International Business Machines Corporation

Ramot at Tel-aviv University Ltd.

Tufts College

Shell Oil Comapny

University of Massachusetts

University of British Columbia

Tufts University

Tel Aviv-yafo University

Xtract Technologies Inc.

Lior Horesh

Raya Horesh

Guy M Cohen

Giacomo Nannicini

Vasileios Kalantzis

Marco Pistoia

Mark Steven Squillante

Haim Avron

Andrew R Conn

Theodore Gerard Van Kessel

Shashanka Ubaru

Kenneth Lee Clarkson

John A Gunnels

Amos Cahan

Omer Tripp

Matthias Kormaksson

Moshood Omolade Saliu

Misha E Kilmer

Tal Kachman

Tayfun Gokmen

Ulisses Mello

Oguzhan Murat Onen

Wilfried Haensch

David Nahamoo

Chai Wah Wu

Tomasz J Nowicki

Wang Zhou

Jonathan Lenchner

Tara N Sainath

Oki Gunawan

Robert L Wisnieff

Aleksandr Y Aravkin

Edwin Peter Dawson Pednault

Malte Johannes Rasch

Ismail Yunus Akhalwaya

Gijs Van Essen

Elizabeth Newman

Dimitri Kanevsky

Catherine Helen Crawford

Brian E D Kingsbury

Oktay Gunluk

Sanjeeb Dash

Sippe Douma

Eduardo Antonio Arismendi Jimenez

Eduardo Antonio Jimenez Arismendi

Anthony J Annunziata

Claudius Feger

Shai Halevi

David James Frank

Richard Chen

Karthik Visweswariah

Ramesh Natarajan

Christopher Wildsmith

James Jeffrey Wynne

Dario Gil

Ankur Narang

Jayram S Thathachar

Parikshit Ram

Alexander Gray

Cristina Cornelio

Georgios Kollias

Gal Shulkind

Francesca Rossi

Leo S Liberti

Osman Asif Malik

Jiani Zhang

Sara Magliacane

Ogunzhan Murat Onen

Michael Widman

Ning Hao

Eldad Haber

Maria J B Moura

Gijs Michiel Van Essen

Sippe Geert Douma

Arya Mazumdar

Elliot Holtham

A processor performing machine learning including spectral clustering can receive data from the sensor. Graph Laplacian of the data can be created and stored in a memory device. Spectral characteristic can be created by applying density of states and spectral gaps can be detected in an unsupervised manner in the spectral characteristic to determine r as number of clusters to cluster the data. A range space of a rational matrix of the graph Laplacian can be determined. K-means clustering can be performed on the range space of rational matrix of the graph Laplacian using r as the number of clusters, the K-means clustering returning r clusters of the received data.

Spectral clustering of high-dimensional data

Methods and systems for determining control inputs to a manufacturing apparatus to manufacture a product are described. A processor may receive model data including initial state data indicating an initial state of an input material, a set of model control inputs, and target measurement data associated with a target product. The processor may learn a causal predictive model based on the target data. Each state of the causal predictive model may be based on an application of the model control inputs on a previous state of the causal predictive model. The processor may compare a final state of the causal predictive model with the target measurement data to determine a difference. The processor may determine, based on the difference, a set of control inputs to be assigned to one or more controls. The one or more controls may define a design of the manufacturing process of an end product.

Free-form production based on causal predictive models

Techniques are provided to implement hardware accelerated computing of eigenpairs of a matrix. For example, a system includes a processor, and a resistive processing unit coupled to the processor. The resistive processing unit includes an array of cells which include respective resistive devices, wherein at least a portion of the resistive devices are tunable to encode values of a given matrix which is storable in the array of cells. When the given matrix is stored in the array of cells, the processor is configured to determine an eigenvector of the stored matrix by executing a process which includes performing analog matrix-vector multiplication operations on the stored matrix to converge an initial vector to an estimate of the eigenvector of the stored matrix.

Hardware acceleration for computing eigenpairs of a matrix

A method is presented for computing an equilibrium distribution of Markov processes. The method includes storing weight values in an analog crossbar array of transition probability matrices, where the analog crossbar array of transition probability matrices represents a weight matrix with m rows and n columns, computing an eigenvector associated with a real eigenvalue of modulus one for each of the transition probability matrices, applying a gradient-based eigenvalue solver to converge to a dominant eigenpair, and determining a probability of changing from one state to another state in a stochastic entity based on outcomes of the gradient-based eigenvalue solver.

Markov processes using analog crossbar arrays

Techniques and a system to facilitate estimation of a quantum phase, and more specifically, to facilitate estimation of an expectation value of a quantum state, by utilizing a hybrid of quantum and classical methods are provided. In one example, a system is provided. The system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include an encoding component and a learning component. The encoding component can encode an expectation value associated with a quantum state. The learning component can utilize stochastic inference to determine the expectation value based on an uncollapsed eigenvalue pair.

Short-depth active learning quantum amplitude estimation without eigenstate collapse

A system, method, and computer program product are disclosed. The method includes loading a first set of data as an initial matrix and determining a truncated singular value decomposition (SVD) of the initial matrix. The method also includes loading a second set of data as a new matrix, generating a first projection matrix, which approximates k leading left singular vectors of the updated matrix, and generating a second projection matrix, which approximates k leading right singular vectors of the updated matrix. Further, the method includes determining based on the initial matrix, the new matrix, the SVD of the existing matrix, and the first or second projection matrix, an approximate truncated SVD of the updated matrix.

Projection-based techniques for updating singular value decomposition in evolving data sets

A medication dispenser system and use thereof for providing exact personal dosing for a patient are provided. In one aspect, a method for dispensing medications is provided. The method includes the steps of: obtaining patient information and a list of the medications for the patient; calculating a personal dose of each of the medications for the patient using the patient and cross-medication interaction information; and preparing treatments each containing the personal dose of each of the medications in a single treatment. In another aspect a system for dispensing medications includes: a patient information component for obtaining patient information and a list of the medications for the patient; a dosage calculator component for calculating a personal dose of each of the medications for the patient using the patient information; and a medication dispenser component for preparing treatments each containing the personal dose of each of the medications in a single treatment.

Medication dispenser system with exact personal dosing

A method includes computing a diffusion vector starting with a seed, querying nodes for connections, reweighting diffusion vector based on the degrees, sorting nodes based upon magnitude in the reweighted diffusion vector which is obtained through wave relaxation solution of a time-dependent initial value problem, detecting a community through a sweep over the nodes according to their rank, and selecting a prefix that minimizes or maximizes an objective function.

System and method for discovering ad-hoc communities over large-scale implicit networks by wave relaxation

Techniques for generating and managing, including simulating and training, deep tensor neural networks are presented. A deep tensor neural network comprises a graph of nodes connected via weighted edges. A network management component (NMC) extracts features from tensor-formatted input data based on tensor-formatted parameters. NMC evolves tensor-formatted input data based on a defined tensor-tensor layer evolution rule, the network generating output data based on evolution of the tensor-formatted input data. The network is activated by non-linear activation functions, wherein the weighted edges and non-linear activation functions operate, based on tensor-tensor functions, to evolve tensor-formatted input data. NMC trains the network based on tensor-formatted training data, comparing output training data output from the network to simulated output data, based on a defined loss function, to determine an update. NMC updates the network, including weight and bias parameters, based on the update, by application of tensor-tensor operations.

Generating and managing deep tensor neural networks

An example operation may include one or more of invoking, via an operating system, execution of a plurality of software programs having a first mode of operation that causes the plurality of software programs to operate in a first resource consuming mode, monitoring physical resources of a computing device that are consumed by the plurality of software programs, determining to reduce or allow expanded consumption of the physical resources of the computing device by the plurality of software programs based on the monitored physical resources, and in response to the determination, switching from a first mode of operation of a software program from among the plurality of software programs and to a second mode of operation of the software program that causes the software program to operate in a second resource consuming mode that consumes either less or more physical resources than the first resource consuming mode.

Operating system based on dual system paradigm

A method for performing sparse quantum Fourier transform computation includes defining a set of quantum circuits, each quantum circuit comprising a Hadamard gate and a single frequency rotation operator, said set of quantum circuits being equivalent to a quantum Fourier transform circuit. The method includes constructing a subset of said quantum circuits in a quantum processor, said quantum processor having a quantum representation of a classical distribution loaded into a quantum state of said quantum processor. The method includes executing said subset of said quantum circuits on said quantum state, and performing a measurement in a frequency basis to obtain a frequency distribution corresponding to said quantum state.

Quantum sparse Fourier transform

Techniques are provided to implement hardware accelerated application of preconditioners to solve linear equations. For example, a system includes a processor, and a resistive processing unit coupled to the processor. The resistive processing unit includes an array of cells which include respective resistive devices, wherein at least a portion of the resistive devices are tunable to encode entries of a preconditioning matrix which is storable in the array of cells. When the preconditioning matrix is stored in the array of cells, the processor is configured to apply the preconditioning matrix to a plurality of residual vectors by executing a process which includes performing analog matrix-vector multiplication operations on the preconditioning matrix and respective ones of the plurality of residual vectors to generate a plurality of output vectors used in one or more subsequent operations.

Hardware acceleration with preconditioners

A computer implemented method for speeding up execution of a convex optimization operation one or more quadratic complexity operations to be performed by an analog crossbar hardware switch, and identifying one or more linear complexity operations to be performed by a CPU. At least one of the quadratic complexity operations is performed by the analog crossbar hardware, and at least one of the linear complexity operations is performed by the CPU. An iteration of an approximation of a solution to the convex optimization operation is updated by the CPU.

Accelerated Quasi-Newton methods on analog crossbar hardware

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

Systems, computer-implemented methods, and computer program products that can facilitate quantum topological classification are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a topological component that employs one or more quantum computing operations to identify one or more persistent homology features of a topological simplicial structure. The computer executable components can further comprise a topological classifier component that employs one or more machine learning models to classify the topological simplicial structure based on the one or more persistent homology features.

Quantum topological classification

Growing community of inventors

North Salem, NY, United States of America

Lior Horesh