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1. 发明申请

WO2020167465A1 ACCELERATOR FOR COMPUTING COMBINATORIAL COST FUNCTION 审中-公开
公开(公告)号：WO2020167465A1
公开(公告)日：2020-08-20
申请号：PCT/US2020/015514
申请日：2020-01-29
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： TROYER, Matthias , KATZGRABER, Helmut Gottfried , PATTISON, Christopher Anand
IPC分类号： G06N5/00 , G06F9/38 , G06Q10/04 , G06N3/12 , G06F17/10
摘要： A computing device, including memory, an accelerator device, and a processor. The processor may generate a plurality of data packs that each indicate an update to a variable of one or more variables of a combinatorial cost function. The processor may transmit the plurality of data packs to the accelerator device. The accelerator device may, for each data pack, retrieve a variable value of the variable indicated by the data pack and generate an updated variable value. The accelerator device may generate an updated cost function value based on the updated variable value. The accelerator device may be further configured to determine a transition probability using a Monte Carlo algorithm and may store the updated variable value and the updated cost function value with the transition probability. The accelerator device may output a final updated cost function value to the processor.

2. 发明申请

WO2021146011A1 CLUSTER UPDATE ACCELERATOR CIRCUIT 审中-公开
公开(公告)号：WO2021146011A1
公开(公告)日：2021-07-22
申请号：PCT/US2020/065046
申请日：2020-12-15
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： PATTISON, Christopher Anand , KATZGRABER, Helmut Gottfried , TROYER, Matthias
IPC分类号： G06F17/11 , G06N5/00 , G06Q10/04 , G06F9/5061 , G06N5/003 , G06N5/022 , G06N7/005
摘要： A computing device is provided, including a cluster update accelerator circuit configured to receive signals encoding a combinatorial cost function of a plurality of variables and a connectivity graph for the combinatorial cost function. In an energy sum phase, the cluster update accelerator circuit may determine a respective plurality of accumulated energy change values for the combinatorial cost function based at least in part on the connectivity graph. In an update phase, the cluster update accelerator circuit may determine a respective update indicator bit for each accumulated energy change value. In an encoder phase, based on the plurality of update indicator bits, the cluster update accelerator circuit may select a largest update-indicated cluster of the variables included in the connectivity graph. The cluster update accelerator circuit may output an instruction to update the variables included in the largest update-indicated cluster.

3. 发明申请

WO2021067402A1 COMPUTING STOCHASTIC SIMULATION CONTROL PARAMETERS 审中-公开
公开(公告)号：WO2021067402A1
公开(公告)日：2021-04-08
申请号：PCT/US2020/053480
申请日：2020-09-30
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： STEIGER, Damian, Silvio , KATZGRABER, Helmut, Gottfried , TROYER, Matthias , PATTISON, Christopher, Anand
IPC分类号： G06F30/25
摘要： A computing device is provided, including memory storing a cost function of a plurality of variables. The computing device may further include a processor configured to, for a stochastic simulation algorithm, compute a control parameter upper bound. The processor may compute a control parameter lower bound. The processor may compute a plurality of intermediate control parameter values within a control parameter range between the control parameter lower bound and the control parameter upper bound. The processor may compute an estimated minimum or an estimated maximum of the cost function using the stochastic simulation algorithm with the control parameter upper bound, the control parameter lower bound, and the plurality of intermediate control parameter values. A plurality of copies of the cost function may be simulated with a respective plurality of seed values.

4. 发明申请

WO2016022507A1 RECURSIVE HIERARCHICAL PROCESS FOR COMBINATORIAL OPTIMIZATION AND STATISTICAL SAMPLING 审中-公开
标题翻译：用于组合优化和统计抽样的综合分层过程
公开(公告)号：WO2016022507A1
公开(公告)日：2016-02-11
申请号：PCT/US2015/043515
申请日：2015-08-04
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： HASTINGS, Matthew B. , TROYER, Matthias , ZINTCHENKO, Ilia
IPC分类号： G06F17/11 , G06N3/12
CPC分类号： G06F17/18 , G06F17/11 , G06F17/5009 , G06N99/002
摘要： In some examples, techniques and architectures for solving combinatorial optimization or statistical sampling problems use a hierarchical approach. Such a hierarchical approach may be applied to a system or process in a patch-like fashion. A set of elements of the system correspond to a first tier. An objective function associates the set of elements with one another. The set of elements are partitioned into patches corresponding to a second tier. The patches individually include second tier elements that are subsets of the set of elements, and the individual patches have an energy configuration. The second tier elements of the patches are randomly initialized. Based, at least in part, on the objective function, a combinatorial optimization operation is performed on the second tier elements of the individual patches to modify the second tier elements of the individual patches.
摘要翻译：在一些示例中，用于解决组合优化或统计抽样问题的技术和架构使用分层方法。这种分层方法可以以贴片状的方式应用于系统或过程。系统的一组元素对应于第一层。一个目标函数将这组元素相互关联起来。该组元素被分割成与第二层相对应的补丁。补丁单独地包括作为该组元素的子集的第二层元素，并且各个补丁具有能量配置。补丁的第二层元素随机初始化。至少部分地基于目标函数，对各个补丁的第二层元素执行组合优化操作，以修改各个补丁的第二层元素。

5. 发明申请

WO2020167466A1 QUANTUM ALGORITHMS FOR SUPERVISED TRAINING OF QUANTUM BOLTZMANN MACHINES 审中-公开
公开(公告)号：WO2020167466A1
公开(公告)日：2020-08-20
申请号：PCT/US2020/015516
申请日：2020-01-29
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： WIEBE, Nathan O. , BOCHAROV, Alexei , SMOLENSKY, Paul , TROYER, Matthias , SVORE, Krysta
IPC分类号： G06N3/04 , G06N3/08 , G06N10/00 , G06N5/02
摘要： A method of training a quantum computer employs quantum algorithms. The method comprises loading, into the quantum computer, a description of a quantum Boltzmann machine, and training the quantum Boltzmann machine according to a protocol, wherein a classification error is used as a metric for the protocol.

6. 发明申请

WO2020061338A1 A QUANTUM-WALK-BASED ALGORITHM FOR CLASSICAL OPTIMIZATION PROBLEMS 审中-公开
公开(公告)号：WO2020061338A1
公开(公告)日：2020-03-26
申请号：PCT/US2019/051968
申请日：2019-09-19
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： TROYER, Matthias , SVORE, Krysta , HEIM, Bettina , POULIN, David
IPC分类号： G06N10/00
摘要： Example circuit implementations of Szegedy's quantization of the Metropolis-Hastings walk are presented. In certain disclosed embodiments, a quantum walk procedure of a Markov chain Monte Carlo simulation is implemented in which a quantum move register is reset at every step in the quantum walk. In further embodiments, a quantum walk procedure of a Markov chain Monte Carlo simulation is implemented in which an underlying classical walk is obtained using a Metropolis-Hastings rotation or a Glauber dynamics rotation. In some embodiments, a quantum walk procedure is performed in the quantum computing device to implement a Markov Chain Monte Carlo method; during the quantum walk procedure, an intermediate measurement is obtained; and a rewinding procedure of one or more but not all steps of the quantum walk procedure is performed if the intermediate measurement produces an incorrect outcome.

7. 发明申请

WO2017100016A1 SYSTEM, METHOD AND COMPUTER-READABLE MEDIUM FOR THE PARTIAL REINITIALIZATION OF OPTIMIZERS 审中-公开
标题翻译：用于优化器的部分优化的系统，方法和计算机可读介质
公开(公告)号：WO2017100016A1
公开(公告)日：2017-06-15
申请号：PCT/US2016/063744
申请日：2016-11-25
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： HASTINGS, Matthew B. , WIEBE, Nathan , ZINTCHENKO, Ilia , TROYER, Matthias
IPC分类号： G06F17/50 , G06F17/11
CPC分类号： G06N5/022 , G06F17/11 , G06F17/50 , G06F2217/08 , G06N99/005
摘要： In some examples, techniques and architectures for solving combinatorial optimization or statistical sampling problems use a recursive hierarchical approach that involves reinitializing various subsets of a set of variables. The entire set of variables may correspond to a first level of a hierarchy. In individual steps of the recursive process of solving an optimization problem, the set of variables may be partitioned into subsets corresponding to higher-order levels of the hierarchy, such as a second level, a third level, and so on. Variables of individual subsets may be randomly initialized. Based on the objective function, a combinatorial optimization operation may be performed on the individual subsets to modify variables of the individual subsets. Reinitializing subsets of variables instead of reinitializing the entire set of variables may allow for preservation of information gained in previous combinatorial optimization operations.
摘要翻译：在一些示例中，用于求解组合优化或统计抽样问题的技术和体系结构使用递归分层方法，其涉及重新初始化一组变量的各种子集。整个变量集合可以对应于层级的第一级别。在解决优化问题的递归过程的各个步骤中，该组变量可以被划分成对应于层级的更高级别级别的子集，诸如第二级别，第三级别等等。个别子集的变量可以随机初始化。基于目标函数，可以对各个子集执行组合优化操作以修改各个子集的变量。重新初始化变量的子集而不是重新初始化整个变量集可能允许保留在先前的组合优化操作中获得的信息。

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