System, method and related method for mounting fluctuation equivalent variable quantum circuit for quantum machine learning

Abstract

To provide a system for mounting a symmetric quantum circuit.SOLUTION: A system and method for mounting a symmetric quantum circuit includes a data processing module for processing input data, a quantum circuit construction module for using a symmetric quantum operation to construct the symmetric quantum circuit related to a series of quantum bits, an optimization module for using an optimization technique to optimize a circuit parameter, and an output generation module for generating output data for new input data. The symmetric quantum circuit may be mounted on various quantum hardware and provides faster convergence and training and more excellent accuracy.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a system and method for implementing a variational equivalent variable quantum circuit in quantum machine learning. The circuit can be implemented on various quantum hardware and is constructed using the symmetry of the data set to be learned. The system includes a data module, an equivalent variable variational quantum circuit, a classical optimization algorithm, and an interference (inference) module.

Background Art

[0002] Quantum machine learning is a rapidly evolving field that leverages the principles of quantum mechanics to improve machine learning algorithms. Quantum circuits are the basic units of quantum computing and play an important role in this area. These circuits are established based on qubits (quantum bits), which can exist in multiple states simultaneously, unlike classical bits that can only be in one state at a time. This property of qubits allows quantum circuits to process an enormous amount of information simultaneously and, in some cases, can lead to faster and more efficient machine learning algorithms. However, designing quantum circuits that can efficiently harness this potential is a challenging task. One of the important difficulties is to ensure that the quantum circuit respects the high degree of symmetry in the dataset being learned. This is important because the high degree of symmetry that can be used to improve the performance of machine learning algorithms can provide useful insights into the structure of the data. Another difficulty is to optimize the parameters of the quantum circuit to minimize the cost function for the training set. This is usually done using classical optimization algorithms, but these algorithms can be computationally expensive and may not always converge to the optimal solution. Moreover, the quantum circuit needs to be able to make accurate predictions for a new set of data points when it is trained. This requires an efficient inference mechanism, which is an area of ongoing research in quantum machine learning. Summary of the Invention Means for Solving the Problems

[0003] According to an embodiment, a system for implementing a symmetric quantum circuit is provided. The system includes a data processing module for processing input data from a data source, a quantum circuit construction module for constructing a symmetric quantum circuit that respects data characteristics regarding a series of qubits using symmetric quantum operations, an optimization module for optimizing circuit parameters to minimize a performance metric using optimization techniques, and an output generation module for generating output data for new input data. The symmetric quantum circuit provides faster convergence and training, better accuracy, and can be implemented on various quantum hardware.

[0004] According to other embodiments, a method for implementing a symmetric quantum circuit is provided. The method includes the steps of processing input data from a data source, constructing a symmetric quantum circuit that respects data characteristics regarding a series of qubits using symmetric quantum operations, optimizing circuit parameters to minimize a performance metric using optimization techniques, and generating output data for new input data. The method further includes the steps of using the high symmetry of a dataset to construct the quantum circuit, using a training set for optimizing the quantum circuit, using a cost function for optimizing the quantum circuit, using quantum gates when constructing the quantum circuit, using a series of qubits when constructing the quantum circuit, and using quantum machine learning when implementing the quantum circuit.

Brief Description of the Drawings

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Mode for Carrying Out the Invention

[0006] Steps 100 and its sub-step 100-a from step 6 include processing of input data from a data source. This is represented as reading and expressing classical data from a dataset (Claim 9).

[0007] The components involved in this operation are data processing modules that can be understood as the part of a quantum computing system designed to handle and process data. In this case, the data is classical data referring to conventional non-quantum data used as input for the quantum machine learning process from a data set.

[0008] The data processing module reads this classical data, which is the first step in the method of implementing a symmetric quantum circuit. This step provides the necessary input data for the rest of the system to perform its function. The data processing module reads data from a data set, which can be any set of data relevant to the task at hand.

[0009] The reason the data processing module reads classical data is that this data serves as the basis for the subsequent quantum computation. Classical data is used to construct a symmetric quantum circuit, optimize its parameters, and generate output data. Without this input data, the quantum machine learning process cannot begin.

[0010] Regarding the mechanism of this operation, the data processing module is likely to use algorithms and computer calculation processes to read and process classical data. The details of these processes will depend on the nature of the data and the requirements of the specific quantum machine learning task.

[0011] In summary, step 100 and its sub-step 100-a include the data processing module reading classical data from a data set, which serves as the first step in the method of implementing a symmetric quantum circuit.

[0012] Step 102 and its sub-steps from step 6 include the construction of a symmetric quantum circuit that respects the data characteristics of a series of qubits using symmetric quantum operations. This is represented as the establishment (building) of an equivalent variable variational quantum circuit (Claim 10).

[0013] The component involved in this operation is a quantum circuit construction module. This module is the part of the quantum computing system responsible for creating quantum circuits. The quantum circuit is an equivalent variable variational quantum circuit, which is a specific type of quantum circuit that respects the high symmetry of the dataset.

[0014] The quantum circuit construction module establishes a quantum circuit using the high symmetry of the dataset (Claim 15), quantum gates (Claim 18), and a series of qubits (Claim 19). The high symmetry of the dataset is a mathematical property designed to be respected by the quantum circuit. Quantum gates are the basic building blocks of quantum circuits and function on a small number of qubits, which are the basic units of quantum information.

[0015] The quantum circuit construction module establishes the quantum circuit to ensure that the quantum circuit can perform the quantum calculations required for the quantum machine learning process. The equivalent variable variational quantum circuit is designed to provide faster convergence and training, as well as better accuracy (Claim 14).

[0016] Regarding the mechanism of this operation, the quantum circuit construction module is likely to use quantum algorithms and computer computational processing to establish the quantum circuit. The details of these processes will depend on the nature of the high symmetry of the dataset, the specific quantum gates used, and the number and arrangement of the qubits.

[0017] In summary, Step 102 and its sub-steps include the quantum circuit construction module establishing an equivalent variable variational quantum circuit using the high symmetry of the dataset, quantum gates, and a series of qubits. This is a step in the method of implementing a symmetric quantum circuit.

[0018] Steps 104 and its sub-steps from step 6 involve the optimization of circuit parameters to minimize performance metrics using optimization techniques. This is represented as the precise tuning of the parameters of an equivalent variable-variant quantum circuit using conjugate gradient descent or the like (Claim 11).

[0019] The component involved in this operation is the optimization module. This module is the part of the quantum computing system responsible for optimizing the parameters of the quantum circuit. The parameters of the quantum circuit are the variables that determine the behavior of the quantum circuit.

[0020] The optimization module uses conjugate gradient descent or the like (Claim 11), a training set (Claim 16), and a cost function (Claim 17) to precisely tune the parameters of the quantum circuit. Conjugate gradient descent is an optimization algorithm used to minimize a cost function, which is a measure of the error or loss of the quantum circuit. The training set is a set of data used to adjust the parameters of the quantum circuit.

[0021] The optimization module precisely tunes the parameters of the quantum circuit to minimize the cost function and improve the performance of the quantum circuit. The goal is to enable the quantum circuit to execute quantum computations as accurately and efficiently as possible.

[0022] Regarding the mechanism of this operation, the optimization module is likely to use mathematical processes and computer calculation processes to precisely tune the parameters of the quantum circuit. The details of these processes will depend on the nature of the parameters, the cost function, and the training set.

[0023] In summary, step 104 and its sub-steps involve the optimization module precisely tuning the parameters of the equivalent variable-variant quantum circuit using conjugate gradient descent or the like, a training set, and a cost function. This is a step in the method of implementing a symmetric quantum circuit.

[0024] Step 106 and its sub-steps from step 6 involve generating output data for new input data. This is expressed as making predictions for a new set of data points (claim 12).

[0025] The component involved in this operation is the output generation module. This module is the part of the quantum computing system responsible for generating the output of the quantum machine learning process. The output data is the result of the quantum computation executed by the quantum circuit and is generated for a new set of data points.

[0026] The output generation module makes predictions for a new set of data points. These predictions are the output data of the quantum machine learning process and are generated based on the optimized parameters of the quantum circuit and the new input data.

[0027] The output generation module makes predictions for a new set of data points in order to provide the results of the quantum machine learning process. These results are the predictions made by the quantum circuit based on the new input data.

[0028] Regarding the mechanism of this operation, the output generation module is likely to use quantum algorithms and computer computational processes to generate predictions. The details of these processes will depend on the nature of the new input data, the optimized parameters of the quantum circuit, and the specific quantum machine learning task.

[0029] In summary, step 106 and its sub-steps include the output generation module making predictions for a new set of data points. This is a step in the method of implementing a symmetric quantum circuit and provides the result of the quantum machine learning process.

[0030] Step 108 from step 6 includes the implementation of quantum hardware. This is represented as the ability to implement quantum hardware on superconducting qubits, ion traps, Rydberg atoms, photonic systems, and solid-state quantum dots (Claim 13).

[0031] The component involved in this operation is quantum hardware. This refers to the physical system used to perform quantum computing. Quantum hardware can be implemented on various systems including superconducting qubits, ion traps, Rydberg atoms, photonic systems, and solid-state quantum dots.

[0032] Quantum hardware is implemented on these systems to enable quantum computing. Each of these systems has unique properties that make them suitable for different types of quantum computing.

[0033] Superconducting qubits are very small circuits that can carry charge and exist in multiple states simultaneously. Ion traps use individual ions held in place by electromagnetic fields as qubits. Rydberg atoms are highly excited state atoms with strong interactions that can be used for quantum computing. Photonic systems use particles of light (photons) as qubits, while solid-state quantum dots use the spin of electrons in small semiconductor dots as qubits.

[0034] Quantum hardware is implemented on these systems to perform quantum computing. The choice of system will depend on the specific requirements of the quantum machine learning task.

[0035] To summarize, step 108 includes the implementation of quantum hardware on various systems including superconducting qubits, ion traps, Rydberg atoms, photonic systems, and solid-state quantum dots. This step enables the quantum computing necessary for the quantum machine learning process.

[0036] Step 110 from step 6 includes faster convergence and training by a symmetric quantum circuit, and providing better accuracy. This is expressed as the symmetric quantum circuit providing faster convergence and training, and better accuracy (claim 14).

[0037] The component involved in this operation is a symmetric quantum circuit. This refers to a quantum circuit constructed by a quantum circuit construction module and optimized by an optimization module. A symmetric quantum circuit is an equivalent variable fluctuation type quantum circuit, which is a specific type of quantum circuit that respects the high symmetry of the dataset.

[0038] A symmetric quantum circuit provides faster convergence and training, and better accuracy. Convergence refers to the process by which an optimization algorithm approaches an optimal solution. In the context of quantum machine learning, faster convergence means that the quantum circuit can reach the optimal solution more quickly. Training refers to the process of adjusting the parameters of a quantum circuit based on a training set to minimize a cost function. Faster training means that the quantum circuit can learn to perform a task more quickly. Better accuracy refers to the accuracy of the predictions made by the quantum circuit.

[0039] A symmetric quantum circuit provides faster convergence and training, and better accuracy to improve the performance of the quantum machine learning process. Faster convergence and training can make the quantum machine learning process more efficient, while better accuracy can make the results of the quantum machine learning process more accurate.

[0040] Regarding the mechanism of this operation, symmetric quantum circuits are likely to use quantum algorithms and computer calculation processes in order to provide faster convergence and training, as well as better accuracy. The details of these processes will depend on the nature of the quantum circuit, the optimization techniques used, and the specific quantum machine learning task.

[0041] In summary, step 110 includes that the symmetric quantum circuit provides faster convergence and training, as well as better accuracy. This step improves the performance of the quantum machine learning process.

[0042] Step 112 from step 6 includes the use of quantum machine learning in the implementation of the quantum circuit. This is represented as the implementation and representation of a variational equivalent variable quantum circuit for quantum machine learning (Claim 20).

[0043] The component involved in this operation is quantum machine learning. This refers to the application of the principles of quantum computing to machine learning tasks. Quantum machine learning can provide computational advantages over classical machine learning for certain types of tasks.

[0044] In this step, quantum machine learning is used to implement the quantum circuit. The quantum circuit is a constructed and optimized equivalent variable variational quantum circuit.

[0045] The use of quantum machine learning in the implementation of the quantum circuit is done to utilize the computational advantages of quantum computing. The quantum circuit is implemented to perform quantum calculations related to the machine learning task.

[0046] Regarding the mechanism of this operation, the implementation of a quantum circuit using quantum machine learning includes the use of quantum algorithms and computer calculation processes designed for quantum machine learning tasks. The details of these processes will depend on the nature of the quantum circuit, the specific quantum machine learning task, and the quantum hardware on which the quantum circuit is implemented.

[0047] In summary, step 112 involves the use of quantum machine learning in the implementation of a quantum circuit. This step exploits the computational advantages of quantum computing for machine learning tasks.

[0048] The equivalent variable quantum circuit system numbered 200 is part of the quantum machine learning process. It exploits the high symmetry of the dataset to bring about faster convergence and training, as well as better accuracy.

[0049] The system is composed of several main components. The first component is a data ingestion component (202) that processes input data from a data source. This includes reading classical data from a dataset and converting it into a form suitable for quantum computing.

[0050] The quantum circuit builder (204) is another part of the system. It constructs a symmetric quantum circuit that respects the data characteristics regarding a series of qubits using symmetric quantum operations. This includes the construction of an equivalent variable variational quantum circuit established using the high symmetry of the dataset. This is further decomposed into an equivalent variable circuit constructor (204-a), which is a sub-component that specifically handles the construction of the equivalent variable variational quantum circuit.

[0051] The parameter tuner (206) optimizes the circuit parameters to minimize a performance metric using optimization techniques. This includes using the conjugate gradient descent method or a similar method to precisely tune the parameters of the equivalent variable variational quantum circuit. The goal is to minimize the cost function for the training set, which helps improve the efficiency and accuracy of the quantum circuit.

[0052] The prediction generator (208) is responsible for generating output data for new input data. This includes making predictions for a new set of data points using the trained quantum circuit.

[0053] Finally, the quantum hardware integrator (210) ensures that the quantum hardware can be implemented on various quantum systems. This includes superconducting qubits, ion traps, Rydberg atoms, photonic systems, and solid-state quantum dots. This component is for the physical implementation of quantum circuits on various types of quantum hardware.

[0054] The equivalent variable quantum circuit system (200) starts its operation together with the data ingestion component (202). This component handles the input data from the data source. This component reads classical data from the dataset and converts it into a form suitable for quantum computing.

[0055] When the data is prepared, the quantum circuit builder (204) starts its activity. This component constructs a symmetric quantum circuit that respects the data properties regarding a series of qubits using symmetric quantum operations. The construction of the circuit is done to respect the relevant high symmetry of the dataset. Here, the equivalent variable circuit constructor (204-a) starts its activity. This sub-component specifically handles the construction of the equivalent variable variational quantum circuit. The use of the high symmetry in the dataset for constructing the circuit helps to improve the efficiency and accuracy of the quantum circuit.

[0056] After the construction of the quantum circuit, the parameter tuner (206) optimizes the circuit parameters. This is done to minimize the performance metric using optimization techniques. The optimization module uses the conjugate gradient descent method or a similar method to precisely tune the parameters of the equivalent variable variational quantum circuit. The goal is to minimize the cost function for the training set, which helps to improve the efficiency and accuracy of the quantum circuit.

[0057] When the circuit parameters are optimized, the prediction generator (208) generates output data for new input data. This includes making predictions for a new set of data points using the trained quantum circuit.

[0058] Finally, the quantum hardware integrator (210) ensures that the quantum hardware can be implemented on various quantum systems. This includes superconducting qubits, ion traps, Rydberg atoms, photonic systems, and solid-state quantum dots. This component is for the physical implementation of the quantum circuit on various types of quantum hardware.

[0059] In summary, the equivalent variable quantum circuit system (200) functions by processing input data, constructing a symmetric quantum circuit, optimizing the circuit parameters, and generating output data. Each component and sub-component plays a role in contributing to the efficiency and accuracy of the system in quantum machine learning applications in this process.

[0060] The data ingestion component numbered 202 is part of the equivalent variable quantum circuit system. This component plays a role in the first stage of the quantum machine learning process. This component handles the input data from the data source. This component reads classical data from the dataset and converts it into a form suitable for quantum computing. This step ensures that the data is in the correct form to be processed by the quantum circuit.

[0061] The data ingestion component (202) initiates the operation of the equivalent variable quantum circuit system. This component handles the input data from the data source. This component reads classical data from the dataset and converts it into a form suitable for quantum computing. This step relates to reading the data and ensuring that the data is in the correct form to be processed by the quantum circuit. The conversion of classical data into a form suitable for quantum computing is an important aspect of the function of this component. This process is executed under the condition that the data is classical and is provided from the dataset. The method includes reading the data and then converting it into a suitable form. This is the first step in the process and sets the stage for the subsequent components of the system. Therefore, the data ingestion component (202) is part of the system that sets the foundation for the quantum machine learning process.

[0062] The quantum circuit builder numbered 204 is part of the equivalent variable quantum circuit system. It is involved in the construction of a symmetric quantum circuit that respects the data characteristics regarding a series of qubits using symmetric quantum operations. This includes the construction of an equivalent variable variational quantum circuit established using the high symmetry of the dataset.

[0063] This component is further decomposed into an equivalent variable circuit constructor (204-a), which is a sub-component that particularly handles the construction of the equivalent variable variational quantum circuit. The use of the high symmetry in the dataset for constructing the circuit helps to improve the efficiency and accuracy of the quantum circuit. The quantum circuit builder and its sub-component, the equivalent variable circuit constructor, are part of the system that contributes to the construction of the quantum circuit.

[0064] The quantum circuit builder (204) is part of an equivalent variable quantum circuit system. This component constructs a symmetric quantum circuit that respects the data characteristics regarding a series of qubits using symmetric quantum operations. The construction of the circuit is performed to respect the relevant high degree of symmetry of the dataset. Here, the equivalent variable circuit constructor (204-a) starts its activity. This sub-component specifically handles the construction of an equivalent variable variational quantum circuit. The use of the high degree of symmetry in the dataset for constructing the circuit helps improve the efficiency and accuracy of the quantum circuit.

[0065] After the data is prepared by the data ingestion component (202), the quantum circuit builder (204) starts its activity. This component constructs a symmetric quantum circuit that respects the data characteristics regarding a series of qubits using symmetric quantum operations. The construction of the circuit is performed to respect the relevant high degree of symmetry of the dataset. Here, the equivalent variable circuit constructor (204-a) starts its activity. This sub-component specifically handles the construction of an equivalent variable variational quantum circuit. The use of the high degree of symmetry in the dataset for constructing the circuit helps improve the efficiency and accuracy of the quantum circuit.

[0066] The quantum circuit builder (204) and its sub-component, the equivalent variable circuit constructor (204-a), play a role in the construction of the quantum circuit. The construction of the circuit is performed to respect the relevant high degree of symmetry of the dataset. The use of the high degree of symmetry in the dataset for constructing the circuit helps improve the efficiency and accuracy of the quantum circuit. This process is executed under the condition that the data is classical and is brought about from the dataset. The method includes reading the data and then converting it into a suitable form. This is the first step in the process and sets the stage for the subsequent components of the system. Therefore, the quantum circuit builder (204) and its sub-component, the equivalent variable circuit constructor (204-a), are part of the system that sets the foundation for the quantum machine learning process.

[0067] The parameter tuner numbered 206 is part of an equivalent variable quantum circuit system. It is involved in optimizing circuit parameters to minimize a performance metric using optimization techniques. This includes using conjugate gradient descent methods or similar methods to precisely tune the parameters of an equivalent variable variational quantum circuit. The goal is to minimize a cost function for a training set, which helps improve the efficiency and accuracy of the quantum circuit.

[0068] After the quantum circuit is constructed by the quantum circuit builder (204) and its sub-component, the equivalent variable circuit constructor (204-a), the parameter tuner (206) starts its activity. This component optimizes the circuit parameters to minimize a performance metric using optimization techniques. The optimization module uses conjugate gradient descent methods or similar methods to precisely tune the parameters of an equivalent variable variational quantum circuit. The goal is to minimize a cost function for a training set, which helps improve the efficiency and accuracy of the quantum circuit. This process is executed under the condition that the data is classical and is provided from a data set. The method includes reading the data and then converting it into a suitable form. This is the first step in the process and sets the stage for subsequent components of the system. Thus, the parameter tuner (206) is part of the system that sets the foundation for the quantum machine learning process.

[0069] The prediction generator numbered 208 is part of an equivalent variable quantum circuit system. It is responsible for generating output data for new input data. This includes making predictions for a new set of data points using a trained quantum circuit.

[0070] A quantum circuit is constructed by a quantum circuit builder (204) and its sub-component, an equivalent variable circuit constructor (204-a). After the circuit parameters are optimized by a parameter tuner (206), a prediction generator (208) starts its operation. This component generates output data for new input data. This includes making predictions for a new set of data points using the trained quantum circuit. This process is executed under the condition that the data is classical and is provided from a data set. The method includes reading the data and then converting it into a suitable form. This is the final step in the process and sets the stage for the subsequent components of the system. Thus, the prediction generator (208) is part of the system that sets the foundation for the quantum machine learning process.

[0071] The quantum hardware integrator numbered 210 is part of an equivalent variable quantum circuit system. It ensures that quantum hardware can be implemented on various quantum systems. This includes superconducting qubits, ion traps, Rydberg atoms, photonic systems, and solid-state quantum dots. This component is for the physical implementation of quantum circuits on various types of quantum hardware.

[0072] A quantum circuit is constructed by a quantum circuit builder (204) and its sub-component, an equivalent variable circuit constructor (204-a). After the circuit parameters are optimized by a parameter tuner (206) and output data is generated by a prediction generator (208), a quantum hardware integrator (210) starts its activity. This component ensures that quantum hardware can be implemented on various quantum systems, including superconducting qubits, ion traps, Rydberg atoms, photonic systems, and solid-state quantum dots. This process is executed under the condition that the data is classical and is derived from a data set. The method includes reading the data and then converting it into a suitable format. This is the final step in the process and sets the stage for subsequent components of the system. Thus, the quantum hardware integrator (210) is part of the system that sets the foundation for the quantum machine learning process.

Claims

1. 1. A system for implementing a symmetric quantum circuit, comprising: a data processing module for processing input data from a data source; a quantum circuit construction module for constructing a symmetric quantum circuit using symmetric quantum operations that respects data properties on a set of qubits; an optimization module for optimizing circuit parameters to minimize a performance metric using optimization techniques; and an output generation module for generating output data for new input data.

2. 2. The system of claim 1, wherein the data processing module reads classical data from a data set.

3. 3. The system of claim 2, wherein the symmetric quantum circuit construction module establishes an equivalent variable fluctuation quantum circuit.

4. 4. The system of claim 3, wherein the optimization module uses conjugate gradient descent or the like to fine-tune the parameters of the equivalent variable variation quantum circuit.

5. 5. The system of claim 4, wherein the output generation module performs predictions on new sets of data points.

6. 10. The system of claim 1, wherein the quantum hardware can be implemented on superconducting qubits, ion traps, Rydberg atoms, photonic systems, and solid-state quantum dots.

7. 10. The system of claim 1, wherein the symmetric quantum circuit provides faster convergence and training, and greater accuracy.

8. 1. A method for implementing a symmetric quantum circuit, comprising: processing input data from a data source; constructing a symmetric quantum circuit using symmetric quantum operations to respect data properties on a set of qubits; optimizing the circuit parameters using optimization techniques to minimize a performance metric; and generating output data for new input data.

9. 9. The method of claim 8, wherein the input data is classical data from a dataset.

10. 10. The method of claim 9, wherein the quantum circuit is an equivalent variable fluctuation type quantum circuit.

11. 11. The method of claim 10, wherein the optimization technique is conjugate gradient descent or similar to fine-tune the parameters of the equivalent variable variation quantum circuit.

12. 12. The method of claim 11, wherein the output data is a prediction for a new set of data points.

13. 10. The method of claim 8, wherein the quantum hardware can be implemented on superconducting qubits, ion traps, Rydberg atoms, photonic systems, and solid-state quantum dots.

14. 10. The method of claim 8, wherein the symmetric quantum circuit provides faster convergence and training, and greater accuracy.

15. 10. The method of claim 8, further comprising using a high degree of symmetry of a data set to establish the quantum circuit.

16. 10. The method of claim 8, further comprising using a training set for the optimization of the quantum circuit.

17. 9. The method of claim 8, further comprising using a cost function for the optimization of the quantum circuit.

18. 10. The method of claim 8, further comprising using quantum gates in said construction of said quantum circuit.

19. 10. The method of claim 8, further comprising using a set of qubits in said construction of said quantum circuit.

20. 10. The method of claim 8, further comprising using quantum machine learning in the implementation of the quantum circuit.

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