公开(公告)号：US20220269751A1

公开(公告)日：2022-08-25

申请号：US17213731

申请日：2021-03-26

申请人： MULTIVERSE COMPUTING S.L.

发明人： Samuel MUGEL ,  Román ORÚS ,  Saeed JAHROMI ,  Serkan SAHIN

IPC分类号： G06F17/11 ,  G06N20/00

摘要： A computer-implemented method is provided whereby an equation with a cost function for minimization is solved by a tensor network. Coefficients of tensors of the tensor network are modified so as to reduce a value of the cost function in an iterative process until convergence is reached, at which point the concerned Unconstrained Optimization problem is solved and the values of the variables of the cost function are provided.

公开(公告)号：US20230342644A1

公开(公告)日：2023-10-26

申请号：US17729585

申请日：2022-04-26

申请人： Multiverse Computing, S.L.

发明人： Román ORÚS ,  Samuel MUGEL ,  Saeed JAHROMI

IPC分类号： G06N7/00

CPC分类号： G06N7/005

摘要： A computer-implemented method including: receiving data including a probability distribution of a dataset or a multivariate probability distribution about a target, the probability distribution relating to a plurality of discrete random variables; providing a tensor codifying the probability distribution such that each configuration of the plurality of discrete random variables has its respective probability codified therein; encoding the tensor into a tensor network in the form of a matrix product state, where an external index of each tensor of the tensor network represents one discrete random variable of the plurality discrete random variables, and an internal index or internal indices of each tensor of the tensor network represents correlation between the tensor and the corresponding adjacent tensor of the tensor network; and computing at least one moment of the probability distribution by processing the tensor network for sampling of the probability distribution.

公开(公告)号：US20240177809A1

公开(公告)日：2024-05-30

申请号：US18087779

申请日：2022-12-22

申请人： Multiverse Computing, S.L.

发明人： Román ORÚS ,  Saeed JAHROMI

IPC分类号： G16C10/00 ,  G06N3/091 ,  G06N10/20

CPC分类号： G16C10/00 ,  G06N3/091 ,  G06N10/20

摘要： A computer-implemented method includes processing a predetermined machine learning routine of a tensor network that defines layers of tensors in the routine, which is adapted for a regression problem of fermionic systems that are molecules or chemical reactions. Each tensor of the tensor network of the predetermined machine learning routine is converted into a parity preserving tensor. A sign swap tensor is introduced in the tensor network at each crossing of legs of different tensors in the tensor network. Thus, implementing anticommutation fermionic operator; inputting a first many-body problem modeling a first fermionic system in the processed predetermined machine learning routine, the first fermionic system being a molecule or a chemical reaction; and outputting from the processed predetermined machine learning routine at least one parameter for the first fermionic system after having inputted the first many-body problem. At least one parameter is inferred by the processed predetermined machine learning routine.

公开(公告)号：US20230341824A1

公开(公告)日：2023-10-26

申请号：US17729575

申请日：2022-04-26

申请人： Multiverse Computing, S.L.

发明人： Román ORÚS ,  Samuel MUGEL ,  Serkan SAHIN ,  Saeed JAHROMI ,  Chia-Wei HSING ,  Raj PATEL ,  Samuel PALMER

IPC分类号： G05B13/02 ,  G06N3/08

CPC分类号： G05B13/027 ,  G06N3/084

摘要： A device or system configured to: receive a set of data associated with a monitored target, the set of data having N features, where N is a natural number greater than one; input the N features of the received set of data into a trained neural network for determining a condition or characteristic of the target with a plurality of sets of historical data associated with the target, each set of the plurality of sets of historical data having N features, the neural network at least having N inputs and one or more outputs, the neural network having one or more hidden layers, each hidden layer being a tensor network in the form of a matrix product operator, MPO, with a respective plurality of tensors and having a respective predetermined activation function per hidden layer or per tensor in the MPO; and input the N features into the neural network, determining a condition or characteristic of the target by processing the one or more outputs. Also, a device or system configured to train such neural network.