Quantum computer system and method for controlling same

Abstract

The present invention makes it possible, by using a quantum computer, to carry out a process for requesting a greater number of quantum bits than the number of quantum bits that can be processed by the quantum computer. A quantum computer system (2) comprises an information compression device (6), which is a classical computer, and a quantum computer (5). The information compression device (6) acquires a plurality of first quantum bits, converts a tensor including the acquired plurality of first quantum bits to a tensor network, and performs information compression on the converted tensor network, thereby compressing the plurality of first quantum bits to a plurality of second quantum bits for which the number of quantum bits is smaller than that of the plurality of first quantum bits and outputting the information-compressed plurality of second quantum bits to the quantum computer (5). The quantum computer (5) performs quantum calculation on the plurality of second quantum bits.

Description

Quantum computer system and control method thereof

[0001] The present invention relates to a quantum computer system including a quantum computer and a control method thereof.

[0002] A quantum computer is a computer that applies the principles of quantum mechanics to calculations, while conventional binary digital computers are called classical computers.

[0003] Quantum computers are said to be capable of parallel computation on a scale that is impossible with classical computers. However, even the most recent quantum computers can process only a maximum of 100 qubits, making it difficult to process data with a large number of parameters.

[0004] Japan Special Table No. 2023-510706

[0005] Meanwhile, as a method for simulating quantum circuits on a classical computer, a method using a tensor network, as described in Patent Document 1, has recently attracted attention. However, in the case of the method using the tensor network, the amount of calculation increases significantly as the quantum computation becomes more complex.

[0006] An object of one aspect of the present invention is to enable, using a quantum computer, processing that requires more quantum bits than the number of quantum bits that can be processed by the quantum computer.

[0007] In order to solve the above problems, a computer system according to a first aspect of the present invention comprises a classical computer and a quantum computer, wherein the classical computer comprises an acquisition unit that acquires a plurality of first quantum bits, a compression unit that converts a tensor including the plurality of first quantum bits into a tensor network to compress information, thereby compressing the plurality of first quantum bits into a plurality of second quantum bits having a smaller number of quantum bits than the plurality of first quantum bits, and an output unit that outputs the plurality of second quantum bits to the quantum computer, and the quantum computer performs quantum computation on the plurality of second quantum bits.

[0008] A computer system according to aspect 2 of the present invention may be the same as that according to aspect 1, wherein the compression unit of the classical computer converts the tensor into a tensor network using singular value decomposition or Schmidt decomposition, and performs a low-rank approximation using singular value decomposition or Schmidt decomposition on the converted tensor network to perform a contraction calculation, thereby compressing information.

[0009] A computer system according to aspect 3 of the present invention is the same as in aspect 1, wherein the compression unit of the classical computer compresses the plurality of first quantum bits into a plurality of second quantum bits and a plurality of third quantum bits, each of which has a smaller number of quantum bits than the plurality of first quantum bits, and the classical computer may perform quantum computation on the plurality of third quantum bits using a tensor network.

[0010] A computer system according to Aspect 4 of the present invention is the computer system of Aspect 1, further comprising a plurality of edge computers communicatively connected to the classical computer, each of the plurality of edge computers communicatively connected to a plurality of terminals and configured to acquire tensors that are a plurality of pieces of data from the plurality of terminals; a normalization unit that normalizes values ​​included in the tensors and converts them into tensors including a plurality of fourth qubits; a compression unit that converts the tensors including the plurality of fourth qubits into a tensor network and performs information compression, thereby compressing the plurality of fourth qubits into a plurality of fifth qubits having a smaller number of qubits than the plurality of fourth qubits; and a transmission unit that transmits the plurality of fifth qubits to the classical computer, wherein the acquisition unit of the classical computer may acquire the plurality of fifth qubits from each of the plurality of edge computers as the first qubits.

[0011] In a computer system according to aspect 5 of the present invention, in the above-described aspect 4, the quantum computer may perform machine learning using the plurality of second quantum bits, and transmit the machine-learned learning model to at least one of the plurality of edge computers via the classical computer.

[0012] A control program according to aspect 6 of the present invention is a control program for causing a computer to function as the quantum computer system described in aspects 1 to 5, and is a control program for causing a computer to function as the compression unit.

[0013] A control method for a computer system according to a seventh aspect of the present invention is a control method for a quantum computer system comprising a classical computer and a quantum computer, and includes the steps of: a step in which the classical computer acquires a plurality of first quantum bits; a step in which the classical computer compresses information by converting a tensor including the plurality of first quantum bits into a plurality of second quantum bits having a smaller number of quantum bits than the plurality of first quantum bits; a step in which the classical computer outputs the plurality of second quantum bits to the quantum computer; and a step in which the quantum computer performs quantum computation on the plurality of second quantum bits.

[0014] According to one aspect of the present invention, a quantum computer can be used to perform processing that requires more quantum bits than the number of quantum bits that can be processed by the quantum computer.

[0015] FIG. 1 is a block diagram showing an overview of a network system according to one embodiment of the present invention. FIG. 2 is a block diagram showing a schematic configuration of an information compression device in the network system. FIG. 3 is a flowchart showing the flow of information compression processing in the information compression device. FIG. 4 is a block diagram showing a schematic configuration of an information compression device in a network system according to another embodiment of the present invention. FIG. 5 is a flowchart showing the flow of information compression processing in the information compression device. FIG. 6 is a diagram showing an overview of a distributed network system according to yet another embodiment of the present invention. FIG. 7 is a block diagram showing a schematic configuration of an edge server in the distributed network system. FIG. 8 is a flowchart showing the flow of information compression processing in the edge server. FIG. 9 is a block diagram showing a schematic configuration of a distributed network system according to an example of the present invention.

[0016] Hereinafter, embodiments of the present invention will be described in detail. For the sake of convenience, the same reference numerals will be used to designate components having the same functions as those in the embodiments, and the description thereof will be omitted where appropriate.

[0017] First Embodiment An embodiment of the present invention will be described with reference to FIGS.

[0018] (Network System) Fig. 1 is a block diagram showing an outline of a network system according to this embodiment. As shown in Fig. 1, the network system 1 includes a quantum computer system 2, a communication network 3, and a plurality of terminals 4.

[0019] The quantum computer system 2 includes a quantum computer 5 and an information compression device 6. The quantum computer 5 is a NISQ (Noisy Intermediate-Scale Quantum) machine, which has been used recently. Currently, NISQ machines are capable of quantum calculations of approximately 100 to 1000 qubits. The quantum computer 5 is communicatively connected to the information compression device 6. Details of the quantum computer 5 are publicly known, and therefore will not be described here.

[0020] The information compression device 6 is a classical computer, and is communicatively connected to a plurality of terminals 4 via a communication network 3 such as the Internet. Examples of the terminals 4 include drones, surveillance cameras, automobiles, mobile terminals, and IoT (Internet of Things) devices. The information compression device 6 collects various data from the plurality of terminals 4. Examples of the data include location data of the terminals 4, detection data from sensors in the terminals 4, video data from cameras in the terminals 4, and various parameters of the terminals 4.

[0021] The data from each terminal 4 is a vector including a plurality of values. Therefore, the plurality of data from the plurality of terminals 4 is a tensor including a plurality of vectors.

[0022] Furthermore, each of the multiple values ​​contained in the tensor is normalized to a value between 0 and 1. This value corresponds to the state of one quantum bit. The state of one quantum bit can be expressed as a superposition of two defined vectors |0> and |1>. Therefore, the multiple values ​​contained in the tensor correspond to multiple quantum bits (first quantum bits).

[0023] The data compression device 6 converts the tensor into a tensor network, which is a network of tensors with a dimension smaller than that of the tensor. Specifically, the data compression device 6 converts the tensor into a tensor network in a matrix product state or a tensor product state using singular value decomposition or Schmidt decomposition. The data compression device 6 also performs low-rank approximation on the tensor network using singular value decomposition or Schmidt decomposition.

[0024] The information compression device 6 then performs a reduction calculation on the low-rank approximated tensor network. This reduction calculation may be performed sequentially starting from a certain data, or may be performed for each piece of data from the terminal 4. If there are multiple tensor networks, the reduction calculation may be performed for each of the multiple tensor networks and then combined into a single tensor. Alternatively, a tensor network may be created for each type of terminal 4, and the reduction calculation may be performed for each of the multiple tensor networks created and then combined into a single tensor.

[0025] This allows the plurality of pieces of data to be compressed into a smaller number of pieces of data than the plurality of pieces of data. Note that the information compression device 6 can perform any processing that allows information compression, such as mean field approximation, in addition to low-rank approximation.

[0026] The multiple values ​​included in the information-compressed data correspond to multiple quantum bits (second quantum bits). The number of second quantum bits becomes smaller than the number of first quantum bits due to the information compression. The information compression device 6 outputs the multiple second quantum bits included in the information-compressed data to the quantum computer 5.

[0027] For example, suppose the number of the second quantum bits is 1 / 10,000 of the number of the first quantum bits. In this case, if the number of the first quantum bits is 1,000,000, the information compression device 6 compresses the information into second quantum bits with a quantum bit count of 100. This allows the quantum computer 5 to perform quantum computation on the second quantum bits. As a result, the network system 1 configured as described above becomes capable of processing that requires a larger number of first quantum bits (1,000,000 in the above example) than the number of second quantum bits that can be processed by the quantum computer 5 (100 in the above example).

[0028] 2 is a block diagram showing a schematic configuration of the information compression device 6. As shown in FIG. 2, the information compression device 6 includes a control unit 10, a communication unit 11, and an output unit 12.

[0029] The control unit 10 comprehensively controls the operation of various components of the information compression device 6, and is configured by, for example, a computer including a GPU (Graphics Processing Unit) and memory. The operation of the various components is controlled by causing the computer to execute a control program. The control unit 10 will be described in detail later.

[0030] The communication unit 11 transmits and receives information to and from an external communication device. The communication unit 11 includes a communication device such as a transmission / reception circuit. The output unit 12 outputs the quantum bits generated by the control unit 10 to the quantum computer 5.

[0031] In this embodiment, the control unit 10 is configured to include an acquisition unit 20, a normalization unit 21, a tensor network conversion unit 22 (compression unit), and an information compression unit 23 (compression unit). The acquisition unit 20 acquires tensors, which are multiple pieces of data, from multiple terminals 4 via the communication network 3 and the communication unit 11. The acquisition unit 20 sends the acquired tensors to the normalization unit 21.

[0032] The normalization unit 21 normalizes the multiple values ​​included in the tensor from the acquisition unit 20 to values ​​between 0 and 1 to generate multiple first quantum bits. The normalization unit 21 sends the tensor including the multiple first quantum bits to the tensor network conversion unit 22.

[0033] The tensor network conversion unit 22 converts the tensor from the normalization unit 21 into the tensor network. The tensor network conversion unit 22 sends the converted tensor network to the information compression unit 23.

[0034] The information compression unit 23 performs a contraction calculation by performing low-rank approximation using singular value decomposition on the tensor network from the tensor network conversion unit 22. This compresses the information of the multiple first quantum bits included in the tensor into multiple second quantum bits having a smaller number of quantum bits than the first quantum bits. The information compression unit 23 outputs the compressed information of the multiple second quantum bits to the quantum computer 5 via the output unit 12.

[0035] (Information Compression Process) FIG. 3 is a flowchart showing the flow of the information compression process in the information compression device 6 of this embodiment. As shown in FIG. 3, first, the acquisition unit 20 acquires tensors, which are multiple pieces of data, from multiple terminals 4 (S10). Next, the normalization unit 21 normalizes the multiple values ​​included in the tensor to values ​​between 0 and 1 to generate multiple first quantum bits (S11). Next, the tensor network conversion unit 22 converts the tensor including the multiple first quantum bits into a tensor network (S12). Next, the information compression unit 23 performs a contraction calculation by performing low-rank approximation using singular value decomposition on the tensor network, thereby compressing the multiple first quantum bits included in the tensor into multiple second quantum bits with fewer quantum bits than the first quantum bits (S13). The information compression unit 23 then outputs the compressed multiple second quantum bits to the quantum computer 5 via the output unit 12 (S14). The information compression process then ends.

[0036] [Embodiment 2] Another embodiment of the present invention will be described with reference to Figures 4 and 5. The network system 1 according to this embodiment differs from the network system 1 shown in Figures 1 to 3 in the configuration of the information compression device 6, but the other configurations are the same.

[0037] Fig. 4 is a block diagram showing a schematic configuration of the information compression device 6 of this embodiment. The information compression device 6 shown in Fig. 4 differs from the information compression device 6 shown in Fig. 2 in that an information compression unit 24 (compression unit) is provided instead of the information compression unit 23, and in that a quantum computing unit 25 is added, but the other configurations are the same.

[0038] The information compression unit 24 of this embodiment compresses the plurality of first quantum bits included in the tensor from the normalization unit 21 into a plurality of second quantum bits and a plurality of third quantum bits, each of which has a smaller number of quantum bits than the first quantum bits. The information compression unit 23 outputs the plurality of second quantum bits to the quantum computer 5 via the output unit 12, and sends the plurality of third quantum bits to the quantum computing unit 25.

[0039] A quantum circuit in the quantum computer 5 can be represented by a tensor network, and quantum computation of the quantum circuit corresponds to contracting the tensor network. Therefore, the quantum computation unit 25 includes a tensor network corresponding to a certain quantum circuit, inputs the third quantum bit from the information compression unit 24 to the tensor network, and contracts the tensor network to execute quantum computation of the quantum circuit.

[0040] This allows the number of qubits used in the quantum computation to be increased to the sum of the number of second qubits and the number of third qubits. Furthermore, by having the quantum computer 5 perform complex quantum computations and the data compression device 6 perform simple quantum computations, it is possible to suppress an increase in the amount of computation required by the data compression device 6, which is a classical computer.

[0041] Fig. 5 is a flowchart showing the flow of information compression processing in the information compression device 6 of this embodiment. The information compression processing shown in Fig. 5 differs from the information compression processing shown in Fig. 3 in that it includes step S20 instead of step S13 and in that step S21 is added after step S14, but the other steps are the same.

[0042] In step S20, the information compression unit 24 compresses the multiple first quantum bits included in the tensor from the normalization unit 21 into multiple second quantum bits and multiple third quantum bits, which have fewer quantum bits than the first quantum bits.

[0043] In step S21, the quantum computing unit 25 inputs the third quantum bit into a tensor network corresponding to a certain quantum circuit and contracts the tensor network, thereby performing quantum computation of the quantum circuit. The information compression process is then terminated. Note that steps S14 and S21 may be performed either first or simultaneously.

[0044] Third Embodiment Still another embodiment of the present invention will be described with reference to FIGS.

[0045] Fig. 6 is a diagram showing an overview of a distributed network system 1a according to this embodiment. The distributed network system 1a shown in Fig. 6 differs from the network system 1 shown in Figs. 1 to 3 in that an edge server 7 and a base station 8 are provided between the communication network 3 and the terminal 4, but the other configurations are the same.

[0046] The information compression device 6 is communicably connected to a plurality of edge servers 7 via the communication network 3. Therefore, the information compression device 6 of this embodiment acquires tensors, which are a plurality of pieces of data, from the plurality of edge servers 7.

[0047] Each of the edge servers 7 is communicably connected to a plurality of base stations 8 via wire or wirelessly (radio frequency). Each of the base stations 8 is communicably connected to a plurality of terminals 4 via wirelessly. Note that each of the edge servers 7 may be communicably connected to the terminals 4 via wire.

[0048] The edge server 7 is a classical computer that collects various data from the terminal 4. Data processing that was previously performed by the cloud is now performed by multiple edge servers 7 that are closer to the terminal 4 than the cloud. This allows the edge server 7 to achieve low-latency, low-load data processing.

[0049] In this embodiment, similar to the information compression device 6, the edge server 7 acquires tensors, which are data from multiple terminals 4, and normalizes multiple values ​​included in the acquired tensors to values ​​between 0 and 1 to generate multiple quantum bits (fourth quantum bits). Next, the edge server 7 converts the tensor including the multiple fourth quantum bits into a tensor network, and performs low-rank approximation using singular value decomposition on the tensor network to perform a contraction calculation. This allows the multiple data to be compressed into data that is fewer in number than the multiple data.

[0050] The plurality of values ​​included in the information-compressed data correspond to a plurality of quantum bits (fifth quantum bits). The number of fifth quantum bits becomes smaller than the number of fourth quantum bits due to the information compression. The edge server 7 transmits the plurality of fifth quantum bits included in the information-compressed data to the information compression device via the communication network 3.

[0051] For example, suppose there are 100 edge servers 7, and the number of the fifth quantum bits is 1 / 100 of the number of the fourth quantum bits. In this case, if the number of fourth quantum bits is 1,000,000, each edge server 7 compresses the information into fifth quantum bits, which have 10,000 quantum bits, and transmits the information to the information compression device 6. The information compression device 6 receives 10,000 x 100 fifth quantum bits, i.e., 1,000,000 first quantum bits, from the 100 edge servers 7 and compresses the information into 100 second quantum bits. This allows the quantum computer 5 to perform quantum calculations on the second quantum bits. In other words, the distributed network system 1a configured as described above is capable of processing requiring 1,000,000 x 100 = 100 million quantum bits (fourth quantum bits).

[0052] Furthermore, because the data transmitted by the edge server 7 is a plurality of information-compressed fifth quantum bits, it is difficult to infer the actual data, resulting in improved security. By distributing the edge servers 7 throughout the country, even if the cloud becomes inoperable due to a disaster or other reason, the edge servers 7 can continue to operate, thereby maintaining the network between the edge servers 7 and the terminals 4 communicatively connected to the edge servers 7. As a result, fault tolerance is improved. Furthermore, the amount of long-distance communication between the edge servers 7 and the cloud can be reduced, thereby reducing communication costs.

[0053] Fig. 7 is a block diagram showing a schematic configuration of the edge server 7 of this embodiment. As shown in Fig. 7, the edge server 7 is configured to include a control unit 30 and a communication unit 31 (transmission unit). Note that the control unit 30 and the communication unit 31 shown in Fig. 7 have the same hardware configuration as the control unit 10 and the communication unit 11 shown in Fig. 2, and therefore a description thereof will be omitted.

[0054] In this embodiment, the control unit 30 is configured to include an acquisition unit 40, a normalization unit 41, a tensor network conversion unit 42 (compression unit), and an information compression unit 43 (compression unit). The acquisition unit 40 acquires tensors, which are multiple pieces of data, from multiple terminals 4 via the base station 8 and the communication unit 31. The acquisition unit 40 sends the acquired tensors to the normalization unit 41.

[0055] The normalization unit 41 and the tensor network conversion unit 42 are similar to the normalization unit 21 and the tensor network conversion unit 22 shown in Fig. 2. The information compression unit 43 differs from the information compression unit 23 shown in Fig. 2 in that it transmits compressed data (plurality of fifth quantum bits) to the information compression device 6 via the communication unit 31 and the communication network 3, but is otherwise similar. As a result, the acquisition unit 20 of the information compression device 6 acquires tensors, which are multiple pieces of data from the multiple edge servers 7. Furthermore, since the multiple values ​​included in this tensor have already been normalized, they are treated as multiple first quantum bits, and the normalization unit 21 is omitted.

[0056] FIG. 8 is a flowchart showing the flow of information compression processing in the edge server 7 of this embodiment. As shown in FIG. 8, first, the acquisition unit 40 acquires tensors, which are multiple pieces of data, from multiple terminals 4 (S30). Next, the normalization unit 41 normalizes the multiple values ​​included in the tensor to values ​​between 0 and 1 to generate multiple fourth quantum bits (S31). Next, the tensor network conversion unit 42 converts the tensor including the multiple fourth quantum bits into a tensor network (S32). Next, the information compression unit 43 performs a contraction calculation by performing low-rank approximation using singular value decomposition on the tensor network, thereby compressing the multiple fourth quantum bits included in the tensor into multiple fifth quantum bits, which have fewer quantum bits than the fourth quantum bits (S33). Then, the information compression unit 23 transmits the compressed multiple fifth quantum bits to the information compression device 6 via the communication unit 31 and the communication network 3 (S34). The information compression process then ends.

[0057] (Additional Notes) The edge server 7 may perform quantum computation using a tensor network, similar to the information compression device 6 shown in Fig. 4. In this case, the number of quantum bits for performing quantum computation can be further increased.

[0058] (Example) An example of the distributed network system 1a shown in Figures 6 to 8 will be described with reference to Figure 9. This example is a system for identifying abnormalities in financial transactions.

[0059] 9 is a block diagram showing a schematic configuration of a distributed network system 1a according to this embodiment. As shown in FIG. 9, an information compression device 6 is communicatively connected to edge servers 7 of financial institutions A, B, and C via a communication network 3. The edge servers 7 of each company are communicatively connected to terminals 4, such as automated teller machines (ATMs) and smartphones, via a communication network 9.

[0060] Financial transaction data is accumulated in the edge server 7 of each company. Therefore, the edge server 7 of each company can identify anomalies in financial transactions within its own company by performing machine learning using the financial transaction data. However, it is difficult to identify anomalies in financial transactions between different companies.

[0061] Therefore, in this embodiment, each company's edge server 7 compresses the accumulated financial transaction data (fourth quantum bit) and transmits it to the information compression device 6 via the communication network 3. The information compression device 6 further compresses the data (plurality of first quantum bits) from each company's edge server 7 and outputs the compressed plurality of second quantum bits to the quantum computer 5. The quantum computer 5 performs machine learning using the second quantum bits to generate a learning model that identifies anomalies in financial transactions. Note that the quantum computer 5 may perform machine learning together with the information compression device 6, which is a classical computer.

[0062] The learning model generated by the quantum computer 5 is transmitted from the information compression device 6 to each company's edge server 7 via the communication network 3. By using the learning model, each company's edge server 7 can identify anomalies not only in financial transactions within its own company, but also in financial transactions between different companies.

[0063] [Example of implementation using software] The functions of the information compression device 6 and the edge server 7 (hereinafter referred to as "devices") can be realized by a program that causes a computer to function as the device, and a program that causes a computer to function as each control block of the device (particularly each part included in the control units 10 and 30).

[0064] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The functions described in each of the above embodiments are realized by executing the program using the control device and storage device.

[0065] The program may be non-transitory and may be recorded on one or more computer-readable recording media. The recording media may or may not be included in the device. In the latter case, the program may be supplied to the device via any wired or wireless transmission medium.

[0066] Furthermore, some or all of the functions of the control blocks can be realized by logic circuits. For example, an integrated circuit in which a logic circuit that functions as each of the control blocks is formed is also included in the scope of the present invention. In addition, the functions of the control blocks can also be realized by, for example, a quantum computer.

[0067] Furthermore, each process described in each of the above embodiments may be executed by AI (Artificial Intelligence). In this case, the AI ​​may run on the control device or on another device (for example, an edge computer or a cloud server).

[0068] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention.

[0069] REFERENCE SIGNS LIST 1 Network system 1a Distributed network system 2 Quantum computer system 3, 9 Communication network 4 Terminal 5 Quantum computer 6 Information compression device (classical computer) 7 Edge server 8 Base station 10, 30 Control unit 11 Communication unit 12 Output unit 20, 40 Acquisition unit 21, 41 Normalization unit 22, 42 Tensor network conversion unit (compression unit) 23, 24, 43 Information compression unit (compression unit) 25 Quantum calculation unit 30 Control unit 31 Communication unit (transmission unit)

Claims

1. A quantum computer system comprising a classical computer and a quantum computer, wherein the classical computer comprises: an acquisition unit that acquires a plurality of first quantum bits; a compression unit that converts a tensor including the plurality of first quantum bits into a tensor network and performs information compression to compress the plurality of first quantum bits into a plurality of second quantum bits having a smaller number of quantum bits than the plurality of first quantum bits; and an output unit that outputs the plurality of second quantum bits to the quantum computer, wherein the quantum computer performs quantum computation on the plurality of second quantum bits.

2. The quantum computer system of claim 1, wherein the compression unit of the classical computer converts the tensor into a tensor network using singular value decomposition or Schmidt decomposition, and compresses information by performing a contraction calculation on the converted tensor network through low-rank approximation using singular value decomposition or Schmidt decomposition.

3. The quantum computer system of claim 1, wherein the compression unit of the classical computer compresses the plurality of first quantum bits into a plurality of second quantum bits and a plurality of third quantum bits, each of which has a smaller number of quantum bits than the plurality of first quantum bits, and the classical computer performs quantum computation on the plurality of third quantum bits using a tensor network.

4. The quantum computer system of claim 1, further comprising: a plurality of edge computers communicatively connected to the classical computer, each of the plurality of edge computers communicatively connected to a plurality of terminals, and comprising: an acquisition unit that acquires tensors, which are a plurality of pieces of data, from the plurality of terminals; a normalization unit that normalizes values ​​included in the tensors and converts them into tensors including a plurality of fourth qubits; a compression unit that converts the tensors including the plurality of fourth qubits into a tensor network and performs information compression, thereby compressing the plurality of fourth qubits into a plurality of fifth qubits having a smaller number of qubits than the plurality of fourth qubits; and a transmission unit that transmits the plurality of fifth qubits to the classical computer, wherein the acquisition unit of the classical computer acquires the plurality of fifth qubits from each of the plurality of edge computers as the first qubits.

5. The quantum computer system according to claim 4, wherein the quantum computer performs machine learning using the plurality of second quantum bits and transmits the machine-learned learning model to at least one of the plurality of edge computers via the classical computer.

6. A control program for causing a computer to function as the quantum computer system according to claim 1, the control program causing the computer to function as the compression unit.

7. A control method for a quantum computer system comprising a classical computer and a quantum computer, comprising: a step in which the classical computer acquires a plurality of first quantum bits; a step in which the classical computer compresses information by converting a tensor including the plurality of first quantum bits into a tensor network, thereby compressing the plurality of first quantum bits into a plurality of second quantum bits having a smaller number of quantum bits than the plurality of first quantum bits; a step in which the classical computer outputs the plurality of second quantum bits to the quantum computer; and a step in which the quantum computer performs quantum computation on the plurality of second quantum bits.

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