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WO2018160599A1 - Calcul quantique en tant que service - Google Patents

Calcul quantique en tant que service Download PDF

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Publication number
WO2018160599A1
WO2018160599A1 PCT/US2018/020033 US2018020033W WO2018160599A1 WO 2018160599 A1 WO2018160599 A1 WO 2018160599A1 US 2018020033 W US2018020033 W US 2018020033W WO 2018160599 A1 WO2018160599 A1 WO 2018160599A1
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WO
WIPO (PCT)
Prior art keywords
platform
user service
service requests
processing devices
quantum processing
Prior art date
Application number
PCT/US2018/020033
Other languages
English (en)
Inventor
Matthew C. JOHNSON
David A.B. HYDE
Peter Mcmahon
Kin-Joe Sham
Kunle Tayo OGUNTEBI
Asier Ozaeta RODRIGUEZ
Original Assignee
QC Ware Corp.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US15/446,973 external-priority patent/US10614370B2/en
Application filed by QC Ware Corp. filed Critical QC Ware Corp.
Publication of WO2018160599A1 publication Critical patent/WO2018160599A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/76—Architectures of general purpose stored program computers
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
    • G06N10/40—Physical realisations or architectures of quantum processors or components for manipulating qubits, e.g. qubit coupling or qubit control
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
    • G06N10/80—Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computers; Platforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • This invention relates to the field of quantum computing, and more specifically to quantum computing as a service.
  • Quantum processing devices exploit the laws of quantum mechanics in order to perform computations. Quantum processing devices commonly use so-called qubits, or quantum bits, rather than the bits used in classical computers. Classical bits always have a value of either 0 or 1. Roughly speaking, qubits have a non-zero probability of existing in a superposition, or linear combination, of 0 and 1. Certain operations using qubits and control systems for computing using qubits are further described in U.S. Patent Application Ser. No. 09/872,495, "Quantum Processing System and Method for a Superconducting Phase Qubit," which is hereby incorporated by reference in its entirety.
  • the present disclosure overcomes the limitations of the prior art by providing access to "quantum computing as a service” (QCaaS).
  • QaaS quantum computing as a service
  • Such a service may enable, for instance, development of software for quantum processing devices in a manner that reduces programmer burden and that offers cross-platform compatibility for different devices.
  • service offerings allow users to use one or more quantum processing devices available through the service platform, for development, testing, and execution of quantum software, as well as for other such use cases reasonably afforded by quantum processing devices.
  • Various software modules may provide additional functionality for users of quantum processing devices.
  • FIG. 1 is a logical diagram of one instance of a platform component of a QCaaS architecture and system, including certain related infrastructure components, described in accordance with one or more embodiments of the invention.
  • FIG. 2 is a logical diagram of some of the various software modules and components of the Server-Side Platform Library in FIG. 1, described in accordance with one or more embodiments of the invention.
  • FIG. 3 is a logical diagram of some of the various software modules and components of the Client-Side Platform Library in FIG. 1, described in accordance with one or more embodiments of the invention.
  • FIG. 1 is a logical diagram of one instance of a platform component of a QCaaS (quantum computing as a service) architecture and system, including certain related infrastructure components, described in accordance with one or more embodiments of the invention.
  • dashed lines indicate boundaries where information passes between different machines and/or logical realms.
  • an end user 110 working on a machine 100 interfaces with a client-side platform library 115 (explained in more detail in FIG. 3). This interaction may be, for example, the user inputting a problem in software code and calling a function in the client-side platform library 115 to pass that problem into the QCaaS system.
  • the client-side platform library 115 then may communicate with a client- side web service or remote procedure call (RPC) interface 120 in order to transmit information about the problem to the remote QCaaS platform, for example in the form of user service requests.
  • RPC remote procedure call
  • the client-side platform library 115 may have any number of features to expose functionality for and ease the programming burden of the user. For instance, FIG. 3 shows several different modules that may be contained within the client-side library. This example includes a collection of general-purpose software routines, data structures, API endpoints, etc. in block 118. Additionally, the client-side library 115 may include any number of domain-specific libraries: collections of software routines, data structures, API endpoints, etc. that are designed specifically to aid with performing computations for specific domains.
  • Examples include graph analytics 117A, finance 117B, machine learning 117C, or any other domains 117N.
  • the additional libraries 117N need not be domain specific libraries but may be any such additional libraries or modules that add value and functionality to the client-side platform library 115.
  • the client-side platform library 115 is designed in such a way as to be extensible by any other such possible modules.
  • the next step is to organize the user requests, for example by routing the information through a load balancer and/or queuing system 125. For instance, if many users are simultaneously using the QCaaS platform, and there are only limited computational resources available through the platform, some of the system preferable will schedule and order the processing of various users' submitted tasks in a reasonable manner. Any number of standard load balancing and queuing algorithms and policies may be used. For instance, one may use a standard round robin algorithm for load balancing.
  • a frontend server 101 may authenticate the user 110 using a database 135 and library 130.
  • the frontend server 101 may also log the information supplied by the user via a logging library 130 and store that information in a database 135.
  • the frontend server might record copies of the problems that users submit to the platform.
  • the load balancer / queuing system 125 deems that a problem is ready to run on the QCaaS system, the information is passed to one or more backend servers 102 in a format governed by a server-side web service / RPC interface 140.
  • the backend servers 102 generally process the user problems to a form suitable for use with quantum processing devices.
  • the problem information arrives at the server-side platform library 145 (explained in more detail in FIG. 2).
  • the platform library 145 may interact with debugging libraries 150 or other similar libraries (e.g. logging libraries).
  • the platform library 145 contains a variety of algorithms for taking a
  • the platform library 145 is not necessarily limited to interacting with just quantum processing devices.
  • One or more classical solver libraries 160 for conventional processing devices may also be used by the platform library 145 for various purposes (e.g. to solve some part of a problem that is not well-suited to a quantum processing solution, or to compare an answer obtained on a quantum processing device to an answer obtainable via a classical solver library).
  • the server-side platform library 145 processes a computational problem and any relevant information about it and passes that processed form onto one or more quantum computing interfaces, such as quantum processing device vendor APIs and/or SDKs 155.
  • quantum processing device vendor APIs and/or SDKs 155 For example, if the end user 110 is solving a quadratic binary optimization problem, this problem may be converted by 145 into a form amenable for a D- Wave quantum processing device, whereupon the server-side platform library 145 passes the processed form of the problem to the low-level D-Wave API 155. Low-level APIs like these directly interact with the underling quantum processing devices 103. The quantum processing devices return one or more solutions, and possibly other related information, which are propagated back up the chain, to 155 and then to 145.
  • Solutions and information e.g. from 150, 155, and 160, are passed to and coalesced by 145.
  • the resulting coalesced data returns to the user, for example via a reverse path through the server-side interface 140, load balancer 125, client-side interface 120, and client-side platform library 115, to finally arrive back at the end user 110.
  • the quantum processing devices 103 may be one or more physical devices that perform processing especially based upon quantum effects, one or more devices that act in such a way, one or more physical or virtual simulators that emulate such effects, or any other devices or simulators that may reasonably be interpreted as exhibiting quantum processing behavior.
  • quantum processing devices include, but are not limited to, the devices produced by D-Wave Systems Inc., such as the quantum processing devices (and devices built upon the architectures and methods) described in U.S. Patent Application Ser. No. 14/453,883, "Systems and Devices for Quantum Processor Architectures" and U.S. Patent Application Ser. No. 12/934,254, "Oubit [sic] Based Systems, Devices, and Methods for Analog Processing," both of which are hereby incorporated by reference in their entirety.
  • Other quantum processing devices are under development by various companies, such as Google and IBM.
  • Shor's algorithm describes how a quantum processing device can be used to efficiently factor large integers, which has significant applications and implications for cryptography.
  • Graver's search algorithm describes how a quantum processing device can be used to efficiently search a large set of information, such as a list or database. For further examples, see e.g. Shor, 1997, SIAM J. of Comput. 26, 1484; Graver, 1996, Proc. 28th STOC, 212 (ACM Press, New York); and Kitaev, LANL preprint quant- ph/9511026, each of which is hereby incorporated by reference in their entireties.
  • FIG. 2 is a logical diagram of some of the various software modules and components of the Server-Side Platform Library 145 in FIG. 1, described in accordance with one or more embodiments of the invention. Data flows into and out of the platform library 145 via the interfaces 140,155 at the top and bottom of the diagram, corresponding to connections to other components in FIG. 1.
  • the platform library 145 may contain one or more domain-specific libraries 200 that may be useful for developing software for or solving problems on quantum processing devices.
  • Each domain-specific library may include software routines, data models, and other such resources as may typically appear in a software library, just as in the case of the client- side platform library 115 shown in FIG 3.
  • FIG. 2 specifically shows graph analytics 200A, finance 200B, and machine learning 200C as domains where domain-specific libraries and routines may be especially useful, but library 200N emphasizes that any domain-specific library may be incorporated at this layer of the platform library.
  • the numbering 200A-N emphasizes the extensible nature of the platform library. Based upon the components lower down in the diagram, any number of domain-specific libraries 200 may be written and integrated into the platform library 145.
  • the API 205 exposes the functions, data structures, models, and other core interfaces of the platform library 145.
  • the API 205 may connect with one or more libraries 200A-N and/or may directly communicate with the server-side web service / RPC interface 140, depending on the information being supplied to the platform library 145.
  • the API 205 is responsible for examining a problem and whatever information is supplied to the platform library 145 and determining how to execute the problem on quantum processing devices and/or classical solver libraries, with the help of the remaining modules shown in FIG. 2.
  • TSP Traveling Salesman Problem
  • Benders' method an optimization problem is split into an alternating sequence of linear and integer optimization problems that are each easier to solve than the overall optimization problem, which may be nonlinear, non-integer, etc. Benders' method offers certain bounds showing that a solution obtained via the decomposition method is within some approximation of the overall problem's optimal solution.
  • Another potential problem decomposition approach is based on the "randomized search" idea used in, for example, U.S. Patent Application Ser. No. 13/332,721, which is incorporated by reference here in its entirety.
  • problem decomposition is especially useful in the context of QCaaS since the currently commercially available quantum processing devices have significantly limited memory.
  • problems of practical size must be decomposed in order to fit into the memories of the available quantum processing devices.
  • the modules, 215, 220, and 225 relate to taking a discrete optimization problem of one form and converting it into a quadratic binary unconstrained form. While certainly not all problems solved on quantum processing devices are discrete optimization problems, the relatively popular D-Wave quantum processing devices have been found to be especially well-suited for this class of problems.
  • the platform library 145 includes these special modules 215, 220, and 225.
  • Module 215 uses heuristics to convert an integer optimization problem into a binary optimization problem.
  • One such heuristic operates when the integer formulation of the optimization problem is over a finite set of choices. For instance, if a variable in an integer optimization problem may take on the values 2, 3, or 4, then this may be recast as three binary variables corresponding to (a) whether or not the value is 2, (b) whether or not the value is 3, and (c) whether or not the value is 4, along with some mutual exclusivity constraints between the three binary variables (e.g., the sum of the three binary variables must equal one). While this is a standard heuristic used for integer to binary optimization problem conversion, other heuristics and algorithms may be implemented by module 215 as appropriate.
  • Module 220 uses heuristics to convert a higher-order polynomial binary optimization problem into a quadratic binary optimization problem.
  • module 220 use a third-party software library to provide such functionality (indeed, this is generally true of all components of the platform). For instance, module 220 may use the "reduce degree" utility in the D-Wave low-level API.
  • Another approach is based on adding penalty terms to ensure that ancillas get the correct values (if penalty terms are too small, ancillas may easily obtain the wrong values). Such an approach is documented in arXiv:0801.3625v2 [quant-ph], which is incorporated by reference here in its entirety.
  • Module 225 uses heuristics involving penalty terms to convert a constrained binary optimization problem into an unconstrained binary optimization problem.
  • One implementation of this module is documented in Pierre Hansen, "Methods of Nonlinear 0-1 Programming," Annals of Discrete Mathematics, Elsevier, 1979, which is incorporated by reference here in its entirety. Other mathematical techniques for constrained to unconstrained conversion for binary optimization problems will be apparent.
  • modules 215, 220, 225 may be used in order to prepare the problem for solution on the quantum processing devices and/or other solver libraries underlying the platform.
  • Other such modules are certainly possible and may also be used within the platform.
  • one such module converts formulations of problems that can be run on quantum annealing processing devices (such as the quantum processing devices developed by D-Wave) to formulations that can be run on gate-model quantum processing devices.
  • quantum annealing processing devices such as the quantum processing devices developed by D-Wave
  • Such a module may work via "Trotterization," see e.g. arXiv: 1611.00204v2 [quant-ph] (which is incorporated by reference here in its entirety).
  • Another such possible module would convert gate-model formulations of certain problems into formulations amenable to solution on quantum annealing processing devices.
  • Other additional relevant modules may be included at this layer of the platform. The above list is not exhaustive.
  • Module 230 provides optimizations for the processed problem in order to improve the quality of the solution obtained via the platform.
  • the operations performed are documented in arXiv: 1503.01083vl [quant-ph], which is incorporated by reference here in its entirety. Roughly speaking, we first obtain the strict embedding (the percentage of chains whose qubits all take the same value) for different values of intracoupling strength ] E . We then fit this data with a Sigma curve. Next, we use the Sigma curve to determine the point at which the strict embedding is 0.5. Then, for a defined range of values centered at 0.5, we calculate the 7r eiite value (the best percentage of the obtained logic energies). Finally, we use the ] E value that results in the best n elite .
  • embedding tools 235, 240 may be run to fit the problem onto a model of the particular hardware architecture of a target quantum processing device. For instance, if a problem is to be solved using a D-Wave quantum processing device, these tools will map the problem onto the chimera graph architecture of that device.
  • the embedding tool 235 may be vendor-supplied or a third-party library, whereas tool 240 can take the output of another embedding tool 235 and provide additional optimizations to make the embedding as compact as possible.
  • Tool 240 may function by comparing an input problem to similar problems input in the past, and using the good embeddings found for those prior problems as starting points for computing good embeddings for the current input problem.
  • 240 may operate by running the embedding tool 235 multiple times, and choosing the best result to use as the embedding (such may be the mode of operation when tool 235 produces different outputs for different executions).
  • the "best" output of tool 235 may be the embedding with the fewest number of qubits used, the embedding with the shortest chain lengths, or some other criteria that may be specified.
  • Other techniques may be incorporated into the platform for selecting and optimizing embeddings.
  • the embedded problem (output of tools 235 and/or 240) is then optimized for execution on a specific device via modules 245, 250, 255.
  • One such process is essentially the same process as performed by embedding tool 230.
  • the gauge selection module 250 is based on the techniques in arXiv: 1503.01083vl [quant-ph], which is incorporated by reference here in its entirety.
  • the collection of modules in the server-side platform library may be executed iteratively or in the style of a "feedback loop," where one or more of the modules are executed repeatedly, either in parallel or in serial. For example, one may wish to re- execute both the embedding routines 235 and then the automated parameter selection 245 in order to obtain a better embedding and better parameters for the embedded problem.
  • the platform may include options and modes for executing some or all of the modules repeatedly in the style of a feedback loop in order to obtain more suitable results.
  • the platform does not restrict which modules may be run in this iterative fashion.
  • the optimized problem is dispatched to one or more vendor device APIs / SDKs 155.
  • solutions are returned and are passed back to the end user, as described above and as shown in FIG. 1.
  • classical solver libraries 165 may be interfaced with FIG. 2 via connections to, for example, one or more of modules 205, 210, 215, 220, and 225. Depending on the classical solver library, other connections may also be possible or appropriate.
  • the QCaaS platform described above is just one example of how quantum processing devices may be offered on an "as a service" basis.
  • the user remotely accesses a service provider's software and/or hardware resources, as opposed to "on-prem" (on-premise) solutions that are collocated with the end user.
  • this use model can be applied to many types of quantum computing-related resources, including software, platform and infrastructure.
  • Software as a service SaaS
  • SaaS Software as a service
  • PaaS is typically accessed via the Internet and encompasses a broad variety of applications, from enterprise accounting tools to consumer social media websites.
  • Platinum as a service provides software and/or hardware resources— typically for use by application developers— to clients.
  • IaaS Infrastructure as a Service
  • infrastructure for instance, database server hardware and software
  • QCaaS it is also possible to have a scheduler for multiple quantum processing devices that is hidden from the end users.
  • This scheduler can be used, for example, to automatically load balance and scale runs across multiple quantum processing devices and to automatically load balance and queue the workloads of multiple users on the same resources. Even as a scheduler for multiple runs on the same computer, it will not be serial like if run directly on a quantum processing device.
  • QCaaS can encompass a way to determine how many runs is good enough for the answer to a given problem, so the number of runs would be different for each problem and would be determined in a way that need not be exposed to the end user. This is different from the conventional model of using a quantum processing device, where the number of runs is explicitly specified.
  • Alternate embodiments are implemented in computer hardware, firmware, software, and/or combinations thereof. Implementations can be implemented in a computer program product tangibly embodied in a machine-readable storage device for execution by a programmable processor; and method steps can be performed by a programmable processor executing a program of instructions to perform functions by operating on input data and generating output. Embodiments can be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device.
  • Each computer program can be implemented in a high-level procedural or object- oriented programming language, or in assembly or machine language if desired; and in any case, the language can be a compiled or interpreted language.
  • Suitable processors include, by way of example, both general and special purpose microprocessors.
  • a processor will receive instructions and data from a read-only memory and/or a random access memory.
  • a computer will include one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks;
  • magneto-optical disks and optical disks.
  • Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto- optical disks; and CD-ROM disks. Any of the foregoing can be supplemented by, or incorporated in, ASICs (application-specific integrated circuits) and other forms of hardware.
  • ASICs application-specific integrated circuits
  • module is not meant to be limited to a specific physical form.
  • modules can be implemented as hardware, firmware, software, and/or combinations of these.
  • different modules can share common components or even be implemented by the same components. There may or may not be a clear boundary between different modules, even if drawn as separate elements in the figures.

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Abstract

L'invention concerne une architecture informatique en nuage et un système capables d'interagir avec des dispositifs de traitement quantique et d'utiliser ces derniers. Un aspect de l'invention concerne une plate-forme unifiée constituant un service d'interaction avec différents dispositifs de traitement quantique. Un autre aspect concerne une architecture et une méthodologie permettant d'accéder à divers dispositifs de traitement quantique et d'utiliser ces derniers. D'autres aspects de l'invention englobent différents modules logiciels qui offrent des fonctionnalités supplémentaires aux utilisateurs de dispositifs de traitement quantique.
PCT/US2018/020033 2017-03-01 2018-02-27 Calcul quantique en tant que service WO2018160599A1 (fr)

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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11514134B2 (en) 2015-02-03 2022-11-29 1Qb Information Technologies Inc. Method and system for solving the Lagrangian dual of a constrained binary quadratic programming problem using a quantum annealer
EP4105812A1 (fr) * 2021-06-17 2022-12-21 Multiverse Computing S.L. Méthode et système pour fournir du calcul quantiqueen en tant que service à une feuille de calcul
US11797641B2 (en) 2015-02-03 2023-10-24 1Qb Information Technologies Inc. Method and system for solving the lagrangian dual of a constrained binary quadratic programming problem using a quantum annealer
US11947506B2 (en) 2019-06-19 2024-04-02 1Qb Information Technologies, Inc. Method and system for mapping a dataset from a Hilbert space of a given dimension to a Hilbert space of a different dimension
US12051005B2 (en) 2019-12-03 2024-07-30 1Qb Information Technologies Inc. System and method for enabling an access to a physics-inspired computer and to a physics-inspired computer simulator
US12353965B2 (en) 2018-12-06 2025-07-08 1Qb Information Technologies Inc. Artificial intelligence-driven quantum computing
US12423374B2 (en) 2017-12-01 2025-09-23 1Qb Information Technologies Inc. Systems and methods for stochastic optimization of a robust inference problem

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020188578A1 (en) * 2001-06-01 2002-12-12 D-Wave System, Inc. Quantum processing system for a superconducting phase qubit
US6671681B1 (en) * 2000-05-31 2003-12-30 International Business Machines Corporation System and technique for suggesting alternate query expressions based on prior user selections and their query strings
US20110022820A1 (en) * 2008-03-24 2011-01-27 D-Wave Systems Inc. Systems, devices, and methods for analog processing
US20130166353A1 (en) * 2011-12-21 2013-06-27 Oracle International Corporation Price optimization using randomized search
US20150046681A1 (en) * 2013-08-07 2015-02-12 D-Wave Systems Inc. Systems and devices for quantum processor architectures
US20150106413A1 (en) * 2013-10-10 2015-04-16 1Qb Information Technologies Inc. Method and system for solving a convex integer quadratic programming problem using a binary optimizer
WO2016089711A1 (fr) * 2014-12-05 2016-06-09 Microsoft Technology Licensing, Llc Apprentissage en profondeur quantique
US20160224515A1 (en) * 2015-02-03 2016-08-04 1Qb Information Technologies Inc. Method and system for solving the lagrangian dual of a constrained binary quadratic programming problem using a quantum annealer
US9537953B1 (en) * 2016-06-13 2017-01-03 1Qb Information Technologies Inc. Methods and systems for quantum ready computations on the cloud

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6671681B1 (en) * 2000-05-31 2003-12-30 International Business Machines Corporation System and technique for suggesting alternate query expressions based on prior user selections and their query strings
US20020188578A1 (en) * 2001-06-01 2002-12-12 D-Wave System, Inc. Quantum processing system for a superconducting phase qubit
US20110022820A1 (en) * 2008-03-24 2011-01-27 D-Wave Systems Inc. Systems, devices, and methods for analog processing
US20130166353A1 (en) * 2011-12-21 2013-06-27 Oracle International Corporation Price optimization using randomized search
US20150046681A1 (en) * 2013-08-07 2015-02-12 D-Wave Systems Inc. Systems and devices for quantum processor architectures
US20150106413A1 (en) * 2013-10-10 2015-04-16 1Qb Information Technologies Inc. Method and system for solving a convex integer quadratic programming problem using a binary optimizer
WO2016089711A1 (fr) * 2014-12-05 2016-06-09 Microsoft Technology Licensing, Llc Apprentissage en profondeur quantique
US20160224515A1 (en) * 2015-02-03 2016-08-04 1Qb Information Technologies Inc. Method and system for solving the lagrangian dual of a constrained binary quadratic programming problem using a quantum annealer
US9537953B1 (en) * 2016-06-13 2017-01-03 1Qb Information Technologies Inc. Methods and systems for quantum ready computations on the cloud

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
GUNNERUD ET AL.: "Dantzig-Wolfe decomposition for real-time optimization-applied to the Troll west oil rim", IFAC PROCEEDINGS, 23 April 2016 (2016-04-23), XP055538897, Retrieved from the Internet <URL:https://pdfs.semanticscholar.org/d8d0/d74939b16ab798525ae0772d6f1186556542.pdf> *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11514134B2 (en) 2015-02-03 2022-11-29 1Qb Information Technologies Inc. Method and system for solving the Lagrangian dual of a constrained binary quadratic programming problem using a quantum annealer
US11797641B2 (en) 2015-02-03 2023-10-24 1Qb Information Technologies Inc. Method and system for solving the lagrangian dual of a constrained binary quadratic programming problem using a quantum annealer
US11989256B2 (en) 2015-02-03 2024-05-21 1Qb Information Technologies Inc. Method and system for solving the Lagrangian dual of a constrained binary quadratic programming problem using a quantum annealer
US12423374B2 (en) 2017-12-01 2025-09-23 1Qb Information Technologies Inc. Systems and methods for stochastic optimization of a robust inference problem
US12353965B2 (en) 2018-12-06 2025-07-08 1Qb Information Technologies Inc. Artificial intelligence-driven quantum computing
US11947506B2 (en) 2019-06-19 2024-04-02 1Qb Information Technologies, Inc. Method and system for mapping a dataset from a Hilbert space of a given dimension to a Hilbert space of a different dimension
US12051005B2 (en) 2019-12-03 2024-07-30 1Qb Information Technologies Inc. System and method for enabling an access to a physics-inspired computer and to a physics-inspired computer simulator
EP4105812A1 (fr) * 2021-06-17 2022-12-21 Multiverse Computing S.L. Méthode et système pour fournir du calcul quantiqueen en tant que service à une feuille de calcul

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