Abstract: Provided is a low noise amplification circuit, comprising: a RF amplification circuit, one end of which is connected with a signal input end, another end is connected with the output impedance matching circuit; an output impedance matching circuit, one end of which is connected with the RF amplification circuit, another end is connected with the attenuation network; an attenuation network, one end of which is connected with the output impedance matching circuit, another end is connected with a signal output end; a passive attenuation path, one end of which is connected with the signal input end, another end is connected with a connection node between the matching circuit and the network; and an impedance regulation network, configured to, in a first working mode, resonate with a parasitic capacitance generated by the network, in a second working mode, resonate with a parasitic capacitance generated by the network and the attenuation path.

Abstract: Methods for creating a knowledge graph for a video are disclosed. Aspects include obtaining the video, processing the video to extract audio information and video information, and storing the extracted audio information and video information with a timestamp corresponding its occurrence in the video. Aspects also include creating a plurality of groups of the extracted audio information and video information based at least in part on the timestamps and extracting two or more keywords from each of the plurality of groups. Aspects further include identifying a relationship between the two or more keywords based on the extracted audio information and video information and creating a graph having a plurality of nodes and a plurality of links that connect a pair of nodes of the plurality of nodes. Each node corresponds to an extracted keyword and each link corresponds to the identified relationship between the pair of nodes.

Abstract: A system comprises a network of computers comprising a master computer and slave computers. For a machine learning problem that is partitioned into a number of correlated sub-problems, each master computer is configured to store tasks associated with the machine learning problem, and each of the slave computers is assigned one of the correlated sub-problems. Each slave computer is configured to store variables or parameters or both associated with the assigned one of the correlated sub-problems; obtain information about one or more tasks stored by the master computer without causing conflict with other slave computers with regard to the information; perform computations to update the obtained information and the variables or parameters or both of the assigned sub-problem; send the updated information to the master computer to update the information stored at the master computer; and store the updated variables or parameters or both of the assigned sub-problem.

Abstract: The present invention belongs to the technical field of control of aero-engines, and proposes an adaptive boosting algorithm-based turbofan engine direct data-driven control method. First, a turbofan engine controller is designed based on the Least Squares Support Vector Machine (LSSVM) algorithm, and further, the weight of a training sample is changed by an adaptive boosting algorithm so as to construct a turbofan engine direct data-driven controller combining a plurality of basic learners into strong learners. Compared with the previous solution only adopting LS SVM, the present invention enhances the control precision, improves the generalization ability of the algorithm, and effectively solves the problem of sparsity of samples by the adaptive boosting method. By the adaptive boosting algorithm-based turbofan engine direct data-driven control method designed by the present invention.

Abstract: A vehicle running status field model-based information transmission frequency optimization method in the Internet of Vehicles belongs to the technical field of network communications. The method establishes a running status field model according to the real-time running status of a road vehicle to describe the degree of risk of the vehicle, the degree of risk can be used to dynamically adjust the transmission frequency of safety-critical information, and the transmission frequency of non-safety-critical information is adjusted through the real-time transmission frequency of safety-critical information to achieve the purpose of improving the utilization ratio of link. The method establishes the running status field model of a moving vehicle, uses the risk intensity of the vehicle in the running status field to describe the current running risk of the vehicle, and takes account of different application scenarios, thereby having generality.

Abstract: Methods, systems, and apparatus for quantum phase estimation. In one aspect, an apparatus includes a quantum circuit comprising a first quantum register comprising at least one ancilla qubit, quantum gates, comprising at least (i) two Hadamard gates, (ii) a phase gate, (iii) a unitary operator, and (iv) a measurement operator, a second quantum register comprising one or more qubits, wherein the second quantum register is prepared in an arbitrary quantum state that is not an eigenstate of the unitary operator; and a phase learning system, configured to perform phase estimation experiments on the quantum circuit, comprising repeatedly measuring the state of an ancilla qubit for each phase estimation experiment to determine an expectation value of the state of the ancilla qubit and learn phases of the eigenvalues of the unitary operator.

Abstract: Methods and apparatus for estimating the fidelity of quantum hardware. In one aspect, a method includes accessing a set of quantum gates; sampling a subset of quantum gates from the set of quantum gates, wherein the subset of quantum gates defines a quantum circuit; applying the quantum circuit to a quantum system and performing measurements on the quantum system to determine output information of the quantum system; calculating output information of the quantum system based on application of the quantum circuit to the quantum system; and estimating a fidelity of the quantum circuit based on the determined output information and the calculated output information of the quantum system.

Abstract: Methods, systems, and apparatus for quantum phase estimation. In one aspect, an apparatus includes a quantum circuit comprising a first quantum register comprising at least one ancilla qubit, quantum gates, comprising at least (i) two Hadamard gates, (ii) a phase gate, (iii) a unitary operator, and (iv) a measurement operator, a second quantum register comprising one or more qubits, wherein the second quantum register is prepared in an arbitrary quantum state that is not an eigenstate of the unitary operator; and a phase learning system, configured to perform phase estimation experiments on the quantum circuit, comprising repeatedly measuring the state of an ancilla qubit for each phase estimation experiment to determine an expectation value of the state of the ancilla qubit and learn phases of the eigenvalues of the unitary operator.

Abstract: Methods and apparatus for estimating the fidelity of quantum hardware. In one aspect, a method includes accessing a set of quantum gates; sampling a subset of quantum gates from the set of quantum gates, wherein the subset of quantum gates defines a quantum circuit; applying the quantum circuit to a quantum system and performing measurements on the quantum system to determine output information of the quantum system; calculating output information of the quantum system based on application of the quantum circuit to the quantum system; and estimating a fidelity of the quantum circuit based on the determined output information and the calculated output information of the quantum system.

Abstract: Processes are described for extracting metals from a combination derived from spent lithium-ion batteries and comprising such metals, a liquid, an acid, and other components.

Abstract: The present invention belongs to the technical field of control of aero-engines, and proposes an adaptive boosting algorithm-based turbofan engine direct data-driven control method. First, a turbofan engine controller is designed based on the Least Squares Support Vector Machine (LSSVM) algorithm, and further, the weight of a training sample is changed by an adaptive boosting algorithm so as to construct a turbofan engine direct data-driven controller combining a plurality of basic learners into strong learners. Compared with the previous solution only adopting LS SVM, the present invention enhances the control precision, improves the generalization ability of the algorithm, and effectively solves the problem of sparsity of samples by the adaptive boosting method. By the adaptive boosting algorithm-based turbofan engine direct data-driven control method designed by the present invention.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a quantum oracle to make inference in complex machine learning models that is capable of solving artificial intelligent problems. Input to the quantum oracle is derived from the training data and the model parameters, which maps at least part of the interactions of interconnected units of the model to the interactions of qubits in the quantum oracle. The output of the quantum oracle is used to determine values used to compute loss function values or loss function gradient values or both during a training process.

Abstract: A method for active fault tolerant control of a turbofan engine control system designs a linear parameter varying gain scheduling robust tracking controller with good dynamic performance. Adaptive estimation of fault amplitude of a sensor and an actuator is realized according to the change of the operating state of the turbofan engine to accurately reconfigure a fault signal. An active fault tolerant control strategy based on a virtual actuator is designed according to a fault estimation result. Without redesigning the controller, through the designed active fault tolerant control strategy, on the premise of ensuring the stability of the control system, a control effect similar to that of the control system without fault is obtained.

Abstract: A braking force control system is configured to, when a vehicle is decelerated by driving support control, on an assumption that a predetermined deceleration is generated in the vehicle, set a distribution ratio between a braking force that is applied to a front wheel and a braking force that is applied to a rear wheel such that a degree of a specific position in which a front of the vehicle is lower than a rear of the vehicle is not greater than a predetermined degree.

Abstract: A vehicle running status field model-based information transmission frequency optimization method in the Internet of Vehicles belongs to the technical field of network communications. The method establishes a running status field model according to the real-time running status of a road vehicle to describe the degree of risk of the vehicle, the degree of risk can be used to dynamically adjust the transmission frequency of safety-critical information, and the transmission frequency of non-safety-critical information is adjusted through the real-time transmission frequency of safety-critical information to achieve the purpose of improving the utilization ratio of link. The method establishes the running status field model of a moving vehicle, uses the risk intensity of the vehicle in the running status field to describe the current running risk of the vehicle, and takes account of different application scenarios, thereby having generality.

Abstract: Methods, systems, and apparatus for quantum phase estimation. In one aspect, an apparatus includes a quantum circuit comprising a first quantum register comprising at least one ancilla qubit, quantum gates, comprising at least (i) two Hadamard gates, (ii) a phase gate, (iii) a unitary operator, and (iv) a measurement operator, a second quantum register comprising one or more qubits, wherein the second quantum register is prepared in an arbitrary quantum state that is not an eigenstate of the unitary operator; and a phase learning system, configured to perform phase estimation experiments on the quantum circuit, comprising repeatedly measuring the state of an ancilla qubit for each phase estimation experiment to determine an expectation value of the state of the ancilla qubit and learn phases of the eigenvalues of the unitary operator.

Abstract: Methods, systems, and apparatus for quantum phase estimation. In one aspect, an apparatus includes a quantum circuit comprising: a first quantum register comprising at least one ancilla qubit, a second quantum register comprising one or more qubits, wherein the second quantum register is prepared in a quantum state that is not an eigenstate of a unitary operator operating on the first and second quantum register; and a phase learning system, configured to learn phases of the eigenvalues of the unitary operator.

Abstract: A system comprises a network of computers comprising a master computer and slave computers. For a machine learning problem that is partitioned into a number of correlated sub-problems, each master computer is configured to store tasks associated with the machine learning problem, and each of the slave computers is assigned one of the correlated sub-problems. Each slave computer is configured to store variables or parameters or both associated with the assigned one of the correlated sub-problems; obtain information about one or more tasks stored by the master computer without causing conflict with other slave computers with regard to the information; perform computations to update the obtained information and the variables or parameters or both of the assigned sub-problem; send the updated information to the master computer to update the information stored at the master computer; and store the updated variables or parameters or both of the assigned sub-problem.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a quantum oracle to make inference in complex machine learning models that is capable of solving artificial intelligent problems. Input to the quantum oracle is derived from the training data and the model parameters, which maps at least part of the interactions of interconnected units of the model to the interactions of qubits in the quantum oracle. The output of the quantum oracle is used to determine values used to compute loss function values or loss function gradient values or both during a training process.

Abstract: Methods and apparatus for estimating the fidelity of quantum hardware. In one aspect, a method includes accessing a set of quantum gates; sampling a subset of quantum gates from the set of quantum gates, wherein the subset of quantum gates defines a quantum circuit; applying the quantum circuit to a quantum system and performing measurements on the quantum system to determine output information of the quantum system; calculating output information of the quantum system based on application of the quantum circuit to the quantum system; and estimating a fidelity of the quantum circuit based on the determined output information and the calculated output information of the quantum system.