Abstract: Quantum computing systems and methods for determining a state of a physical system are provided. In some examples, a method can include obtaining a defined function associated with the physical system, the defined function encoding an estimated value of at least one property to be simulated by a quantum computing system as a gradient of the defined function. The method can include implementing a quantum circuit on a plurality of qubits in a quantum computing system to perform a quantum operation (e.g., the Gilyén quantum gradient algorithm) on the plurality of qubits. The quantum operation is operable to determine the gradient of the defined function. The method can include determining the estimated value for the at least one property based at least on at least one of the plurality of qubits after implementation of the quantum operation.

Abstract: Methods, systems and apparatus for preparing a target quantum state of a quantum system, where the target quantum state is stationary with respect to a parameterized many-body qubit operator. In one aspect a method includes preparing an initial quantum state as an input state for a first iteration; iteratively evolving the initial quantum state and subsequent input quantum states as inputs for subsequent iterations until an approximation of the target stationary quantum state is obtained, comprising, for each iteration: computing, by quantum computation, parameter values of the many-body qubit operator for the iteration; computing, by quantum computation, an evolution time for the iteration, comprising evaluating changes in elements of a 2-RDM for the iteration; and evolving the initial quantum state or the subsequent input quantum state for the iteration using the computed parameter values and evolution time to generate a subsequent input quantum state for the subsequent iteration.

Abstract: Methods and servers for processing a textual input for generating an audio output are disclosed. The audio output is a sequence of waveform segments generated in real-time by a trained Convolutional Neural Network. The method includes, at a given iteration, generating a given waveform segment which includes storing first tensor data computed by a first hidden layer during the given iteration, and where the first tensor data has tensor-chunk data. The tensor-chunk data is used during the given iteration for generating the given waveform segment and is to be used during a next iteration for generating a next waveform segment. The method includes, at the next iteration, generating the next waveform segment, which comprises storing second tensor data computed by the first hidden layer during the next iteration. The second tensor data excludes redundant tensor-chunk data that is identical to the tensor-chunk data from the first tensor data.

Abstract: The disclosure describes various aspects of a practical implementation of multi-qubit gate architecture. A method is described that includes enabling ions in the ion trap having three energy levels, enabling a low-heating rate motional mode (e.g., zig-zag mode) at a ground state of motion with the ions in the ion trap; and performing a Cirac and Zoller (CZ) protocol using the low-heating rate motional mode as a motional state of the CZ protocol and one of the energy levels as an auxiliary state of the CZ protocol, where performing the CZ protocol includes implementing the multi-qubit gate. The method also includes performing one or more algorithms using the multi-qubit gate, including Grover's algorithm, Shor's factoring algorithm, quantum approximation optimization algorithm (QAOA), error correction algorithms, and quantum and Hamiltonian simulations. A corresponding system that supports the implementation of a multi-qubit gate architecture is also described.

Abstract: Methods and systems are described for improvements to the use of distributed computer networks. For example, conventional systems may rely on the distribution of network or application traffic across multiple servers and may maintain load balancers to maintain that distribution in an efficient manner. Each load balancer may sit between client devices and backend servers, receiving and then distributing incoming requests to any available server capable of fulfilling them. The load balancers may ensure that no one server is overworked based on the number of processing requests directed to that server, which could degrade performance.

Abstract: The present disclosure provides an interface access processing method and apparatus, a computer device and a storage medium, and the method includes: acquiring target access behavior sequences of an access party within a first specified time range, the target access behavior sequences including an access action behavior sequence and an access time interval sequence; calling an identification model for prediction processing to obtain an identification result based on the access action sequence and the access time interval sequence, the identification result being used for representing a classification probability that the access party has abnormal behaviors; and controlling an interface access request within a second specified time range of the access party according to the identification result, a start time of the second specified time range being later than an end time of the first specified time range.

Abstract: Disclosed is an Artificial Intelligence (AI)-based livestock management system and a livestock management method therefor. According to the inventive concept, it is possible to determine whether the animal has abnormal symptoms by learning the standard livestock stall management data and analyzing the body temperature information and behavior information of livestock from livestock image data obtained in real time from livestock such as cattle, thus giving the manager confidence.

Abstract: The present application relates to an intention recognition method and apparatus, a readable medium, and an electronic device. The method includes: by means of a preset intention recognition quantification model, performing a quantification operation on a dot product of a query vector and a key vector which correspond to each character in a target text, so as to obtain a fixed-point type target vector of a first bit; according to the fixed-point type target vector, determining, by means of a target mapping relationship, a floating-point type attention weight of a second bit corresponding to each character; and according to the floating-point type attention weight, determining a target intention corresponding to the target text, the first bit being smaller than the second bit.

Abstract: Performing state reversal on a quantum spin chain includes: providing qubits in a quantum spin chain in an input state, such that the quantum spin chain includes: first and second terminal qubits and one or more intermediate qubits, such that: the qubits have a transverse field strength; the first and terminal qubits a longitudinal field strength; and nearest neighbor qubit pair has an Ising coupling strength; and evolving the quantum spin chain from the input state to a final state for an evolution period to perform state reversal on the quantum spin chain.

Abstract: In general, the disclosure describes techniques for machine learning for translation to structured computer readable representation. An example method to generate a training set for a natural language translation model includes receiving, by a computing system, a grammar comprising rules, one or more of the rules being associated with random biases; generating, by the computing system, at least one of random trees or random graphs based on the random biases in the grammar; for each of the random trees or random graphs, by the computing system, generating a natural language sample; and generating, by the computing system, the training set with the random trees or random graphs and the corresponding natural language samples.

Abstract: Methods, systems, and apparatus for determining frequencies at which to operate interacting qubits arranged as a two dimensional grid in a quantum device. In one aspect, a method includes the actions of defining a first cost function that characterizes technical operating characteristics of the system. The cost function maps qubit operation frequency values to a cost corresponding to an operating state of the quantum device; applying one or more constraints to the defined first cost function to define an adjusted cost function; and adjusting qubit operation frequency values to vary the cost according to the adjusted cost function such that the operating state of the quantum device is improved.

Abstract: Training a user-specific perturbation generator for an audio feature detection model includes receiving one or more positive audio samples of a user, each of the one or more positive audio samples including an audio feature; receiving one or more negative audio samples of the user, each of the one or more negative audio samples sharing an acoustic similarity with at least one of the one or more positive audio samples; and adversarially training a user-specific perturbation generator model to generate a user-specific perturbation, the training based on the one or more positive audio samples and the one or more negative audio samples. Perturbing audio samples of the user with the user-specific perturbation can cause an audio feature detection model to recognize the audio feature in audio samples that include the audio feature and/or to refrain from recognizing the audio feature in audio samples that do not include the audio feature.

Abstract: Encoding/decoding an immersive voice and audio services (IVAS) bitstream comprises: encoding/decoding a coding mode indicator in a common header (CH) section of an IVAS bitstream, encoding/decoding a mode header or tool header in the tool header (TH) section of the bitstream, the TH section following the CH section, encoding/decoding a metadata payload in a metadata payload (MDP) section of the bitstream, the MDP section following the CH section, encoding/decoding an enhanced voice services (EVS) payload in an EVS payload (EP) section of the bitstream, the EP section following the CH section, and on the encoder side, storing or streaming the encoded bitstream, and on the decoder side, controlling an audio decoder based on the coding mode, the tool header, the EVS payload, and the metadata payload or storing a representation of same.

Abstract: Disclosed are systems and methods for a frontend capture module of a video conferencing application, which can modify an input signal, received from a microphone device to match predetermined signal characteristics, such as voice signal level and expected noise floor. An Input stage, a suppression module and an output stage amplify the voice signal portion of the input signal and suppress the noise signal of input signal to predetermined ranges. The input stage selectively applies gains defined by a gain table, based on signal level of the input signal. The suppression module selectively applies a suppression gain to the input signal based on presence or absence of voice signal in the input signal. The output stage further amplifies the input signal in portions having a voice signal and applies a gain table to maintain a consistent noise floor.

Abstract: Method for quantizing a trained long short-term memory (LSTM) neural network having a plurality of weights, the method comprising: obtaining data specifying trained floating-point values for each of the weights of the trained LSTM neural network, the trained LSTM neural network comprising one or more LSTM layers, each LSTM layer having a plurality of gates and each of the plurality of gates being associated with an input weight matrix and a recurrent weight matrix; quantizing the trained LSTM neural network, comprising: for each gate, quantizing the elements of the input weight matrix to a target fixed bit-width; for each gate, quantizing the elements of the recurrent weight matrix to the target fixed bit-width; and providing data specifying a quantized LSTM neural network for use in performing quantized inference.

Abstract: A tree-of-thought (ToT) system is presented that improves problem-solving capabilities of machine learning models, such as auto-regressive large language models (LLMs). The TOT system can solve complex reasoning tasks through trial and error. In this process, the system explores the solution space through a tree-like thought process, allowing for backtracking when necessary. The system augments an LLM with additional modules including a prompter agent, a checker module, a memory module, and a ToT controller. These modules engage in a multi-round conversation with the LLM. The memory module records the conversation and state history of the problem-solving process, which allows the system to backtrack to the previous steps of the thought-process and explore other solution paths. This new system can be applied to a blockchain and/or a distributed computing system.

Abstract: Many microbes are capable of synthesizing anti-cancer products, however existing state of art is limited by focus on industrial production of the said products. A method and system for preparing a knowledgebase of microbes and microbial functions to identify good and bad microbes have been provided. The present disclosure therefore further describes methods and compositions for the risk assessment, prevention and management of various forms of cancer by using microbes, microbial products utilizing the knowledgebase of microbes and microbial function. The method is configured to priming the microbes inside the host for boosting the immune response against cancer initiation, progression, recurrence and associated side effects. The use of microbes and microbial products can be provided in the form of probiotics, supplements, and prebiotics etc. along with creation of right sets of nutrition conditions in the host for the proper functioning of the microbes and microbial products.

Abstract: Techniques for reducing latency in processing of audio data, where the latency may be caused in detecting audio of interest in the audio data, are described. A device that captures audio data may include a detection component to determine when the audio data includes audio of interest (e.g., device-directed speech), and an audio embedding generator to generate embedding vectors for the captured audio data while the detection component processes the audio data. The device may generate an embedding vector for audio data captured at the device for a duration of time; determine, at the end of the duration of time, that the audio data represents audio of interest; and send the embedding vector to an audio processing component (e.g., an automatic speech recognition component) for processing.

Abstract: A system and method code an object-based audio signal comprising audio objects in response to audio streams with associated metadata. In the system and method, an audio stream processor analyses the audio streams. A metadata processor is responsive to information on the audio streams from the analysis by the audio stream processor for coding the metadata. The metadata processor uses a logic for controlling a metadata coding bit-budget. An encoder codes the audio streams.

Abstract: Video processing with a multi-quality loop filter using a multi-task neural network is performed by at least one processor and includes generating a first set of masked weight parameters, based on an input and a plurality of quantization parameter values with a corresponding first set of masks and first plurality of weight parameters, for a first set of shared neural network layers, selecting a second set of task specific neural network layers for the plurality of quantization parameter values with a second plurality of weight parameters, based on the plurality of quantization parameter values, computing an inference output, based on the first set of masked weight parameters and the second plurality of weight parameters, and outputting the computed inference output as an enhanced result.