21 results about "Atomic system" patented technology

Disclosed are an atomic system and an atomic arrangement method, relating to the technical field of quantum computing. In the atomic system, atoms are captured by using a pair of first optical tweezers and second optical tweezers, respectively, such that a single atom captured by each first optical tweezer is bound to a first layer of an atom array, and an atomic ensemble captured by each second optical tweezer is bound to a second layer of the atom array. The atomic system uses intrinsic dependent array light to rearrange an initially loaded first atom array, and moves atoms in the atomic ensemble at the second layer to vacant first optical tweezers at the first layer, thereby successfully loading physical bits matching first information to a plurality of first optical tweezers. The intrinsic dependent array light moves a plurality of atoms at the second layer in parallel, and the intrinsic dependent array light does not need to move atoms one by one from outside the atom array, and does not need to serially fill vacant first optical tweezers in the atom array, thereby solving the problems of long loading time and low atom loading efficiency of a quantum bit array.

The invention relates to a potential function model construction method, system and device and a storage medium, and belongs to the technical field of digital data processing. The construction method of the potential function model comprises the following steps: determining a neighbor atom set for each atom in an atom system according to a truncation radius; constructing a neighbor environment descriptor of each atom based on the neighbor atom set of each atom; inputting the neighbor environment descriptor of each atom into an independently configured neural network corresponding to the element type of the neighbor environment descriptor, and performing forward propagation calculation from an input layer to an output layer of the neural network to obtain energy of each atom; performing back propagation calculation and descriptor back calculation on the energy of each atom from the output layer to the input layer through each independently configured neural network to obtain partial derivative of the energy of each atom to the position of the adjacent atom; and obtaining the stress of each atom based on the partial derivative of the energy of each atom to the position of the adjacent atom. The potential function model has both precision and calculation efficiency.

A quantum memory device for storing a signal field is provided. The quantum memory device comprises an optical cavity comprising a first reflective surface and a second reflective surface; a first optical element and a second optical element arranged within the optical cavity; an atomic system situated between the first optical element and the second optical element, the atomic system configured to store the signal field via an atomic transition; and one or more magnets configured to apply an axial magnetic field across the atomic system.

The invention discloses a method for estimating potential track microstructure damage of a GaN device under an electron energy loss mechanism. The method comprises the following steps: establishing a GaN device structure model; simulating the motion trail of the high-energy heavy ion incidence device through numerical calculation; calculating energy deposition space-time distribution of the high-energy heavy ions along the incident trajectory under the leading of the electron energy loss mechanism; solving the coupling dual-temperature heat conduction equation of the electronic system and the lattice atomic system to obtain the temperature space-time distribution of the lattice atomic system; according to the temperature space-time distribution of the lattice atomic system, whether latent track microstructure damage is generated or not is judged by comparing the highest temperature of the lattice atomic system with the melting point of a device crystal material; and under the condition of judging that the latent track damage is generated, drawing a temperature change curve chart of lattice atoms at different positions, and determining the position of the crystal atom of which the highest temperature is equal to the melting point of the crystal material as the radius size of the latent track microstructure damage. According to the method, the latent track microstructure damage characteristics of the device can be quantitatively and quickly obtained at low cost.

In various examples, a technique for modeling molecular dynamics includes generates a first three-dimensional representation of a structural state of an atomic system at a given time step, generating a condition embedding associated with the atomic system, processing, via a machine learning model, the first three-dimensional representation and the condition embedding to generate a second three-dimensional representation of a structural state of the atomic system at a next time step, and generating a visual output representing the structural state of the atomic system at the next time step based on the second three-dimensional representation, wherein the machine learning model, during the processing, generates transport predictions for the atomic system at sub-time steps in between the given time step and the next time step based on a noise-perturbed latent space that is conditioned on at least the structural state of the atomic system at the given time step.

A machine learning inter-atomic potential (MLIP) method of determining physical states of interactions between atomic systems from one or more physical attributes of atoms. The method includes dynamically evolving, in a number of simulation steps, a first subset of atoms via MLIP within a central region based on one or more physical properties of the atoms, while fixing a second subset of atoms surrounding the central region in a shell layer during at least a portion of the number of simulation steps, and determining the physical state of the interaction between the atoms. Physical states of interactions between atoms may be used to control chemical systems.

The embodiment of the application discloses a high-dimensional NOT gate implementation method based on a lattice cold atom system and a related device. The method comprises sequentially exciting the first n energy levels of the lattice cold atom system through respective excitation frequencies, exciting k energy levels upwards, exciting the first n-1 energy levels after the upward excitation k-1 energy levels downwards, and exciting the n energy level after the upward excitation to the first energy level, thereby obtaining a high-dimensional NOT gate with a dimension of n, wherein k is an integer greater than n-1, so that the high-dimensional NOT gate is directly realized in the lattice cold atom system. The embodiment of the application is beneficial to reducing the depth of the quantum circuit, thereby reducing the accumulation and amplification of errors, and realizing high-dimensional quantum computing.

This invention discloses an optical non-reciprocal information encryption method and apparatus based on a time crystal. The method includes the following steps: S1, providing a three-level atomic system containing rubidium atoms, and setting a probe light and a coupling light, wherein the probe light is incident on the atomic system in a fixed direction; S2, controlling the coupling light to propagate collinearly in the opposite direction to the probe light, causing the atomic system to enter a time crystal state and generate a transmission signal with a stable oscillation frequency; S3, acquiring the oscillation frequency of the transmission signal and encoding the oscillation frequency as key information; S4, placing the atomic system in a non-time crystal state, where the transmission signal does not contain the oscillation frequency, thereby achieving non-reciprocal encryption of the key information. This invention causes the atomic system to enter or leave a time crystal state to generate or suppress a transmission signal with a stable oscillation frequency, and encodes and encrypts the oscillation frequency as key information.

The invention discloses an atom system and an atom arrangement method, and relates to the technical field of quantum computing. The atom system captures atoms by adopting first optical tweezers and second optical tweezers which are paired one by one, so that single atoms captured by the first optical tweezers are bound to a first layer of the atom array, and atom ensembles captured by the second optical tweezers are bound to a second layer of the atom array. The atomic system rearranges the initially loaded first atomic array by using internal state dependent array light, and moves atoms in the atomic ensemble in the second layer to the first optical tweezers vacant in the first layer, thereby successfully loading the physical bits matched with the first information to the plurality of first optical tweezers. Wherein the internal state dependent array light moves a plurality of atoms in the second layer in parallel, the internal state dependent array light does not need to move atoms from the outside of the atom array one by one, and does not need to serially fill the vacant first optical tweezers in the atom array, so that the problems that the quantum bit array is relatively long in loading time and relatively low in atom loading efficiency are solved.

A method for training and subsequently executing a machine learning network of both an autoencoder and a machine learning model in the context of machine learning inter-atomic potential is disclosed. The systems described herein are configured to embed atomic locations and categories of a given atomic system, and apply them to an autoencoder to learn auxiliary attributes, and to a machine learning model to learn local energy. The auxiliary attributes are then used to generate an auxiliary Hamiltonian description. Attributes, such as the total energy of the atomic system, are determined by combining both the auxiliary Hamiltonian description and the local energy. By processing machine learning via both an autoencoder and a machine learning model, such methods ensure that both long range and short range effects are considered while also appropriately achieving realistic discontinuities and / or transitions within potential energy surfaces.

A method for training and subsequently executing a machine learning network of one or more machine learning (ML) models is disclosed. The systems described herein are configured to embed atomic locations and categories of a given atomic system and apply them to ML model (s) to learn charge transfer attributes and local energy. By constructing atomic charges from the learned charge transfer attributes, local and global charge neutrality is ensured. Atomic charges are then used to generate an auxiliary Hamiltonian description. Attributes, such as the total energy of the atomic system, are determined by combining the auxiliary Hamiltonian description and the learned local energy. By determining the total energy from the local energy described and learned by the auxiliary Hamiltonian, such a method ensures that both long range and short range effects are considered while also appropriately achieving true discontinuities and / or transitions within potential energy surfaces.

Methods for a machine learning network that trains and subsequently executes one or more machine learning (ML) models are disclosed. The system described herein is configured to embed atomic positions and species of a given atomic system and apply those to ML model(s) to learn charge transfer properties and local energies. By constructing atomic charges from learned charge transfer properties, both local and global charge neutrality is ensured. The atomic charges are then used to generate an auxiliary Hamiltonian description. By combining both the auxiliary Hamiltonian description and the learned local energies, properties such as total energy of the atomic system are determined. By determining total energy from the auxiliary Hamiltonian description and the learned local energies, such methods ensure that long and short range effects are accounted for, while also appropriately enabling for realistic discontinuities and / or transitions within the potential energy surface.

The application relates to a tunable light-quantum memory entanglement generation device based on atomic transition interference and belongs to the technical field of quantum information science technology. In the application, a multi-energy-level atomic system is used as an atomic medium for preparing a quantum memory; atoms in an initial state in the atomic medium are transitioned to a coherent superposition state of multiple excited states under the action of excitation light; the atoms in the coherent superposition state of the multiple excited states are transitioned to a ground state, forming a coherent superposition state of multiple light-quantum memory entangled states, that is, atomic transition interference occurs; the frequency of the excitation light can be adjusted to change the coefficients of the coherent superposition of the light-quantum memory entangled states, so that the photons output from the atomic medium and the quantum memory of the spin state of the storage atom are in a tunable quantum entangled state. In the application, each component for preparing the tunable light-quantum memory entanglement can be obtained from a mature optoelectronic device.

A computing device usable in an atomistic system, comprising: one or more processors configured to execute code: compute descriptors of atomic environments of all atoms in the atomistic system, determine a short-range energy for each atom, define a long-range neural network to map invariant features of each atom to one or more hidden variables, perform an Ewald summation on the hidden variables to determine a long-range energy of the system, and sum the short-range energy and the long-range energy to determine total energy of the system. A computer-implemented method of augmenting an existing machine learning interatomic potential systems (MLIP) in an atomistic system, defining a long-range neural network to map invariant features of each atom to a hidden variable, and performing an Ewald summation on the hidden variables to determine a long-range energy of the system.

This application discloses a method for enhancing the low-frequency sensitivity of a quantum sensor's magnetic field, relating to the field of quantum sensor technology. The method includes: during the detection of a longitudinal magnetic field by the target quantum sensor, adding a driving magnetic field to an atomic system in the transverse direction; the transverse direction being perpendicular to the longitudinal magnetic field to be measured; adding a feedback magnetic field to a y-direction coil on the atomic gas cell side to form a coil feedback-induced quantum coupling system; the y-direction being perpendicular to both the direction of the longitudinal magnetic field to be measured and the direction of the probe light; and adjusting the interaction between different atoms in the quantum coupling system under the quantum coupling system so that the frequency of the driving magnetic field differs from the rate of change of the longitudinal magnetic field to be measured by the gyromagnetic ratio by a set value, and then measuring the longitudinal magnetic field to be measured based on this. This application reduces the influence of low-frequency noise, improves the sensitivity of longitudinal low-frequency magnetic field measurement, and thus improves the accuracy of magnetic measurement.

This invention relates to the field of quantum control technology, specifically disclosing a device for generating chaos in a multi-cluster Rydberg atomic ensemble based on radio frequency (RF) control. Using a multi-cluster Rydberg atomic ensemble as the working medium, the device achieves controllable excitation and observation of chaotic phases by controlling the interactions between atoms through an RF field. First, a spatially controllable multi-cluster Rydberg atomic ensemble is prepared using two-photon excitation. Second, a control signal with a specific frequency and voltage is applied through the RF field to induce interactions between the atomic ensembles into a nonlinear region. Finally, the acquired signals are analyzed to observe the emergent characteristics of the chaotic phase. This invention overcomes the limitation that a single-cluster atomic ensemble cannot generate chaos, enabling the observation of dynamic transitions from a time-crystalline phase to a chaotic phase in a three-cluster Rydberg atomic system, providing a new platform for quantum encrypted communication and the study of complex dynamics.

A controller of an atomic system controls voltage sources to cause first and second atomic objects of an object crystal to experience a coupling force. The object crystal is confined at a target location of a confinement apparatus that defines an axis thereat. The coupling force includes a component that is perpendicular to the axis. The controller causes the voltage sources to generate voltage signals that cause the object crystal to experience an axial confinement corresponding to a transition region for selected motional modes of the object crystal. When the atomic objects experience the coupling force and the object crystal experiences the axial confinement corresponding to the transition region, the selected motional modes form mixed motional modes. The controller causes manipulation sources to generate manipulation signals that are incident on the target location and that address the mixed motional modes to cause an entangling interaction between the atomic objects.

Methods for a machine learning network that train and then execute both an autoencoder and a machine learning model within a context of machine-learned interatomic potentials are disclosed. The system described here is configured to embed atomic positions and species of a given atomic system and apply these to an autoencoder to learn an auxiliary property, and to a machine learning model to learn local energies. The auxiliary property is then used to generate a Hamiltonian auxiliary description. By combining both the Hamiltonian auxiliary description and the local energies, properties such as the total energy of the atomic system are determined.By processing machine learning through both an autoencoder and a machine learning model, such methods ensure that long-range and short-range effects are taken into account, while also conveniently enabling realistic discontinuities and / or transitions within the potential energy surface.

Methods for a machine learning network that train and subsequently execute both an autoencoder and a machine learning model within a context of machine-learning interatomic potentials are disclosed. The system described herein is configured to embed atomic positions and species of a given atomic system and apply those to an autoencoder in order to learn an auxiliary property and to a machine learning model in order to learn local energies. The auxiliary property is then used to generate an auxiliary Hamiltonian description. By combining both the auxiliary Hamiltonian description and the local energies, properties such as total energy of the atomic system are determined. By processing the machine learning through both an autoencoder and a machine learning model, such methods ensure that long and short range effects are accounted for, while also appropriately enabling for realistic discontinuities and / or transitions within the potential energy surface.

Methods for a machine learning network that trains and then executes one or more machine learning (ML) models are disclosed. The system described here is configured to embed atomic positions and types of a given atomic system and apply these to ML model(s) to learn charge transfer properties and local energies. By constructing atomic charges from learned charge transfer properties, both local and global charge neutrality is ensured. The atomic charges are then used to generate a Hamiltonian auxiliary description. By combining both the Hamiltonian auxiliary description and the learned local energies, properties such as the total energy of the atomic system are determined.By determining the total energy from Hamilton's auxiliary description and the learned local energies, such methods ensure that long-range and short-range effects are taken into account, while also allowing for appropriately realistic discontinuities and / or transitions within the potential energy surface.

The invention belongs to the technical field of power system measurement, and relates to a direct-current electric field rapid measurement and harmonic identification method for a Rydberg atomic system, which comprises the following steps of: 1, constructing an optical detection channel, and forming a coherent superposition state (dark state) by the atomic system by adopting a two-photon excitation mode of coupling light and detection light, therefore, a characteristic narrow-linewidth EIT transparent window can appear in a transmitted spectrum; 2, a direct-current electric field is introduced, and the second-order displacement characteristic of Rydberg state energy level is caused by the Stark effect, so that an EIT transparent window generates frequency deviation corresponding to the electric field intensity in a quantitative mode; 3, through analysis and calculation, establishing a mapping relation between the frequency shift amount and the external electric field intensity for the target Rydberg state; according to the invention, on the premise of not changing an optical structure, low-frequency disturbance components such as power frequency and frequency multiplication harmonic waves thereof in a power system can be identified and quantified at the same time; therefore, the method can be widely applied to live detection, electric field distribution surveying and mapping, electric energy quality monitoring and other scenes of power system equipment.