based on the proposition. For example, a proposition is "A is B" or "C is A". Based on such a proposition, a logical relationship of "C is B" is derived. By quantum encoding data representing the logical relationship of such a proposition, high-speed quantum computation by a quantum computer is realized.

[0021] Fig. 1 shows a block diagram of an information processing system 1. The information processing system 1 includes an information processing device 2, a quantum computer 3, a user terminal 4, and a knowledge database NB, which are communicatively connected. Although only one user terminal 4 is shown in Fig. 1, there may be multiple user terminals 4. The quantum computer 3 may be configured to be mounted on the information processing device 2.

[0022] The information processing device 2 includes, as functional components, a text acquisition unit 21, a conversion unit 22, an instruction unit 23, a storage processing unit 24, a collation unit 25, and a circuit generation unit 26. The conversion unit 22 includes a vector conversion unit 221, an analysis unit 222, and a graph conversion unit 223.

[0023] In this embodiment, the quantum computer 3 configures quantum bits using any hardware (for example, a superconducting quantum circuit or an optical quantum circuit) and performs quantum computation by performing quantum gate operations on the quantum bits. The quantum computer 3 performs quantum computation based on the quantum circuit.

[0024] The user terminal 4 accepts input of sentences to be stored in the knowledge database NB and sentences to be compared against the knowledge database NB. The user terminal 4 can build a knowledge database NB by storing sentences by transmitting the sentences to the information processing device 2. The user terminal 4 can refer to the comparison results by transmitting the sentences to be compared to the information processing device 2. In this embodiment, the sentences are processed as text data that can be processed by a computer.

[0025] Fig. 2(a) shows a hardware configuration diagram of the information processing device 2. The information processing device 2 includes, as its hardware configuration, a control device 201, a storage device 202, and a communication device 203. In this embodiment, the information processing device 2 may use a computer device such as a server device or a personal computer. Note that the information processing device 2 may be configured by a plurality of computers, and is not limited to the configuration shown in Fig. 2(a) as long as it can realize the above-mentioned functional components (21-26) as a whole.

[0026] The control device 201 is configured with one or more processors such as a CPU and a GPU (Graphics Processing Unit), and executes an information processing program, an OS (Operating System), and other applications to control the overall processing in the information processing device 2. The storage device 202 is a hard disk drive (HDD), a solid state drive (SSD), a flash memory, a RAM (Random Access Memory), etc., and stores the information processing program and various data. The communication device 203 controls communication with a communication network, and realizes data communication with the user terminal 4, the quantum computer 3, etc.

[0027] 2(b) shows a hardware configuration diagram of the user terminal 4. The user terminal 4 includes, as its hardware configuration, a control device 401, a storage device 402, a communication device 403, an input device 404, and an output device 405. In this embodiment, the user terminal 4 may be a smartphone, a personal computer, a tablet terminal, or the like.

[0028] The control device 401 is composed of one or more processors such as a CPU, and controls the overall processing in the user terminal 4 by executing terminal programs, an OS, other applications, etc. The storage device 402 is an HDD, SSD, flash memory, RAM, etc., and stores a browser application and various data. The communication device 403 controls communication with a communication network, and realizes data communication with at least the information processing device 2. The input device 404 is an input interface that accepts input operations by a user, and is composed of a microphone, touch panel, mouse, keyboard, etc. The output device 405 is composed of a display that displays and outputs, etc.

[0029] <Knowledge database construction> FIG. 3 shows a processing sequence of the information processing system 1 in the process of constructing a knowledge database.

[0030] The user terminal 4 transmits text to the information processing device 2 (S101). The information processing device 2 stores the text received from the user terminal 4 in the knowledge database NB (S102). The information processing device 2 also stores text vectors obtained by vectorizing the text received from the user terminal 4 in the knowledge database NB (S103). The information processing device 2 also stores graph data and / or graph Laplacian obtained by graphing the text received from the user terminal 4 in the knowledge database NB (S104).

[0031] <Knowledge database comparison> 4 shows a processing sequence of the information processing system 1 in a process of checking the knowledge database constructed by the process of constructing the knowledge database. In this embodiment, checking the knowledge database means that when a user inputs a sentence (target sentence) to be checked, the target sentence is interpreted as a proposition and checked against sentences stored in the knowledge database NB, thereby obtaining a truth / false judgment result for the proposition. Note that, as with conventional knowledge databases, it is of course possible to search and view sentences using any search query, etc.

[0032] The user terminal 4 transmits a text to be compared to the information processing device 2 (S201). The information processing device 2 vectorizes the received text and transmits a comparison request to the knowledge database NB (S202). The information processing device 2 may store the received text or text vector in the knowledge database NB. The information processing device 2 receives the comparison result as graph data or graph Laplacian (S203). If the information processing device 2 cannot obtain a corresponding comparison result, it outputs a result indicating that comparison is not possible to the user terminal 4. If the information processing device 2 obtains graph data as the comparison result, it performs graph Laplacianization. The information processing device 2 outputs a quantum encoding instruction to the quantum computer 3 for applying the graph Laplacian to the unitary matrix (S204). The quantum computer 3 performs a quantum phase estimation calculation according to the quantum encoding instruction (S205). The information processing device 2 receives a quantum calculation result of the quantum phase estimation in response to the quantum encoding instruction (S206). The information processing device 2 performs a comparison judgment on the quantum phase estimation result (S207). The information processing device 2 transmits an output based on the comparison judgment result to the user terminal 4 (S208). Here, the output based on the comparison judgment includes a truth or falsity result of the proposition. The truth or falsity result of the proposition may be that the proposition has a logical connection and is true, that the proposition has a logical connection and is false but is false, that there is no logical connection and is highly likely to be false, or that it is considered to be false, etc.

[0033] In this embodiment, sentences are represented as a knowledge graph. Sentences are treated as nodes. Logical relationships between sentences are treated as edges. Specifically, edges represent logical operators such as AND, OR, NOT, and IMP(→).

[0034] Fig. 5 shows an outline of the logical relationship between sentences in this embodiment. In Fig. 5, sentences A, B, and C have a logical relationship of "A AND B -> C". Sentences A to C are represented as nodes N1 to N3, respectively. Edge E1 represents the logical relationship of AND. Edge E2 represents the logical relationship of conclusion. Here, node N4 of sentence D has no edge connection with sentences A to C, and is therefore treated as having no logical relationship.

[0035] Sentences and logical relationships between sentences can be represented by sentence vectors obtained by vectorizing sentences. Sentence vectorization can use an existing natural language processing model, and sentences are input into the natural language processing model to output sentence vectors. Sentence vectors are multidimensional vectors, and similar sentences have similar vectors, so they are mapped onto a close vector space. Furthermore, multiple sentence vectors having a logical relationship between sentences are each mapped onto a relatively close vector space corresponding to the logical relationship. For example, the logical relationship between similar sentence vectors can be an inclusion relationship IMP.

[0036] When a proposition in one sentence is similar to a proposition in another sentence, it is preferable to interpret these propositions as being the same. In addition, regarding the logical relationship between sentences, it is preferable to expand the interpretation of similar propositions so that propositions that did not previously have a logical relationship are interpreted as having a logical relationship.

[0037] In this embodiment, a geometric analysis method is adopted to give the proposition of the sentence a wider interpretation range. More specifically, the sentence vector is interpreted by an analysis method that applies persistent homology. For a detailed explanation of persistent homology, please refer to Non-Patent Document 1. In the analysis method using persistent homology, for example, an n-th order homology group can be considered for a vector space. According to this analysis, information such as the number of connected components in a vector space can be obtained from the zeroth order homology group, the number of one-dimensional holes (rings) in a vector space from the first order homology group, and the number of two-dimensional holes (cavities) in a vector space from the second order homology group can be obtained. The information obtained by these geometric analyses is called Betti numbers. Betti numbers are used in analyses such as classifying structures that are considered to be the same by continuous transformation as the same.

[0038] The analysis method of this embodiment is applied to interpreting the logical relationship between sentence vectors mapped to a vector space based on the Betti number. Specifically, the zero-dimensional Betti number represents the number of connected components, and if the zero-dimensional Betti number is 1, it can be analyzed that the structural connected component of the analysis target is one. In other words, it can be analyzed that the analysis target may have a series of connections as a logical relationship. Conversely, if the zero-dimensional Betti number is 2 or more, the analysis target can be analyzed as being divided as a logical relationship and having no connections. Therefore, by acquiring the zero-dimensional Betti number, it is possible to determine the presence or absence of a logical relationship.

[0039] 6 is a schematic diagram for explaining persistent homology. Here, text vectors are considered to be mapped as points in a vector space. In other words, text vectors are considered to be point cloud data in the vector space.

[0040] FIG. 6(a) shows point cloud data corresponding to the sentence vectors mapped onto a vector space. In persistent homology, each point of the point cloud is gradually expanded. FIG. 6(b) shows the process of expanding each point of the point cloud data. Here, it is interpreted that the number of connected components is reduced by connecting some points to each other. In FIG. 6(c), each point of the point cloud data is further expanded, so that all points are connected to form a torus. As shown in FIG. 6(a) to (c), by expanding each point, a torus is finally formed, so that the number of holes becomes 1, and the Betti number changes. As the Betti number changes, the interpretation of the proposition of the sentence and the logical relationship between sentences also changes, and it is understood that the range of ambiguity of the meaning of the sentence can be adjusted.

[0041] FIG. 7 is a flowchart showing detailed processing for constructing the knowledge database NB in ​​the information processing device 2. In FIG.

[0042] The text acquisition unit 21 acquires text related to knowledge to be stored in the knowledge database NB from the user terminal 4 (S301).

[0043] The vector conversion unit 221 converts the acquired sentences into sentence vectors and maps them to a vector space (S302). The sentence vectors mapped to the vector space are called distributed representations. The distributed representations are stored in the knowledge database NB.

[0044] The analysis unit 222 determines the similarity between the text vectors. In this embodiment, the similarity between the text vectors is determined by cosine similarity. In cosine similarity, two text vectors are normalized with a value ranging from -1 to 1. If the cosine similarity is 1, the angle between the text vectors is 0 degrees, and the similarity between the text vectors is determined to be high. If the cosine similarity is -1, the angle between the text vectors is 180 degrees, and the similarity between the text vectors is determined to be low. Note that the determination of the similarity between the text vectors is not limited to the determination by cosine similarity, and methods such as Euclidean distance and KL divergence may be adopted.

[0045] The analysis unit 222 expands each point by a radius r using the sentence vectors mapped to the vector space as point cloud data, as in the above-mentioned analysis method. Here, the radius r is defined as a numerical value used to adjust the similarity of the sentences described above, and can be set arbitrarily depending on the extent to which the width of the interpretation of the sentences is to be expanded. For example, when there are two similar propositions that are targets to be regarded as identical, a radius r is set at which the points of the sentence vectors corresponding to those propositions are connected in the vector space. If the radius r is zero, only the identity of sentences that exactly match is permitted, and if the distance between the sentence vectors is equal to or greater than a certain value (this number is set as the radius r), it becomes possible to identify the sentences with ambiguity in meaning included.

[0046] In this embodiment, the radius r is set as a constant, but an arbitrary radius r may be set via the user terminal 4. Also, if a matching result cannot be obtained in response to a matching request, the radius r may be set to a larger value and a matching request may be made again. The radius r may be set to a different constant depending on the attributes and use of the knowledge stored in the knowledge database NB.

[0047] The analysis unit 222 can take the Betti number that changes by adjusting the radius r as the analysis result. The Betti number represents a geometric characteristic of a structure made up of points that constitute the point cloud data. The analysis unit 222 can take the identity of the proposition as the analysis result by the geometric transformation of the point cloud data.

[0048] The graph conversion unit 223 performs graph conversion based on the propositions of the sentences to graph the logical relationships between the propositions (S303). Specifically, the graph conversion unit 223 generates graph data based on the sentence vectors mapped to the vector space. The graph conversion unit 223 also generates a graph Laplacian by further converting the graph data. Here, the graph conversion unit 223 can generate graph data by treating two similar propositions as the same proposition using the analysis result of the analysis unit 222.

[0049] In this embodiment, the graph data is composed of start point data and end point data. Fig. 8(a) shows an example of the configuration of graph data. The logical connection between propositions A to C has, for example, a logical relationship of "A ≒ B" and a logical relationship of "B is C". When proposition A is set as the start point data, the first line of the graph data has proposition A as the start point data and proposition B as the end point data. When proposition B is set as the start point data, the second line of the graph data has proposition B as the start point data and propositions A and C as the end point data. Note that the logical relationship of "B is C" does not have an inverse logical relationship, so a line of graph data having proposition C as the start point data is not generated.

[0050] When the graph conversion unit 223 can obtain an analysis result that determines that similar propositions A and B are the same proposition, it can generate graph data as shown in FIG. 8(b). In FIG. 8, the first line of the graph data has proposition B as the starting point data and proposition C as the end point data, and indicates the logical relationship "B is C". Here, since proposition A and proposition B are determined to be the same proposition, the graph data in the first line of FIG. 8(a) does not exist. As shown in FIG. 8(b), the graph conversion unit 223 can generate graph data equivalent to FIG. 8(a) by having the analysis result by the analysis unit 222 even if there is no sentence vector indicating the logical relationship "A ≒ B". This makes it possible to perform matching by expanding and interpreting the proposition even if the number of sentences stored in the knowledge database NB is small.

[0051] Quantum encoding of data represented by nodes and edges will be described with reference to Non-Patent Document 2. Here, data that can be represented by nodes and edges shown in FIG. 9 is considered. In this embodiment, these data correspond to graph data. According to FIG. 9, the target data is composed of nodes N1 to N5, and nodes N1 to N3 compose a partial structure k. This entire structure K is expressed as formula (1).

[0052]

number

[0053] Equations (2) and (3) show the boundary operator ∂2 of the two-dimensional simplex of the structure. Each column of the boundary operator ∂1 of the one-dimensional simplex represents the relationship between the nodes N1 to N5. For example, the first column represents the relationship between the nodes N1 and N2. The reinforcement operator ∂2 of the two-dimensional simplex represents the partial structure k.

[0054]

number

number

[0055] From these boundary operators, we obtain the graph Laplacian Δ1 shown in equation (4).

[0056]

number

[0057] The graph Laplacian Δ1 is padded to have the data length required for quantum computation. Here, since the graph Laplacian Δ1 is a 6x6 matrix, it is padded to an 8x8 matrix, which is a power of 2. Note that padding is performed appropriately according to the matrix size of the target data. The padded graph Laplacian Δ1' is shown in formula (5).

[0058]

number

[0059] The graph Laplacian Δ1 is a Hermitian matrix. The dimension of the eigenspace of the zero eigenvalue in a Hermitian matrix is ​​the Betti number. The zero eigenvalue is the kernel dimension of a matrix and is the multiplicity of a matrix whose eigenvalue is 0. In equation (5), the zero eigenvalue is 1, and the one-dimensional Betti number calculated in this example is 1. However, in general, the larger the dimension of the matrix, the more difficult the calculation. A quantum computer can quickly calculate eigenvalues ​​using a quantum phase estimation algorithm.

[0060] Next, the graph Laplacian Δ1 is transformed into a Hermitian matrix H, which is a linear combination of the tensor products of the Pauli operators shown in equation (6).

[0061]

number

[0062] By using the Hermitian matrix H, it is possible to express the unitary matrix U shown in equation (7). In this transformation, the kernel dimension information of the Hermitian matrix is ​​preserved, so by finding the kernel dimension of this unitary matrix, the eigenvalues, i.e., Betti numbers, can be derived. Quantum computers are known to have a high-speed eigenvalue estimation algorithm (also called a phase estimation algorithm) for the unitary matrix U, which enables high-speed calculations by the quantum computer by converting the target data into a unitary matrix. In the controlled unitary gate of the quantum circuit, a controlled unitary operator U is placed. In the quantum circuit, multiple power gates of the controlled unitary gate are placed.

[0063]

number

[0064] As described above, the graph transformation unit 223 can further transform the graph data into a graph Laplacian, thereby graphing the text and generating information necessary for quantum encoding by the quantum computer 3.

[0065] The memory processing unit 24 stores information required for quantum encoding in the knowledge database NB (S304). The information required for quantum encoding is graph data or graph Laplacian. The knowledge database NB stores at least a sentence, a sentence vector corresponding to the sentence, and graph data. It is preferable that the knowledge database NB further stores a corresponding graph Laplacian. The knowledge database NB stores graph data or graph Laplacian that is a matching result in response to a matching request by a sentence or sentence vector to be matched. In this embodiment, if graph data can be obtained as a matching result, it can be converted into a graph Laplacian and a quantum encoding instruction can be output, but it is more preferable to obtain a graph Laplacian as a matching result, since the process of converting graph data into a graph Laplacian in the matching stage can be omitted.

[0066] As one aspect, the vector conversion unit 221 generates a sentence vector by vectorizing the sentence stored in the knowledge database NB. The graph conversion unit 223 generates graph data by graphing the sentence vector stored in the knowledge database NB. The instruction unit 23 generates a graph Laplacian from the graph data stored in the knowledge database NB, and generates quantum encoded information by having the quantum computer 3 process quantum encoding based on the graph Laplacian. The timing of each data process is not limited to the timing of acquiring a new sentence, and may be appropriately executed when a certain amount of data is accumulated in the knowledge database NB. This makes it possible to update the logical relationship between sentences at any timing.

[0067] FIG. 10 is a flowchart showing the detailed process of checking the knowledge database NB in ​​the information processing device 2. In FIG.

[0068] First, the text acquisition unit 21 acquires a target text to be checked via the user terminal 4 (S401). In this embodiment, the target text is information about which the user wants to know the truth or falsity, input as a text. For example, if the knowledge database NB stores knowledge related to in-house operations, an in-house operation whose truth or falsity is unknown can be input as a text, and a checking result indicating whether it is correct or not can be obtained as an output.

[0069] The vector conversion unit 221 converts the target sentence into a sentence vector (target sentence vector) (S402). The collation unit 25 requests the knowledge database NB to collate the converted target sentence vector (S403).

[0070] The analysis unit 222 may execute an analysis process in which the target sentence vector is treated as point cloud data on persistent homology, and the ambiguity of the meaning is inflated by the inverse number r of the distance between the vectors. The analysis unit 222 may receive an instruction input regarding the abstraction level of the interpretation of the proposition via the user terminal 4. The analysis unit 222 determines the radius r of the point cloud data according to the inputted abstraction level.

[0071] The collation unit 25 acquires a collation result from the knowledge database NB (S404). The collation result is graph data or a graph Laplacian of a sentence vector corresponding to the target sentence vector stored in the knowledge database NB. When graph data is acquired as the collation result, the graph conversion unit 223 converts the acquired graph data into a graph Laplacian.

[0072] The instruction unit 23 outputs a quantum encoding instruction to the quantum computer 3 based on the acquired graph Laplacian (S405). Specifically, the instruction unit 23 generates a quantum encoding instruction for applying the graph Laplacian to the unitary matrix. The quantum computer 3 can perform quantum phase estimation according to the quantum encoding instruction.

[0073] The quantum computer 3 performs quantum phase estimation from the quantum encoding instruction to be collated, and transmits the calculation result to the information processing device 2. The calculation result is an eigenvalue of the quantum encoded unitary matrix.

[0074] The collation unit 25 obtains the calculation result including the eigenvalues ​​of the unitary matrix from the quantum computer 3 (S406).

[0075] The collation unit 25 judges whether or not there is a logical relationship from the eigenvalues ​​(S407). Specifically, the collation unit 25 obtains a zero-dimensional Betti number from the eigenvalues, and if the zero-dimensional Betti number is 1, it judges that there is a logical connection (YES in S407), and performs a logical calculation in the following S408. If the zero-dimensional Betti number is 2 or more, the collation unit 25 judges that there is no logical connection (NO in S407), and the logical calculation can be omitted. The logical calculation by a classical computer requires a high calculation cost, but the quantum phase estimation by the quantum computer 3 makes it possible to exclude collation targets that have no logical relationship from the logical calculation, thereby reducing the calculation cost.

[0076] The collation unit 25 performs logical calculations on the collation targets determined to have a logical connection (S408). The logical calculations are performed by referring to logical relationships such as graph data of the collation results. The collation unit 25 outputs a truth / false judgment result of the proposition of the collation target sentence as a result of the logical calculations. A false truth / false judgment result is a result indicating that there is no logical relationship, but the result may be output separately from the judgment result of S407 that there is no logical connection. The judgment result of S407 that there is no logical connection cannot strictly be true or false, but indicates a result that is highly likely to be false due to the lack of logical connection, or that is presumed to be false.

[0077] The collation unit 25 generates an output based on the calculation result and transmits it to the user terminal 4 (S409). The output based on the collation result includes information indicating whether or not the target sentences have a logical connection. Furthermore, the output based on the collation result may include a sentence that forms a logical connection with the result of a truth / false judgment when the sentence is treated as a proposition based on the information stored in the knowledge database NB if a logical relationship exists. This allows the user to effectively utilize accumulated knowledge by, for example, referring to the sentences that serve as evidence.

[0078] <Quantum circuit generation> In this embodiment, the quantum circuit constituting the quantum computer 3 includes a controlled unitary gate. Fig. 11 shows an example of a configuration diagram of a quantum circuit QC including a controlled unitary gate. The quantum circuit QC includes a quantum phase estimation circuit QC1 and a quantum superposition state preparation circuit QC2. The quantum circuit QC is composed of quantum gates such as a Hadamard gate, a measurement, and further controlled unitary gates G1 to G3.

[0079] In this embodiment, the quantum phase estimation circuit QC1 is used for the calculation of quantum phase estimation. In particular, the controlled unitary gates G1 to G3 are quantum gates used for the calculation of quantum phase estimation, and affect the speed of quantum calculation.

[0080] Although the quantum phase estimation circuit QC1 shown in Fig. 11 is simplified, an actual quantum circuit is long. Therefore, it is preferable to make the quantum circuit as short as possible and to improve its efficiency by arranging quantum gates efficiently.

[0081] The circuit generator 26 generates a quantum circuit that processes quantum phase estimation. The circuit generator 26 generates the quantum circuit by generating a gene tree based on a genetic algorithm.

[0082] Fig. 11 shows a process flowchart for generating a quantum circuit. The circuit generation unit 26 generates a quantum circuit using a genetic algorithm in accordance with the process flowchart shown in Fig. 12.

[0083] First, the circuit generation unit 26 randomly generates parent quantum circuit data (S501). The circuit generation unit 26 evaluates the fitness of the quantum circuit data, which is a gene tree generated by the evaluation function (S502). The circuit generation unit 26 selects the parent quantum circuit data evaluated as having high fitness (S503). The circuit generation unit 26 randomly takes two quantum circuit data generated as a gene tree at a certain ratio and partially replaces the nodes to generate quantum circuit data (S504). The circuit generation unit 26 randomly takes one quantum circuit data generated as a gene tree at a certain ratio and replaces one node with a randomly selected node to generate quantum circuit data (S505). If the circuit generation unit 26 determines that the termination condition is not satisfied (NO in S506), it returns to S502 and repeats the process. If the circuit generation unit 26 determines that the termination condition is satisfied (YES in S506), it completes the process. Based on the generated quantum circuit data, the quantum phase estimation circuit QC1 is designed.

[0084] The constraints of the genetic algorithm in this embodiment are explained. The gene tree is a multi-way tree, and is composed of child nodes branching from a parent node. A node is a one-qubit gate or an n-th qubit. A terminal node is a qubit. A qubit gate that handles multiple qubits is a controlled unitary gate. When a controlled unitary gate is a parent node, a child node is multiple qubits. When a parent node is a quantum gate, the quantum gate processes the qubit of the child node. Nodes other than the terminal node are unitary gates. A unitary gate is a gate capable of performing quantum phase estimation calculation in this embodiment. A controlled unitary gate refers to a unitary gate that handles multiple input signals.

[0085] The evaluation function in this embodiment has the following characteristics. When a superposition of possible quantum states is given as an input to the Pauli Strings basis vector, the evaluation function evaluates the difference between the measurement result to be obtained and the measurement result obtained, and evaluates the gene tree as being closer to 0 as having a higher fitness. L1 norm, L2 norm, etc. can be used to calculate the difference, but L2 norm is particularly suitable. The evaluation function evaluates the gene tree as being higher in fitness as the number of nodes is smaller in order to converge the genetic algorithm to a circuit as short as possible. The evaluation function evaluates the gene tree as being higher in fitness as the distance between the quantum bit to be controlled and the quantum gate controlling the quantum bit is closer in order to converge the genetic algorithm to an arrangement that promotes interaction between quantum bits as close as possible. The evaluation function evaluates the gene tree as being lower in fitness when it has multiple quantum bits as child nodes, or when it has the same quantum bit as a child node. The evaluation function evaluates the gene tree as being higher in fitness as the number of high-cost T gates is smaller.

[0086] In this embodiment, the circuit generation unit 26 can generate quantum circuit data by applying a genetic algorithm to each tensor product of the Pauli operator. The circuit generation unit 26 integrates the quantum circuit data generated for each tensor product to generate a quantum circuit used for quantum encoding in which the graph Laplacian is applied to a unitary matrix. If a quantum circuit for quantum encoding is generated all at once using a genetic algorithm, problems such as the algorithm not converging or generation taking a long time occur. The above-mentioned problems can be solved by using a method of integrating circuits for calculating tensor products.

[0087] The circuit generation unit 26 generates quantum circuit data by a gene tree using the constraint conditions and evaluation function described above. When the circuit generation unit 26 generates a controlled unitary gate as a parent node, it generates child nodes related to quantum bits that become at least two or more input signals. At this time, two or more child nodes are generated. This makes it possible to generate an efficient quantum circuit including an arrangement of controlled unitary gates used in the calculation of quantum phase estimation according to this embodiment. [Explanation of symbols]

[0088] 1. Information Processing Systems 2. Information processing equipment 21 Text acquisition part 22 Conversion section 221 Vector conversion section 222 Analysis Department 223 Graph Transformation Unit 23 Instruction section 24 Memory Processing Unit 25 Matching section 26 Circuit generation section 3. Quantum Computers 4. User terminal NB Knowledge Base

Claims

1. An information processing system that enables knowledge database matching using a quantum computer, The system includes a text acquisition unit, a conversion unit, and an instruction unit, The text acquisition unit acquires a plurality of texts, the conversion unit obtains a graph Laplacian by converting a logical relationship between propositions based on the propositions of the sentence; The instruction unit outputs a quantum encoding instruction to the quantum computer for applying the graph Laplacian to a unitary matrix.

2. The information processing system according to claim 1 , further comprising a comparison unit that acquires eigenvalues ​​from the quantum computer as quantum computation results for the quantum encoding instructions, and determines whether or not a logical relationship exists from the eigenvalues.

3. a knowledge database for storing information necessary for the quantum encoding instructions; The information processing system according to claim 2 , wherein the matching unit matches the sentence to be matched with information necessary for a quantum encoding instruction including graph data or a graph Laplacian, and outputs a quantum encoding instruction using the information necessary for the quantum encoding instruction which is the matching result.

4. The conversion unit is an analysis unit that maps text vectors obtained by vectorizing text as point cloud data and outputs, as an analysis result, identity between text vectors connected by changes in radius of each point; The information processing system according to claim 1 , further comprising: a graph conversion unit configured to generate the graph Laplacian based on the sentence vector and the analysis result.

5. The information processing system according to claim 4 , further comprising a collation unit that acquires an eigenvalue as a quantum computation result for the quantum encoding instruction, and determines that there is no logical connection when a zero-dimensional Betti number derived from the eigenvalue is not 1.

6. a circuit generation unit that generates a quantum circuit for processing the quantum encoded information based on a gene tree that is a multi-way tree; The information processing system according to claim 1, wherein the circuit generation unit generates a quantum circuit based on a constraint for generating a gene tree consisting of a parent node having a unitary gate and a child node which is either a quantum bit, a unitary gate for a single quantum bit, or a unitary gate having one or more control bits.

7. The information processing system according to claim 6 , wherein the circuit generation unit generates a quantum circuit based on a constraint for generating a gene tree in which nodes other than a terminal node are unitary gates.

8. 8. The information processing system according to claim 6, wherein the circuit generation unit evaluates the generated quantum circuit using an evaluation function that evaluates a quantum circuit with a small number of nodes and a closeness of an arrangement of interacting quantum bits.

9. An information processing method that enables knowledge database matching using a quantum computer, comprising the steps of: The computer acquires a number of sentences, Obtaining a graph Laplacian by transforming logical relations between propositions based on the propositions of the sentence; and outputting quantum encoding instructions to the quantum computer for applying the graph Laplacian to a unitary matrix.

Images