Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to learning and leveraging quantum hardware noise for quantum machine learning (QML). For example, according to an embodiment, a system is provided. The system can comprise a memory that can store computer-executable components. The system can further comprise a processor that can execute the computer-executable components stored in the memory, where the computer-executable components can comprise a noise learning component that can learn, based on an ansatz circuit and an input dataset, quantum hardware noise. The computer-executable components can further comprise a QML component that can employ the quantum hardware noise in an adaptive QML process.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to data classification based on quantum kernels. For example, a system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute the computer executable components stored in the memory, where the computer executable components can comprise an access component that can access an input dataset. The computer executable components can further comprise a data generation component that can generate, based on the input dataset, a plurality of new datasets by employing a plurality of quantum kernels.

Abstract: Systems and techniques that facilitate Gibbs state-based quantum optimization for combinatorial optimization problems are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory that can execute at least one of the computer executable components that can prepare a Gibbs state of a quantum system that represents a combinatorial optimization problem, wherein the Gibbs state is a quantum state that minimizes free energy of the quantum system. The at least one of the computer executable components can further initialize a quantum optimization algorithm using a set of parameters that define the Gibbs state to solve the combinatorial optimization problem, wherein solving the combinatorial optimization problem comprises determining a ground state of a Hamiltonian of the quantum system.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to identifying training data for quantum machine learning models. A system can comprise a processor that can execute computer executable components stored in memory, wherein the computer executable components can comprise a training component that can employ a training dataset to train a hybrid machine learning model to generate predictions, wherein training the hybrid machine learning model can comprise assigning, via a combination model, respective first weights to a first subset of training data comprised in the training dataset, assigning, via the combination model, respective second weights to a second subset of the training data, training the at least one classical machine learning model based on the first subset of the training data, and training the at least one quantum machine learning model based on the second subset of the training data.

Abstract: Techniques regarding generating an ensemble of quantum kernel-based learners for one or more quantum machine learning models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an ensemble component that can generate an ensemble of quantum kernel-based learners by selecting a quantum kernel at multiple iterations of a boosting procedure that analyzes a range of feature maps employable by a quantum machine learning model.

Abstract: A method, system, and computer program product for enabling quantum machine learning to be used effectively with classical data. Classical data, which may consist of a large sample size and a large number of features, is mapped into quantum state space forming quantum data using a classical machine learning model. Classical data refers to data subject to the laws of classical physics. Quantum state space refers to an abstract space in which different “positions” represent, not literal locations, but rather quantum states of a physical system. The dimensionality of the quantum state space corresponds to 2 raised to the power of the number of qubits. Quantum machine learning may then be performed on a quantum computer using the formed quantum data. As a result, quantum machine learning is enabled to be used effectively with classical data while utilizing a small number of qubits.

Abstract: A system to train multiple combined quantum classical kernels can comprise a memory that stores, and a processor that executes, computer executable components that perform operations comprising determining a set of kernel bandwidths, calculating a plurality of kernels, based on the kernel bandwidths, for subsets of features of a feature map, centering the plurality of kernels within a feature space of the feature map, regularizing parameters to combine the plurality of kernels, and combining the plurality of kernels into a combined kernel. Feature subsampling and data subsampling can be employed to compute a finite set subsampled kernels. Furthermore, the finite set of subsampled kernels can be combined classically to create a combined kernel that can represent an arbitrary target kernel function of a target dataset.

Abstract: Techniques are described herein regarding utilizing a quantum transformation to generate a transformed dataset from an original dataset and a quantum feature map. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include operations to transform a qubit of a quantum feature map from an initial state to a transformed state based on an input value of a first classical dataset. The computer executable components can further include operations that generate a second classical dataset based on the input value and the transformed state.

Abstract: Systems and methods for quantum machine learning are described. A plurality of qubits can be entangled to create a cluster state. The plurality of qubits can include at least an input qubit, an output qubit, and at least one ancilla qubit. The input qubit can represent data among a training data set of a machine learning model represented by a unitary operation. Sequential local measurements of the cluster state can be performed to generate a plurality of measurement outcomes. At least one of the plurality of qubits can be rotated according to the plurality of measurement outcomes and rotation parameters of the unitary operation. The sequential local measurements and rotation of the plurality of qubits can transform an input state of the input qubit into an output state of the output qubit. The machine learning model can be trained based on the output state of the output qubit.

Abstract: A processor can control quantum hardware to transform qubit states associated with a plurality of pairs of data points in a training dataset using a circuit parameter representing a rotation angle. Inner products of transformed qubit states associated with the plurality of pairs of data points can be computed. The processor can minimize an objective function based on the inner products, where the minimizing finds a target circuit parameter representing a target rotation angle that minimizes the objective function. A processor can build a kernel matrix based on the inner products computed for a sample dataset and the target circuit parameter passed to the quantum hardware. A classification algorithm can use the kernel matrix to classify the sample dataset.

Abstract: Mechanisms are provided to implement a privacy enhanced location service for determining a granularity of location information to return to a requestor computing device. The privacy enhanced location service receives, from a requestor computing device, a location query requesting location information for a subject. The privacy enhanced location service retrieves a selected subject privacy policy data structure, selected from a set of subject privacy policy data structures corresponding to the subject identified in the location query. The privacy enhanced location service applies the selected subject privacy policy data structure to location information associated with the subject to generate modified location information having a granularity of location information specified in the selected subject privacy policy data structure. The privacy enhanced location service transmits the modified location information to the requestor computing device.

Abstract: Techniques regarding generating an ensemble of quantum kernel-based learners for one or more quantum machine learning models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an ensemble component that can generate an ensemble of quantum kernel-based learners by selecting a quantum kernel at multiple iterations of a boosting procedure that analyzes a range of feature maps employable by a quantum machine learning model.

Abstract: Embodiments herein describe a return network simulation system that can simulate changes in a retailer's return network to determine the impact of those changes. Advantageously, being able to accurately simulate the retailer's return network means changes can be evaluated without first making those adjustments in the physical return network. Doing so avoids the cost of implementing the changes on the return network without first being able to predict whether the changes will have a net positive result (e.g., a positive result that offsets any negative results). A retailer can first simulate the change on the return network, review how the change affects one or more KPIs, and then decide whether to implement the change in the actual return network. As a result, the retailer has a reliable indicator whether the changes will result in a desired effect.

Abstract: A computer-implemented method for a machine learning based design framework includes receiving input data, generating a design proposal based on the input data using a machine learning model, receiving feedback for the design proposal from a designated reviewer of the design proposal, updating a user preference profile associated with the designated reviewer using data generated by a different machine learning model based on the feedback for the design proposal, updating the design proposal to replace the candidate design with a new candidate design based on the user preference profile, and generating a final design based on the design proposal. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Techniques for a machine learning based tiered graphical user interface (GUI) are described herein. Aspects of the invention include receiving a set of offered products. The set of offered products is sorted into a plurality of tiers, and an initial tiered GUI is generated based on the sorting. Based on receiving customer feedback via the tiered GUI, the sorting of the set of offered products into the plurality of tiers is updated, and an updated tiered GUI is generated based on the updated sorting.

Abstract: A hypergraph is constructed based on historical shopping cart data. A node of the hypergraph corresponds to a shopping basket, and a hyperedge of the hypergraph corresponds to a unique product, the hyperedge connecting all nodes of the hypergraph representing baskets containing the unique product. A hypergraph partition algorithm identifies a cluster of shopping baskets represented in the hypergraph and determined to be similar to a given basket. Based on the cluster of shopping baskets a dual-level return prediction is performed. The dual-level return prediction includes predicting whether the given basket will be returned, and based on predicting that the given basket will be returned, predicting a probability that a product in the given basket will be returned. Based on predicting that the given basket will be returned, an ameliorative action is performed to reduce the probability.

Abstract: Aspects of the invention include obtaining product hierarchy information for an early lifecycle product offered for sale by a retailer and obtaining order data for each order of the early lifecycle product during an early lifecycle period. The aspects also include obtaining customer data for a customer associated with each order of the early lifecycle product during the early lifecycle period and determining an expected return rate for the early lifecycle product based by inputting the product hierarchy information, the order data and the customer data into a trained return prediction model. Aspects also include performing an action based on a stored profile of the retailer based on a determination that the expected return rate exceeds a threshold value.

Abstract: The embodiments herein provide techniques for selecting an optimal return location from a plurality of candidate return locations for returning an item based on an expected recovery associated with each location. As discussed above, using predesignated return location(s) ignores many factors that can increase costs that affect returning items such as shipping costs, inventory, handling costs, operational transfer costs, as well as several predicted costs. Further, these techniques do not consider expected future revenue (which can offset these costs). In one embodiment, a net expected recovery is determined for each location using the costs and future revenues discussed above. By comparing the net expected recovery associated with each candidate return location, the optimal return location can be identified.

Abstract: Embodiments herein describe a return network simulation system that can simulate changes in a retailer's return network to determine the impact of those changes. Advantageously, being able to accurately simulate the retailer's return network means changes can be evaluated without first making those adjustments in the physical return network. Doing so avoids the cost of implementing the changes on the return network without first being able to predict whether the changes will have a net positive result (e.g., a positive result that offsets any negative results). A retailer can first simulate the change on the return network, review how the change affects one or more KPIs, and then decide whether to implement the change in the actual return network. As a result, the retailer has a reliable indicator whether the changes will result in a desired effect.

Abstract: A system, method and program product for authenticating a device. An authentication service is provided having: a data management system for periodically collecting and storing signature data from each of a set of registered devices, wherein the signature data includes a plurality of data points, and wherein at least one of the data points includes a device usage characteristic; a system for obtaining a temporal signature state (TSS) vector of a device in response to a transaction request from the device, wherein the TSS vector includes values for a selected subset of the data points forming the signature data; and an authenticator for comparing the TSS vector of the device with stored signature data in order to authenticate the device.