International Business Machines Corporation

Edwin Peter Dawson Pednault

John A Gunnels

Lior Horesh

Giacomo Nannicini

Naoki Abe

Robert L Wisnieff

Ramesh Natarajan

Hyun Kyu Seo

Mark C Bullock

Harlan Crowder

Eric Bibelnieks

Michael Haydock

Mindy Schiller

Wayne Kugel

Nancy Soderquist

Shivakumar Vaithyanathan

Berthold Reinwald

Christoph Lingenfelder

Yuanyuan Tian

Rajasekar Krishnamurthy

Richard D Lawrence

Shirish Tatikonda

Vikas Sindhwani

Douglas Ronald Burdick

Fateh A Tipu

Upendra D Chitnis

Prem Melville

David L Jensen

Amol N Ghoting

Cezar Pendus

James J Bennett

Vincent Philip Thomas

Chandan Karrem Reddy

Described herein is a simulation of an input quantum circuit, comprising a machine-readable specification of a quantum circuit. Aspects include partitioning the input quantum circuit into a group of sub-circuits based on at least two groups of qubits identified for tensor slicing, wherein the resulting sub-circuits have associated sets of qubits to be used for tensor slicing. The simulating can occur in stages, one stage per sub-circuit. A set of qubits associated with a sub-circuit can be used to partition the simulated quantum state tensor for the input quantum state circuit into quantum state tensor slices, and the quantum gates in that sub-circuit can used to update the quantum state tensor slices into updated quantum state tensor slices. The updated quantum state tensor slices are stored to secondary storage as micro slices.

Simulating quantum circuits on a computer using hierarchical storage

Techniques and a system for quantum circuit decomposition by integer programming are provided. In one example, a system includes a quantum circuit decomposition component and a simulation component. The quantum circuit decomposition component generates graphical data for a quantum circuit that is indicative of a graphical representation of the quantum circuit. The graphical representation is formatted as a hypergraph. The simulation component simulates the quantum circuit based on the graphical data associated with the hypergraph.

Quantum circuit decomposition by integer programming

A computer implemented method includes receiving a digital description of a quantum circuit, partitioning the digital description of the quantum circuit into a plurality of quantum sub-circuits wherein each quantum sub-circuit of the plurality of quantum sub-circuits comprises one or more quantum gates, determining sub-circuit dependencies for the plurality of quantum sub-circuits, simulating the plurality of quantum sub-circuits according to the sub-circuit dependencies to produce simulation results for each quantum sub-circuit of the plurality of quantum sub-circuits, wherein a first and a second quantum sub-circuit of the plurality of quantum sub-circuits each contain one or more gates that are applied to a common qubit, and wherein the first and the second quantum sub-circuit are simulated independently using an entangled tensor index. A corresponding computer system and computer program product are also disclosed herein.

Simulating quantum circuits

Techniques and a system for visualization or interaction with a quantum processor are provided. In one example, a system includes a quantum programming component and a visualization component. The quantum programming component manages a quantum programming process to generate topology data for a quantum processor that is indicative of a physical topology of a set of qubits associated with the quantum processor. The visualization component generates visualization data for the topology data that comprises a set of planar slice elements arranged to correspond to the physical topology of the set of qubits. The set of planar slice elements indicate one or more operations performed at a time step associated with the quantum programming process.

Visualizing or interacting with a quantum processor

A process of transforming data residing in databases, such as relational databases, into forms suitable as input to data analysis tools, such as predictive modeling tools includes the steps of defining a business process problem to be solved and identifying data requirements. For example, the business process problem may relate to predicting a customer's propensity to make purchases in the future or a store's requirements for inventory in the future. In the process, a computer implemented method is used for automatically transforming data for data analysis such as predictive modeling. Database metadata that describe database tables, their interrelationships, dimensional information, fact tables and measures are accessed. A mining transformation profile is created to encapsulate aggregations and transformation on data stored in relational databases in order to convert the data to forms suitable for predictive mining tools. The mining transformation profile specifies data transformations relative to the data base metadata. Executable data transformation codes is then generated from the database metadata and the mining transformation profile. Execution of this code results in aggregation and transformation of data residing in a database for input to a data analysis tool such as a predictive modeling tool. The data transformation code can be used by, for example, the predictive modeling tool to generate an output that provides a solution to a business process problem.

System and method of transforming data for use in data analysis tools

Systems and methods for processing Machine Learning (ML) algorithms in a MapReduce environment are described. In one embodiment of a method, the method includes receiving a ML algorithm to be executed in the MapReduce environment. The method further includes parsing the ML algorithm into a plurality of statement blocks in a sequence, wherein each statement block comprises a plurality of basic operations (hops). The method also includes automatically determining an execution plan for each statement block, wherein at least one of the execution plans comprises one or more low-level operations (lops). The method further includes implementing the execution plans in the sequence of the plurality of the statement blocks.

Systems and methods for processing machine learning algorithms in a MapReduce environment

The present invention generally relates to computer databases and, more particularly, to data mining and knowledge discovery. The invention specifically relates to a method for constructing segmentation-based predictive models, such as decision-tree classifiers, wherein data records are partitioned into a plurality of segments and separate predictive models are constructed for each segment. The present invention contemplates a computerized method for automatically building segmentation-based predictive models that substantially improves upon the modeling capabilities of decision trees and related technologies, and that automatically produces models that are competitive with, if not better than, those produced by data analysts and applied statisticians using traditional, labor-intensive statistical techniques. The invention achieves these properties by performing segmentation and multivariate statistical modeling within each segment simultaneously. Segments are constructed so as to maximize the accuracies of the predictive models within each segment. Simultaneously, the multivariate statistical models within each segment are refined so as to maximize their respective predictive accuracies.

Method for constructing segmentation-based predictive models

A method of marketing optimization with respect to brand lifetime management formulates a problem of brand equity maximization utilizing Markov Decision Process (MDP) thereby casting brand equity management as a long term regard optimization problem in MDP, The marketing mix is optimized by formulating the mix as actions in MDP and, utilizing historical marketing and transaction data, aspects of the MDP are estimated.

System and method for marketing mix optimization for brand equity management

Feature importance information available in a predictive model with correlation information among the variables is presented to facilitate more flexible choices of actions by business managers. The displayed feature importance information combines feature importance information available in a predictive model with correlational information among the variables. The displayed feature importance information may be presented as a network structure among the variables as a graph, and regression coefficients of the variables indicated on the corresponding nodes in the graph. To generate the display, a regression engine is called on a set of training data that outputs importance measures for the explanatory variables for predicting the target variable. A graphical model structural learning module is called that outputs a graph on the explanatory variables of the above regression problem representing the correlational structure among them. The feature importance measure, output by the regression engine, is displayed for each node in the graph, as an attribute, such as color, size, texture, etc, of that node in the graph output by the graphical model structural learning module.

Method and apparatus for presenting feature importance in predictive modeling

The present invention employs data processing systems to handle debt collection by formulation the collections process as a Markov Decision Process with constrained resources, thus making it possible automatically to generate an optimal collections policy with respect to maximizing long-term expected return throughout the course of a collections process, subject to constraints on the available resources possibly in multiple organizations. This is accomplished by coupling data modeling and resource optimization within the constrained Markov Decision Process formulation and generating optimized rules based on constrained reinforcement learning process comprising applied on the basis of past historical data.

Method and system for debt collection optimization

A predictive model method (and structure) includes receiving an input data into an initial model to develop an initial model output and receiving both of the input data and the initial model output as input data into a first transform/regression stage.

Method and structure for transform regression

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Cortlandt Manor, NY, United States of America

Edwin Peter Dawson Pednault