Abstract: Methods, systems, and apparatus for designing a quantum control trajectory for implementing a quantum gate using quantum hardware. In one aspect, a method includes the actions of representing the quantum gate as a sequence of control actions and applying a reinforcement learning model to iteratively adjust each control action in the sequence of control actions to determine a quantum control trajectory that implements the quantum gate and reduces leakage, infidelity and total runtime of the quantum gate to improve its robustness of performance against control noise during the iterative adjustments.

Abstract: A method may include accessing a data processing architecture associated with a neural network to determine dependencies between intermediate data layers of the neural network; obtaining dimensions of the intermediate data layers in the neural network; calculating a minimum number of data storage portions for executing the neural network based on the dependencies; determining a memory allocation size for each respective data storage portion of the data storage portions based on the dimensions and dependencies; allocating memory on a storage device for each data storage portion in accordance with its respective determined memory allocation size.

Abstract: Embodiments of a method are disclosed. The method includes performing decentralized distributed deep learning training on a batch of training data. Additionally, the method includes determining a training time wherein the learner performs the decentralized distributed deep learning training on the batch of training data. Further, the method includes generating a table having the training time and other processing times for corresponding other learners performing the decentralized distributed deep learning training on corresponding other batches of other training data. The method also includes determining that the learner is a straggler based on the table and a threshold for the training time. Additionally, the method includes modifying a processing aspect of the straggler to reduce a future training time of the straggler for performing the decentralized distributed deep learning training on a new batch of training data in response to determining the learner is the straggler.

Abstract: The disclosure discloses a method of executing dynamic graph for neural network computation and the apparatus thereof. The method of executing dynamic graph includes the following steps: S1: constructing and distributing an operator and a tensor; S2: deducing an operator executing process by an operator interpreter; S3: constructing an instruction of a virtual machine at runtime by the operator interpreter; S4: sending the instruction to the virtual machine at runtime by the operator interpreter; S5: scheduling the instruction by the virtual machine; and S6: releasing an executed instruction by the virtual machine. According to the method of executing dynamic graph for neural network computation and the apparatus thereof provided by the disclosure, runtime is abstracted to be the virtual machine, and the virtual machine acquires a sub-graph of each step constructed by a user in real time through the interpreter and schedules, the virtual machines issues, and executes each sub-graph.

Abstract: Systems and methods related to a cryogenic-CMOS interface for controlling qubit gates are provided. A system for controlling qubit gates includes a first device comprising a quantum device including qubit gates. The system further includes a second device comprising a control system configured to operate at the cryogenic temperature. The control system includes charge locking circuits, where each of the charge locking circuits is coupled to at least one qubit gate via an interconnect such that each of the charge locking circuits is configured to provide a voltage signal to at least one qubit gate. The control system further includes a control circuit comprising a finite state machine configured to provide at least one control signal to selectively enable at least one of the charge locking circuits and to selectively enable a provision of a voltage signal to a selected one of the charge locking circuit.

Abstract: Various techniques are described for automatically suggesting variation parameters used to generate a tailored synthetic dataset to train a particular machine learning model. A seeding taxonomy associates a plurality of machine learning scenarios with corresponding subsets of variation parameters. A selected machine learning scenario is used to retrieve a corresponding subset of variation parameters associated with the selected machine learning scenario by the seeding taxonomy. The seeding taxonomy may be adaptable using a feedback loop that tracks selected variation parameters and updates the seeding taxonomy. The suggested variation parameters are presented as suggestions to assist users to identify and select relevant variation parameters faster and more efficiently. Further embodiments relate to pre-packaging synthetic datasets for common or anticipated machine learning scenarios.

Abstract: Methods and systems for generating a multi-model ensemble of global climate simulation data from a plurality of pre-existing global climate simulation model (GCM) datasets, are disclosed. The methods and systems perform steps of computing a GCM dataset validation measure based on at least one sample statistic for at least one climate variable from the pre-existing GCM dataset; selecting a validated subset of the plurality of pre-existing GCM datasets; selecting a subset of GCM datasets; generating one or more candidate ensembles of GCM datasets; computing an ensemble forecast skill score for each candidate ensemble of GCM datasets; generating the multi-model ensemble of GCM datasets by selecting a candidate ensemble of GCM datasets with a best ensemble forecast skill score; and training the NN-based climate forecasting model using the multi-model ensemble of GCM datasets.

Abstract: Methods and systems for quantum computing based sample analysis include computing cross-correlations of two images using a quantum processing system, and computing less noisy image based of two or more images using a quantum processing system. Specifically, the disclosure includes methods and systems for utilizing a quantum computing system to compute and store cross correlation values for two sets of data, which was previously believed to be physically impossible. Additionally, the disclosure also includes methods and systems for utilizing a quantum computing system to generate less noisy data sets using a quantum expectation maximization maximum likelihood (EMML). Specifically, the disclosed systems and methods allow for the generation of less noisy data sets by utilizing the special traits of quantum computers, the systems and methods disclosed herein represent a drastic improvement in efficiency over current systems and methods that rely on traditional computing systems.

Abstract: A data set is stored in memory circuitry that is indicative of a state of a semiconductor fabrication process or of semiconductor structure fabricated thereby. Features in the data set are discernable to an extent limited by a data resolution. A machine-learning model comprising parameters having respective values assigned thereto as constrained by a model training process is also stored in the memory circuitry. Processor circuitry communicatively coupled to the memory circuitry generates an output data set from the data set in accordance with the machine-learning model such that features in the output data set are discernable to an extent limited by an output data resolution that is finer than the data resolution of the data set.

Abstract: An encoded artificial intelligence (AI) behavior specification is received. A data generation configuration specification is received. And a deep neural network configuration specification is received. A training data set based on the data generation configuration specification is generated. An AI behavior deep neural network that conforms to the deep neural network configuration specification is trained using at least a subset of the generated training data. The trained AI behavior deep neural network is provided from a remote AI add-in service to a development environment.

Abstract: Systems and methods identify a behavior in multivariate time-series data. The systems and methods receive data representing a time series having steps each representing an event associated with a time stamp and being associated with one or more attributes; transform the data into a tensor; train a model using training data comprising a set of tensors; identify the behavior using predictions from the trained model and a target pattern; and provide an indication of the presence or absence of the behavior.

Abstract: The illustrative embodiments provide a method, system, and computer program product for quantum feature kernel alignment using a hybrid classical-quantum computing system. An embodiment of a method for hybrid classical-quantum decision maker training includes receiving a training data set. In an embodiment, the method includes selecting, by a first processor, a sampling of objects from the training set, each object represented by at least one vector. In an embodiment, the method includes applying, by a quantum processor, a set of quantum feature maps to the selected objects, the set of quantum maps corresponding to a set of quantum kernels. In an embodiment, the method includes evaluating, by a quantum processor, a set of parameters for a quantum feature map circuit corresponding to at least one of the set of quantum feature maps.

Abstract: The present disclosure provides multi-sampling model training methods and devices. One exemplary training method includes: performing multi-sampling on samples to obtain a training set and a validation set in each sampling; using the training set and the validation set obtained in each sampling as a group, and performing model training and obtaining a trained model using the training set in each group; evaluating the trained model using the training set and the validation set in each group separately; eliminating or retaining the trained model based on the evaluation results and a predetermined elimination criterion; obtaining prediction results of the samples using retained models; and obtaining a final model by performing combined model training on the retained models using the prediction results. The final model obtained using embodiments of the present disclosure can be more robust and stable, and can provide more accurate prediction results, thus greatly improving efficiency of modeling.

Abstract: A post-processing method, system, and computer program product for post-hoc improvement of instance-level and group-level prediction metrics, including training a bias detector that learns to detect a sample that has an individual bias greater than a predetermined individual bias threshold value with constraints on a group bias, applying the bias detector on a run-time sample to select a biased sample in the run-time sample having a bias greater than the predetermined individual bias threshold bias value, and suggesting a de-biased prediction for the biased sample.

Abstract: A quantum computing device including a first plurality of qubits having a first resonance frequency and a second qubit having a second resonance frequency, the second resonance frequency being different from the first resonance frequency; and a first tunable frequency bus configured to couple the first plurality of qubits to the second qubit.

Abstract: Methods, systems, and apparatus for nonlinear calibration of quantum computing apparatus. In one aspect, elements in a set of experimental data correspond to a respective configuration of control biases for the quantum computing apparatus. An initial physical model comprising one or more model parameters of the quantum computing apparatus is defined. The model is iteratively adjusted to determine a revised physical model, where at each iteration: a set of predictive data corresponding to the set of experimental data is generated, and elements in the predictive data represent a difference between the two smallest eigenvalues of a Hamiltonian characterizing the system qubits for the previous iteration, and are dependent on at least one model parameter of the physical model for the previous iteration; and the model for the previous iteration is adjusted using the obtained experimental data and the generated set of predictive data for the iteration.

Abstract: VQE is accelerated by performing receiving a qubit Hamiltonian representing a linear combination of a plurality of Pauli strings. Selecting, among the plurality of Pauli strings, one or more Pauli strings that have less influence than a threshold on an eigenvalue of the qubit Hamiltonian. Grouping, based on joint measurability, the unselected Pauli strings among the plurality of Pauli strings into a plurality of groups of jointly measurable Pauli strings Determining that one or more of the selected one or more Pauli strings is jointly measurable with Pauli strings in one of the plurality of groups And adding one or more of the selected one or more Pauli strings to the one of the plurality of groups.

Abstract: Request flow log retrieval can include extracting one or more keywords from a natural language description of an action, the action being a system response to a user request submitted to a resource-provisioning system during a user session. Request flow log retrieval can also include determining a classification of the action based on a correlation value generated by a classifier model trained using machine learning to classify actions performed by the resource-provisioning system, the classification based on the one or more keywords. Additionally, request flow log retrieval can include automatically identifying a request flow associated with the action based on the classification of the action and returning at least one system log entry corresponding to the request flow.

Abstract: Methods, system, and apparatus for pre-computing data metrics. A method can include receiving, by one or more computers, (i) data from a user including a plurality of data metrics, and (ii) historical data associated with the user; generating, by the one or more computers and based at least on the historical data associated with the user, a forecast for the user using an artificial neural network, the forecast indicating a prediction of future user behaviors; determining, by the one or more computers and based at least on the forecast for the user, a subset of data metrics, from among the plurality of data metrics, that are likely to be used by the user; performing, by the one or more computers, one or more automatic data operations on the subset of data metrics; and providing, by the one or more computers, the processed subset of data metrics for output.

Abstract: Configuring a quantum computing system to determine a solution to an optimization problem includes encoding the optimization problem in an encoding language to produce an encoded optimization model. The encoded optimization model is transformed into a unconstrained model. The encoded optimization model includes an objective function having one or more terms. The one or more terms are converted to one or more Pauli terms. An Ising Hamiltonian is generated using the one or more terms. The Ising Hamiltonian corresponds to the optimization problem. An instruction indicative of the Ising Hamiltonian is provided to the quantum computing system.