A method for preparing internal state compression of large spin atoms based on reinforcement learning and application thereof in magnetic field measurement

Abstract

This invention discloses a method for preparing the compressed internal state of large-spin atoms based on reinforcement learning and its application in magnetic field measurement. The method includes: constructing environmental observations, an evolutionary action set, and a reward function based on the quantum state and total angular momentum operator of the large-spin atom; an actor network selecting an action from the evolutionary action set based on the environmental observations of the large-spin atom system at the current time step, executing the action, calculating the reward value at the current time step, and updating the environmental observations for the next time step; a critic network obtaining a state value estimate based on the environmental observations experienced by the actor network at each time step, calculating the advantage function by combining it with the expected cumulative reward at the current time step, and training both the actor network and the critic network; inputting the environmental observations constructed from the quantum state and total angular momentum operator of the large-spin atom into the trained actor network to generate an optimal control pulse sequence for preparing and maintaining the compressed state of the large-spin atom.

Description

Technical Field

[0001] This invention relates to the field of quantum precision measurement technology, specifically to a method for preparing large-spin atomic internal states based on reinforcement learning and its application in magnetic field measurement. Background Technology

[0002] Atomic magnetometers are a class of quantum sensors that utilize the response of atomic spin to an external magnetic field to achieve highly sensitive measurements. Theoretically, their measurement accuracy is limited by the Standard Quantum Limit (SQL). In 1993, Kitagawa et al. proposed reducing quantum fluctuations in the magnetic field measurement direction by preparing spin-squeezed states (SSS), thereby achieving higher measurement accuracy. A common dynamic model is the one-axis twisting (OAT) model, which generates spin squeezing through nonlinear interactions. Furthermore, introducing additional pulse modulation can improve OAT dynamics, enhance squeezing efficiency, and prepare quantum states with higher sensitivity. This approach offers advantages such as high operability and simple measurement schemes. Spin-squeezed states have made significant progress in the field of atomic interferometers.

[0003] In existing research, spin-squeezed states are typically prepared between multiple two-level atoms, requiring complex and fragile quantum entanglement resources. However, in a weak magnetic field, the second-order Zeeman Effect (QZE) experienced by the energy levels within atoms can generate dynamic processes equivalent to OAT (Optical Atom-On-Track), representing a natural nonlinear interaction that can construct spin-squeezed states within atoms. However, in practice, the persistent QZE continuously alters the distribution of atomic states, causing the squeezing direction to rotate and reducing the metrological advantage of the measurement direction. To address this issue, existing techniques typically employ spin-locking schemes by introducing additional pulses to mitigate or compensate for the distortion effects of QZE on the quantum state.

[0004] However, in existing research, the two types of methods mentioned above are often studied independently, lacking a unified control strategy that can simultaneously achieve efficient generation and long-term stable maintenance of squeezed states within the same control framework. This makes it difficult to fully utilize squeezed resources, thereby reducing the overall efficiency of quantum-enhanced measurements and limiting the application of existing schemes in practical systems. Furthermore, researchers typically employ traditional quantum optimal control algorithms to modulate the system's evolution process, such as Gradient Ascent Pulse Engineering (GRAPE). These methods usually require pre-defined time segments and numerous optimization parameters, making them difficult to handle complex, high-dimensional quantum control problems. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method for preparing large-spin atomic internal state compression based on reinforcement learning and its application in magnetic field measurement. By optimizing and controlling the pulse modulation atomic state through reinforcement learning, the method achieves efficient preparation of the compressed state inside the atom and enables the compressed state to maintain an effective magnetic field measurement gain for a longer period of time.

[0006] To achieve the above technical objectives, the present invention adopts the following technical solution:

[0007] A reinforcement learning-based method for preparing large-spin atom internal states through compression includes the following steps: Step S1: Construct the environmental observables, evolutionary action set, and reward function of the large spin atom system based on the quantum state and total angular momentum operator of the large spin atom; Step S2: The actor network selects an action from the set of evolutionary actions based on the environmental observations of the large spin atom system at the current time step. The agent executes the action, calculates the reward value for the current time step according to the reward function, and updates the environmental observations for the next time step. Step S3: Repeat step S2 until a complete large-spin atom internal state squeezing evolution process is completed; Step S4: After the actor network completes multiple full large-spin atom internal state compression evolution processes, the critic network obtains the state value estimate based on the environmental observations at each time step experienced by the actor network, calculates the advantage function by combining the expected cumulative reward at the current time step, and updates the parameters of the actor network and the critic network. Step S5: Repeat steps S2-S4 until the maximum number of iterations is reached to complete the training of the actor network and the critic network; Step S6: Input the quantum state and total angular momentum operator of the large spin atom into the trained actor network to generate the action at each time step step by step, forming the optimal control pulse sequence, which is used to prepare and maintain the squeezed state of the large spin atom.

[0008] Furthermore, the large-spin atom system employs a total angular momentum quantum number of In an atomic system, the internal states of a large-spin atomic system are determined by the same hyperfine level. Each Zeeman sublevel is characterized, and the Zeeman sublevel is denoted as... ,in, For the total angular momentum in Magnetic quantum number in direction, Values .

[0009] Furthermore, the evolution process of the quantum state of the large-spin atom is as follows:

[0010] in, Indicates the first The quantum state that evolves within a time step Indicates the first The amplitude of the control pulse is controlled by each time step. , This represents the Rabi frequency of the microwave field. This represents the step size at each time step. Represents the total angular momentum operator of the third atom. Indicates the first The direction of the control pulse at each time step is... The atomic total angular momentum operator, Pick or , The coefficient representing the second-order Zeeman effect. It represents the imaginary unit.

[0011] Furthermore, the process of constructing environmental observations for the large-spin atom system is as follows:

[0012] in, Indicates the first Environmental observations at each time step Represents the total angular momentum operator of the first atom In the The quantum state that evolves within a time step Expected value on Represents the total angular momentum operator of the second atom In the The quantum state that evolves within a time step Expected value on The operator representing the total angular momentum of the third atom In the The quantum state that evolves within a time step The expected value.

[0013] Furthermore, the process of constructing the set of evolutionary actions is as follows:

[0014] in, Represents a set of evolutionary actions. This indicates that no control pulse is applied. Indicates applying around Control pulses for shaft rotation Indicates circling The rotation angle of the axis, , , Represents the total angular momentum operator for the first atom; Indicates applying around Control pulses for shaft rotation Indicates circling The rotation angle of the axis, , , This represents the total angular momentum operator for the second atom.

[0015] Furthermore, the process of constructing the reward function is as follows: i: Operator for calculating the total angular momentum of the second atom In the The quantum state that evolves within a time step variance ; ii: For any unit vector perpendicular to the average atomic total angular momentum operator ,definition Atomic total angular momentum operator ,calculate In the The quantum state that evolves within a time step variance ,in, Represents the average spin vector. ; iii: According to and the The square of the evolution of the average total atomic angular momentum operator within each time step Calculate the first Wineland compression parameters at each time step ,in, ; iv: According to Calculate the first Compression parameters at each time step in a fixed measurement direction ; v: According to the first Wineland compression parameters at each time step Compression parameters in a fixed measurement direction And the reward function is calculated for the action selected from the set of evolutionary actions.

[0016] Furthermore, the calculation process of the reward function is as follows:

[0017] in, Indicates the first The action selected from the set of evolutionary actions at each time step. Indicates a penalty item. These represent the weighting coefficients of the reward function at different stages. Indicates Wineland compression parameters The time step to reach the compression threshold.

[0018] Furthermore, step S4, which involves obtaining a state value estimate based on environmental observations at each time step experienced by the actor network, is as follows:

[0019] in, Indicates according to the first Environmental observations at each time step The calculated state value estimate Indicates the first The expected cumulative return at each time step , This represents the total number of time steps in a complete large-spin atom internal state squeezing evolution process. express index, Indicates the first The reward value calculated based on the reward function at each time step. Indicates the discount factor. It expresses expectation.

[0020] Furthermore, the present invention also provides an application of the squeezed state of large-spin atoms in magnetic field measurement, comprising the following steps: Step A1: Select a control pulse sequence in the continuous time step of the spin compression stabilization stage from the optimal control pulse sequence obtained by the reinforcement learning-based large spin atom internal state compression preparation method, and use it for magnetic field measurement of the atomic magnetometer. Step A2: The magnetic field signal generates phase accumulation through Larmor precession, and the evolution of the quantum state of large spin atoms is carried out in combination with the selected control pulse sequence; Step A3: Estimate the magnitude of the magnetic field by measuring the expected change of the total angular momentum operator of the second atom based on the completed large-spin atom quantum state.

[0021] Furthermore, the evolution process of the large-spin atom quantum state in step A2 is as follows:

[0022] in, This represents the quantum state of a large-spin atom after its evolution is complete. This indicates the number of consecutive time steps selected. express index, The coefficient representing the second-order Zeeman effect. Indicates the total evolution time. This represents the phase accumulation of the magnetic field signal. , The gyromagnetic ratio of a large-spin atom is represented by its magnetic gyromagnetic ratio. Indicates magnetic field strength. Represents the total angular momentum operator of the third atom. This represents the step size at each time step. This indicates that large-spin atoms are in the first... The quantum state that evolves within a time step This represents the optimal control pulse sequence. In the optimal control pulse sequence, the first... k The action at each time step.

[0023] Compared with the prior art, the present invention has the following beneficial effects: (1) The large-spin atom internal state compression preparation method based on reinforcement learning in this invention utilizes the strong adaptability of the reinforcement learning framework to model the evolution process of the quantum state of large-spin atoms. By designing a piecewise reward function, it simultaneously achieves the rapid generation of spin-compressed states and the long-term stable maintenance of measurement-related compression directions. This invention enables the compression resources to be continuously utilized throughout the measurement process, thereby significantly improving the overall efficiency of quantum-enhanced measurement; (2) The reinforcement learning-based method for preparing large-spin atomic internal states uses low-dimensional environmental observations as inputs to the agent, realizing a quantum control strategy compatible with experimental measurements. In the reinforcement learning environment design, this invention uses low-order spin moments as observations, avoiding the direct use of complete quantum state information, thus possessing high experimental feasibility. Simultaneously, this invention utilizes the second-order Zeeman effect inside the atom under weak magnetic field conditions as a compression generation mechanism, and optimizes the control pulse modulation of the atomic state through reinforcement learning, achieving efficient preparation of compressed states inside the atom. (3) The compressed state of the large spin atoms prepared in this invention is used for magnetic field measurement of an atomic magnetometer, which greatly improves the accuracy of magnetic field measurement. Attached Figure Description

[0024] Figure 1 This is a flowchart of the large-spin atom internal state compression preparation method based on reinforcement learning according to the present invention; Figure 2 A schematic diagram of the evolution of the dysprosium-161 atom ground state control pulse sequence and the corresponding spin-squeezing parameters over time, obtained through this invention; Figure 3 The sensitivity curve of the dysprosium-161 atom ground state control pulse sequence obtained by the present invention in magnetic field measurement. Detailed Implementation

[0025] The technical solution of the present invention will be further explained and described below with reference to the accompanying drawings.

[0026] like Figure 1 This is a flowchart of the large-spin atom internal state squeezing preparation method based on reinforcement learning according to the present invention. The large-spin atom internal state squeezing preparation method includes the following steps: Step S1: Construct the environmental observables, evolutionary action set, and reward function of the large-spin atom system based on the quantum states and total angular momentum operator of the large-spin atom. The specific process is as follows: Large-spin atom systems employ a total angular momentum quantum number of In an atomic system, the internal states of a large-spin atomic system are determined by the same hyperfine level. Each Zeeman sublevel is characterized, and the Zeeman sublevel is denoted as... ,in, For the total angular momentum in Magnetic quantum number in direction, Values .

[0027] Along Under the condition of applying a weak magnetic field in the direction of the field and simultaneously applying a transverse microwave control field, let the reduced Planck constant be... The effective Hamiltonian of a large-spin atom system can then be expressed as:

[0028] in, These are the total angular momentum operators for the first, second, and third atoms, respectively. The coefficient of the second-order Zeeman effect is given. The Rabi frequency of the microwave field. Let be the phase of the microwave field. The first term represents the nonlinear evolution caused by the second-order Zeeman effect, and the second term is the microwave field itself. The evolution process can be controlled by adjusting the parameters of the microwave field. Let the initial quantum state of the large-spin atom system be... For along Directional polarized spin coherent states This state can be obtained by passing through a magnetically polarized state. Microwave field preparation.

[0029] To construct the environmental dynamics model corresponding to reinforcement learning, it is necessary to include the continuous-time control process within the total control duration. Internal discretization is There are time steps, where the length of each time step is _ ... Within each time step, the microwave control field is approximated as a short, strong pulse, and its duration is assumed to be negligible relative to the QZE-induced evolution timescale. Therefore, the control field can be equivalent to an instantaneous rotational operation within that time step. Between adjacent control pulses, the large-spin atom system primarily operates in the second-order Zeeman term. Under the influence of [the specific element], it evolves freely. Therefore, the quantum state of a large-spin atom is determined in the [specific context]. The evolution process within each time step can be approximated as:

[0030] in, Indicates the first The quantum state that evolves within a time step Indicates the first The amplitude of the control pulse is controlled by each time step. , This represents the Rabi frequency of the microwave field. This represents the step size at each time step. Represents the total angular momentum operator of the third atom. Indicates the first The direction of the control pulse at each time step is... The atomic total angular momentum operator, Pick or , by phase Decide, The coefficient representing the second-order Zeeman effect. It represents the imaginary unit.

[0031] In this invention, the environmental observables of the large-spin atom system reflect the average spin direction and quantum fluctuations of the quantum state. The construction process is as follows:

[0032] in, Indicates the first Environmental observations at each time step Represents the total angular momentum operator of the first atom In the The quantum state that evolves within a time step Expected value on ; Represents the total angular momentum operator of the second atom In the The quantum state that evolves within a time step Expected value on ; The operator representing the total angular momentum of the third atom In the The quantum state that evolves within a time step Expected value on .

[0033] Reinforcement learning agents in the first A time step from a discrete set of evolutionary actions Select a control pulse Each action in the action set corresponds to a Hamiltonian. The second item The evolution operator corresponds to a discrete time step. Therefore, the process of constructing the set of evolutionary actions is as follows:

[0034] in, Represents a set of evolutionary actions. This indicates that no control pulse is applied. Indicates applying around Control pulses for shaft rotation Indicates circling The rotation angle of the axis, , , Represents the total angular momentum operator for the first atom; Indicates applying around Control pulses for shaft rotation Indicates circling The rotation angle of the axis, , , This represents the total angular momentum operator for the second atom.

[0035] The environmental observations of a large-spin atom system are directly related to the compressed evaluation quantity in the reward function: among which, and Together they determine the direction and length of the average spin vector, and and This is directly related to spin fluctuations in a fixed direction. Therefore, without explicitly inputting a complete quantum state, environmental observations can still provide the agent with sufficient information to optimize spin-squeezing generation and stabilize the squeezing direction. To achieve rapid generation of spin-squeezed states and stable squeezing directions, this invention employs a staged reward function construction, the specific process of which is as follows: i: Operator for calculating the total angular momentum of the second atom In the The quantum state that evolves within a time step variance ; ii: For any unit vector perpendicular to the average atomic total angular momentum operator ,definition Atomic total angular momentum operator ,calculate In the The quantum state that evolves within a time step variance ,in, Represents the average spin vector. ; iii: According to and the The square of the evolution of the average total atomic angular momentum operator within each time step Calculate the first Wineland compression parameters at each time step ,in, , This represents the minimum variance perpendicular to the average spin direction; iv: According to Calculate the first Compression parameters at each time step in a fixed measurement direction ; v: According to the first Wineland compression parameters at each time step Compression parameters in a fixed measurement direction And the action selected from the set of evolved actions, and the reward function calculated:

[0036] in, Indicates the first The action selected from the set of evolutionary actions at each time step. Indicates a penalty item. These represent the weighting coefficients of the reward function at different stages. Indicates Wineland compression parameters The time step to reach the compression threshold.

[0037] Step S2: The actor network selects an action from the set of evolutionary actions based on the environmental observations of the large spin atom system at the current time step. The agent executes the action, calculates the reward value for the current time step according to the reward function, and updates the environmental observations for the next time step.

[0038] Step S3: Repeat step S2 until a complete large-spin atom internal state compression evolution process is completed.

[0039] Step S4: After the actor network completes multiple full large-spin atom internal state squeezing evolution processes, the critic network obtains the state value estimate based on the environmental observations at each time step experienced by the actor network. The advantage function is calculated by combining the expected cumulative return at the current time step. Update the parameters of the actor network and the critic network.

[0040] in, Indicates according to the first Environmental observations at each time step The calculated state value estimate Indicates the first The expected cumulative return at each time step , This represents the total number of time steps in a complete large-spin atom internal state squeezing evolution process. express index, Indicates the first The reward value calculated based on the reward function at each time step. Indicates the discount factor. It expresses expectation.

[0041] In this invention, the update objective of the actor network is to increase the selection probability of high-dominance actions under corresponding environmental observations, thereby gradually optimizing the impulse selection strategy. Its objective function is... ,in, , Indicates the actor network in parameters Enter below Output The probability of; This indicates the parameters of the actor network before the update. Enter below Output The probability, For the preset cropping parameters, actor network parameters The update process is as follows: , Represents the actor network objective function Actor network parameters gradient, This represents the learning rate of the actor network, used to control the parameters of the actor network each time. Update step size.

[0042] In this invention, the update objective of the critic network is to reduce the state value estimation. With cumulative returns The objective function is to reduce the deviation between the two, thereby improving the accuracy of the evaluation of the current strategy's performance. ,in, This indicates that the critic network has parameters Enter below Output state value estimation, critic network parameters The update process is as follows: , Represents the objective function of the critic network. Commentator network parameters gradient, This represents the learning rate of the commentator network, used to control the parameters of the commentator network each time. Update step size.

[0043] Step S5: Repeat steps S2-S4 until the maximum number of iterations is reached to complete the training of the actor network and the critic network.

[0044] Step S6: Input the quantum state and total angular momentum operator of the large spin atom into the trained actor network to generate the action at each time step step by step, forming the optimal control pulse sequence, which is used to prepare and maintain the squeezed state of the large spin atom.

[0045] This invention presents a reinforcement learning-based method for preparing large-spin atom internal states through squeezing. Leveraging the strong adaptability of the reinforcement learning framework, it models the evolution of quantum states in large-spin atoms. By designing a piecewise reward function, it simultaneously achieves rapid generation of spin-squeezed states and long-term stable maintenance of measurement-related squeezing directions. This invention allows for continuous utilization of squeezing resources throughout the measurement process, significantly improving the overall efficiency of quantum-enhanced measurements. Furthermore, this invention uses low-dimensional environmental observations as agent inputs to implement a quantum control strategy compatible with experimental measurements. In the reinforcement learning environment design, this invention uses low-order spin moments as observations, avoiding the direct use of complete quantum state information, thus demonstrating high experimental feasibility. Additionally, this invention utilizes the second-order Zeeman effect within the atom under weak magnetic fields as a squeezing generation mechanism and optimizes the control pulses through reinforcement learning to modulate the atomic states, achieving efficient preparation of internally squeezed states.

[0046] In one technical solution of the present invention, an application of the squeezed state of large-spin atoms in magnetic field measurement is also provided, comprising the following steps: Step A1: Obtaining the optimal control pulse sequence from a reinforcement learning-based large-spin atom internal state squeezing preparation method. Selected in the spin-compression stable phase A sequence of control pulses in continuous time steps is used for magnetic field measurement by an atomic magnetometer; Step A2: The magnetic field signal generates phase accumulation through Larmor precession, which, combined with the selected control pulse sequence, enables the evolution of the quantum state of large-spin atoms.

[0047] in, This represents the quantum state of a large-spin atom after its evolution is complete. This indicates the number of consecutive time steps selected. express index, The coefficient representing the second-order Zeeman effect. Indicates the total evolution time. , This represents the phase accumulation of the magnetic field signal. , The gyromagnetic ratio of a large-spin atom is represented by its magnetic gyromagnetic ratio. Indicates magnetic field strength. Represents the total angular momentum operator of the third atom. This represents the step size at each time step. This indicates that large-spin atoms are in the first... The quantum state that evolves within a time step This represents the optimal control pulse sequence. In the optimal control pulse sequence, the first... k The action at each time step.

[0048] Step A3: Estimate the magnitude of the magnetic field by measuring the expected change of the total angular momentum operator of the second atom based on the completed large-spin atom quantum state.

[0049] This invention addresses the uncertainty of phase measurement. To reflect the sensitivity of magnetic field measurement, among which, express exist The standard deviation below, , express exist The expected value under, .

[0050] The following example uses the ground state of the dysprosium-161 atom, and its total angular momentum quantum number. Set the coefficient of the second-order Zeeman effect. Total evolution time , Set the weight coefficients in the reward function. , .

[0051] The large-spin atom internal state squeezing preparation method based on reinforcement learning of this invention yields a set of optimal control pulse sequences. These optimal control pulse sequences employ several pairs of pulses during the squeezing state preparation stage. Spinning pulses enable large-spin atom systems to reach compression levels approaching those of a biaxial inverse twist model more quickly. Once spin compression reaches the target threshold, the reinforcement learning policy automatically enters a compression-direction stabilization phase. In this phase, periodic pulses effectively shift the squeezed state to a direction with minimal impact on QZE, allowing the quantum state to maintain a stable squeezed structure under sustained QZE. Figure 2As shown, the optimal control pulse sequence and compression parameters are illustrated. , The evolution over time is illustrated in the first half, which presents the discrete control impulses for agent optimization, and the second half, which presents the compression parameters. , With the second Zeeman strength The change; the gray solid line represents the change under the influence of the second-order Zeeman effect only. The gray dashed line represents the equivalent biaxial torsion model. The red curve represents the optimal control pulse sequence. The green curve represents the optimal control pulse sequence. .Depend on Figure 2 It can be seen that the optimal control pulse sequence prepares the compressed state in a shorter time than the equivalent biaxial torsion model and stabilizes it at -4 to -5 dB. The optimal control pulse sequence is then used for magnetic field measurements with an atomic magnetometer. Figure 2 middle The time step corresponding to time is ,like Figure 3 This demonstrates the variation of phase measurement sensitivity relative to the standard quantum limit under different control schemes, with the horizontal axis representing the signal encoding time. The vertical axis represents the phase sensitivity of SQL compared to the phase sensitivity of different schemes, in dB. The blue curve represents the "compressed state, no-pulse" scheme, i.e., initially using a compressed state, but without applying control pulses during subsequent signal accumulation; the green curve represents the "compressed state, no-pulse" scheme. The "axis pulse" scheme; the red curve represents the optimal control sequence using this invention. It can be seen that the phase sensitivity of the optimal control pulse sequence... Compared to SQL, the accuracy is improved by 5dB and can be maintained under QZE for a long time, indicating that applying the squeezed state of large spin atoms to the magnetic field measurement of atomic magnetometers greatly improves the accuracy of magnetic field measurement.

[0052] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for preparing large-spin atom internal states based on reinforcement learning, characterized in that, Includes the following steps: Step S1: Construct the environmental observables, evolutionary action set, and reward function of the large spin atom system based on the quantum state and total angular momentum operator of the large spin atom; Step S2: The actor network selects an action from the set of evolutionary actions based on the environmental observations of the large spin atom system at the current time step. The agent executes the action, calculates the reward value for the current time step according to the reward function, and updates the environmental observations for the next time step. Step S3: Repeat step S2 until a complete large-spin atom internal state squeezing evolution process is completed; Step S4: After the actor network completes multiple full large-spin atom internal state compression evolution processes, the critic network obtains the state value estimate based on the environmental observations at each time step experienced by the actor network, calculates the advantage function by combining the expected cumulative reward at the current time step, and updates the parameters of the actor network and the critic network. Step S5: Repeat steps S2-S4 until the maximum number of iterations is reached to complete the training of the actor network and the critic network; Step S6: Input the quantum state and total angular momentum operator of the large spin atom into the trained actor network to generate the action at each time step step by step, forming the optimal control pulse sequence, which is used to prepare and maintain the squeezed state of the large spin atom.

2. The method for preparing large-spin atom internal states based on reinforcement learning according to claim 1, characterized in that, The large-spin atom system employs a total angular momentum quantum number of... In an atomic system, the internal states of a large-spin atomic system are determined by the same hyperfine level. Each Zeeman sublevel is characterized, and the Zeeman sublevel is denoted as... ,in, For the total angular momentum in Magnetic quantum number in direction, Values .

3. The method for preparing large-spin atom internal states based on reinforcement learning according to claim 1, characterized in that, The evolution process of the quantum state of the large-spin atom is as follows: in, Indicates the first The quantum state that evolves within a time step Indicates the first The amplitude of the control pulse is controlled by each time step. , This represents the Rabi frequency of the microwave field. This represents the step size at each time step. Represents the total angular momentum operator of the third atom. Indicates the first The direction of the control pulse at each time step is... The atomic total angular momentum operator, Pick or , The coefficient representing the second-order Zeeman effect. It represents the imaginary unit.

4. The method for preparing large-spin atom internal states based on reinforcement learning according to claim 3, characterized in that, The process of constructing environmental observations for the large-spin atom system is as follows: in, Indicates the first Environmental observations at each time step Represents the total angular momentum operator of the first atom In the The quantum state that evolves within a time step Expected value on Represents the total angular momentum operator of the second atom In the The quantum state that evolves within a time step Expected value on The operator representing the total angular momentum of the third atom In the The quantum state that evolves within a time step The expected value.

5. The method for preparing large-spin atom internal states based on reinforcement learning according to claim 2, characterized in that, The process of constructing the set of evolutionary actions is as follows: in, Represents a set of evolutionary actions. This indicates that no control pulse is applied. Indicates applying around Control pulses for shaft rotation Indicates circling The rotation angle of the axis, , , Represents the total angular momentum operator for the first atom; Indicates applying around Control pulses for shaft rotation Indicates circling The rotation angle of the axis, , , This represents the total angular momentum operator for the second atom.

6. The method for preparing large-spin atom internal states based on reinforcement learning according to claim 4, characterized in that, The process of constructing the reward function is as follows: i: Operator for calculating the total angular momentum of the second atom In the The quantum state that evolves within a time step variance ; ii: For any unit vector perpendicular to the average atomic total angular momentum operator ,definition Atomic total angular momentum operator ,calculate In the The quantum state that evolves within a time step variance ,in, Represents the average spin vector. ; iii: According to and the The square of the evolution of the average total atomic angular momentum operator within each time step Calculate the first Wineland compression parameters at each time step ,in, ; iv: According to Calculate the first Compression parameters at each time step in a fixed measurement direction ; v: According to the first Wineland compression parameters at each time step Compression parameters in a fixed measurement direction And the reward function is calculated for the action selected from the set of evolutionary actions.

7. The method for preparing large-spin atom internal states based on reinforcement learning according to claim 6, characterized in that, The calculation process of the reward function is as follows: in, Indicates the first The action selected from the set of evolutionary actions at each time step. Indicates a penalty item. These represent the weighting coefficients of the reward function at different stages. Indicates Wineland compression parameters The time step to reach the compression threshold.

8. The method for preparing large-spin atom internal states based on reinforcement learning according to claim 1, characterized in that, Step S4 involves obtaining the state value estimate based on the environmental observations at each time step experienced by the actor network. in, Indicates according to the first Environmental observations at each time step The calculated state value estimate Indicates the first The expected cumulative return at each time step , This represents the total number of time steps in a complete large-spin atom internal state squeezing evolution process. express index, Indicates the first The reward value calculated based on the reward function at each time step. Indicates the discount factor. It expresses expectation.

9. An application of the squeezed state of a large-spin atom in magnetic field measurement, characterized in that, Includes the following steps: Step A1: Select a control pulse sequence in the continuous time step of the spin compression stabilization stage from the optimal control pulse sequence obtained by the reinforcement learning-based large spin atom internal state compression preparation method according to any one of claims 1-8, and use it for magnetic field measurement of the atomic magnetometer. Step A2: The magnetic field signal generates phase accumulation through Larmor precession, and the evolution of the quantum state of large spin atoms is carried out in combination with the selected control pulse sequence; Step A3: Estimate the magnitude of the magnetic field by measuring the expected change of the total angular momentum operator of the second atom based on the completed large-spin atom quantum state.

10. The application of the squeezed state of a large-spin atom in magnetic field measurement according to claim 9, characterized in that, The evolution of the large-spin atom quantum state in step A2 is as follows: in, This represents the quantum state of a large-spin atom after its evolution is complete. This indicates the number of consecutive time steps selected. express index, The coefficient representing the second-order Zeeman effect. Indicates the total evolution time. This represents the phase accumulation of the magnetic field signal. , The gyromagnetic ratio of a large-spin atom is represented by its magnetic gyromagnetic ratio. Indicates magnetic field strength. Represents the total angular momentum operator of the third atom. This represents the step size at each time step. This indicates that large-spin atoms are in the first... The quantum state that evolves within a time step This represents the optimal control pulse sequence. In the optimal control pulse sequence, the first... k The action at each time step.

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