Quantum Computing System Model Training

Abstract

We provide quantum computing system model training. [Solution] The method involves performing a first task to obtain a first quantum gate set ansatz and generate a second quantum gate set ansatz, the second quantum gate set ansatz then performs a second task related to the first task and trains the parameters of the second quantum gate set ansatz using the first dataset, the training includes adjusting electromagnetic waves applied to the qubits of quantum hardware according to the second quantum gate set and the first dataset, initializing the parameters of the first quantum gate set ansatz based on the trained parameters, and training the parameters of the first quantum gate set ansatz using the second dataset related to the first dataset.

Description

Technical Field

[0001] The present disclosure generally relates to quantum computing system model training.

Background Art

[0002] A quantum computer can use qubits (quantum bits) that can represent information as 1, 0, or both 1 and 0 simultaneously on a quantum gate to perform quantum computing operations. A quantum computer can train the parameters of a quantum computing system model to perform some types of quantum computing operations (such as optimization, graph partitioning, quadratic programming, etc.) more efficiently and / or accurately than a classical computer.

[0003] The subject matter claimed in the present disclosure is not limited to embodiments that solve any disadvantages or that operate only in the environments as described above. Rather, this background is provided only to illustrate an exemplary technical area in which some embodiments described in the present disclosure may be implemented.

Summary of the Invention

Means for Solving the Problems

[0004] According to one aspect of one embodiment, the method may include obtaining a first quantum gate set ansatz configured to perform a first task. A second quantum gate set ansatz may be generated using the first quantum gate set ansatz. The second quantum gate set ansatz may be configured to perform a second task related to the first task. The parameters of the second quantum gate set ansatz may be trained using the first dataset. The training includes tuning electromagnetic waves applied to the qubits of quantum hardware according to the second quantum gate set ansatz and the first dataset. The parameters of the first quantum gate set ansatz may be initialized based on the trained parameters of the second quantum gate set ansatz. The parameters of the first quantum gate set ansatz may be trained using a second dataset related to the first dataset. The training includes tuning electromagnetic waves applied to the qubits of quantum hardware according to the first quantum gate set ansatz and the second dataset. The trained parameters are used to tune the qubits of the quantum hardware used to generate the output.

[0005] The objectives and advantages of the embodiments are realized and achieved at least by the elements, features, and combinations specifically indicated in the claims. It is understood that the above general description and the following detailed description are for illustrative purposes only and do not limit the claimed invention. [Brief explanation of the drawing]

[0006] Exemplary embodiments are described and illustrated with further specificity and detail through the use of the accompanying drawings.

[0007] [Figure 1] This shows an exemplary operational flow of a quantum computing system.

[0008] [Figure 2] This provides an exemplary environment related to training quantum computing model parameters.

[0009] [Figure 3A] An example of quantum gate-set-ansatz is shown.

[0010] [Figure 3B] Another exemplary quantum gate-set-ansatz is shown.

[0011] [Figure 4] A flowchart illustrating an exemplary method for training quantum computing model parameters is shown.

[0012] [Figure 5] An exemplary block diagram of a computing system is shown, all of which are according to one or more embodiments of the present disclosure. [Modes for carrying out the invention]

[0013] Quantum computers use quantum bits, or "qubits," which can be configured to store values ​​of 0, 1, or a superposition of both 0 and 1. Because quantum bits can store multiple values / exist in multiple states simultaneously, quantum computers can perform calculations faster and / or more accurately than classical computers that use only classical bits that can store either 0 or 1. As a result, quantum computers can more efficiently train quantum computing system models related to complex calculations and / or improve computing in various technological fields such as physics, chemistry, finance, and machine learning (ML).

[0014] As the number of qubits in a quantum computer increases, the parameter search space can expand exponentially, making it difficult or impractical to train quantum computing system models. For example, training a quantum computing system model to perform an optimization task can be inefficient (e.g., it cannot be solved in polynomial time). Additionally or alternatively, quantum computing system model training can suffer from a barren plateau, where the size of the gradient across the parameter landscape approaches zero exponentially. Quantum computing system model training can also suffer from local minimum / maximum traps, which are misclassified as global minimums / maximums during gradient descent. Therefore, it can be beneficial to train quantum computing system models with higher quality initial parameters (e.g., non-zero and / or non-random) (e.g., providing a "warm start"). A warm start can improve the computing power of a quantum computer by enabling faster convergence to one or more solutions and / or reducing the likelihood of training getting trapped in a suboptimal solution space.

[0015] Some embodiments of this disclosure may describe systems and / or methods for training quantum computing system model parameters. For example, this disclosure may describe a method for obtaining initial values ​​for one or more parameters for a quantum computing system model. In these and other embodiments, the initial values ​​for the quantum computing system model may be obtained by training a simplified version of the quantum computing system model. For example, the quantum computing system model may include a quantum gate set ansatz. The quantum gate set ansatz may include a configuration of quantum gates that can be used to generate the quantum computing system model. In these and other embodiments, a simplified version of the quantum gate set ansatz, such as a subset of the quantum gate set ansatz, may be obtained. Parameters may be trained using the simplified quantum gate set ansatz. The parameter values ​​trained using the simplified quantum gate set ansatz may be used to initialize the parameter values ​​for the original quantum gate set ansatz for training the quantum computing system model. Using initialized values, the time and / or resources used to train a quantum computing system model can be reduced, and / or better parameters can be generated for the quantum computing system model. As a result, the computing process and performance of a quantum computer can be improved by creating a method and system for training quantum computing system model parameters more efficiently and / or accurately according to this disclosure.

[0016] Embodiments of the present disclosure will be described with reference to the accompanying drawings. Figure 1 shows an exemplary operation flow 100 of a quantum computing system according to one or more embodiments of the present disclosure. The operation flow 100 may be configured to train quantum computing model parameters.

[0017] In general, a quantum computing system may operate to perform quantum computations using a set of quantum gates that act on the quantum bits of the quantum computing system, such as qubits. Generally, quantum gates are configured to manipulate the quantum states of qubits. The quantum states of a qubit may include a basic state, a superposition state that can be represented by any point on the surface of a sphere (two opposing points on the sphere represent the 1 and 0 ground states of the qubit), and an entangled state where the qubit state is based on the state of another qubit. The quantum states of a qubit can be tuned. For example, a quantum gate may tune the superposition state of a qubit by rotating the state of the qubit from a first position to a second position. In these and other embodiments, quantum gates may represent operations that can be performed on a qubit. Thus, quantum gates may be implemented by controlling the quantum hardware that encodes the qubit, such as by manipulating the energy levels of atoms, ions, photons, or superconducting circuits that form the quantum hardware. In these and other embodiments, the quantum hardware may be controlled by the application of electromagnetic waves, such as lasers, microwaves, or other electromagnetic waves.

[0018] In these and other embodiments, how a quantum gate tunes a qubit may be determined based on the values ​​of the quantum gate's parameters. For example, a gate may be configured to tune the superposition of qubits. In this example, the gate's parameters could represent operators applied to the qubit by the gate, such as the rotation angle of the qubit. In another example, a gate may be configured to tune the intensity of entanglement between one qubit and another. Thus, each quantum gate may have one or more distinct parameters that can be tuned. Different parameters of a quantum gate can be implemented by tuning one or more properties of an electromagnetic wave applied to the quantum hardware. For example, the amplitude, pulse shape, duration, wavelength, or phase of an electromagnetic wave, or other properties, may be set to specific settings to achieve different parameters of a quantum gate. For example, a microwave pulse with a specific duration may be applied to a qubit to rotate it by a specific amount (e.g., 45 degrees) around a specific idealized axis. In these and other embodiments, other properties of the microwave pulse may be set to specific settings to help achieve the correct tuning of the qubit. Therefore, in order to adjust the parameters of a quantum gate, the properties of electromagnetic waves that can be applied to quantum hardware can be adjusted.

[0019] Quantum gates can be organized in a particular way to implement quantum algorithms. For example, a quantum algorithm may be written to perform a particular task. For example, the task may be a quantum Fourier transform or optimization problem, such as how to select stocks to form a portfolio that achieves a desired gain and risk tolerance. The optimization problem may be encoded into a quantum algorithm. A quantum algorithm can be represented by a particular set of quantum gates organized in a particular way that encodes the variables and operations of the quantum algorithm into a sequence of quantum gates. A set of quantum gates organized in a particular sequence may be referred to in this disclosure as a quantum gate set ansatz.

[0020] A quantum gate set ansatz may include quantum gates for solving an optimization problem. However, the quantum gate set ansatz may not include the values of the parameters of the quantum gates within the quantum gate set ansatz. The selection of specific parameter values for each of the quantum gates within the quantum gate set ansatz can be achieved by parameter training. Parameter training can include sequentially and iteratively adjusting the parameters of the gates using classical optimization techniques. Generally, before training, none of the parameters of the quantum gates may be known. Thus, each parameter can be initialized to zero or some random number. During training, values from a dataset can be provided to the quantum gates and results can be generated. The generated results can be compared with the known results for those values. Based on the difference between the generated results and the known results, the values of the parameters can be adjusted or updated. Updating the parameters can result in the adjustment of one or more characteristics of the electromagnetic wavelengths applied to the qubits of the quantum hardware to generate the results. For example, based on known gradient-based or gradient-free methods, the parameters may be updated to minimize or maximize a value calculated from the generated output. Training can continue until the difference between the generated results and the known results is within a specific threshold or until some other outcome, such as a limit on the number of sequential iterations or processing time, is reached.

[0021] As mentioned above, it can be difficult to train parameters about quantum gate set ansatz by initializing all parameters with zero or random numbers. In some embodiments, the operation flow 100 may be configured to train parameters. In these embodiments and other embodiments, the operation flow 100 may be configured to train parameters in two stages. For example, the parameters may be trained in the first stage using a first quantum gate set ansatz. After training the parameters in the first stage, the parameter values ​​from the first stage may be used as initial values ​​in the second stage. Training in the second stage may be achieved using a second quantum gate set ansatz. In these embodiments and other embodiments, the first quantum gate set ansatz may be a subset of the second quantum gate set ansatz.

[0022] With respect to Figure 1, the operation flow 100 may include a first quantum gate set ansatz 104 and a second quantum gate set ansatz 108. In some embodiments, the first quantum gate set ansatz 104 may be a subset of the second quantum gate set ansatz 108. That the first quantum gate set ansatz 104 is a subset of the second quantum gate set ansatz 108 may indicate that the quantum gates of the first quantum gate set ansatz 104 may be included in the second quantum gate set ansatz 108. Alternatively or additionally, that the first quantum gate set ansatz 104 is a subset of the second quantum gate set ansatz 108 may indicate that the quantum gates and configurations in the first quantum gate set ansatz 104 are consistent with the quantum gates and configurations in the second quantum gate set ansatz 108.

[0023] In some embodiments, the second quantum gate set ansatz 108 may correspond to a quantum algorithm. For example, the second quantum gate set ansatz 108 may correspond to a quantum optimization algorithm. In these and other embodiments, the first quantum gate set ansatz 104 may correspond to a part of the quantum algorithm. For example, the quantum algorithm may include a plurality of terms. In these and other embodiments, the second quantum gate set ansatz 108 may represent all terms in the quantum algorithm, and the first quantum gate set ansatz 104 may represent some of the terms in the quantum algorithm.

[0024] In some embodiments, the second quantum gate set ansatz 108 may represent a quantum algorithm comprising one or more quadratic and linear terms. For example, one or more quadratic and linear terms may be associated with applying idealized z-axis, x-axis, or y-axis rotations to a single qubit, or with applying coupled x-axis, y-axis, or z-axis rotations to a pair of qubits, respectively. In these and other embodiments, the second quantum gate set ansatz 108 may represent all terms of the quantum algorithm, and the first quantum gate set ansatz 104 may represent the linear terms of the quantum algorithm for a given rotation. For example, the first quantum gate set ansatz 104 may represent only the linear terms of the quantum algorithm for a given rotation. For example, a phase-separating gate that applies idealized z-axis rotation and coupled z-axis rotation to a pair of qubits may not be included in the first quantum gate set ansatz 104, while rotation gates associated with x-axis and y-axis rotations and mixtures with coupled x-axis and y-axis rotations of multiple qubits may be included in the first quantum gate set ansatz 104. As another example, a quadratic term in a quantum algorithm may not be represented by the first quantum gate set ansatz 104, thereby allowing the use of the first quantum gate set ansatz 104 to solve using a quantum algorithm that has a quadratic term in measurement without considering the quadratic term of the quantum algorithm.

[0025] In some embodiments, the first quantum gate set ansatz 104 may be generated from the second quantum gate set ansatz 108. For example, one or more quantum gates may be removed from the second quantum gate set ansatz 108 to generate the first quantum gate set ansatz 104. In some embodiments, the second quantum gate set ansatz 108 may satisfy the property of being a generator of a particular Lie algebra. In these and other embodiments, the first quantum gate set ansatz 104, after being generated from the second quantum gate set ansatz 108, may satisfy the property of being a generator of a Lie algebra.

[0026] In some embodiments, one or more quantum gates removed from the second set of quantum gates 108 may correspond to one or more quadratic terms of the quantum algorithm. The one or more quantum gates corresponding to one or more quadratic terms may be quantum gates that represent the quadratic terms and can be used to solve the quadratic terms in the quantum algorithm. As a result, the first set of quantum gates 104 can represent the quantum algorithm as if one or more variables of the quadratic terms were set to zero. When the quantum algorithm is an optimization algorithm, the variables may correspond to boundary coefficients.

[0027] Since the first quantum gate set ansatz 104 is generated from the second quantum gate set ansatz 108, the first quantum gate set ansatz 104 may be a simplified version of the second quantum gate set ansatz 108. In these and other embodiments, the fact that the first quantum gate set ansatz 104 is a simplified version may include the first quantum gate set ansatz 104 containing fewer quantum gates than the second quantum gate set ansatz 108. Thus, fewer quantum gate parameters may need to be tuned when training quantum gate parameters using the first quantum gate set ansatz 104 compared to training quantum gate parameters using the second quantum gate set ansatz 108.

[0028] In some embodiments, the operation flow 100 can train quantum gate parameters using a dataset related to the quantum problem to be solved. For example, the operation flow 100 may acquire a first dataset 102 and a second dataset 112 for use when training quantum gate parameters. In some embodiments, the first dataset 102 may include data that can be used to train the quantum gate parameters of a first quantum gate set ansatz 104. Alternatively or additionally, the second dataset 112 may include data that can be used to train the quantum gate parameters of a second quantum gate set ansatz 108.

[0029] In some embodiments, the first dataset 102 and / or the second dataset 112 may include a single dataset or multiple datasets. In some embodiments, the first dataset 102 and / or the second dataset 112 may include multiple different data items and may include a compilation of data that may be arranged in multiple different configurations. In some embodiments, the first dataset 102 and / or the second dataset 112 may include data from or representing financial / business data, statistical metrics, biological / medical / pharmacological data, technical data, and / or any other type of data. In some embodiments, the first dataset 102 and / or the second dataset 112 may be input by a user, generated by a computing device (e.g., a quantum computing device, a classical computing device, etc.), retrieved from a network (e.g., downloaded), and / or generated by devices such as sensors, cameras, satellites, bioinformatics devices, healthcare devices, audio devices, video devices, and / or any other devices, or a combination thereof. In some embodiments, the second dataset 112 may include multiple features (e.g., stock name / identifier, investment date, historical / expected rate of return, covariance, etc.).

[0030] In some embodiments, the second dataset 112 and the first dataset 102 may be related. For example, the first dataset 102 may be a subset of the data contained in the second dataset 112 (for example, the first dataset 102 may exclude data corresponding to one or more features contained in the second dataset 112). In these and other embodiments, the second dataset 112 may contain more features than the first dataset 102. In these and other embodiments, features contained in the second dataset 112 but not in the first dataset 102 may correspond to quantum gates contained in the second quantum gate set ansatz 108 but not in the first quantum gate set ansatz 104.

[0031] For example, the first dataset 102 and / or the second dataset 112 may include data relating to financial investments, such as financial returns from a specific asset or a group of different assets within a financial portfolio. In some embodiments, the financial returns dataset may relate to a linear term of a quantum algorithm. In some embodiments, the second dataset 112 may also include covariance data relating to financial investments. The covariance data may relate to a quadratic term of a quantum algorithm that may be represented in the second quantum gate-set ansatz 108 but not in the first quantum gate-set ansatz 104. In these and other embodiments, the first quantum gate-set ansatz 104 may not include quantum gates configured to be trained using covariance data, and therefore the first dataset 102 may not include covariance data. The absence of quantum gates configured to be trained using covariance data can have a dramatic effect on the associated Lie algebra and its mathematical properties, such as the dimension associated with the Lie algebra, because the gate set has changed.

[0032] The operation flow 100 may be accompanied by a training operation 106. The training operation 106 may include training the quantum gate parameters of the quantum gate of the first quantum gate set ansatz 104, and is referred to in this disclosure as training the first quantum gate set ansatz 104. First, the parameters can be initialized. In these and other embodiments, the parameters may be initialized to zero, random numbers, or some other number. Parameter initialization may set specific properties of electromagnetic wavelengths that can be used to interact with qubits. After initialization, the first quantum gate set ansatz 104 may be trained using a first dataset 102. For example, data from the first dataset 102 may be provided to the first quantum gate set ansatz 104, and an output may be generated. The generated output may be compared to a known output. The difference between the known output and the generated output may be used to tune the parameters of the first quantum gate set ansatz 104. Tuning the parameters of the first quantum gate set ansatz 104 may include tuning the electromagnetic wave properties applied to the qubits of the quantum hardware.

[0033] In some embodiments, gradient or non-gradient methods, among others, may be used to tune the parameters of the second quantum gate set ansatz 104 in an attempt to minimize or maximize a value calculated from the generated output. Other data may be provided to the first quantum gate set ansatz 104 and training may continue. In continued training, the tuned electromagnetic waves may be applied to qubits of quantum hardware to change the state of the qubits, thereby changing the output generated by the qubits in response to the first dataset 102. In these and other embodiments, multiple sequential iterations of training may be performed using the complete first dataset 102 or a portion of the first dataset 102. Any amount of training may be considered within the scope of this disclosure. After training the first quantum gate set ansatz 104 with the first dataset 102, the parameters of the first quantum gate set ansatz 104 may include a specific trained value. Therefore, in some embodiments, each parameter for each quantum gate in the first quantum gate set ansatz 104 may include a specific trained value.

[0034] The operation flow 100 may further include an initialization operation 110. In the initialization operation 110, the parameters of the second quantum gate set ansatz 108 may be initialized. In these and other embodiments, one or more of specific trained values ​​from the first quantum gate set ansatz 104 may be used as initial values ​​for the quantum gate parameters of the second quantum gate set ansatz 108. To initialize the parameters of the second quantum gate set ansatz 108, common gates between the first quantum gate set ansatz 104 and the second quantum gate set ansatz 108 may be identified. For example, quantum gates of the same type and located in the same position within the quantum gate sets of the first quantum gate set ansatz 104 and the second quantum gate set ansatz 108 may be considered common gates. In these and other embodiments, specific trained values ​​for the quantum gates of the first quantum gate set ansatz 104 may be used as initialization values ​​for the quantum gates of the second quantum gate set ansatz 108, which are common gates. For example, gates F1 and F2 of the first quantum gate set ansatz 104 may be common to gates S1 and S2 of the second quantum gate set ansatz 108, respectively. In these and other embodiments, specific trained values ​​for gate F1 may be used as initial parameter values ​​for gate S1, and specific trained values ​​for gate F2 may be used as initial parameter values ​​for gate S2. In these and other embodiments, all or only some of the specific trained values ​​for the quantum gates of the first quantum gate set ansatz 104 may be used as initial parameter values ​​for the quantum gates of the second quantum gate set ansatz 108.

[0035] In some embodiments, other quantum gates included in the second quantum gate set ansatz 108 but not included in the first quantum gate set ansatz 104 may be initialized to other values. For example, other quantum gates may be initialized to zero, random numbers, or some other number.

[0036] The operation flow 100 can proceed to the training operation 114. The training operation 114 may include training the quantum gate parameters of the quantum gate of the second quantum gate set ansatz 108, which is referred to in this disclosure as training the second quantum gate set ansatz 108. The second quantum gate set ansatz 108 may be trained using a second dataset 112. For example, data from the second dataset 112 may be provided to the second quantum gate set ansatz 108, which may generate an output. The generated output may be compared to a known output. The difference between the known output and the generated output may be used to tune the parameters of the second quantum gate set ansatz 108. Tuning the parameters of the first quantum gate set ansatz 104 may include tuning the characteristics of the electromagnetic waves applied to the qubits of the quantum hardware. In some embodiments, gradient or non-gradient methods, among others, may be used to tune the parameters of the second quantum gate set ansatz 108 to attempt to minimize or maximize a value calculated from the generated output. Other data may be provided to the second quantum gate set ansatz 108 and training may continue. In these and other embodiments, multiple sequential iterations of training may be performed using the complete second dataset 112 or a portion of the second dataset 112. Any amount of training may be considered within the scope of this disclosure.

[0037] After training the second quantum gate set-ansatz 108 using the second dataset 112, the parameters of the second quantum gate set-ansatz 108 may contain specific trained values. Thus, each parameter for each quantum gate in the second quantum gate set-ansatz 108 may contain specific trained values. Note that the parameters set in initialization operation 110 do not have to remain static. Rather, the parameters set in initialization operation 110 of the second quantum gate set-ansatz 108 may be further adjusted and refined during training operation 114. However, training operation 114 may be simplified by initializing some of the parameters of the second quantum gate set-ansatz 108. For example, training time, duration, or processing power may be reduced. Furthermore, the training operation 114 may be simplified to such an extent that the time it takes to perform operation flow 100 may be shorter than the time it takes to train the second quantum gate set ansatz 108 without performing operation flow 100, which includes initializing the parameters of the second quantum gate set ansatz 108 with values ​​from the first quantum gate set ansatz 104 after training. Alternatively or additionally, the training operation 114 may achieve better results by initializing some of the parameters of the second quantum gate set ansatz 108.

[0038] Modifications, additions, or omissions may be made to the operation flow 100 without departing from the scope of this disclosure. For example, the designation of different elements in the manner described is intended to help illustrate the concepts described herein, and is not intended to limit it. For example, in some embodiments, the operation flow 100 may be depicted in a particular manner described to help illustrate the concepts described herein, but such depiction is not intended to limit it. Furthermore, the operation flow 100 may include any number of other elements or may be performed in other systems or contexts other than those described.

[0039] Figure 2 shows an exemplary environment 220 related to training quantum computing system model parameters. Environment 220 may include a quantum computing system 200, parameter values ​​202, a dataset 204, and a gate-set-ansat 206. The quantum computing system 200 may take the parameter values ​​202, the dataset 204, and the gate-set-ansat 206 as input and be configured to update one or more of the parameter values ​​202 with specific trained values.

[0040] The gate-set-ansatz 206 may include a first gate-set-ansatz and a second gate-set-ansatz. The first and second gate-set-ansatz may be analogous to the first quantum gate-set-ansatz 104 and the second quantum gate-set-ansatz 108 in Figure 1, respectively. Therefore, no further explanation is provided for Figure 2. The dataset 204 may include a first dataset and a second dataset. The first dataset may correspond to a first gate-set-ansatz, and the second dataset may correspond to a second gate-set-ansatz. The correspondence between the dataset and the gate-set-ansatz may indicate that the dataset contains features represented within the gate-set-ansatz. For example, since the second gate-set-ansatz contains additional quantum gates, the second dataset may contain additional features corresponding to the additional quantum gates. The first and second datasets may be similar to the first dataset 102 and the second dataset 112 in Figure 1, respectively. Therefore, no further explanation is provided for Figure 2.

[0041] Parameter value 202 can be the initial value for the quantum gate parameter of gate set ansatz 206. In the initial state, the parameter value can be set to zero, a random number, or any other number.

[0042] In some embodiments, the quantum computing system 200 may include quantum hardware 208. For example, the quantum hardware 208 may include a quantum processor that includes one or more qubits and the ability to store qubits. In some embodiments, qubits may be physically implemented using, for example, photons, trapped ions, electrons, one or more nuclei, superconducting circuits, and / or quantum dots. For example, qubits may be physically implemented in a variety of ways, including the polarization state of a single photon, the spatial optical path of a single photon, two different energy states of an atom or ion, and / or the spin orientation of a particle or multiple particles such as a nucleus. In some embodiments, the quantum processor may include at least two qubits and at least one coupler capable of coupling those qubits. Storing qubits may include, for example, supercooling the qubits to maintain them in a suitable environment for quantum computation.

[0043] In some embodiments, the quantum hardware 208 may include a quantum circuit 210. The quantum circuit 210 may be formed by a suitable arrangement of quantum gates and may act on qubits contained in the quantum hardware 208. In some embodiments, the quantum gates of the quantum circuit 210 may be configured according to one of the gate set ansatz 206. For example, during a first period, the quantum circuit 210 may be configured according to a first gate set ansatz from the gate set ansatz 206. During a second period, the quantum circuit 210 may be configured according to a second gate set ansatz from the gate set ansatz 206. The quantum circuit 210 may determine the characteristics of electromagnetic waves that can be applied to the qubits of the quantum hardware 208 to tune the state of the qubits.

[0044] In some embodiments, the parameters of the quantum gates of the quantum circuit 210 may be initialized with values ​​from parameter values ​​202. Initially, parameter values ​​202 may be set to zero, a random number, or another selected value. After processing, the quantum computing system 200 may be configured to update one or more of the parameter values ​​202. For example, the quantum computing system 200 may update one or more of the parameter values ​​202 using specific trained values ​​based on computations performed by the quantum computing system 200. Updating the values ​​may include updating the properties of electromagnetic waves that can be applied to the qubits of the quantum hardware 208 to tune the state of the qubits.

[0045] The processing system 212 may be any configuration of a non-quantum processing device and / or system. For example, the processing system 212 may include one or more elements of the computing system 500. In these and other embodiments, the processing system 212 may be configured to control the quantum hardware 208, provide data to the quantum hardware 208, retrieve data from the quantum hardware 208, and / or otherwise interact with the quantum hardware 208 to assist the quantum hardware 208 in performing its functions.

[0046] In some embodiments, parameter values ​​202, dataset 204, and / or gate set ansatz 206 may be provided to the retrieval / quantum computing system 200 via one or more physical networks, cloud networks, random access memory (RAM) drives, flash memory devices (e.g., solid-state memory devices), and / or any other means by which data can be transferred between devices and / or systems.

[0047] An example of the operation of the quantum computing system 200 is provided here. The quantum circuit 210 may be configured according to a first gate-set-ansatz. The parameters of the first gate-set-ansatz may be initialized using parameter values ​​202 corresponding to the quantum gates in the first gate-set-ansatz. The quantum hardware 208 can train the parameters of the first gate-set-ansatz by performing one or more operations using the first dataset of the dataset 204. Training the parameters may include tuning the electromagnetic wave properties that can be applied to the qubits of the quantum hardware 208 to tune the state of the qubits. Training the parameters of the first gate-set-ansatz may result in specific trained values ​​for one or more parameters of one or more quantum gates in the first gate-set-ansatz. In these and other embodiments, the quantum computing system 200 may update the parameter values ​​202 for one or more quantum gates using the specific trained values.

[0048] After performing operations with respect to the first gate-set ansatz, the quantum circuit 210 may be configured according to the second gate-set ansatz. The parameters of the second gate-set ansatz may be initialized using parameter values ​​202 corresponding to the quantum gates of the second gate-set ansatz. Note that some of the parameter values ​​used to initialize some of the quantum gates of the second gate-set ansatz may be specific trained values. In these and other embodiments, the quantum hardware 208 can train the parameters of the second gate-set ansatz by performing one or more operations using the second dataset. Training the parameters may include tuning the electromagnetic wave properties that can be applied to the qubits of the quantum hardware 208 to tune the state of the qubits. Training the parameters of the second gate-set ansatz may result in specific trained values ​​for one or more parameters of one or more of the quantum gates of the second gate-set ansatz. The specific trained values ​​of the second gate-set-ansatz can be used as model parameters for a quantum algorithm that is partially or entirely represented by the gate-set-ansatz 206. Using the model parameters, certain properties of the electromagnetic wave may be known, which can be used to tune the qubits of the quantum hardware 208 to place the qubits in the correct state to produce a desired output.

[0049] After training using a second gate-set-ansatz, the data is provided to quantum hardware 208 and can be processed to generate an output. To process the data, the qubit states can be set using electromagnetic waves with specific properties. The output can be a solution for a quantum algorithm given the data provided to quantum hardware 208.

[0050] Without departing from the scope of this disclosure, modifications, additions, or omissions may be made to the environment 220. For example, the quantum computing system 200 may include one or more additional components. Alternatively or additionally, the quantum computing system 200 may not include the processing system 212. In these and other embodiments, the processing system 212 is separate from the quantum computing system 200 and may be networked with it. Alternatively or additionally, the environment 220 may include one or more additional components.

[0051] Figure 3A shows an exemplary quantum gate set 300. Quantum gate set Ansatz 300 may be an example of the first quantum gate set Ansatz 104 in Figure 1. Quantum gate set Ansatz 300 may include the first quantum gates 302. The first quantum gates 302 may include the first configuration as shown. The first quantum gates 302 include the rotation gate R z It may also include an XY gate.

[0052] Figure 3B shows an exemplary quantum gate set-ansatz 350. Quantum gate set-ansatz 350 may be an example of the second quantum gate set-ansatz 108 in Figure 1. Quantum gate set-ansatz 350 may include the first quantum gates 302 as shown in Figure 3A, or it may include the second quantum gates 352. The second quantum gates 352 may include the second configuration shown, or it may be connected to the first quantum gates 302. The second quantum gates 352 is an Ising gate R zz It may include.

[0053] It should be noted that Quantum Gate Set Ansatz 300 is a subset of Quantum Gate Set Ansatz 350. For example, each quantum gate in Quantum Gate Set Ansatz 300 is included in Quantum Gate Set Ansatz 350. Furthermore, the configuration, for example, the interconnections between quantum gates in Quantum Gate Set Ansatz 300 may be the same as or similar to the interconnections between quantum gates in Quantum Gate Set Ansatz 350.

[0054] As illustrated, the quantum gates of Quantum Gate Set Ansatz 300 are not continuous in the configuration of Quantum Gate Set Ansatz 350. For example, the rotational R gate of Quantum Gate Set Ansatz 300 is not directly coupled to the XY gate of Quantum Gate Set Ansatz 300 in Quantum Gate Set Ansatz 350. Instead, it is coupled to the Ising gate R zz However, it is located between the rotating R gate and the XY gate. Note that the connection between the quantum gate set Ansatz 350 and the rotating R gate and the XY gate between the quantum gate set Ansatz 350 is an Ising gate R zz It is maintained even if it is removed. Therefore, quantum gate set ansatz 300, for example, subset ansatz, may not be a continuous grouping of quantum gates from quantum gate set ansatz 350. Rather, subset ansatz, for example quantum gate set ansatz 300, can be constructed from the original ansatz, for example quantum gate set ansatz 350, by removing one or more continuous portions of quantum gates from quantum gate set ansatz 350. For example, the continuous portion may be at the beginning, middle, or end of the original ansatz.

[0055] Without departing from the scope of this disclosure, modifications, additions, or omissions may be made to the quantum gate set ansatz 300 and quantum gate set ansatz 350. For example, one or more gates may be added to or removed from the quantum gate set ansatz 300 and quantum gate set ansatz 350.

[0056] Figure 4 is a flowchart of an exemplary method 400 for training quantum computing system model parameters using a constrained set of quantum gates on a quantum computer, according to one or more embodiments of the present disclosure. Method 400 can be performed by any suitable system, apparatus, or device. For example, a quantum computing system 200, quantum hardware 208, and / or processing system 212 may perform one or more of the operations related to Method 400. Although shown in discrete blocks, the steps and operations related to one or more of the blocks of Method 400 may be divided into additional blocks, combined into fewer blocks, or deleted, depending on the specific implementation.

[0057] Method 400 may begin at block 402, and a first quantum gate set-ansatz configured to perform the first task may be obtained.

[0058] In block 404, a second quantum gate set ansatz may be generated using the first quantum gate set ansatz. In these and other embodiments, the second quantum gate set ansatz may be configured to perform a second task related to the first task.

[0059] In some embodiments, generating a second quantum gate-set ansatz may involve removing one or more quantum gates from the first quantum gate-set ansatz. In these and other embodiments, both the first and second quantum gate-set ansatz may satisfy the properties of a Lie algebra generator.

[0060] In some embodiments, the one or more quantum gates to be removed may be used in solving one or more quadratic terms in the quantum algorithm on which the first quantum gate set ansatz is based. In these and other embodiments, the removal of one or more quantum gates may result in setting one or more variables in the quadratic terms of the quantum algorithm to zero. In some embodiments, the variables are boundary coefficients.

[0061] In some embodiments, the second quantum gate-set-ansatz may include quantum gates used to solve one or more linear terms in the quantum algorithm on which the first quantum gate-set-ansatz is based. In these and other embodiments, the quantum algorithm may be a quantum optimization algorithm.

[0062] In block 406, the parameters of a second quantum gate set Ansatz may be trained using a first dataset. The second quantum gate set Ansatz may be trained using quantum hardware. In some embodiments, the first dataset may include financial return data corresponding to financial assets. In some embodiments, training involves tuning electromagnetic waves applied to the qubits of the quantum hardware according to the second quantum gate set and the first dataset.

[0063] In block 408, the parameters of the first quantum gate set ansatz may be initialized based on the trained parameters of the second quantum gate set ansatz. In some embodiments, initializing the parameters of the first quantum gate set ansatz may include identifying common quantum gates between the first and second quantum gate set ansatz, and for the identified common quantum gates, setting the parameters of the first quantum gate set ansatz to the trained parameters of the second quantum gate set ansatz. In these and other embodiments, initialization may further include setting the remaining parameters of the first quantum gate set ansatz to zero.

[0064] In block 410, the parameters of the first quantum gate set ansatz may be trained using a second dataset related to the first dataset. The second quantum gate set ansatz may be trained using quantum hardware. In some embodiments, training involves tuning electromagnetic waves applied to the qubits of the quantum hardware according to the first quantum gate set ansatz and the second dataset. In some embodiments, the trained parameters may be used to configure the quantum hardware to produce a desired output using other data.

[0065] In some embodiments, the second dataset may include covariance data corresponding to the financial return data of the first dataset. In these and other embodiments, the first and second tasks may each include the task of identifying a set of financial assets.

[0066] Method 400 may be modified, added to, or omitted without departing from the scope of this disclosure. For example, the designation of different elements in the manner described is intended to be helpful in illustrating the concepts described herein, and is not limiting. Furthermore, Method 400 may include any number of other elements, or may be carried out in other systems or contexts other than those described.

[0067] Figure 5 shows an exemplary computing system 500 according to one or more embodiments of the present disclosure. The computing system 500 may include a processor 502, memory 504, data storage 506, and / or a communication unit 508, all of which may be communicatively coupled. For example, the processing system 212 in Figure 2 may include one or more components of the computing system 500.

[0068] Generally, the processor 502 may include any suitable dedicated or general-purpose computer, computing entity, or processing device, including various computer hardware or software modules, and may be configured to execute instructions stored in any applicable computer-readable storage medium. For example, the processor 502 may include a microprocessor, microcontroller, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or any other digital or analog circuitry configured to interpret and / or execute program instructions and / or process data.

[0069] Although shown as a single processor in Figure 5, it will be understood that the processor 502 may include any number of processors distributed across any number of network or physical locations, configured to individually or collectively perform any number of operations described in this disclosure. In some embodiments, the processor 502 may interpret and / or execute program instructions stored in memory 504, data storage 506, or memory 504 and data storage 506, and / or process data. In some embodiments, the processor 502 may fetch program instructions from data storage 506 and load the program instructions into memory 504.

[0070] After the program instructions are loaded into memory 504, the processor 502 may execute program instructions, such as instructions that cause the computing system 500 to perform some of the operations of method 400 in Figure 4. For example, the computing system 500 may execute program instructions to generate a second quantum gate set ansatz using a first quantum gate set ansatz.

[0071] The memory 504 and data storage 506 may include computer-readable storage media or one or more computer-readable storage media for storing computer-executable instructions or data structures. Such computer-readable storage media may be any available media that can be accessed by a general-purpose or dedicated computer, such as a processor 502. In some embodiments, the computing system 500 may or may not include either the memory 504 or the data storage 506.

[0072] Such computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), compact disk read-only memory (CD-ROM) or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory devices (e.g., solid-state memory devices), or any other storage media that may be used to store desired program code in the form of computer-executable instructions or data structures and that can be accessed by a general-purpose or dedicated computer. Combinations of the above may also be included within the scope of computer-readable storage media. Computer-executable instructions may include, for example, instructions and data configured to cause processor 502 to perform a particular operation or set of operations.

[0073] The communication unit 508 may include any component, device, system, or combination thereof configured to transmit or receive information over a network. In some embodiments, the communication unit 508 may communicate with other devices located at other locations, at the same location, or even with other components within the same system. For example, the communication unit 508 may include modems, network cards (wireless or wired), optical communication devices, infrared communication devices, wireless communication devices (such as antennas), and / or chipsets (such as Bluetooth® devices, 802.6 devices (e.g., Metropolitan Area Network (MAN)), WiFi devices, WiMAX devices, cellular communication equipment, etc.). The communication unit 508 may enable data to be exchanged with the network and / or any other devices or systems described herein. For example, the communication unit 508 may enable computing system 500 to communicate with other systems such as computing devices and / or other networks.

[0074] A person skilled in the art will recognize, after reviewing this disclosure, that modifications, additions, or omissions may be made to the computing system 500 without departing from the scope of this disclosure. For example, the computing system 500 may include more or fewer components than those expressly illustrated and described.

[0075] The foregoing disclosure is not intended to limit this disclosure to the exact form or specific field of use disclosed herein. Therefore, it is conceivable that various alternative embodiments and / or modifications to this disclosure are possible in light of this disclosure, whether expressly described or implied herein. While embodiments of this disclosure have been described in this manner, it is recognized that changes may be made in form and detail without departing from the scope of this disclosure. Therefore, this disclosure is limited solely by the claims.

[0076] In some embodiments, the different components, modules, engines, and services described herein may be implemented as objects or processes (for example, as separate threads) running on a computing system. While some of the systems and methods described herein are generally described as being implemented in software (stored and / or executed on general-purpose hardware), specific hardware implementations or combinations of software and specific hardware implementations are also possible and conceivable.

[0077] In accordance with common practice, the various features shown in the drawings may not be depicted to a fixed scale. The drawings presented in this disclosure are not intended to be actual diagrams of any particular apparatus (e.g., a device, system, etc.) or method, but are merely idealized representations used to illustrate various embodiments of this disclosure. Accordingly, the dimensions of various features may be enlarged or reduced as appropriate for clarity. In addition, some of the drawings may be simplified for clarity. Thus, the drawings do not necessarily show all components of a given apparatus (e.g., a device) or all operations of a particular method.

[0078] In this specification, terms used in particular in the appended claims (for example, the body of the appended claims) are generally intended to be “open” terms (for example, the term “contains” should be interpreted as “contains, but is not limited to,” the term “has” should be interpreted as “has at least…,” and the term “includes” should be interpreted as “contains, but is not limited to,” etc.).

[0079] Furthermore, if a specific number of claims to be introduced is intended, such intention is explicitly stated in the claim; if no such statement is present, such intention does not exist. For example, for the sake of understanding, the attached claims below may include the use of the introductory phrases “at least one” and “one or more” to introduce a claim. However, the use of such phrases should not be interpreted as implying that the introduction of a claim by the indefinite article “a” or “an” limits any particular claim containing such introduced claim to embodiments containing only one such claim. This is also true if the same claim includes the introductory phrase “one or more” or “at least one” and an indefinite article such as “a” or “an” (for example, “a” and / or “an” should be interpreted as meaning “at least one” or “one or more”). The same applies to the use of definite articles used to introduce a claim.

[0080] In addition, even if a specific number of claims being introduced is explicitly stated, it should be understood that such a statement should be interpreted as meaning at least the stated number (for example, the statement “two statements” without other modifiers means at least two statements, or two or more statements). Furthermore, when idiomatic expressions similar to “at least one of A, B, and C, etc.” or “one or more of A, B, and C, etc.” are used, such constructions are generally intended to include A only, B only, C only, A and B together, A and C together, B and C together, or A, B and C together, etc.

[0081] Furthermore, any separate word or phrase that presents two or more alternative terms should be understood, whether in this paper, claims, or drawings, as considering the possibility of including one of the terms, either of the terms, or both of the terms. For example, the phrase “A or B” should be understood as including the possibilities of “A” or “B” or “A and B.”

[0082] Furthermore, the use of terms such as “first,” “second,” and “third” does not necessarily imply a particular order or number of elements in this specification. Generally, terms such as “first,” “second,” and “third” are used as general identifiers to distinguish different elements. If terms such as “first,” “second,” and “third” do not indicate that they imply a particular order, these terms should not be understood as implying a particular order. Furthermore, if terms such as “first,” “second,” and “third” do not indicate that they imply a particular number of elements, these terms should not be understood as implying a particular number of elements. For example, a first widget may be described as having a first side, and a second widget may be described as having a second side. The use of the term “second side” with respect to a second widget may be to distinguish such a side of the second widget from the “first side” of the first widget, and not to imply that the second widget has two sides.

[0083] All examples and conditional statements described herein are intended for educational purposes to help the reader understand the present invention and the concepts to which the inventors contribute to advancing the art, and should be construed as not being limited to such specifically described examples and conditions. While embodiments of this disclosure have been described in detail, it should be understood that various changes, substitutions, and modifications can be made thereto without departing from the spirit and scope of this disclosure.

[0084] The following additional information is disclosed regarding embodiments including those described above. (Note 1) The steps include obtaining a first quantum gate set Ansatz configured to perform the first task; A step of generating a second quantum gate set ansatz using the first quantum gate set ansatz, wherein the second quantum gate set ansatz is configured to perform a second task related to the first task; A step of training the parameters of the second quantum gate set ansatz using a first dataset, wherein the training includes adjusting the electromagnetic waves applied to the qubits of the quantum hardware according to the second quantum gate set ansatz and the first dataset; A step of initializing the parameters of the first quantum gate set-ansatz based on the trained parameters of the second quantum gate set-ansatz; A step of training the parameters of the first quantum gate set ansatz using a second dataset related to the first dataset, wherein the training includes tuning electromagnetic waves applied to the qubits of quantum hardware according to the first quantum gate set ansatz and the second dataset, and the trained parameters are used to tune the qubits of quantum hardware used to generate an output. Methods that include... (Note 2) The method according to Appendix 1, wherein generating the second quantum gate set ansatz includes removing one or more quantum gates included in the first quantum gate set ansatz. (Note 3) The method described in Appendix 2, wherein both the first quantum gate-set-ansatz and the second quantum gate-set-ansatz satisfy the properties of a Lie algebra. (Note 4) The method according to Appendix 2, wherein the one or more quantum gates to be removed are used in solving one or more quadratic terms in a quantum algorithm on which the first quantum gate set ansatz is based. (Note 5) The method according to Appendix 4, wherein removing one or more quantum gates results in setting one or more quadratic terms variables in the quantum algorithm to zero. (Note 6) The aforementioned variable is a boundary coefficient, as described in Appendix 5. (Note 7) The method according to Appendix 2, wherein the second quantum gate-set-ansatz includes a quantum gate used to solve one or more linear terms in the quantum algorithm on which the first quantum gate-set-ansatz is based. (Note 8) The aforementioned quantum algorithm is a quantum optimization algorithm, as described in Appendix 7. (Note 9) Initializing the parameters of the first quantum gate set-ansatz: The steps include identifying a common quantum gate between the first quantum gate set ansatz and the second quantum gate set ansatz; The steps include setting the parameters of the first quantum gate set ansatz to the trained parameters of the second quantum gate set ansatz for the identified common quantum gate; The steps include setting the remaining parameters of the first quantum gate set ansatz to zero and The method described in Appendix 1, including the method described in Appendix 1. (Note 10) The method according to Appendix 1, wherein the first dataset includes financial return data corresponding to financial assets, the second dataset includes covariance data corresponding to the financial return data, and the first task and the second task each include the task of identifying a set of financial assets. (Note 11) A system having a quantum computing system, wherein the quantum computing system is: A first quantum gate set Ansatz configured to perform the first task; A second quantum gate set ansatz generated based on the first quantum gate set ansatz, the second quantum gate set ansatz configured to perform a second task related to the first task; It is quantum hardware: A step of training the parameters of the second quantum gate set ansatz using a first dataset, wherein the training includes adjusting electromagnetic waves applied to the qubits of quantum hardware according to the second quantum gate set ansatz and the first dataset, and A step of training the parameters of the first quantum gate set ansatz using a second dataset associated with the first dataset, wherein the training includes adjusting electromagnetic waves applied to the qubits of quantum hardware according to the first quantum gate set ansatz and the second dataset, and the parameters of the first quantum gate set ansatz are initialized based on the trained parameters of the second quantum gate set ansatz. Quantum hardware configured to perform, A system that has (Note 12) The system described in Appendix 11, wherein the second quantum gate set ansatz is generated by removing one or more quantum gates included in the first quantum gate set ansatz. (Note 13) The system described in Appendix 12, wherein both the first quantum gate-set-ansatz and the second quantum gate-set-ansatz satisfy the properties of a Lie algebra. (Note 14) The system according to Appendix 12, wherein the one or more quantum gates to be removed are used in solving one or more quadratic terms in the quantum algorithm on which the first quantum gate set ansatz is based. (Note 15) The system according to Appendix 14, wherein removing one or more of the quantum gates results in setting one or more of the quadratic terms' variables in the quantum algorithm to zero. (Note 16) The aforementioned variables are boundary coefficients, as described in Appendix 15. (Note 17) The system according to Appendix 11, wherein the second quantum gate-set-ansatz includes a quantum gate used to solve one or more linear terms in the quantum algorithm on which the first quantum gate-set-ansatz is based. (Note 18) The quantum computing system initializes the parameters of the first quantum gate set-anzatz as follows: Identify the common quantum gate between the first quantum gate set ansatz and the second quantum gate set ansatz; Set the parameters of the first quantum gate set ansatz to the trained parameters of the second quantum gate set ansatz for the identified common quantum gate; Set the remaining parameters of the first quantum gate set-ansatz to zero. thing The system described in Appendix 11, configured to be executed by... (Note 19) The system as described in Appendix 11, wherein the first dataset includes financial return data corresponding to financial assets, the second dataset includes covariance data corresponding to the financial return data, and the first task and the second task each include the task of identifying a set of financial assets. (Note 20) A non-temporary computer-readable medium configured to store instructions for performing an action when executed by a system, wherein the action is: The steps include obtaining a first quantum gate set Ansatz configured to perform the first task; A step of using the first quantum gate set ansatz to instruct the generation of a second quantum gate set ansatz, wherein the second quantum gate set ansatz is configured to perform a second task related to the first task; Steps include: directing the training of the parameters of the second quantum gate set ansatz using a first dataset, wherein the training includes adjusting the electromagnetic waves applied to the qubits of the quantum hardware according to the second quantum gate set ansatz and the first dataset; A step of instructing the initialization of the parameters of the first quantum gate set ansatz based on the trained parameters of the second quantum gate set ansatz; Steps include: instructing to train the parameters of the first quantum gate set ansatz using a second dataset related to the first dataset, wherein the training includes tuning electromagnetic waves applied to the qubits of quantum hardware according to the first quantum gate set ansatz and the second dataset, and the trained parameters are used to tune the qubits of quantum hardware used to generate an output; and Computer-readable media, including [specific examples of computer-readable media]. [Explanation of Symbols]

[0085] 102 First dataset 104 The first quantum gate set-ansatz 106 Training the parameters of the first quantum gate set 108 The second quantum gate set-ansatz 110 Initialize the parameters of the second quantum gate set. 112 Second dataset 114 Training the parameters of the second quantum gate set 200 Quantum Computing Systems 202 Parameter values 204 datasets 206 Gate Set Ansatz 208 Quantum Hardware 210 Quantum Circuit 212 Processing Systems 402 Obtain the first set of quantum gates configured to perform the first task. 404. Use the first set of quantum gates to generate a second set of quantum gates. The second set of quantum gates is configured to perform a second task related to the first task. 406 Train the parameters of the second quantum gate set using the first dataset. 408 Initialize the parameters of the first quantum gate set based on the trained parameters of the second quantum gate set. 410 Train the parameters of the first quantum gate set using the second dataset related to the first dataset. 500 Computing Systems 504 memory 506 Data Storage 508 Communication Unit

Claims

1. The steps include: obtaining a first quantum gate set Ansatz configured to perform the first task; A step of generating a second quantum gate set ansatz using the first quantum gate set ansatz, wherein the second quantum gate set ansatz is configured to perform a second task related to the first task; A step of training the parameters of the second quantum gate set ansatz using a first dataset, wherein the training includes adjusting the electromagnetic waves applied to the qubits of the quantum hardware according to the second quantum gate set ansatz and the first dataset; A step of initializing the parameters of the first quantum gate set ansatz based on the trained parameters of the second quantum gate set ansatz; A step of training the parameters of the first quantum gate set ansatz using a second dataset associated with the first dataset, wherein the training includes tuning electromagnetic waves applied to the qubits of quantum hardware according to the first quantum gate set ansatz and the second dataset, and the trained parameters are used to tune the qubits of quantum hardware used to generate an output. Methods that include...

2. The method according to claim 1, wherein generating the second quantum gate set ansatz comprises removing one or more quantum gates included in the first quantum gate set ansatz.

3. The method according to claim 2, wherein both the first quantum gate set ansatz and the second quantum gate set ansatz satisfy the properties of a Lie algebra.

4. The method according to claim 2, wherein the one or more quantum gates to be removed are used in solving one or more quadratic terms in a quantum algorithm on which the first quantum gate set ansatz is based.

5. The method according to claim 4, wherein removing one or more quantum gates results in setting one or more quadratic terms variables in the quantum algorithm to zero.

6. The method according to claim 5, wherein the aforementioned variable is a boundary coefficient.

7. The method according to claim 2, wherein the second quantum gate set ansatz includes a quantum gate used to solve one or more linear terms in a quantum algorithm on which the first quantum gate set ansatz is based.

8. The method according to claim 7, wherein the quantum algorithm is a quantum optimization algorithm.

9. Initializing the parameters of the first quantum gate set Ansatz can be done as follows: The steps include identifying a common quantum gate between the first quantum gate set ansatz and the second quantum gate set ansatz; The steps include setting the parameters of the first quantum gate set ansatz to the trained parameters of the second quantum gate set ansatz for the identified common quantum gate; The steps include setting the remaining parameters of the first quantum gate set ansatz to zero and The method according to claim 1, including the method described in claim 1.

10. The method according to claim 1, wherein the first dataset includes financial return data corresponding to financial assets, the second dataset includes covariance data corresponding to the financial return data, and the first task and the second task each include a task of identifying a set of financial assets.

11. A system having a quantum computing system, wherein the quantum computing system is: With a first quantum gate set Ansatz configured to perform the first task; A second quantum gate set ansatz generated based on the first quantum gate set ansatz, the second quantum gate set ansatz configured to perform a second task related to the first task; It is quantum hardware: A step of training the parameters of the second quantum gate set ansatz using a first dataset, wherein the training includes adjusting electromagnetic waves applied to the qubits of quantum hardware according to the second quantum gate set ansatz and the first dataset, and Steps include training the parameters of the first quantum gate set ansatz using a second dataset associated with the first dataset, wherein the training includes adjusting electromagnetic waves applied to the qubits of quantum hardware according to the first quantum gate set ansatz and the second dataset, and the parameters of the first quantum gate set ansatz are initialized based on the trained parameters of the second quantum gate set ansatz. Quantum hardware configured to perform, A system that has

12. The system according to claim 11, wherein the second quantum gate set ansatz is generated by removing one or more quantum gates included in the first quantum gate set ansatz.

13. The system according to claim 12, wherein both the first quantum gate set ansatz and the second quantum gate set ansatz satisfy the properties of a Lie algebra.

14. The system according to claim 12, wherein the one or more quantum gates to be removed are used in solving one or more quadratic terms in a quantum algorithm on which the first quantum gate set ansatz is based.

15. The system according to claim 14, wherein removing one or more quantum gates results in setting one or more quadratic terms variables in the quantum algorithm to zero.

16. The system according to claim 15, wherein the aforementioned variable is a boundary coefficient.

17. The system according to claim 11, wherein the second quantum gate set ansatz includes a quantum gate used to solve one or more linear terms in a quantum algorithm on which the first quantum gate set ansatz is based.

18. The quantum computing system initializes the parameters of the first quantum gate set Ansatz as follows: Identify the common quantum gates between the first quantum gate set Ansatz and the second quantum gate set Ansatz; The parameters of the first quantum gate set Ansatz are set to the trained parameters of the second quantum gate set Ansatz for the identified common quantum gate; Set the remaining parameters of the first quantum gate set ansatz to zero. thing The system according to claim 11, configured to be executed by

19. The system according to claim 11, wherein the first dataset includes financial return data corresponding to financial assets, the second dataset includes covariance data corresponding to the financial return data, and the first task and the second task each include a task of identifying a set of financial assets.

20. A non-temporary computer-readable medium configured to store instructions for performing an action when executed by a system, wherein the action is: The steps include: obtaining a first quantum gate set Ansatz configured to perform the first task; A step of using the first quantum gate set ansatz to instruct the generation of a second quantum gate set ansatz, wherein the second quantum gate set ansatz is configured to perform a second task related to the first task; Steps include: instructing the training of the parameters of the second quantum gate set ansatz using a first dataset, wherein the training includes adjusting the electromagnetic waves applied to the qubits of the quantum hardware according to the second quantum gate set ansatz and the first dataset; A step of instructing the initialization of the parameters of the first quantum gate set ansatz based on the trained parameters of the second quantum gate set ansatz; Steps include: instructing to train the parameters of the first quantum gate set ansatz using a second dataset associated with the first dataset, wherein the training includes tuning electromagnetic waves applied to the qubits of quantum hardware according to the first quantum gate set ansatz and the second dataset, and the trained parameters are used to tune the qubits of quantum hardware used to generate an output; and Computer-readable media, including [specific examples of computer-readable media].

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