Information processing device, information processing method, information processing system, robot system, and program

Abstract

An information processing device (10) is provided with: a first acquisition unit (12) for acquiring input data; a generation unit for generating reservoir input data to be inputted to a quantum reservoir (20) that has a hierarchy of a plurality of sub-reservoirs from the input data; a second acquisition unit (12) for acquiring an output result of the quantum reservoir obtained by inputting the reservoir input data; and an output data generation unit (14) that references the output result to generate output data.

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, an information processing system, a robot system, and a program. [Background technology]

[0002] In recent years, artificial intelligence (AI) has been utilized in various fields, and new AI technologies are being developed. As a type of next-generation AI, a machine learning technique called reservoir computing has attracted attention. Research into robot motion control and various types of information processing using reservoir computing has also attracted attention.

[0003] Reservoir computing is a method based on recurrent neural networks, which are complex models with nonlinear properties. Reservoir computing consists of an input layer, a reservoir layer, and an output layer. After transforming time-series input data using a reservoir layer, which is a fixed intermediate layer during training, only the read portion is trained using a simple learning method such as linear regression, enabling rapid learning. Therefore, it is expected to be applied in situations where data processing and analysis are performed in real time.

[0004] The reservoir layer plays an important role in reservoir computation, and since it only needs to generate useful spatiotemporal patterns from input data, methods have been suggested to replace it with complex physical systems. In theory, it is possible to create high-dimensional space-time by utilizing the nonlinearity of complex physical systems with a large degree of freedom. Data can be easily learned in this high-dimensional space-time. Such reservoirs consume little power and enable high-speed processing, making them suitable for controlling robots and various types of information processing.

[0005] However, it is difficult to design a physical system used as a reservoir layer to achieve high learning accuracy. The reason for this is that although reservoir computation methods using soft robotics, tensagrity, etc. as reservoirs have been proposed, it is not self-evident what kind of physical system should be designed to achieve high accuracy (see, for example, Non-Patent Documents 1 and 2).

[0006] To solve these problems, various physical systems have been used as reservoirs, and recently a method has been proposed that uses the dynamics of quantum systems, such as qubits (quantum bits), a highly flexible and complex quantum computer, for time series learning. This quantum dynamics reproduced by a quantum computer with n qubits can be used to extract features from time series data in a 2n-dimensional quantum space, and it is expected that highly accurate learning can be achieved by making good use of this.

[0007] Although the design of such reservoir layers using quantum computers is still in the research stage, it has been proposed in recent years to design reservoir layers using the quantum state of Qubits (see, for example, Non-Patent Documents 3 and 4). [Prior art documents] [Non-patent literature]

[0008] [Non-Patent Document 1] Caluwaerts, K., Despraz, J. Iscen,A., Sabelhaus, AP, Bruce, J., Schrauwen, B., & SunSpiral, V. (2014).Design and control of compliant tensegrity robots through simulation and hardware validation. JR Soc. Interface 11, 98. [Non-patent document 2] Nakajima, K., Hauser, H., Li, T., & Pferfer, R. (2015). Information Processing via physical soft body. Sci.Rep. 5, 10487. [Non-patent document 3] Fujii, K., & Nakajima, K. (2017 ). Harnessing disordered-ensemble quantum dynamics for machine learning. Physical Review Applied, 8(2), 024030. [Non-patent document 4] Chen, J., Nurdin, HI, &Yamamoto, N. (2020). Temporal information processing on noisy quantum computers.arXiv preprint arXiv: 2001.09498. Summary of the Invention [Problem to be solved by the invention]

[0009] In the method described in Non-Patent Document 3, the learning accuracy is greatly affected by the performance of the quantum computer. However, existing quantum computers are medium-scale devices with errors. Therefore, reservoirs using existing quantum computers produce outputs that differ from those of ideal quantum computers (large-scale, error-free Qubit-based quantum computers), and there is a problem that they cannot learn with high accuracy. Such quantum computers are called NISQ (Noisy Intermediate-Scale Quantum) devices, and it is predicted that the era of NISQ devices will continue at least until around 2030 to 2040. In order to achieve the practical application of robot control and various types of information processing using reservoirs using quantum computers, there is a need to develop quantum reservoirs that function with NISQ devices.

[0010] An object of the present invention is to provide a technology that can suitably perform various types of information processing, including robot control, using a quantum reservoir that uses a quantum computer with a small number of qubits (NISQ device). [Means for solving the problem]

[0011] As a result of extensive research, the inventors have found that errors occurring in NISQ devices are themselves caused by quantum noise of Qubits, and that this noise can also be considered as a type of quantum dynamics.The present invention is based on this finding and has been made possible by utilizing the Qubit noise in the design of the reservoir.

[0012] An information processing device according to one embodiment of the present invention includes a first acquisition unit that acquires input data, a generation unit that generates reservoir input data from the input data to be input to a quantum reservoir having multiple levels of sub-reservoirs, a second acquisition unit that acquires an output result of the quantum reservoir to which the reservoir input data has been input, and an output data generation unit that generates output data by referring to the output result.

[0013] An information processing device according to one embodiment of the present invention includes a first acquisition unit that acquires training data including input data and label data, a generation unit that generates reservoir input data from the input data to be input to a quantum reservoir having multiple levels of sub-reservoirs, a second acquisition unit that acquires the output results of the quantum reservoir to which the reservoir input data has been input, and an output data generation unit that generates output data by referring to the output results, and a learning unit that trains the above using the label data.

[0014] An information processing device according to one embodiment of the present invention includes a quantum reservoir having multiple levels of sub-reservoirs, a first acquisition unit that acquires reservoir input data generated from time series data, a second acquisition unit that acquires an output result of the quantum reservoir after inputting the reservoir input data to the quantum reservoir, and an output unit that outputs the output result.

[0015] An information processing system according to one aspect of the present invention is an information processing system including a first information processing device and a second information processing device, wherein the first information processing device comprises a first acquisition unit that acquires input data, a generation unit that generates, from the input data, reservoir input data to be input to a quantum reservoir having multiple levels of sub-reservoirs, a second acquisition unit that acquires an output result of the quantum reservoir to which the reservoir input data has been input, and an output data generation unit that generates output data by referring to the output result, and the second information processing device comprises a quantum reservoir having multiple levels of sub-reservoirs, a third acquisition unit that acquires reservoir input data generated by the generation unit of the first information processing device, a fourth acquisition unit that acquires the output result of the quantum reservoir after the reservoir input data has been input to the quantum reservoir, and an output unit that outputs the output result.

[0016] An information processing method according to one aspect of the present invention includes a first acquisition step of acquiring input data, a generation step of generating reservoir input data from the input data to be input to a quantum reservoir having multiple levels of sub-reservoirs, a second acquisition step of acquiring an output result of the quantum reservoir to which the reservoir input data has been input, and an output data generation step of generating output data by referring to the output result.

[0017] An information processing method according to one aspect of the present invention includes a first acquisition step of acquiring training data including input data and label data, a generation step of generating reservoir input data from the input data to be input to a quantum reservoir having multiple levels of sub-reservoirs, a second acquisition step of acquiring an output result from the quantum reservoir to which the reservoir input data has been input, and a learning step of training an output data generation unit, which generates output data by referring to the output result, using the label data.

[0018] An information processing method according to one embodiment of the present invention includes a first acquisition step of acquiring reservoir input data generated from time series data, a second acquisition step of acquiring an output result of a quantum reservoir having multiple levels of sub-reservoirs after inputting the reservoir input data into the quantum reservoir, and an output step of outputting the output result.

[0019] A program according to one aspect of the present invention is a program for causing a computer to function as an information processing device, and causes the computer to execute a first acquisition step of acquiring input data, a generation step of generating, from the input data, reservoir input data to be input to a quantum reservoir having multiple levels of sub-reservoirs, a second acquisition step of acquiring an output result of the quantum reservoir to which the reservoir input data has been input, and an output data generation step of generating output data by referring to the output result.

[0020] Furthermore, a program according to one aspect of the present invention is a program for causing a computer to function as an information processing device, and causes the computer to execute a first acquisition step of acquiring training data including input data and label data, a generation step of generating, from the input data, reservoir input data to be input to a quantum reservoir having multiple levels of sub-reservoirs, a second acquisition step of acquiring an output result of the quantum reservoir to which the reservoir input data has been input, and a learning step of using the label data to train an output data generation unit that generates output data by referring to the output result. [Effects of the Invention]

[0021] According to the present invention, it is possible to suitably perform information processing that takes advantage of the advantages of a quantum reservoir using a quantum computer with a small number of qubits (NISQ device). [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a schematic diagram of a robot system according to an embodiment of the present invention. [Figure 2] FIG. 1 is a conceptual diagram of a reservoir layer. [Figure 3] FIG. 10 is a diagram showing an example of construction of a reservoir layer. [Figure 4] FIG. 1 is a schematic diagram of a quantum circuit. [Figure 5] 1 is a conceptual diagram of a reservoir layer using a quantum computer. [Figure 6] FIG. 1 is a schematic diagram of an information processing system that uses a quantum computer through the cloud. DETAILED DESCRIPTION OF THE INVENTION

[0023] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] The present invention will be described in detail below with reference to the accompanying drawings, in which:

[0017] As shown in Fig. 1, a robot system according to this embodiment includes a robot 40, and a robot control system having a determination device 10 and a controller 30 for controlling the robot 40.

[0024] The robot 40 has a manipulator 42 and a sensor 44. For example, if the robot 40 is a humanoid robot, the arm corresponds to the manipulator 42. The sensor 44 senses the work of the manipulator 42 and generates a time-series signal. The sensor 44 according to this embodiment mainly senses the motion of the robot 40 in time series. The sensor 44 may be incorporated into the manipulator 42, or may be provided in a position different from the manipulator 42.

[0025] For example, in the case of a robot hand in which the manipulator 42 grasps and lifts an object, the sensor 44 may be a pressure sensor provided at the location where the manipulator 42 grasps the object. It is preferable that the pressure sensor does not distinguish between two values, that is, whether or not an object has come into contact with the object, but rather is one that can continuously detect and output changes in pressure.

[0026] When the robot hand grasps an object, the pressure sensor outputs a continuous change in pressure according to the grasping force. The output value of the pressure sensor is sent to a computer and learned, allowing the robot 40 to grasp a specific object with an appropriate force.

[0027] A vibration sensor may be attached as a sensor 44 to the manipulator 42 in addition to the gripping portion, and the situation may be analyzed based on vibrations when gripping an object. The vibration sensor outputs continuous vibration changes when the manipulator 42 carries an object. By transmitting the output value of the vibration sensor to a computer and learning from it, the robot 40 can correct the posture of the arm gripping the object, thereby achieving stable transportation of the object.

[0028] A camera sensor may be attached to the manipulator 42 as the sensor 44 to analyze the surrounding situation when grasping an object. For example, when several manipulators work together to carry an object in the same place, a camera sensor mounted on one manipulator outputs a time-series image file that identifies the movements of the surrounding manipulators. The output time-series image file is sent to a computer for learning, thereby realizing well-balanced cooperative work with other manipulators.

[0029] Other sensors that can be suitably used include those that can obtain continuous or intermittent data over time, and specifically, sound sensors, tactile sensors, etc.

[0030] The controller 30 controls the manipulator 42 by issuing pre-programmed operation commands to the manipulator 42. The controller 30 may be incorporated into the manipulator 42.

[0031] The manipulator 42 operates in accordance with commands issued from the controller 30. The sensor 44 outputs a time-series signal in response to the operation of the manipulator 42.

[0032] The determination device 10 includes an acquisition unit 12 , a determination unit 14 , and a quantum computer 20 .

[0033] The acquisition unit 12 acquires, via a network, a time-series signal (sensor data) from the sensor 44. The network is a wired or wireless communication network, and may be the Internet or a LAN.

[0034] The quantum computer 20 has an input layer, a reservoir layer, and an output layer. The time-series signal acquired by the acquisition unit 12 is input to the input layer of the quantum computer 20, and the signal is learned according to the schematic diagram of FIG. 2, which will be described later.

[0035] The determination unit 14 determines the next operation that the manipulator 42 is desired to perform from the output of the quantum computer 20, and notifies the controller 30 of the determination result. The controller 30 translates the notified determination result into an operation command for the manipulator 42 and controls the manipulator 42. By repeating this cycle, comprehensive operation control of the manipulator 42 is realized.

[0036] For example, if the manipulator 42 as a robotic hand grasps an object and transports the object to a destination according to the type of object, the output of the sensor 44 is input to the quantum computer 20, and the determination unit 14 notifies the controller 30 of the determination result of the type of object based on the output of the quantum computer 20. The controller 30 controls the manipulator 42 to transport the object to the destination according to the notified type. Note that the object may be waste, food such as fruits or vegetables, or other objects.

[0037] The acquisition unit 12 and the determination unit 14 can be configured by a (classical) computer equipped with a CPU, ROM, RAM, a communication unit, and the like.

[0038] In order to use this robot control system to control the complex movements of the manipulator 42 with high precision, it is necessary to design a reservoir layer with high-precision learning ability. The reservoir layer according to this embodiment will be described with reference to the conceptual diagram shown in FIG.

[0039] The quantum computer 20 is composed of an input layer that inputs time-series data required for learning, a reservoir layer that maps the data into a high-dimensional quantum space, and an output layer that outputs the learning results.

[0040] The reservoir layer contains n qubits (quantum bits) (n is an integer greater than or equal to 2), and in NISQ devices, it is assumed that they are affected by noise that interacts with them through the external environment. In this embodiment, in order to utilize this noise to exploit complex quantum dynamics, the qubits in the reservoir layer are first grouped. Each group contains two or more qubits. Hereinafter, the groups are referred to as subreservoirs.

[0041] Next, for each sub-reservoir, we design a specific quantum circuit according to the time series data to be trained. The quantum circuits in each sub-reservoir can be the same or different.

[0042] Next, the designed quantum circuit is used to input the time-series data to be trained into the sub-reservoirs in chronological order, and the quantum circuit is executed, whereby the training data is mapped into a high-dimensional quantum space through each sub-reservoir.

[0043] Furthermore, when executing a quantum circuit, each subreservoir receives crosstalk noise from surrounding subreservoirs, and the time series data is mapped differently to the quantum space for each subreservoir. This increases the degrees of freedom and nonlinearity of the reservoir layer, and as a result, the output of each subreservoir obtained by observing the execution results of the quantum circuit also differs.

[0044] Finally, the connection weights of the output layer are trained using linear regression to minimize the error between the output values ​​and the target values ​​for those output results.

[0045] In other words, the acquisition unit 12 acquires time series data to be learned (learning data) and a target value (label data associated with the time series data), and the determination device 10 is configured to train the output layer by inputting the time series data into the input layer of the quantum computer 20 so as to minimize the error between the output value output from the output layer and the target value.

[0046] To verify the effectiveness of the reservoir layer of this embodiment, two conditions must be confirmed using a quantum computer. One is that the reservoir system must have nonlinearity. The other is that the reservoir system must have a property called fading memory, in which current inputs have a greater influence on the current internal state than past inputs or states.

[0047] Empirically, to achieve high-performance reservoir computing, the reservoir must possess the properties of nonlinearity and fading memory. To confirm these two properties, we verified the effectiveness of the reservoir layer of this embodiment using a standard benchmark test task called the Nonlinear Autoregressive Moving Average (NARMA) task. Equation (1) shows the NARMA task.

[0048]

number

[0049] Figure 3 shows an example of constructing a quantum reservoir using IBM's 16-Qubit quantum computer (ibmq_16_melbourne) and learning 100 time step data for the NARMA task of equation (1).

[0050] As shown in Figure 3, first, one subreservoir is created using two adjacent qubits (for example, the 0th and 14th qubits). Similarly, one subreservoir is created using another two adjacent qubits (for example, the 1st and 13th qubits). This process is repeated to create subreservoirs, and finally, a reservoir layer is created using multiple connected subreservoirs. Figure 3 shows an example of a reservoir layer constructed with five subreservoirs.

[0051] In data learning for a 100-time-step NARMA task, the quantum circuit shown in Figure 4 is used for each sub-reservoir to input data for each time step of the time series to be learned, perform calculations on the quantum circuit, and obtain output.

[0052] Figure 4 is an example of a quantum circuit using 2 Qubits with time step = 2. Data for time step = 1 and 2 are input to the quantum circuit shown in Figure 4 with time step = 2. Each input contains five quantum gates.

[0053] In this quantum circuit, an X rotation operation is first performed on each quantum bit, with the input data as the rotation angle. Next, a CNOT gate is applied across two quantum bits, followed by a Z rotation operation on the second quantum bit, and then another CNOT gate, creating a strong correlation between the two quantum bits. However, the rotation angle of the Z rotation operation is also the input data.

[0054] By repeatedly operating such a quantum circuit for each time step, a quantum state dependent on time-series data is generated. Note that although this quantum circuit structure is used in this embodiment, other quantum gates may also be used.

[0055] In this manner, in the determination device 10, the time series data acquired by the acquisition unit 12 is divided into data of a plurality of time steps (time slices), thereby generating reservoir input data. Then, data or a representative value of the data at each time step included in the generated reservoir input data is input to each sub-reservoir via the input layer. Here, the division of the time series data into a plurality of time steps may be performed by the input layer or by another component of the determination device 10.

[0056] Furthermore, as described above, one or more quantum operations are executed on the quantum bits that constitute each sub-reservoir in the determination device 10. Information specifying the quantum operation may be included in the data input to the input layer or reservoir layer, for example.

[0057] Furthermore, specific unitary transformations (unitary gates) such as the X rotation operation, CNOT gate, and Z rotation operation for two qubits described above are limited in terms of the overall degrees of freedom of unitary transformations. Using restricted unitary transformations in this way reduces the number of operations in quantum gates, resulting in quantum circuits (hardware-efficient ansatz) that can be executed on NISQ devices. In other words, the complexity of the dynamics in the quantum reservoir is not due to the degrees of freedom of the unitary transformations, but rather to the effects of the noise described above. Therefore, even simple quantum circuits such as restricted unitary transformations can demonstrate the functionality of the quantum reservoir.

[0058] As shown in Figure 3, in a reservoir layer constructed with five subreservoirs, different outputs were obtained even though calculations were performed on each subreservoir using the same quantum circuit. This is because when executing the quantum circuit on each subreservoir, the quantum bit is affected by noise from interactions with the external environment, such as the surrounding subreservoirs.

[0059] This indicates that the quantum noise of Qubits can be utilized in the construction of the reservoir layer of this embodiment. Furthermore, it was also shown that the outputs obtained from the individual subreservoirs differ significantly, creating a high-dimensional quantum space with a large degree of freedom.

[0060] Table 1 shows the NMSE (normalized mean square error) values ​​of the prediction results of the NARMA task. For comparison, the NMSE values ​​of linear regression are also shown. The NMSE value is a standard means of evaluating prediction accuracy and is calculated using equation (2).

[0061]

number

[0062] [Table 1] As shown in Table 1, as the number of subreservoirs in the reservoir layer increases, both the mean value and standard deviation of NMSE decrease, confirming that prediction accuracy improves. From these results, it was confirmed that with two or more subreservoirs, prediction accuracy is several tens of percent better than conventional linear regression learning. Furthermore, with a quantum reservoir using six subreservoirs, prediction accuracy several times higher than the NSME value of linear regression was obtained, demonstrating the superiority of the computational accuracy of the quantum reservoir of this embodiment.

[0063] The robot motion control performance of the robot control system according to this embodiment was verified. An experiment was conducted to determine whether the reservoir could predict the motion of a robot hand, which is a manipulator having a two-fingered robot hand and a robot arm.

[0064] The two-fingered robotic hand used a vacuum-driven soft gripper manufactured by Piab. The robotic arm used a four-axis robotic arm (Dobot Magician, a link mechanism that can be used on a desktop). The movement of the robotic hand was controlled using software called Dobot Studio.

[0065] A film sensor was attached to a two-fingered robot hand. The film sensor has a negatively charged dome-shaped silicon layer and a positively charged nylon layer, with copper and aluminum electrode layers attached to the silicon and nylon layers.

[0066] When the robot hand performs actions such as grasping or releasing an object, pressure is applied to the dome-shaped silicon layer, generating negative and positive frictional charges on the electrodes, which then generates a voltage.

[0067] Three plastic objects (Object A, Object B, and Object C) were prepared, each with approximately the same weight (approximately 16 g) and volume (approximately 27 cm3) but different shapes. Objects A and B are cubes, and Object C is a sphere. Object A is made of ABS synthetic plastic, and Objects B and C are made of PLA synthetic plastic.

[0068] For each object, an experiment was conducted in which a two-fingered robotic hand equipped with a film sensor repeatedly grasped and released the object 25 times. The voltage in the film sensor changed over time in response to the movements of the robotic hand's fingers, and data on the time series of voltage changes was obtained. For each object, voltage data for both fingers was obtained. The researchers verified whether reservoir calculations using a quantum computer could distinguish between objects by learning data on the time series of voltage changes.

[0069] In reservoir training, we first used data from two fingers to embed the data into a 2-qubit sub-reservoir in IBM's 27-qubit quantum computer (ibmq_toronto) shown in Figure 5, and then executed and measured the same quantum circuit as the NARMA task.

[0070] Using the output results obtained from the measurements, we adjusted the output weights to be able to distinguish between the three types of objects, and created a prediction model. Table 2 shows the results of 10-fold cross-validation for the three types of objects.

[0071] [Table 2] As shown in Table 2, the learning accuracy and prediction accuracy were 0.87 and 0.83, respectively, in distinguishing between three types of objects, demonstrating that the reservoir layer of this embodiment can accurately learn the time-series data of the sensor, distinguish between objects, and accurately control the manipulator through the controller.

[0072] In the above embodiment, the robot control system may be a system in which the determination device 10, the controller 30, and the robot 40 are integrated, or may be configured to use the quantum computer 20 via the cloud.

[0073] In other words, the determination device 10 according to this embodiment may be configured in a distributed manner by a determination device (also referred to as the determination device 10a) including the acquisition unit 12 and the determination unit 14, and a quantum computer 20 communicatively connected to the determination device 10a. In this configuration, the determination device 10a and the quantum computer 20 communicate with each other via their respective communication units, and the acquisition unit 12 also acquires various data from the quantum computer 20.

[0074] Furthermore, in the configuration in which the quantum computer 20 is used via the cloud described above, the determination device 10a may be configured to include some of the components of the quantum computer 20. As an example, at least one of the input layer and the output layer may be included in the determination device 10a rather than in the quantum computer 20. For example, in a configuration in which the output layer is included in the determination device 10a, the output layer can be learned without transmitting the connection weights of the output layer to the quantum computer 20 in the learning process of the output layer.

[0075] In this way, in the configuration in which the quantum computer 20 is used via the cloud described above, the robot control system according to this embodiment may be configured to include a determination device 10a and a quantum processing device equipped with a quantum reservoir.

[0076] Here, the determination device 10a, for example, a first acquisition unit (the acquisition unit 12 described above) that acquires a time-series signal from a sensor provided on the robot; a generator (for example, the input layer described above) that generates reservoir input data from the time-series signal to be input to a quantum reservoir (the reservoir layer described above) having multiple layers of sub-reservoirs; a second acquisition unit (the aforementioned acquisition unit 12) that acquires the output result of the quantum reservoir to which the reservoir input data has been input; a determination unit (the output layer and determination unit 14 described above) that determines the robot's behavior by referring to the output result; It is equipped with:

[0077] Furthermore, the above-mentioned quantum processing device equipped with a quantum reservoir may be, for example, A quantum reservoir (the above-mentioned reservoir layer) having multiple layers of sub-reservoirs is provided, Acquire reservoir input data generated from a time series signal obtained from a sensor provided on the robot; obtaining an output result of the quantum reservoir after inputting the reservoir input data into the quantum reservoir; The output result is output to the determination device 10a.

[0078] Furthermore, the determination device 10a that performs the learning process described above includes: a first acquisition unit (the acquisition unit 12 described above) that acquires training data including time-series data and label data; a generator (the input layer described above) that generates reservoir input data from the time-series data to be input to a quantum reservoir (the reservoir layer described above) having multiple layers of sub-reservoirs; a second acquisition unit (the aforementioned acquisition unit 12) that acquires the output result of the quantum reservoir to which the reservoir input data has been input; Equipped with The determination unit (the output layer and determination unit 14 described above) that determines the robot's behavior by referring to the output results is trained using the label data.

[0079] <Additional Note 1> In the above explanation, robot control has been given as an example of inference and learning processing using a quantum reservoir having multiple levels of subreservoirs, but as can be seen from the above explanation, processing using a quantum reservoir having multiple levels of subreservoirs is not limited to robot control and may be applied to various applications, such as medical care, mobility, collection and delivery, and sorting work.

[0080] For example, the acquisition unit 12 may acquire medical-related data, such as vital data such as the subject's brain waves, pulse waves, and respiratory rate, as time-series data, and the determination unit 14 may perform inference regarding the subject's physical condition. The time-series data may also be the subject's blood glucose level, drug concentration, dosage, blood oxygen level, etc. Here, the determination device 10 may output the inference result by the determination unit 14 in the form of advice to the subject in the form of an image, audio, or the like. In such a configuration, the output layer may be trained using training data including the subject's vital data, such as the subject's brain waves, pulse waves, respiratory rate, blood glucose level, drug concentration, dosage, and blood oxygen level, and label data regarding the physical condition associated with the vital data.

[0081] Furthermore, in the above configuration, the acquisition unit 12 and the output unit that outputs the inference result by the determination unit 14 may be provided in separate devices. For example, a configuration may be adopted in which vital data as time-series data is acquired by the acquisition unit 12 provided in an examination device installed in a hospital in one country, the time-series data is input into a quantum reservoir installed in another country, and an image including advice generated by the determination unit 14 based on the output result from the quantum reservoir is displayed on a display terminal outside the hospital.

[0082] As another example, the acquisition unit 12 may acquire sensing data from various sensors provided in the target vehicle as time-series data, and the determination unit 14 may perform driving control of the target vehicle. In such a configuration, the output layer can be trained using training data including sensing data from various sensors provided in the target vehicle and label data related to driving control that is associated with the sensing data.

[0083] Furthermore, the acquisition unit 12 may acquire time-series data from various sensors installed in the target vehicle, as well as data from other vehicles traveling around the target vehicle, cameras installed near the road, and the like. In this way, the time-series data acquired by the acquisition unit 12 may include time-series data from separate devices or equipment. The generation unit may generate reservoir input data based on the sensing data or after applying integration processing to the sensing data. The determination unit 14 may then control the driving of the target vehicle based on the output result of the quantum reservoir to which the reservoir input data has been input. In such a configuration, the number of types and channels of sensing data will increase. Accordingly, it is preferable that the quantum reservoir have a number of quantum bits corresponding to the number of types and channels of sensing data. The invention described herein is useful even when a quantum reservoir with such a large number of quantum bits is used.

[0084] Therefore, the aspects described in this specification can also be expressed as follows.

[0085] (Aspect A1) a first acquisition unit that acquires input data; a generator that generates reservoir input data from the input data to be input to a quantum reservoir having multiple levels of sub-reservoirs; a second acquisition unit that acquires the output result (measurement result) of the quantum reservoir to which the reservoir input data has been input; an output data generation unit that generates output data by referring to the output result; An information processing device comprising:

[0086] (Aspect B1) a first acquisition unit that acquires training data including input data and label data; a generator that generates reservoir input data from the input data to be input to a quantum reservoir having multiple levels of sub-reservoirs; a second acquisition unit that acquires the output result (measurement result) of the quantum reservoir to which the reservoir input data has been input; an output data generation unit that generates output data by referring to the output result; and a learning unit that causes the output data generation unit to learn using the label data. An information processing device comprising:

[0087] (Aspect C1) A quantum reservoir having multiple layers of sub-reservoirs; an acquisition unit that acquires reservoir input data generated from the time series data; a second acquisition unit that acquires an output result (measurement result) of the quantum reservoir after inputting the reservoir input data into the quantum reservoir; an output unit that outputs the output result; An information processing device comprising:

[0088] (Example of cloud-based information processing system configuration) An example of the configuration of an information processing system having the above-described configuration and using a quantum computer via the cloud will be described below with reference to Fig. 6. When the information processing system is applied to robot control, it is realized as the robot control system described above.

[0089] As shown in Fig. 6, the information processing system includes a first information processing device 10 and a second information processing device 20. The first information processing device 10 and the second information processing device 20 are configured to be able to communicate with each other via a network N. The first information processing device 10 has a configuration equivalent to the determination device 10 described above (excluding the configuration equivalent to part or all of the quantum computer 20), as an example, and the second information processing device has a configuration equivalent to the quantum computer 20 described above or the quantum operation device described above, as an example.

[0090] (First information processing device) As shown in FIG. 6, the first information processing device 10 includes, for example, an acquisition unit 12, a generation unit 20a, and an output data generation unit 14.

[0091] The acquisition unit 12 is configured to acquire input data. Here, the input data may be time-series data from sensors provided on the robot, vital data such as brain waves, pulse waves, and respiratory rate of the subject, or sensing data from various sensors provided on the subject vehicle, as described above.

[0092] The generation unit 20a generates reservoir input data from the input data acquired by the acquisition unit 12 to be input to a quantum reservoir (reservoir layer 20c in FIG. 6) having multiple layers of subreservoirs. The generation unit 20a can also be considered as part of the input layer described above. Here, as an example, as described above, the generation unit 20a divides the input data into multiple time slices and generates the reservoir input data using a representative value of the input data in each time slice. Furthermore, as an example, the reservoir input data includes information specifying a quantum operation for the quantum bits that constitute the quantum reservoir, as described above.

[0093] The acquisition unit 12 also functions as a second acquisition unit that acquires the output result of the quantum reservoir to which the reservoir input data has been input.

[0094] The output data generation unit 14 generates output data by referring to the output result of the quantum reservoir. When the acquisition unit 12 is configured to acquire time-series data from a sensor provided on the robot as the input data, as described above, the output data generation unit 14 generates a control signal corresponding to the output result of the quantum reservoir, for controlling the operation of the robot, as described above.

[0095] Furthermore, when the acquisition unit 12 is configured to acquire vital data such as brain waves, pulse waves, respiratory rate, etc. of the subject as the input data, as described above, the output data generation unit 14 generates output data in the form of advice for the subject in the form of images, audio, etc., as described above.

[0096] Furthermore, when the acquisition unit 12 is configured to acquire sensing data from various sensors provided in the target vehicle as the input data, as described above, the output data generation unit 14 generates control data for controlling the driving of the target vehicle, as described above.

[0097] As described above, the acquisition unit 12 may be configured to further acquire label data together with the input data, and function as a learning unit that trains the output data generation unit by referring to the label data.

[0098] (Second information processing device) As shown in FIG. 6, the second information processing device 20 includes, for example, an acquisition unit 20b, a reservoir layer 20c, and an output unit 20d.

[0099] The acquisition unit 20b acquires the reservoir input data generated by the generation unit 20a included in the first information processing device 10. The acquisition unit 20b can also be regarded as part of the above-mentioned input layer. The reservoir input data acquired by the acquisition unit 20b is input to the reservoir layer 20c.

[0100] The reservoir layer 20c is a quantum reservoir having multiple layers of subreservoirs. The quantum reservoir has been described above, so a detailed description will be omitted here.

[0101] The acquisition unit 20b also functions as a second acquisition unit that acquires the output result of the quantum reservoir after inputting the reservoir input data into the quantum reservoir.

[0102] The output unit 20d outputs the output result. The output result is acquired by the acquisition unit 12 of the first information processing device 20, for example.

[0103] (Example of information processing method in information processing system) As can be seen from the above, the information processing according to this embodiment includes, for example, the following information processing methods.

[0104] (Information Processing Method 1) a first acquisition step in which the acquisition unit 12 acquires input data; a generation step in which the generation unit 20a generates reservoir input data to be input to a quantum reservoir having multiple levels of sub-reservoirs from the input data; a second acquisition step in which the acquisition unit 12 acquires the output result of the quantum reservoir to which the reservoir input data has been input; and an output data generation step in which the output data generation unit 14 generates output data by referring to the output result; An information processing method comprising:

[0105] (Information processing method 2) a first acquisition step in which the acquisition unit 12 acquires teacher data including input data and label data; a generation step in which the generation unit 20a generates reservoir input data to be input to a quantum reservoir having multiple levels of sub-reservoirs from the input data; a second acquisition step in which the acquisition unit 12 acquires the output result of the quantum reservoir to which the reservoir input data has been input; and a learning step in which the acquisition unit 12 (learning unit) causes an output data generation unit that generates output data by referring to the output result to learn using the label data; An information processing method comprising:

[0106] (Information Processing Method 3) a first acquisition step in which the acquisition unit 20b acquires reservoir input data generated from the time-series data; A second acquisition step in which the acquisition unit 20b acquires the output result of the quantum reservoir (reservoir layer 20c) after inputting the reservoir input data into the quantum reservoir (reservoir layer 20c) having multiple layers of sub-reservoirs; and an output step in which the output unit 20d outputs the output result; An information processing method comprising:

[0107] <Additional Note 2> Although some of the units included in the first information processing device 10 in the above example have been described above, they may be distributed among a plurality of information processing devices. For example, the first information processing device 10 may be configured with an information processing device 10-1 and an information processing device 10-2, and the information processing device 10-1 may: an acquisition unit 12 as a first acquisition unit that acquires input data; and The above-mentioned generation unit 20a The information processing device 10-2 is provided with: an acquisition unit 12 as a second acquisition unit that acquires the output result of the quantum reservoir; and The above-mentioned output data generation unit 14 However, examples of the distributed arrangement are not limited to the above examples.

[0108] Similarly, the units included in the second information processing device 20 in the above example may be distributed among a plurality of information processing devices. For example, the second information processing device 20 may be configured with an information processing device 20-1 and an information processing device 20-2, and the information processing device 20-1 may: an acquisition unit 20b as a first acquisition unit that acquires reservoir input data; and The reservoir layer 20c described above The information processing device 20-2 is an acquisition unit 20 as a second acquisition unit that acquires the output result of the reservoir layer 20c; and The output section 20d mentioned above However, examples of the distributed arrangement are not limited to the above examples.

[0109] Furthermore, the above-described information processing system may be configured such that data is supplied from a plurality of first information processing devices 10 to one or a plurality of second information processing devices, and the data from the second information processing devices is acquired by the plurality of first information processing devices 10. Here, each of the first information processing devices 10 may be configured in a distributed manner as described above. For example, data from each of the plurality of information processing devices 10-1, or data obtained by integrating such data, may be supplied to the second information processing device 20, and the data from the second information processing device 20 may be acquired and processed by at least one of the plurality of information processing devices 10-2.

[0110] <Additional Note 3> [Software implementation example] The functions of the above-mentioned determination devices 10, 10a, quantum computer 20, quantum processing device, and information processing device (hereinafter referred to as "devices") can be realized by a program for causing a computer to function as the device, and a program for causing a computer to function as each control block of the device.

[0111] 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 control device and storage device execute the program, thereby realizing the functions described in each of the above embodiments.

[0112] 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.

[0113] 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.

[0114] <Additional Note 4> The invention described in this specification includes the following configurations.

[0115] (Aspect D1) an acquisition unit that acquires a time-series signal from a sensor provided on the robot; a determination unit that has a plurality of layers of sub-reservoirs each formed by grouping a plurality of quantum bits, and that determines the behavior of the robot based on the output signal from the sensor using a reservoir layer that has undergone processing learning using the signal as an input and the behavior of the robot as an output; a controller that controls the operation of the robot based on the determination result; A robot control system comprising:

[0116] (Aspect D2) The robotic control system of embodiment D1, wherein the reservoir layer has two or more levels of subreservoirs.

[0117] (Aspect D3) The robotic control system of aspect D1 or D2, wherein the reservoir layer is a nonlinear physical system that utilizes the dynamics of a qubit.

[0118] (Aspect D4) The sensor of any one of aspects D1 to D3 is a pressure sensor, a vibration sensor, or a camera sensor. The robot control system according to any one of the above.

[0119] (Aspect D5) A robot comprising the robot control system according to any one of aspects D1 to D4 and a robot. Net system.

[0120] (Aspect D6) acquiring a time-series signal from a sensor provided on the robot; a step of determining the robot's behavior based on the output signal from the sensor using a reservoir layer having a plurality of layers of sub-reservoirs each formed by grouping a plurality of quantum bits, the reservoir layer having undergone processing learning using the signal as an input and the robot's behavior as an output; controlling the operation of the robot based on the determination result; A robot control method comprising:

[0121] The present invention is not limited to the above-described embodiments, and the components can be modified and embodied in practice without departing from the spirit of the invention. Furthermore, various inventions can be created by appropriately combining multiple components disclosed in the above-described embodiments. For example, some components may be omitted from all the components shown in the embodiments. Furthermore, components from different embodiments may be appropriately combined. [Explanation of symbols]

[0122] 10, 10a Judgment device 20 Quantum Computer 30 Controllers 40 Robot

Claims

1. a first acquisition unit that acquires input data; a generator that generates reservoir input data from the input data to be input to a quantum reservoir having multiple levels of sub-reservoirs; a second acquisition unit that acquires an output result of the quantum reservoir to which the reservoir input data has been input; an output data generation unit that generates output data by referring to the output result; Equipped with The reservoir input data is and information specifying a quantum operation for the quantum bit that constitutes the quantum reservoir. Information processing device.

2. The generation unit Dividing the input data into a plurality of time slices, and generating the reservoir input data using a representative value of the input data in each time slice. The information processing device according to claim 1 .

3. the first acquisition unit further acquires label data together with the input data; The information processing device includes: The information processing apparatus according to claim 1 , further comprising a learning unit that causes the output data generating unit to learn by referring to the label data.

4. The information processing device according to claim 1 , wherein the quantum reservoir has two or more subreservoir layers.

5. The information processing device according to claim 1 , wherein the quantum reservoir is a nonlinear physical system that utilizes the dynamics of a quantum bit.

6. the input data includes a time-series signal from a sensor provided on the robot; the quantum reservoir is a trained quantum reservoir that receives the time-series signal as an input and outputs the robot's behavior, The output data generation unit generates a control signal according to the output result of the quantum reservoir, the control signal being used to control the operation of the robot. The information processing device according to claim 1 .

7. The information processing device according to claim 6 , wherein the sensor is a pressure sensor, a vibration sensor, or a camera sensor.

8. A robot system comprising the information processing device according to claim 6 or 7 and a robot.

9. a first acquisition unit that acquires training data including input data and label data; a generator that generates reservoir input data from the input data to be input to a quantum reservoir having multiple levels of sub-reservoirs; a second acquisition unit that acquires an output result of the quantum reservoir to which the reservoir input data has been input; an output data generation unit that generates output data by referring to the output result; and a learning unit that causes the output data generation unit to learn using the label data. Equipped with The reservoir input data is and information specifying a quantum operation for the quantum bit that constitutes the quantum reservoir. Information processing device.

10. A quantum reservoir having multiple layers of sub-reservoirs; a first acquisition unit that acquires reservoir input data generated from the time series data; a second acquisition unit that acquires an output result of the quantum reservoir after inputting the reservoir input data into the quantum reservoir; an output unit that outputs the output result; Equipped with The reservoir input data is and information specifying a quantum operation for the quantum bit that constitutes the quantum reservoir. Information processing device.

11. An information processing system including a first information processing device and a second information processing device, The first information processing device a first acquisition unit that acquires input data; a generator that generates reservoir input data from the input data to be input to a quantum reservoir having multiple levels of sub-reservoirs; a second acquisition unit that acquires an output result of the quantum reservoir to which the reservoir input data has been input; an output data generation unit that generates output data by referring to the output result; Equipped with The second information processing device A quantum reservoir having multiple layers of sub-reservoirs; a third acquisition unit that acquires reservoir input data generated by the generation unit of the first information processing device; a fourth acquisition unit that acquires an output result of the quantum reservoir after inputting the reservoir input data into the quantum reservoir; an output unit that outputs the output result; Equipped with The reservoir input data is and information specifying a quantum operation for the quantum bit that constitutes the quantum reservoir. Information processing system.

12. One or more processors: a first acquisition step of acquiring input data; a generating step of generating reservoir input data from the input data to be input to a quantum reservoir having a plurality of levels of sub-reservoirs; a second acquisition step of acquiring an output result of the quantum reservoir to which the reservoir input data has been input; an output data generation step of generating output data by referring to the output result; Including, The reservoir input data is and information specifying a quantum operation for the quantum bit that constitutes the quantum reservoir. Information processing methods.

13. One or more processors: a first acquisition step of acquiring training data including input data and label data; a generating step of generating reservoir input data from the input data to be input to a quantum reservoir having a plurality of levels of sub-reservoirs; a second acquisition step of acquiring an output result of the quantum reservoir to which the reservoir input data has been input; a learning step of causing an output data generation unit that generates output data by referring to the output result to learn using the label data; Including, The reservoir input data is and information specifying a quantum operation for the quantum bit that constitutes the quantum reservoir. Information processing methods.

14. One or more processors: a first acquisition step of acquiring reservoir input data generated from the time series data; a second acquisition step of acquiring an output result of a quantum reservoir having a plurality of layers of sub-reservoirs after inputting the reservoir input data into the quantum reservoir; an output step of outputting the output result; Including, The reservoir input data is and information specifying a quantum operation for the quantum bit that constitutes the quantum reservoir. Information processing methods.

15. A program for causing a computer to function as an information processing device, the program comprising: a first acquisition step of acquiring input data; a generating step of generating reservoir input data from the input data to be input to a quantum reservoir having a plurality of levels of sub-reservoirs; a second acquisition step of acquiring an output result of the quantum reservoir to which the reservoir input data has been input; an output data generation step of generating output data by referring to the output result; Execute The reservoir input data is and information specifying a quantum operation for the quantum bit that constitutes the quantum reservoir. program.

16. A program for causing a computer to function as an information processing device, the program comprising: a first acquisition step of acquiring teacher data including input data and label data in the computer; a generating step of generating reservoir input data from the input data to be input to a quantum reservoir having a plurality of levels of sub-reservoirs; a second acquisition step of acquiring an output result of the quantum reservoir to which the reservoir input data has been input; a learning step of causing an output data generation unit that generates output data by referring to the output result to learn using the label data; Execute The reservoir input data is and information specifying a quantum operation for the quantum bit that constitutes the quantum reservoir. program.

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