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公开(公告)号：US20240020564A1
公开(公告)日：2024-01-18
申请号：US17863508
申请日：2022-07-13
发明人： Shashanka Ubaru , Kenneth Lee Clarkson , Ismail Yunus Akhalwaya , Mark S. Squillante , Vasileios Kalantzis , Lior Horesh
摘要： Systems and methods for operating a quantum system are described. A controller of a quantum system can generate a command signal. The quantum system can include quantum hardware having a plurality of qubits. An interface of the quantum system can control the quantum hardware based on the command signal to generate a random state vector represented by the plurality of qubits. The random state vector can include a specific number of independent entries. The interface can control the quantum hardware to determine moments of a matrix based on the random state vector. The controller can be further configured to output the moments of the matrix to a computing device to estimate a trace of the matrix using the moments.
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公开(公告)号：US20220207376A1
公开(公告)日：2022-06-30
申请号：US17134814
申请日：2020-12-28
发明人： Tayfun Gokmen , Oguzhan Murat Onen , Chai Wah Wu , Mark S. Squillante , Malte Johannes Rasch , Tomasz J. Nowicki , Wilfried Haensch , Lior Horesh , Vasileios Kalantzis , Vanessa Lopez-Marrero
摘要： Matrix inversion systems and methods are implemented using an analog resistive processing unit (RPU) array for hardware accelerated computing. A request is received from an application to compute an inverse matrix of a given matrix, and a matrix inversion process is performed in response to the received request. The matrix inversion process includes storing a first estimated inverse matrix of the given matrix in an array RPU cells, performing a first iterative process on the first estimated inverse matrix stored in the array of RPU cells to converge the first estimated inverse matrix to a second estimated inverse matrix of the given matrix, and reading the second estimated inverse matrix from the array of RPU cells upon completion of the first iterative process. An inverse matrix is returned to the application, wherein the returned inverse matrix is based, at least in part, on the second estimated inverse matrix.
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公开(公告)号：US20240127084A1
公开(公告)日：2024-04-18
申请号：US17956065
申请日：2022-09-29
发明人： Yuya Jeremy Ong , Aly Megahed , Mark S. Squillante , Yingdong Lu , Yitao Liang , Pravar Mahajan
IPC分类号： G06N5/04
CPC分类号： G06N5/04
摘要： Methods, systems, and computer program products for a joint prediction and improvement framework for machine learning models are provided herein. A method includes obtaining a machine learning model initialized with a set of parameters; identifying one or more actions based on test inputs corresponding to the machine learning model and historical actions related to a task, where the historical actions are dependent on respective historical outputs of the machine learning model; using the identified one or more actions to jointly compute: one or more first values corresponding to inference loss for the machine learning model; and one or more second values based at least in part on a computing cost function associated with the task; and updating the set of parameters of the machine learning model based on the one or more first values and the one or more second values.
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公开(公告)号：US20220366005A1
公开(公告)日：2022-11-17
申请号：US17245801
申请日：2021-04-30
发明人： Tomasz J. Nowicki , Oguzhan Murat Onen , Tayfun Gokmen , Vasileios Kalantzis , Chai Wah Wu , Mark S. Squillante , Malte Johannes Rasch , Wilfried Haensch , Lior Horesh
摘要： 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.
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公开(公告)号：US11455562B2
公开(公告)日：2022-09-27
申请号：US16573862
申请日：2019-09-17
发明人： Tal Kachman , Lior Horesh , Giacomo Nannicini , Mark S. Squillante , John A. Gunnels , Kenneth L. Clarkson
IPC分类号： G06N10/00 , G06F17/11 , H03K19/195 , G06N5/00 , G06N10/60
摘要： A method of detecting cliques in a graph includes determining, based on a number of nodes in the graph, a number of qubits to be included in a quantum processor. The method includes assigning to each node in the graph, a qubit of the quantum processor. The method includes operating on the qubits with a preparation circuit to create a quantum state in the qubits that corresponds to the graph. The method includes operating on the quantum state with a random walk circuit, and measuring the qubits of the quantum processor to detect cliques in the graph. The preparation circuit comprises a plurality of single- and two-qubit operators, wherein, for each pair of adjacent nodes in the graph, an operator of the plurality of two-qubit operators acts on a pair of qubits corresponding to the pair of adjacent nodes to create the quantum state.
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公开(公告)号：US20220107989A1
公开(公告)日：2022-04-07
申请号：US17065277
申请日：2020-10-07
发明人： Tal Kachman , Mark S. Squillante , Lior Horesh , Kenneth Lee Clarkson , John A. Gunnels , Ismail Yunus Akhalwaya , Jayram Thathachar
摘要： 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.
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公开(公告)号：US20170277568A1
公开(公告)日：2017-09-28
申请号：US15081827
申请日：2016-03-25
CPC分类号： G06Q10/06312 , G06F9/50 , G06Q10/067 , G06Q10/20 , Y02D10/22
摘要： A model is built of benefit of each of a plurality of computing tasks under uncertainty as a function of computing resources invested in each of the computing tasks, and a model of risk is built of each of the computing tasks under uncertainty as a function of the computing resources invested in each of the computing tasks. Risk of a task allocation is calculated with the risk model, and benefit of a task allocation is calculated with the benefit model. An allocation of the computing resources is found to increase the benefit and manage the risk. The allocation of computing resources is applied to the computing tasks.
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公开(公告)号：US20150317646A1
公开(公告)日：2015-11-05
申请号：US14472637
申请日：2014-08-29
发明人： Debarun Bhattacharjya , Lena Granovsky , Saleem Hussain , Yingdong Lu , Irvin J. Lustig , Bonnie K. Ray , Mark S. Squillante , Xiaoting Wang
CPC分类号： G06Q30/02 , G06Q30/0201 , G06Q30/0202 , G06Q40/00
摘要： One or more processors determine a financial target for a plurality of business accounts across a plurality of product brands that are included in a business account level of an organizational hierarchy of a business organization. The organizational hierarchy includes a plurality of levels. One or more processors determine respective financial targets and quotas for a plurality of nodes included in a level of the organizational hierarchy. One or more processors determine respective financial targets for combinations of business accounts and product brands. The determination of the financial targets is based on a statistical model that is fitted at a middle level of the organizational hierarchy, and a risk-based stochastic optimization that is used to set financial targets and quotas at one or more levels of the organizational hierarchy.
摘要翻译： 一个或多个处理器确定跨越商业组织的组织层次结构的商业帐户层级中的多个产品品牌的多个商业帐户的财务目标。 组织层次结构包括多个层次。 一个或多个处理器确定包括在组织层级的级别中的多个节点的相应财务目标和配额。 一个或多个处理器确定用于商业帐户和产品品牌的组合的各自的财务目标。 财务目标的确定基于在组织层次结构的中间层次中拟合的统计模型，以及用于在组织层次结构的一个或多个层次设置财务目标和配额的基于风险的随机优化。
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公开(公告)号：US20240020563A1
公开(公告)日：2024-01-18
申请号：US17863449
申请日：2022-07-13
发明人： Ismail Yunus Akhalwaya , Shashanka Ubaru , Kenneth Lee Clarkson , Mark S. Squillante , Vasileios Kalantzis , Lior Horesh
摘要： Systems and methods for operating quantum systems are described. A controller of a quantum system can generate a command signal. The quantum system can include quantum hardware having a plurality of qubits. An interface of the quantum system can control the quantum hardware based on the command signal to sample an input vector represented by the first set of qubits, where the input vector includes mixed states with different Hamming weights. The interface can control the quantum hardware to entangle the first set of qubits to the second set of qubits, where the second set of qubits represent a count of nonzero elements in the input vector. The interface can control the quantum hardware to generate an output vector based on the entanglement of the first set of qubits to the second set of qubits, where the output vector includes one or more states having a specific Hamming weight.
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公开(公告)号：US11657312B2
公开(公告)日：2023-05-23
申请号：US16778878
申请日：2020-01-31
发明人： Ismail Yunus Akhalwaya , Kenneth Clarkson , Lior Horesh , Mark S. Squillante , Shashanka Ubaru , Vasileios Kalantzis
摘要： 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.
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