System and method for enabling access to physics-inspired computer and to physics-inspired computer simulator

Abstract

To provide a computing system and a method for enabling a processing device to remotely access a computing platform.SOLUTION: In a system 100, a computing platform 120 comprises at least one physics-inspired quantum computer simulator comprising tunable parameters. A computing system 118 comprises: a communications interface configured to receive a request including at least one computational task to process using a physics-inspired computer simulator comprising tunable parameters; a control unit configured to translate a request into instructions for the at least one physics-inspired computer simulator, deliver the instructions, and receive at least one corresponding solution; and a memory configured to store one or more of the at least one computational task, a dataset, the tunable parameters, and the at least one corresponding solution.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] One or more embodiments of the present invention may include a method for communicating with a computing platform over a network. SYSTEM AND METHOD FOR PROVIDING REMOTE ACCESS TO A FORM - Patent application This means that in one or more embodiments, the user can create realistic physics-inspired Choosing between using a physics-inspired computer and its simulator In particular, one or more embodiments of the computing system may Instead of a real physics-inspired computer, we use a physics-inspired computer simulator. This allows the use of a laser to reduce costs. [Background technology]

[0002] Today, the scientific community is faced with many different types of noisy intermediate quantum (NISQ) te-scale quantum) devices, as well as other physics that are constantly being developed, improved, and published. We have devised an apparatus and computer that is inspired by the idea of optimization tasks, stochastic sampling, and and / or other computational tasks, which involve a variety of phenomena in quantum physics and / or other physics. Although it is possible to run it much faster using Access to the site is limited to users in categories such as research groups and startups. This situation is compounded by the high manufacturing and maintenance costs. It is caused by several factors, including:

[0003] At least one of the restrictions associated with access to such computer The need for methods and systems that overcome at least one of the above is addressed herein. It is recognized as such. Summary of the Invention

[0004] According to a broad aspect, a processing device communicates with a computing platform over a network. A computing system has been developed to allow remote access to forms. The computing platform includes at least one variable parameter. Also included is a physics-inspired computer simulator. The system is a communication interface for receiving requests provided by a processing unit. The requirement is to provide at least one physics-inspired computer program that includes variable parameters. A communication interface including at least one computational task for processing using the simulator. At least one physical interface, including a communication interface and variable parameters. a control unit operably connected to a biologically inspired computer simulator; The control unit transmits the received request to at least one physics-inspired computer. converting the instructions into instructions for the data simulator; and and transmitting the obtained computer simulator to execute at least one computational task. and receiving at least one corresponding solution. a control unit and at least one physics-inspired computer a memory operatively connected to the computer simulator, the memory including at least one the computational task, the data set included in the received request, and at least one physics-inspired The variable parameters of the computer simulator obtained and at least one pair of received and a memory for storing one or more of the corresponding solutions.

[0005] According to one or more embodiments, the computing platform includes at least one The claim further includes at least one physics-inspired computer. a physics-inspired computer simulator and at least one physics-inspired computer simulator. Further, the control unit further includes selecting at least one of the data. and further operatively connected to at least one physics-inspired computer. If the choice is about at least one physics-inspired computer The control unit transmits the received request to at least one physics-inspired computer. and converting the instructions into instructions for at least one physics-inspired computer. and transmitting the data to a computer for further use in performing at least one computational task. Furthermore, the control unit is configured to operate in accordance with at least one physics-inspired computer , which is further used to receive at least one corresponding solution.

[0006] According to one or more embodiments, the computing system includes at least one physical Further, the training unit is operatively connected to a computer simulator inspired by The training unit includes at least one physics-inspired computer simulator. It is a training unit for training.

[0007] According to one or more embodiments, the computing system includes at least one physical The present invention includes a training unit operably connected to a biomedically inspired computer simulator. The training unit trains at least one physics-inspired computer simulator. It is a training unit for

[0008] According to one or more embodiments, at least one physics-inspired computer simulation The type of simulator corresponds to a physics-inspired computer. At least using instructions transmitted to a physics-inspired computer, and using at least one corresponding solution obtained from a computer-inspired used to train at least one physics-inspired computer simulator can be.

[0009] According to one or more embodiments, the communication interface receives a plurality of requests. The computing platform waits for a number of requests to be received according to the criteria. It further includes a queuing unit for queuing.

[0010] According to one or more embodiments, at least one computational task and at least one corresponding If the corresponding request contains an indication that the solution is usable for training purposes, The unit communicates with the physics-inspired computer and the corresponding instructions transmitted to the computer. and at least one corresponding solution obtained from the computer that inspired the Both train a physics-inspired computer simulator.

[0011] According to one or more embodiments, a process for remotely accessing a computing system The device is a digital device operably connected to a communication interface via a data network. Includes personal computers.

[0012] According to one or more embodiments, instructions for a physics-inspired computer and at least The instructions for at least one physics-inspired computer simulator are identical. do.

[0013] According to one or more embodiments, the computing platform is a distributed computing Includes the operating system.

[0014] According to one or more embodiments, a physics-inspired computer is a non-classical computer. This includes computers.

[0015] According to one or more embodiments, the non-classical computer may be a NISQ device, a quantum computer, or computer, superconducting quantum computer, ion trap quantum computer, quantum annealing, Optical quantum computers, spin-based quantum dot computers, and photonic-based The quantum computer is selected from the group consisting of:

[0016] According to one or more embodiments, at least one physics-inspired computer simulation The simulator includes a computer-implemented method that generates physics-inspired The output of the computer is then simulated and the training unit is used to simulate the variable parameters. updating at least one of the parameters, thereby improving the corresponding performance; Includes:

[0017] According to one or more embodiments, the training unit may be a tensor processing unit (TPU), a graphics processing unit (GPU), or a Graphical Processing Unit (GPU), Field Programmable Gate Array (FPGA) ), and an application specific integrated circuit (ASIC).

[0018] According to one or more embodiments, at least one physics-inspired computer simulation The generator includes a neural network.

[0019] According to a broad aspect, a method for enabling remote access to a computing platform is provided. A computer-implemented method for implementing the present invention is disclosed. , at least one physics-inspired computer simulation involving variable parameters The method includes receiving a request at a communication interface, the request The request is processed using at least one physics-inspired computer simulator. and at least one computational task for performing the at least one of the received requests. The computational task is suitable for at least one physics-inspired computer simulator. and converting the instructions into instructions that are compatible with at least one physics-inspired computer system. providing the simulator with at least one pair of instructions resulting from the execution of the instructions; receiving corresponding generated solutions; and providing at least one corresponding generated solution. This includes:

[0020] According to one or more embodiments, the computing system includes at least one physical A physics-inspired computer and at least one physics-inspired computer system and a training unit for training the simulator. A physics-inspired computer and a At least one physics-inspired computer simulator and at least one Further, at least one computational task of the request may be based on physics. Inspired computer and at least one physics-inspired computer simulation The data is converted into an instruction suitable for at least one of the following: Depending on the sign, a physics-inspired computer and at least one physics-inspired and a computer simulator.

[0021] According to one or more embodiments, the request is made over a communications interface using a data network. and receiving the data from a digital computer operably connected to the source. One corresponding generated solution is provided to a digital computer.

[0022] According to one or more embodiments, at least one corresponding generated solution comprises at least It is obtained from a physics-inspired computer. Furthermore, there is at least one corresponding and performing at least one physics task using the generated solution and at least one computational task. This involves training a computer simulator inspired by

[0023] According to one or more embodiments, at least one computational task and at least one corresponding If the request includes an indication that the generated solution can be used for training purposes, The training is carried out.

[0024] According to one or more embodiments, training involves comparing at least one corresponding generated solution with at least one Execute a procedure based on a machine learning protocol using at least one computational task. and, accordingly, the variable parameters of the physics-inspired computer simulator. and updating the

[0025] According to one or more embodiments, the method comprises: The method further includes storing the solution.

[0026] One or more implementations of the methods and computing systems disclosed herein The advantage of these forms is that they are physics-inspired computational models trained using real computational tasks. The goal is to provide access to computer simulators, e.g., quantum It is a simulator of a device, which is relatively cheaper than access to a quantum device.

[0027] One or more implementations of the methods and computing systems disclosed herein Another advantage of these forms is that they are compatible with physics-inspired computers such as quantum computers. The aim is to make it possible to imitate

[0028] One or more implementations of the methods and computing systems disclosed herein Another advantage of these forms is that they allow for the use of at least one computational task inspired by physics. The aim is to make it possible to improve the computer simulator obtained.

[0029] In order that one or more embodiments of the present invention may be readily understood, one or more embodiments of the present invention may be readily understood. Embodiments are illustrated by way of example in the accompanying drawings, in which: [Brief explanation of the drawings]

[0030] [Figure 1]FIG. 1 illustrates one embodiment of a system for providing access to a quantum computer and a quantum computer simulator. [Figure 2] FIG. 1 illustrates another embodiment of a system for providing access to a quantum computer and a quantum computer simulator. [Figure 3] 1 is a flow chart illustrating one embodiment of a method for training a physics-inspired computer simulator to improve its performance using a training unit. [Figure 4] 1 is a flow chart illustrating one embodiment of a method for enabling remote access to a physics-inspired computer simulator and a physics-inspired computer. [Figure 5] 1 is a flow chart illustrating one embodiment of a method for enabling remote access to a computing platform including at least one physics-inspired computer simulator. DETAILED DESCRIPTION OF THE INVENTION

[0031] In the following description of one or more embodiments, reference is made to the accompanying drawings, in which: The examples are intended to illustrate examples that can be used.

[0032] <Terminology> The term "invention" and the like means "one or more inventions disclosed in this application" unless otherwise specified. means.

[0033] The terms "aspect," "one embodiment," "embodiment," "this embodiment," "this embodiment," "One or more embodiments," "an embodiment," "particular embodiment," "one embodiment" "Another embodiment," "another embodiment," etc., unless expressly stated otherwise, refers to "one or more (e.g., "some (but not all) embodiments" means "some (but not all) embodiments."

[0034] References to "another embodiment" or "another aspect" when describing an embodiment shall be construed as limiting unless expressly stated. Unless otherwise specified, a referenced embodiment may be combined with another embodiment (e.g., a referenced embodiment described before the referenced embodiment). The embodiments described herein are not necessarily mutually exclusive.

[0035] The terms "include," "comprise," and variations thereof, unless otherwise expressly stated, mean "including but not limited to" and "comprises." This means "including but not limited to."

[0036] The terms "indefinite article," "definite article," and "at least one" are used unless otherwise specified. It means "one or more."

[0037] The term "plurality" means "two or more" unless otherwise specified.

[0038] The term "herein" means "in this application, by reference to" unless otherwise specified. "includes anything that may be incorporated herein."

[0039] The term "whereby" is used herein to refer to the intended use of something explicitly listed above. It is used only before a clause or other group of words that expresses only the result, purpose, or outcome. Therefore, when the term "whereby" is used in a claim, the term "whereby" No modifying clause or other word establishes any specific further limitations in the claim; or Nothing in this document limits the meaning or scope of the claims.

[0040] The term "for example" and similar terms mean "for example," and therefore For example, the sentence "Computers use the Internet" does not limit the terms or phrases described in the sentence. In the phrase "transmitting data (e.g., commands, data structures) via" the term "e.g., "Instructions" are the "data" that a computer may send over the Internet. Explain that this is an example and that the "data structure" is a However, the difference between "commands" and "data" is Both "instructions" and "data structures" are merely examples of "data" and are not "instructions" or "data structures". Other things can be "data".

[0041] The term "that is to say" and similar terms mean "in other words" and therefore Qualify the term or phrase they describe.

[0042] When values are stated as ranges, such disclosure shall not be construed as limiting the scope of the disclosure to a specific numerical value or a specific subrange. All possible circumstances within such scope, whether or not expressly stated It will be understood by one of ordinary skill in the art that the disclosure of numerical ranges, as well as specific numerical values falling within such ranges, is intended to be illustrative and not restrictive. It will be done.

[0043] In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In this context, similar symbols typically identify similar components unless the context dictates otherwise. The exemplary embodiments described in the detailed description, drawings, and claims are intended to be illustrative, not restrictive. without departing from the scope of the subject matter presented herein. , other embodiments may be utilized, and other changes may be made. Aspects of the present disclosure include: As generally described herein and shown in the drawings, the may be combined, substituted, combined, separated, designed, all of which are within the scope of the present specification. It will be readily understood that this is expressly contemplated in the present document.

[0044] As used herein, the term "classical" refers to a calculation or computation. When quantum mechanical superposition and quantum mechanical entanglement are not used, the discrete refers to calculations performed using binary values using bits. Without using quantum mechanical superposition and quantum mechanical entanglement, and 1) may be a digital computer such as a computer using

[0045] As used herein, the term "non-classical" is used in the context of calculation or computation. When used, it generally refers to any method for performing computational procedures outside the classical computation paradigm. Refers to a method or system.

[0046] As used herein, the term "physics-inspired" refers to a calculation or computation. When used in the context of, generally, any physical phenomenon based at least in part on and / or refers to any method or system for performing or imitating a computational procedure.

[0047] As used herein, the term "quantum device" generally refers to a device that is in quantum mechanical superposition. Any quantum mechanical phenomenon that performs computations, such as quantum entanglement and quantum mechanical Refers to a device or system.

[0048] As used herein, the terms "quantum computation," "quantum procedure," "quantum operation," and and "quantum computers" in general are quantum computers that operate on Hilbert spaces represented by quantum devices. Dynamical operations (unitary transformations on quantum channels or CPTP (completely positive trace) -preserving) maps, etc. Point.

[0049] As used herein, the term "quantum computer simulator" generally refers to provides solutions to computational tasks, mimicking the results provided by quantum computers. refers to any computer-implemented method that uses any classical hardware to

[0050] As used herein, the term "physically inspired computer simulation" means a "Data" generally mimics the results provided by physics-inspired computers. , any computer using any classical hardware that provides a solution to a computational task. Refers to a computer implementation method.

[0051] As used herein, the term "noisy intermediate-scale quantum device" (NISQ) are generally capable of performing tasks beyond the capabilities of today's classical digital computers. refers to any quantum device that can

[0052] The present disclosure provides a method for running at least one physics-inspired program in a distributed computing environment. and at least one physics-inspired computer simulator. and a computing system for enabling access to at least one of One or more embodiments of a stem are disclosed.

[0053] Neither the title nor the abstract should be construed as limiting in any way as to the scope of the disclosed invention. The title of this application and the section headings provided herein are for convenience only. This should not be construed as limiting the disclosure in any way.

[0054] Numerous embodiments are described in this application and are presented for illustrative purposes only. The illustrated embodiments are not intended to be limiting in any way. The invention of the present disclosure, as will be readily apparent from the present disclosure, Those skilled in the art will appreciate that the invention of this disclosure may be implemented in a variety of structural and logical modifications. It will be appreciated that the present disclosure may be practiced with various modifications and alterations. Certain features of the invention may be described with reference to one or more specific embodiments and / or drawings. However, such features may not, unless otherwise specified, be used in conjunction with one or more of the particular features described. It should be understood that there is no limitation to the embodiments or use in the drawings.

[0055] It will be appreciated that one or more embodiments of the present invention may be implemented in a number of ways. As used herein, these implementations, or any other form the invention may take, are referred to as systems. can be referred to as a system or technology. Components such as a processor or memory are temporarily configured to perform a task at a given time. Either a general-purpose component that has been configured, or a specific component that has been manufactured to perform a task. Includes or.

[0056] With all of this in mind, one or more embodiments of the present invention may include a method for transmitting a command over a network. Method and computer for providing remote access to a computing platform The computing platform is a system that can handle variable performance. Includes at least one physics-inspired computer simulator that includes the parameters .

[0057] One or more embodiments of the present invention provide at least one Physics-inspired computers and at least one physics-inspired computer The system provides access to at least one of the following data simulators:

[0058] Physics-inspired computer simulators are quantum computers or other physics-based It aims to provide a cost-effective alternative to computer-inspired The allows for faster computations compared to classical counterparts at a relatively low price. It will be appreciated that in one or more embodiments, a user may select a physical Choosing between a computer inspired by biology and a computer simulator inspired by physics The user has the option to select and submits the question to the selected answering machine.

[0059] In one or more embodiments, the questions posed are based on physics, as further described below. It will be understood that the instructions are decoded into instructions suitable for a computer inspired by the In the above embodiment, these instructions are used in a physics-inspired computer or a physics-based computer. Furthermore, instructions can be directed to either a computer simulator inspired by the actual physical When applied to a mechanically inspired computer, the problem posed, along with the results, To improve the performance of physics-inspired computer simulators using learning techniques It can further be used for

[0060] For example, a quantum annealing simulator can include a conditional generative model. The conditional generative model is the Metropolis-Hastings Monte Carlo existing sampling algorithms such as Monte Carlo, or quantum annealing It is pre-trained using samples taken from one of the real quantum devices. The synthesis model can generate corresponding data samples, which can be It is further used to solve the original problem submitted by the user.

[0061] <Physics-inspired computers> The physics-inspired computer is based on an optical parametric oscillator (OPO) and an integrated flash memory. Integrated photonic coherent Ising machine quantum computers such as quantum annealing, or games. quantum computers, simulated annealing, simulated quantum annealing, population annealing, etc. Furthermore, it may include one or more implementations of physics-inspired methods such as quantum Monte Carlo. It will be understood that

[0062] <Quantum device> Any type of quantum computer may be suitable for the techniques disclosed herein. According to the specification, suitable quantum computers include, by way of non-limiting example: Superconducting quantum computers (quantum computers implemented as small superconducting circuits (Josephson junctions)) Clarke et al., "Superconducting quantum bits," Nature 453.7198 (2008): 1031) Ion trap quantum computer (qubits implemented as states of ion traps) )(Kielpinski et al., "Architecture for a large-scale ion-trap quantum computer, " Nature 417.6890 (2002): 709.) Optical lattice quantum computers (quantum computers implemented as states of neutral atoms trapped in an optical lattice) bit)(Deutsch et al., "Quantum computing with neutral atoms in an optical lat tice," arXiv preprint: quant-ph / 0003022 (2000)) Spin-based quantum dot computers (implemented as trapped electron spin states) (Imamoglu et al., "Quantum information processing using quantum dots") t spins and cavity QED," arXiv preprint: quant-ph / 9904096 (1999)) Space-based quantum dot computer (quantity implemented as electron positions in a double quantum dot) (Fedichkin et al., "Novel coherent quantum bit using spatial quantization" ion levels in semiconductor quantum dot," arXiv preprint: quant-ph / 0006097 (2000 )) Coupled quantum wires (quantum wires implemented as pairs of quantum wires coupled by quantum point contacts) bit)(Bertoni et al., "Quantum logic gates based on coherent electron transpo rt in quantum wires," Physical Review Letters 84, no. 25 (2000): 5912.) Nuclear magnetic resonance quantum computers (implemented as nuclear spins probed by radio waves) (Cory et al., "Nuclear magnetic resonance spectroscopy: An experimental arXiv preprint: quant-ph / 970 9001 (1997) Solid-state NMR Cahn quantum computer (implemented as nuclear spin states of phosphorus donors in silicon) Kane, BE, "A silicon-based nuclear spin quantum computer," Nature 393, no. 6681 (1998): 133.) Electrons-on-helium quantum computer (implemented as electron spin) Lyon, SA, "Spin-based quantum computing using electrons on quantum bits" liquid helium," arXiv preprint: cond-mat / 0301581 (2006)) Quantum computers based on resonator quantum electrodynamics (trapped quantum computers coupled with highly precise resonators) qubits implemented as states of atoms (Burell, Z., "An Introduction to Quantum um Computing using Cavity QED concepts," arXiv preprint: arXiv:1210.6512 (2012)) Quantum computers based on molecular magnets (qubits implemented as spin states) (Leuen berger et al., "Quantum computing in molecular magnets," arXiv preprint: cond-ma t / 0011415 (2001)) Fullerene-based ESR quantum computers (atoms or molecules wrapped in fullerenes) Harneit, W., "Quantum Computing with Endo hedral Fullerenes," arXiv preprint: arXiv: 1708.09298 (2017)) Linear optical quantum computers (through linear optical elements such as mirrors, beam splitters, and phase shifters) quantum bits implemented as processing states of different modes of light (Knill et al., "Efficiency ent linear optics quantum computation," arXiv preprint: quant-ph / 0006088 (2000)) Diamond-based quantum computers (electron or nuclear spin of NV centers in diamond) qubits implemented as quantum computers (Nizovtsev et al., "A quantum computer based on NV c enters in diamond: optically detected nutrients of single electron and nuclear s pins," Optics and spectroscopy 99, no. 2 (2005): 233-244.) Quantum computers based on Bose-Einstein condensates (Qubit implemented as two-component BEC) (Byrnes et al., "Macroscopic q uantum computation using Bose-Einstein condensates," arXiv preprint: quantum-ph / 1103.5512 (2011)) Transistor-based quantum computers (as semiconductors coupled to photonic resonators) Implemented quantum bits) (Sun et al., "A single-photon switch and transistor enable d by a solid-state quantum memory," arXiv preprint: quant-ph / 1805.01964 (2018)) Quantum computers based on rare earth ion doped inorganic crystals (rare earth ion doped qubits implemented as hyperfine levels of atomic ground states in inorganic crystals (Ohlsson et al., "Quantum computer hardware based on rare-earth-ion-doped inorganic crystals ," Optics communications 201, no. 1-3 (2002): 71-77.) Quantum computers based on metal-like carbon nanospheres (conductive qubits implemented as electron spins in electrically conductive carbon nanoparticles (Nafradi et al., "Room temperature" emperature manipulation of long lifetime spins in metallic-like carbon nanospheres es," arXiv preprint: cond-mat / 1611.07690 (2016)) D-Wave quantum annealing (qubits implemented as superconducting logic elements) (Johnson et al., "Quantum annealing with manufactured spins," Nature 473, no. 7346 (2011 ): 194-198.)

[0063] <Noisy Intermediate-Scale Quantum Technology (NISQ)> The term "Noisy Intermediate-Scale Quantum Technology (NISQ)" was introduced by John Preskill ("Quantum Computing in the NISQ era and beyond," arXiv:1801.00862). Here, "Noisy" means that the control over qubits is imperfect, and "Intermediate-Scale" refers to the number of qubits, which can range from 50 to several hundred. Several physical systems made from superconducting qubits, artificial atoms, and ion traps have been proposed as viable candidates for building NISQ quantum devices and ultimately universal quantum computers .

[0064] <Quantum Annealing> Quantum annealing is a quantum mechanical system consisting of multiple manufactured qubits

[0065] A bias source called a local field bias is inductively coupled to each qubit. In one embodiment , the bias source is an electromagnetic device used to pass magnetic flux through the qubit in order to provide control over the state of the qubit (see U.S. Patent Application No. 2006 / 0225165 ).

[0066] The local field bias on the qubit is programmable and controllable. In one or more embodiments, a qubit control system including a digital processing unit is for the qubits Connected to the system to program and adjust the local field bias on the qubit This makes it possible.

[0067] The quantum annealing may further include couplings between pairs of qubits. In one embodiment, the coupling between two qubits is such that a magnetic flux passes through both qubits. In the same embodiment, the coupling is a complex joule It can consist of a superconducting circuit interrupted by a Fusson junction. The magnetic flux can pass through the Josephson junction, allowing it to pass through both qubits. (See U.S. Patent Application No. 2006 / 0225165.) This magnetic flux strength is In one embodiment, the coupling strength is , is forced by adjusting the coupling devices in the vicinity of both qubits.

[0068] The coupling strength can be controllable and programmable. In an embodiment, the quantum annealing control system including the digital processing unit includes a plurality of coupled , and the quantum annealing coupling strength can be programmed.

[0069] In one or more embodiments, quantum annealing is performed by applying a transverse magnetic field from the initial configuration to the final configuration. In such an embodiment, a quantum Ising model with a transverse magnetic field is performed. The initial and final configurations of the quantum Ising model are given by their corresponding initial and final half- We provide a quantum system described by a Miltonian.

[0070] Quantum annealing can be used as a heuristic optimization of these energy functions. It will be appreciated that an embodiment of such an analog processor is described by McGeoch et al. Disclosed ("Experimental Evaluation of an Adiabatic Quantum System for Combinator ial Optimization,” Computing Frontiers; May 14-16, 2013), U.S. Patent Application No. 200 This is also disclosed in patent application Ser. No. 6 / 0225165.

[0071] At finite temperature, provide a sample from the Boltzmann distribution of the corresponding Ising model It will be appreciated that quantum annealing can further be used to et al., (2010), "The Ising model: teaching an old problem new tricks", and Ami n et al., (2016), "Quantum Boltzmann Machine" arXiv:1601 .02036.). This sample This method of sampling is called quantum sampling.

[0072] <Optical Computing Device> It is possible to sample near the equilibrium state from the Boltzmann distribution of the Ising model. Another embodiment of a possible analog system is an optical device.

[0073] In one or more embodiments, the optical device may be a device as described in U.S. Patent Application No. 2016 / 0162798 and U.S. Patent Application No. 2016 / 0162798. and optical parametric oscillators (OPOs) such as those disclosed in International Application No. 2015006494. ) network.

[0074] In this embodiment, each spin in the Ising model is an optical parametric It is simulated by an OPO.

[0075] Degenerate optical parametric oscillators (OPOs) undergo a second-order phase transition at the lasing threshold. By phase-sensitive amplification, the degenerate optical parametric oscillator (OPO) , oscillation occurs with a phase of either 0 or π relative to the excitation phase for amplitudes above the threshold. The phase is random and the optical parametric downconversion during the build-up of the oscillation It is affected by quantum noise associated with the conversion. An OPO (Optical Phase Opto-Optic Oscillator) naturally represents a binary number specified by its output phase. Based on this, a degenerate optical parametric oscillator (OPO) system can be used to Each degenerate optical parametric oscillator can be used as a physical representative of the system. The phase of the (OPO) is identified as the Ising spin, and its amplitude and phase are related spins. is determined by the strength and sign of the Ising coupling between the two.

[0076] When pumped by a strong source, a degenerate optical parametric oscillator (OPO) operates in the Ising model The electrons take one of two phase states corresponding to the spin of the electrons +1 or -1. A network of N substantially identical optical parametric oscillators (OPOs) with the same source The Ising spin system is excited using the After a certain period of time, the optical parametric oscillator (OPO) network is stabilized near its thermal equilibrium. approaching normal state.

[0077] The phase state selection process relies on vacuum fluctuations and the mutual coupling of optical parametric oscillators (OPOs). In some embodiments, the pump is modulated at a constant amplitude. In yet another embodiment, the pump is controlled in other ways. can be.

[0078] In one or more embodiments of the optical device, multiple couplings of the Ising model are The multiple configurable couplings used to couple the optical field between the optical power oscillators (OPOs) Configurable couplings can be configured to be off and on. Turning the coupling on and off can be configured to be gradual. If configured to turn on, the configuration can be Depending on the coupling strength of the coupling model, any phase or amplitude can be provided.

[0079] Each optical parametric oscillator (OPO) output is interfered with a phase reference, resulting in , is captured at a photodetector. The output of the optical parametric oscillator (OPO) is For example, in the Ising model, the zero phase represents the spin-1 state The π phase represents a +1 spin state.

[0080] For an Ising model with spin, according to one or more embodiments, multiple optical parameters The resonant cavity of an optical phase oscillator (OPO) has a resonant frequency equal to twice the period of the pulses from the pump source. As used herein, round trip time is defined as The round-trip time period of a resonant cavity is the time it takes for light to travel along one of its recursive paths. The pulses of the pulse train, each with a period equal to , simultaneously change the optical parameters without interfering with each other. It can propagate through an optical power oscillator (OPO).

[0081] In one or more embodiments, the coupling of the optical parametric oscillator (OPO) along the resonant cavity The delay is provided by a plurality of delay lines allocated according to the

[0082] The multiple delay lines include multiple modulators that synchronously control the strength and phase of the coupling. This allows the optical device to be programmed to simulate the imaging model.

[0083] In a network of optical parametric oscillators (OPOs), delay lines and corresponding modulators controls the amplitude and phase of the coupling between every two optical parametric oscillators (OPOs) is sufficient.

[0084] In one or more embodiments, the optimizer capable of sampling from an Ising model is An optical parametric oscillator (OPO), such as that disclosed in patent application no. 2016 / 0162798, It can be manufactured as a network of optical power amplifiers (OPOs).

[0085] In one or more embodiments, a network of optical parametric oscillators (OPOs) and optical Coupling of parametric oscillators (OPOs) is possible with commercially available mode-locked lasers and and optical elements, e.g., telecommunications fiber delay lines, modulators, and other optical devices. Alternatively, a network of optical parametric oscillators (OPOs) and optical parametric oscillators can be used. Optically coupled optical oscillators (OPOs) are used in optical fiber technology, e.g., for telecommunications applications. The coupling is realized using fiber, and the optical It will be appreciated that the optical axis may be controlled by an optical Kerr shutter.

[0086] <Integrated Photonic Coherent Ising Machine> Sampling from the Boltzmann distribution of the Ising model near its equilibrium state Another embodiment of an analog system is described, for example, in U.S. Patent Application No. 2018 / 026 The integrated photonic coherent Ising machine disclosed in the specification of 7937.

[0087] In one or more embodiments, the integrated photonic coherent Ising machine Such an embodiment is a combination of nodes and a connection network that solves the jogging problem. In this paper, the combination of the nodes and the connecting network forms an adiabatic optical computer. In other words, the combination of a node and a connecting network can be The value stored in reaches a steady state and minimizes the energy between the node and the connected network. The Ising problem is solved non-deterministically when the node is assigned with the minimum energy level. The stored values can be related to values that solve a particular Ising problem. The solution is given by the Boltzmann distribution defined by the Hamiltonian corresponding to the Ising problem: It can be used as a sample.

[0088] In such an embodiment, the system includes a plurality of ring resonator photonic nodes. Each one of the multiple ring resonator photonic nodes stores a value. To energize each one of the ring resonator photonic nodes, The photonic node is coupled to each of the plurality of ring resonator photonic nodes via a waveguide. A connected network contains multiple pairs of elements. Each element in the pair of elements The element simplifies the connection network using parameters associated with the encoding of the Ising problem. The interconnection network includes a plurality of ring resonator photonic crystals. The Ising problem involves multiple ring resonances. The minimum energy level is determined by the value stored in each one of the photonic nodes. This is resolved by

[0089] <Digital Annealing> In one or more embodiments, the digital annealing may be performed by a digital annealing unit, e.g., For example, it is understood to refer to those developed by Fujitsu (registered trademark) and the like. There will be.

[0090] <Algorithms implemented using quantum devices> Any kind of algorithm that can be implemented on a quantum device is considered to be within the scope of the present invention. may be suitable for one or more embodiments of the methods and computing systems shown. It will be appreciated that, according to the description herein, suitable algorithms include, but are not limited to: Examples include: A scalable co-design framework for solving chemical problems on quantum computers A variational quantum eigensolver (VQE) (Peruzzo et al., (2014), "A variational eigenvalue solver on a photonic quantum processor," .Nature communications, 5, p.4213., arXiv: 1304.3061, and Nam et al., (2019), "Ground-state energy estimation of the water molecule on a trapped ion quantum c omputer,".arXiv preprint: arXiv:1902.10171 .) A quantum algorithm that allows quadratic speedup compared to classical counterparts for search tasks. A Grover algorithm (Chuang et al., (1998), "Experimental implementation entation of fast quantum searching,".Physical review letters, 80(15), p.3408) Deutsch-Josa, an efficient quantum algorithm for solving the Deutsch-Josa problem Algorithm (Jones et al., (1998), "Implementation of a quantum algorithm on an uclear magnetic resonance quantum computer,".The Journal of chemical physics, 10 9(5), pp.1648-1653., arXiv:quant-ph / 9801027, and Debnath et al., (2016)."Demon stration of a small programmable quantum computer with atomic qubits,".Nature, 5 36(7614), p.63., arXiv: 1603.04512) Factors of integers, which can be exponentially faster than classical modern factorization algorithms Shor's algorithm (Lu et al., (2007) "Demon stration of a compiled version of Shor's quantum factoring algorithm using photo nic qubits,".Physical Review Letters, 99(25), p.250504., arXiv:0705.1684, and Monz et al., (2016)."Realization of a scalable Shor algorithm,". Science, 351( 6277), pp.1068-1070., arXiv:1507.08852)

[0091] <Physics-inspired computer simulator> Any kind of physics-inspired computer simulator and simulation may be implemented in one or more of the methods and computing systems disclosed herein. It will be understood that a physics-inspired simulator may be suitable for various embodiments. solutions to computational tasks, mimicking the results provided by biologically inspired computers. It can be any computer-implemented method using any classical hardware that provides It will be understood that physics-inspired simulators are based on artificial intelligence methods. Physics-inspired simulators include, for example, any machine learning method. The learning method may be, for example, a supervised machine learning method or an unsupervised machine learning method. Both of these can be combined with reinforcement learning methods. The data can include any reinforcement learning method.

[0092] In one or more embodiments, the quantum computer simulator operates in a probabilistic framework. This is useful for generative machine learning models, especially restricted Boltzmann machines (RBMs). Reinforcement learning of the parameters defining the quantum Hamiltonian performed to obtain neural network representations of the ground states and time-dependent physical states. The network weights should generally be complex-valued, and the amplitude and position of the wave function should be The neural network parameters are given by the static variation by Monte Carlo sampling, or time-dependent variations if dynamic properties are of interest It is optimized by Monte Carlo (trained in the language of neural networks) For more details, see Carleo et al., 2017. Solving the quantum many-body problem. m with artificial neural networks.Science, 355(6325), pp.602-606, arXiv:1606.023 18, and Melko et al., (2019)."Restricted Boltzmann machines in quantum physics ".Nature Physics, 15(9), pp.887-892, and Torlai et al., (2018) "Neural-networ k quantum state tomography,” Nature Physics 14, 447, arXiv: 1703.05334 I want to be.

[0093] In one or more other embodiments, the quantum computer simulator is a deep Boltzmann This configuration is represented by a deep Boltzmann machine (Deep Boltzmann Machine). We prove that it is possible to represent the exact ground state of a large class of eigenvalues. In form, two layers of hidden neurons are quantum correlations between the degrees of freedom of physics in the visible layer. This method reproduces the exact imaginary time Hamiltonian expansion and is fully deterministic. A compact and accurate network representation of the ground state is given by The physical quantities are obtained without stochastic optimization. The physical quantities are obtained with both physics and neuronal degrees of freedom. For more information, see Carl eo et al., (2018). "Constructing exact representations of quantum many-body syst ems with deep neural networks," Nature Communications, 9(1), p.5322.

[0094] In one or more alternative embodiments, the quantum computer simulator is a recurrent neural network network (more precisely, several stacked gated recurrent units, or The use of this kind of scalable machine learning procedure is This method allows the reconstruction of both pure and mixed states of quantum systems. It will be appreciated that this is experimentally friendly since it only requires the measurement of with a built-in approximate certificate of reconstruction, No assumptions are made about purity. This is true for prototype states of quantum information, as well as condensed matter. Efficiently calculate a wide variety of composite systems, including the ground states of local spin models common in physics. This procedure converts state tomography into unsupervised learning of quantum measurement statistics. This involves reducing the problem to a state-of-the-art machine learning approach to the verification of complex quantum devices. This further constructs a neural network over mixed states suitable for variational optimization. It can be shown that the quantum circuit is related to the network quantum circuit (Ansatz). For more information, see Carrasquilla et al. (2019). "Reconstructing quantum states with gen erative models," Nature Machine Intelligence, 1 (3), p. 155, arXiv:1810.10584 Illuminate.

[0095] The quantum computer simulator is a living computer trained on quantum state tomography data. It will be understood that the generative model may include a neural network model that represents a quantum state. This may include a network.

[0096] Neural networks are used as functional representations of wave functions that describe quantum states. Neural network quantum state tomography can It will be understood by those skilled in the art that this is one of the possible processes for training quantum states. There will be.

[0097] Quantum State Tomography (QST), the use of measurements to reconstruct quantum states, is a quantum device It is the gold standard for validating and benchmarking equipment in the industry. (Cramer et al., "Efficient quantum state tomography," Nature Communications 1 no. 1, (2010). The number and time of measurements required for this scale exponentially with the size of the system. In neural network tomography, JPEG2025118755000002.jpg951 is reconstructed from a set of measurements of the system. This strategy is based on the learning of neural networks. The learned probability distribution is mapped to a probability representation of the wave function.

[0098] <Digital Computer> In one or more embodiments, the digital computer performs the functions of the digital computer. One or more hardware central processing units (CPUs) for executing the In a digital computer, an operating system configured to execute executable instructions is In one or more embodiments, the digital computer further comprises a computer network. In one or more embodiments, the digital computer is connected to an internet connected to the Internet to access the World Wide Web. In an embodiment, the digital computer is connected to a cloud computing infrastructure. In one or more embodiments, the digital computer is connected to an intranet. In the above embodiments, the digital computer is connected to a data storage device.

[0099] Those skilled in the art will appreciate that various types of digital computers may be used. In practice, suitable digital computers include, by way of non-limiting example, server computers. , desktop computers, laptop computers, notebook computers, Subnotebook computer, Netbook computer, Netbook computer , set-top computers, media streaming devices, handheld computers, internet Internet appliances, mobile smartphones, tablet computers, personal devices This includes digital assistants, video game devices, and vehicles. Suitable for use with one or more embodiments of the methods and systems described in Choosing a TV, video player, and digital music player can be a challenge. In some cases, computer network connectivity may be used to operate the systems described herein. Suitable tablet computers may be suitable for use in one or more embodiments of the systems and methods. The computer is available in booklet, slate, and tablet configurations. It can include data.

[0100] In one or more embodiments, the digital computer includes an operating system. An operating system is configured to execute executable instructions. These can be software that manages the hardware of the device. Those skilled in the art will understand the various types of It will be appreciated that any suitable server operating system may be used. Non-limiting examples of such systems include FreeBSD, OpenBSD, and NetBSD (registered trademark). ), Linux, Apple (registered trademark), Mac OS X Server (registered trademark) ), Oracle (registered trademark), Solaris (registered trademark), Windows Server ver (registered trademark), Novell (registered trademark), and NetWare (registered trademark) Suitable personal computer operating systems include, by way of non-limiting example, Micro soft(registered trademark), Windows(registered trademark), Apple(registered trademark), Mac OS X®, UNIX®, and GNU / Linux® In one or more embodiments, the operating system may include a UNIX-like operating system such as The operating system is provided by cloud computing. Smartphone operating systems include, but are not limited to, Nokia, Symbi an® OS, Apple® iOS®, Research In Motion(R), BlackBerry OS(R), Goo gle(registered trademark) Android(registered trademark), Microsoft(registered trademark) Wi Windows Phone (registered trademark) OS, Microsoft (registered trademark) Windows ws Mobile® OS, Linux®, and Palm® Suitable media streaming devices include the WebOS (trademark) Operating systems include, but are not limited to, Apple TV®, Roku®, trademark), Boxee (registered trademark), Google TV (registered trademark), Google C Chromecast®, Amazon Fire®, and Sam Suitable video devices include: The gaming device operating system may be, by way of non-limiting example, a Sony® PS3™. trademark), Sony® PS4®, Microsoft® X Box 360 (registered trademark), Microsoft Xbox One, Nintendo o (registered trademark) Wii (registered trademark), Nintendo (registered trademark) Wii U trademark), and Ouya®.

[0101] In one or more embodiments, a digital computer includes a storage device and / or a memory device. Those skilled in the art will understand that various types of storage devices and / or memories are used in digital computers. It will be appreciated that the present invention may be used in one or more embodiments, such as in a storage device and and / or memory devices for temporary or permanent storage of data or programs. In one or more embodiments, the device includes one or more physical devices used to store volatile memory. In one or more embodiments, the device includes a memory and requires power to maintain the stored information. The device includes a non-volatile memory and is configured to operate when power is not supplied to the digital computer. In one or more embodiments, the non-volatile memory is a flash memory. In one or more embodiments, the non-volatile memory includes dynamic random access memory. In one or more embodiments, the non-volatile memory includes ferroelectric random access memory (DRAM). In one or more embodiments, the non-volatile memory includes a random access memory (FRAM). In one or more embodiments, the device includes a phase change random access memory (PRAM). Storage devices include, but are not limited to, CD-ROM, DVD, flash, memory devices, magnetic disk drives, magnetic tape drives, optical disk drives, and In one or more embodiments, the storage device includes a cloud computing-based storage device. The device and / or memory device may include a combination of devices such as those disclosed herein. include.

[0102] In one or more embodiments, the digital computer includes a display device. Those skilled in the art will appreciate that various types of display devices are used to provide visual information to users. It will be appreciated that in one or more embodiments, the display device may be a cathode ray tube (CRT) In one or more embodiments, the display device comprises a liquid crystal display (LCD). In the above embodiments, the display device is a thin film transistor liquid crystal display device (TFT-LCD). In one or more embodiments, the display device is an organic light emitting diode (OLED) display device. In one or more embodiments, the OLED display device comprises a passive matrix OLED. (PMOLED) or active matrix OLED (AMOLED) displays. In one or more embodiments, the display device comprises a plasma display device. In one embodiment, the display device comprises a video projector. This includes combinations of devices such as those disclosed herein.

[0103] In one or more embodiments, the digital computer includes an input device to receive information from a user. Those skilled in the art will appreciate that various types of input devices may be used. In one or more embodiments, the input device includes a keyboard. The device includes a pointing device, such as, but not limited to, a mouse. , trackball, trackpad, joystick, game controller, or In one or more embodiments, the input device includes a touchscreen or multimedia device. In one or more embodiments, the input device includes a touch screen. In one or more embodiments, the input device includes a microphone for capturing motion or or a video camera or other sensor for capturing visual input. In one embodiment, the input device includes a Kinect, Leap Motion, etc. In embodiments, the input device includes a combination of devices such as those disclosed herein.

[0104] Referring now to Figure 1, the quantum computer and quantum computer simulator One embodiment of a system 100 for providing access is shown.

[0105] The system 100 includes a digital computer 110. The processor 112 and, among other things, the processor executable components for generating the request. and memory 114 containing a computer program. It will be appreciated that computer 110 can be any type of digital computer. Although an embodiment is disclosed in which the request is generated by a digital computer 110, the request It will be appreciated that various alternative embodiments may be provided.

[0106] In the embodiment disclosed in FIG. 1, the system 100 is a computing platform. The computing platform 120 further includes a variable parameter at least one physics-inspired computer simulator containing and one optional physics-inspired computer. In this embodiment, the computing platform 120 may include at least one physics-inspired This includes a computer simulator that obtains the

[0107] The system 100 further includes a computing system 118. More precisely, The computing system 118 includes a communication interface 140, a control unit 126, , an optional training unit 128, and a memory 130. 10 communicates with a data network (not shown) via a communication interface 140. 118. The computer 118 is operatively connected to the computer system 118 using the

[0108] At least one physics-inspired computing platform of your choice The computer can be of various types. For example, one or more embodiments So, at least one physics-inspired computer of choice is a quantum computer. The quantum computer 122 is an embodiment of a non-classical computer. This will be understood by those skilled in the art.

[0109] It is understood that quantum computer 122 can be a variety of types of quantum computers. Indeed, in one or more embodiments, quantum computer 122 may be a NISQ device. device, superconducting quantum computer, ion trap quantum computer, quantum annealing, light intensity quantum dot computers, spin-based quantum dot computers, and photonic-based It will be understood that the quantum computer is selected from the group consisting of:

[0110] In one or more embodiments, at least one physics-inspired computer simulation The data type corresponds to an optional physics-inspired computer.

[0111] In one or more embodiments, at least one physics-inspired computer simulation The data includes a quantum computer simulator 124. In one or more embodiments, the physics It will be understood by those skilled in the art that computer simulators inspired by the Deaf.

[0112] In one or more embodiments, at least one quantum computer simulator 124: Reinforcement learning is represented by a probabilistic framework. and thus refine the parameters that define the generative machine learning model. In an embodiment, the generative machine learning model is a restricted Boltzmann machine. Generative machine learning models include deep Boltzmann machines, forward propagation neural networks, and and recurrent neural networks. can be complex-valued and provides a complete description of both the amplitude and the phase of the wave function In one or more embodiments, the parameters of the neural network are determined using static variational Monte Carlo. In another embodiment, the dynamics are optimized using neural network sampling. The network parameters are optimized using time-dependent variational Monte Carlo. There is a heart.

[0113] In one or more alternative embodiments, the quantum computer simulator is a deep Boltzmann In such an embodiment, two layers of hidden neurons are represented by a visible This method is used to convey quantum correlations between the degrees of freedom of physics in layers. reproduces the exact imaginary time Hamiltonian expansion and is found to be deterministic. To obtain a ground-state network representation, we use a stochastic model of the network parameters. No optimization is required. The physical quantities are composed of both physical and neuronal degrees of freedom. It will further be appreciated that the temperature may be measured by sampling the

[0114] In one or more alternative embodiments, the quantum computer simulator is a recurrent neural network More precisely, quantum computer simulators include several stacks. This type of scalable architecture includes structures consisting of gated recurrent units or GRUs. Using a simple machine learning procedure makes it possible to reconstruct both pure and mixed states. The method involves measuring the quantum system. The learning procedure involves a built-in approximation of the reconstruction. It includes proofs and makes no assumptions about the purity of the state under examination. The learning procedure is This involves reducing the problem of quantum mechanics to the problem of unsupervised learning of the statistics of quantum measurements.

[0115] Still referring to FIG. 1, the computing system 118 may include a It will be understood that the communication interface 140 includes a It will be appreciated that in one or more embodiments, the processing device may include a digital It is further understood that the request may be provided according to various embodiments. In one or more embodiments, the request may be transmitted by a processing device over a data network. is provided to the communication interface 140 via

[0116] It will be appreciated that communication interface 140 may be implemented according to various embodiments. In one or more embodiments, the communication interface 140 may be configured to allow a user to perform a computational task. and receiving the solution of the computation. It is implemented using a programming interface (API) gateway.

[0117] In one or more embodiments, an application programming interface (API) The gateway is programmed or configured to authenticate users of the computing system. In one embodiment, the application programming interface The API gateway monitors system and data security. An example is an application programming interface. The API gateway uses Secure Sockets Layer (SSL) to encrypt requests and responses. In some embodiments, secure sockets layer (SSL) can be used. In one of the applications, the application programming interface (API) gateway is , programmed or configured to monitor data traffic.

[0118] The request is processed using at least one physics-inspired computer simulator. The system includes at least one computational task for processing the data.

[0119] In one or more embodiments, the request includes at least one optional physics-inspired component. and at least one physics-inspired computer simulator. It will be understood that the method further includes an indication of selection for at least one of

[0120] It will be appreciated that the at least one computational task may be a variety of different types of computational tasks. In one or more embodiments, the at least one computational task includes an optimization task. In one or more alternative embodiments, at least one computational task involves sampling from a probability distribution. In one or more alternative embodiments, at least one computational task includes a database. Source search, solving the Deutsch-Josa problem, solving quantum chemistry related problems, and factoring integers The computational task may include any computational task selected from the group consisting of:

[0121] In one or more embodiments, at least one computational task and at least one corresponding solution and to improve at least one physics-inspired computer simulator. The request may further include an indication that the request may be used for training purposes. It will be understood.

[0122] Still referring to FIG. 1, the computing system 118 further includes a memory 130. The memory 130 includes a control unit 126 and at least one physics-inspired The memory 130 is operatively connected to the computer simulator. The memory 130 is configured to store one or more of the following computational tasks: This data set is further used to store the cases where the The memory 130 includes at least one physical memory required to perform one computational task. It can be further used to store variable parameters of biologically inspired computer simulators. Finally, the memory 130 stores at least one corresponding solution received. It is also used for

[0123] In one or more embodiments, the request includes at least one assignment to perform a computational task. A selection of physics-inspired computers and at least one physics-inspired computer and a computer simulator, Memory 130 is further used to store the selection.

[0124] The memory 130 is connected to the control unit 126 and to at least one physics-inspired computer. It will be understood that the computer simulator may be operatively connected to one or more of the In an embodiment, the memory 130 includes a control unit 126, an optional training unit 128, and and accessed by at least one physics-inspired computer simulator. can be.

[0125] It will further be appreciated that memory 130 may be a variety of types of memory. For example, in one or more embodiments, memory 130 comprises a database. It will be appreciated by those skilled in the art that any such database may be suitable for storing and retrieving data. In one or more embodiments, suitable databases include, by way of non-limiting example, relational data. relational database, non-relational database, object-oriented database, object database entity-relationship model databases, associative data In one or more embodiments, the database includes an XML database. In one or more embodiments, the database is web-based. In the above embodiment, the database is based on cloud computing (e.g., cloud In other embodiments, the database is based on one or more local computer stores. In one or more embodiments, the solution to the problem solved is based on a database. In one or more embodiments, the data sent with the request is held by the data It is also stored in the base.

[0126] Still referring to FIG. 1, the computing system 118 includes a control unit 126 It will be understood that the control unit 126 further includes a communication interface 1. 40, memory 130, optional training unit 128, and computing platform The device is operatively connected to platform 120.

[0127] The control unit 126 may be configured to execute at least one of the computational tasks included in the received request. At least one optional physics-inspired computer and at least one physics into instructions for at least one of the computer simulators Additionally, the control unit 126 may be configured to control at least one optional physical A physics-inspired computer and at least one physics-inspired computer system and transmitting instructions to at least one of the simulators to perform the computational task. It will be understood that the

[0128] The control unit 126 includes at least one optional physics-inspired computer and at least one physics-inspired computer simulator and further used to receive at least one corresponding solution to the computational task from another. It is used.

[0129] In one or more embodiments, the computing system 118 includes a training unit 128 and the training unit 128 further includes at least one physics-inspired computer It will be appreciated that the system may be operably connected to a data simulator.

[0130] The optional training unit 128 includes at least one physics-inspired computer In such an embodiment, at least one The computational task and at least one corresponding solution are inspired by at least one physics To improve the computer simulator, a notice indicating that it can be used for training purposes must be added. If received, the control unit 126 sends the command and the corresponding at least one solution to any It will be understood that the data may be further used for transmission to a training unit 128 of the user's choice. cormorant.

[0131] In one or more embodiments, at least one physics-inspired computer simulation The data type corresponds to a physics-inspired computer, and the training unit 128 At least using instructions transmitted to a computer inspired by physics, and At least one corresponding solution obtained from the computer that inspired the It is also used to train a physics-inspired computer simulator. In one or more embodiments, the method comprises: physics-inspired instructions for a computer; and at least one The instructions for two physics-inspired computer simulators are logically identical. It will be understood.

[0132] In one or more embodiments, at least one physics-inspired computer simulation It will be further understood that the data includes a computer-implemented method. mimicking the output of a physics-inspired computer for a given input and training units Update at least one of the variable parameters mentioned above using the parameter 128, thereby and improving the corresponding performance.

[0133] It is understood that the optional training unit 128 can be a variety of types of training unit. Indeed, in one or more embodiments, at least one physics-inspired Optional training units used to improve the performance of a computer simulator 128 uses artificial intelligence-based methods to develop physics-inspired computer simulations. It will be understood that the present invention is a computer-implemented method for updating variable parameters of data. cormorant.

[0134] In one or more embodiments, the optional training unit 128 trains a neural network Neural networks can be of various types. It will be understood by those skilled in the art that in one or more embodiments, the neural network In one or more other embodiments, the neural network includes a restricted Boltzmann machine. , deep Boltzmann machines. In yet one or more other embodiments, In an alternative embodiment, the neural network comprises a recurrent neural network. The network includes a feedforward neural network. The variable parameters are It will be appreciated that the weights of the network may be included. The refinement unit 128 includes a function approximator. The variable parameters include function approximation parameters. It can be done.

[0135] Those skilled in the art will appreciate that the training unit 128 may be implemented according to various embodiments. More precisely, the training unit 128 may be a tensor processing unit (TPU), Graphical Processing Unit (GPU), Field Programmable Gate Array (FPG) A), and an application specific integrated circuit (ASIC). Those skilled in the art will appreciate that various alternative embodiments are available for implementing the training unit 128. You will understand what you will get.

[0136] In one or more embodiments, the control unit 126 may assign computational tasks to optional physics. A small number of physics-inspired computers and physics-inspired computer simulators To create a worker that performs the transformation for at least one In such an embodiment, the created workers are programmed to perform the computational tasks. and at least one optional physics-inspired computer program according to the selection to and at least one physics-inspired computer simulator Furthermore, in this embodiment, the created workers are and receiving at least one corresponding solution and transmitting the instruction and the at least one corresponding solution to the optional training. The data is transmitted to the kneading unit 128.

[0137] In one or more embodiments, the computing system 118 may include an optional queue. The queue unit may further include a central queue, as shown in FIG. It will be understood that such optional queuing units are not shown in , and is programmed to queue the received requests according to a criterion. In the above embodiment, a given request maintains the order of requests in the queue and does not miss a message. It will be appreciated that the request is placed in a queue to prevent it from being overwritten.

[0138] Referring now to Figure 2, one embodiment of a system 2000 is shown. 0 includes a digital computer 110, a computing platform 120, A computer including at least one of a quantum computer and a quantum computer simulator. providing access to the computing platform 120 to the digital computer 110; and another embodiment of a computing system 200 used to provide the

[0139] Quantum computer 122 is a physics-inspired embodiment of a computer, while Quantum Computer Simulator 124 is a physics-inspired computer simulator. It will be understood that the present invention is an embodiment of the present invention.

[0140] In this embodiment, the computing system 200 includes an API gateway 202 , a memory 130, a control unit 126, and a training unit 128. It will be possible.

[0141] Additionally, as will be further explained, the control unit 126 controls a central queue 204 and It includes a cluster manager 208 , a worker farm 206 , and a central log 210 .

[0142] In this embodiment, a request containing a computational task is processed by the digital computer 110. The received request is then sent to the PI Gateway 202. 26 central queue 204. The central queue 204 processes received requests. Those skilled in the art will appreciate that the queue may be of various sizes depending on the number of requests received. It will be understood that the

[0143] The memory 130 is operatively connected to the central queue 204. In particular, the memory 130 , among other things, communicate with the central queue 204 to record queue status and transactions. Record.

[0144] The central queue 204 also reports the current state of the queue to the cluster manager 208. Send.

[0145] The cluster manager 208 initiates and controls the lifetime of certain types of computing components. More precisely, the cluster manager 208 Start at least one worker in the farm 206 to perform the computation. This is, for example, For example, a digital processor and a quantum processor may be used to convert received instructions into specific quantum computation instructions. The goal of the IEEE is to control the processor to perform computational tasks.

[0146] In the embodiment shown in FIG. 2, the worker farm 206 includes a first worker 212 and A second worker 214 and a third worker 216. Those skilled in the art will appreciate that multiple workers It will be appreciated that any number of workers may be included. Once a worker has completed its assigned task, it is then discarded by the cluster manager 208. It will be understood that

[0147] In the embodiment shown in FIG. 2, the central log 210 is in communication with the worker farm 206. , and records all events. In practice, the central log 210 It will be appreciated that the event handler is responsible for tracking events that occur. In an embodiment, every execution sends a corresponding log of events to the central log 210 . Events are the start of a task execution, the end of a task execution, and errors that occur during a task execution. Those skilled in the art will recognize that various alternative embodiments are possible. It will be appreciated that information may be provided about the event.

[0148] A particular embodiment of a computing system 200 is disclosed in FIG. Those skilled in the art will appreciate that various alternative embodiments of the driving system 200 are possible. It will be done.

[0149] Referring now to Figure 3, optional training units are used to develop physics-inspired computer An embodiment of a method for training a computer simulator is presented. The purpose of training a computer simulator is to improve its performance. It will be understood.

[0150] More precisely, in one or more embodiments, a physics-inspired computer simulation Methods for training the data include using machine learning training procedures.

[0151] A physics-inspired computer simulator may be any suitable physics-inspired computer simulator. The system may be a computer simulator, such as the system 100 disclosed in FIG. or any physical device described herein with respect to the system 2000 disclosed in FIG. It will be understood that this is a computer simulator inspired by physics.

[0152] The optional training unit may be any suitable training unit, such as: For example, as used herein with respect to the computing system 118 disclosed in FIG. In any of the training units described, or in computing system 126 of FIG. training unit 128 described above. The choice to use a computer is made and approval is given to use the computational tasks for training purposes. Given at least one computational task and at least one corresponding solution, For the purposes of training at least one physics-inspired computer simulator, In response, the optional training unit The program runs procedures based on machine learning protocols and has at least one physics-inspired algorithm. The resulting computer simulator's variable parameters can be updated. In an embodiment, at least one optional physics-inspired computer A selection is made and approval is given to use at least one computational task for training purposes. If so, the instructions and at least one corresponding solution are stored in memory.

[0153] Still referring to FIG. 3, according to process step 302, at least one instance of the instruction is The stance and at least one corresponding solution are stored in memory using an optional training unit. The selection can be based on various criteria, such as the storage of the instructions. It will be understood by those skilled in the art that the solution is based on a time series of generated instances and at least one corresponding solution. It will be understood that in one or more embodiments, the selection may be performed by selecting a stored instance of the instructions. and at least one corresponding solution based on at least one priority associated with the solution.

[0154] Still referring to FIG. 3, according to process step 304, at least one selected instruction The total error corresponding to an instance and at least one corresponding solution is calculated. Those skilled in the art will appreciate that various embodiments may be used to calculate the error. In one or more embodiments, the calculation of the total error is based on the mean squared error. In one embodiment, the calculation of the total error is based on cross-entropy. The total error calculation is based on the mean absolute error. The total error calculation is based on a physics-inspired computer It is further understood that the number of iterations may depend on the machine learning procedure used to train the data simulator. It will be understood.

[0155] Still referring to FIG. 3, according to process step 306, a machine learning protocol based The procedure is carried out using an optional training unit. The machine learning protocol can be implemented using various It will be appreciated that the machine learning protocol may be of any type. Machine learning protocols are a group of supervised learning, unsupervised learning, and reinforcement learning. In one or more embodiments, this procedure is a backpropagation This procedure may include calculating derivatives with respect to variable parameters. It will be understood that this procedure is applicable to batch gradient descent, stochastic gradient descent, and mini-gradient The method may further include at least one member selected from the group consisting of: It will be understood by those skilled in the art that this procedure may involve any optimization method. .

[0156] Still referring to FIG. 3, according to process step 308, a physics-inspired simulator The variable parameters of are updated using optional training units. In the present embodiment, it is understood that the variable parameters include the weights of the neural network. Neural networks are classified into restricted Boltzmann machines and deep Boltzmann machines. A group consisting of neural networks, feedforward neural networks, and recurrent neural networks. It will be further understood that the present invention may include at least one member selected from the group .

[0157] The update can be performed according to various embodiments. For example, one or more embodiments In this mode, optional training units are available on physics-inspired computer simulators. In one or more other embodiments, the training unit is a variable parameter set of a physics-inspired computer simulator stored in memory. The physics-inspired computer simulator then updates the stored data in memory. Those skilled in the art will appreciate that physics-inspired computers Various alternative embodiments may be provided for updating the variable parameters of the data simulator. You will understand that.

[0158] Referring now to Figure 4, the physics-inspired computing resources and their An embodiment of a method for enabling remote access to a simulation is presented. In an embodiment, the physics-inspired computing resource comprises at least one This includes any physics-inspired computer. The computer may be any suitable physics-inspired computer, for example, 1 or the system 2000 disclosed in FIG. 2. It is understood that the invention is any physics-inspired computer described in the literature. In one or more embodiments, the optional physics-inspired computer may be a quantum The quantum computer may be any suitable quantum computer. This can be done, for example, in the system 100 disclosed in FIG. 1 or the system disclosed in FIG.

[0033] The present invention is directed to any quantum computer described herein with respect to System 2000.

[0159] Therefore, quantum computers and quantum computer simulators are further developed as shown in Figure 4. While the method disclosed in FIG. 4 is more generally applicable to physics-inspired computing It should be understood that quantum computers allow remote access to the and physics-inspired computer simulators, one example of which is quantum It is a child computer simulator.

[0160] Still referring to FIG. 4, according to process step 402, a request is received, the request , at least one computational task and a A physics-inspired computer and at least one physics-inspired computer and an indication of a selection of at least one of the computer simulators.

[0161] It will be appreciated that the request may be received according to various embodiments. In an embodiment, the request is transmitted via a data network to a communications interface of a computing system. In one or more embodiments, the request is received by The digital signal is transmitted by a digital computer operably connected to the communication interface.

[0162] The digital computer can be any suitable digital computer, for example For example, with respect to the system 100 disclosed in FIG. 1 or the system 2000 disclosed in FIG. It will be understood that any digital computer described herein cormorant.

[0163] It is further understood that the communication interface may be any of a variety of types of communication interfaces. In one or more embodiments, the communication interface may be an API gateway. The selection includes at least one optional physics-inspired computer and at least one and at least one physics-inspired computer simulator. In one or more embodiments, a selection is made for at least one corresponding computational task. At least one corresponding generated solution is available for training purposes. It will be understood that the request further includes the indication, more precisely as further explained below. At least one corresponding generated solution and at least one corresponding calculated solution are The task is then to train at least one physics-inspired computer simulator. It can be used to knead.

[0164] Those skilled in the art will appreciate that the at least one computational task may be a variety of different types of computational tasks. In one or more embodiments, at least one computational task may be an optimization task. In one or more alternative embodiments, at least one computational task comprises: In one or more alternative embodiments, at least one computational task includes sampling. Database search, solving the Deutsch-Josa problem, solving quantum chemistry related problems, and factoring integers Contains any member of the group consisting of number decompositions.

[0165] Still referring to FIG. 4, according to process step 404, the received request is queued. In one or more embodiments, the received request is placed in a computing system. The process steps are performed in a queue using a queue unit located in the system. It will be understood that the group is optional.

[0166] Still referring to FIG. 4, according to process step 406, the received request is translated. The received request is processed as follows, depending on whether or not processing step 404 is performed: It will be understood that a queue may or may not be placed in the queue. The received request is sent to at least one of the quantum computer and quantum computer simulator. It will be understood that the command will be converted into at least one suitable command.

[0167] In one or more embodiments, the conversion is performed using a control unit of a computing system. Various alternative embodiments may be provided for converting the received request. It will be appreciated that the quantum computer simulator may be any suitable quantum computer. The system may be a computer simulator, such as the system 100 disclosed in FIG. Any quantum computer described herein with respect to system 2000 disclosed in FIG. It will be understood that the present invention is a computer simulator.

[0168] Still referring to Figure 4, according to process step 408, a decision is made. is the quantum computer or quantum computer simulator uses to execute instructions. It will be understood that the decision is to determine whether the It will be appreciated that in accordance with one or more embodiments, the determination In one or more alternative embodiments, the decision is made based on a variety of other This is based on the following considerations:

[0169] If it is determined that quantum computers should be used to execute instructions, If so, according to process step 410, the instruction is transmitted to the quantum computer. It will be understood that the quantum computer may be transmitted according to various embodiments known to those skilled in the art. It would be.

[0170] According to process step 414, the instructions are executed using a quantum computer, At least one corresponding solution is generated resulting from the execution.

[0171] A quantum computer simulator should be used to execute the instructions. If so, according to process step 412, the instructions are The instructions are transmitted to the quantum computer according to various implementations known to those skilled in the art. It will be appreciated that the data may be transmitted to a simulator.

[0172] According to process step 416, the instructions then use a quantum computer simulator to The instructions are executed to generate at least one corresponding solution resulting from the execution of the instructions. can be.

[0173] Still referring to FIG. 4, according to process step 418, the resulting instruction execution In one or more embodiments, at least one corresponding generated solution is received. At least one corresponding generated solution is provided to a control unit of the computing system. It is received using

[0174] According to processing step 420, at least one corresponding generated solution is stored in memory. In one or more embodiments, at least one corresponding generated solution is stored. The unit stores the data in the memory of the computing system.

[0175] Still referring to FIG. 4, according to processing step 422, at least one corresponding raw The resulting solution is provided to a digital computer. The corresponding generated solution is then transmitted to a digital computer via a data network. Provided.

[0176] Processing steps 420 and 422 provide at least one corresponding generated solution. It will be understood that this is an embodiment.

[0177] At least one corresponding generated solution is obtained from at least one quantum computer. In a preferred embodiment, the method includes: We further train the quantum computer simulator using a single computational task. It will be understood that the above may include:

[0178] Furthermore, in one or more embodiments, at least one computational task and at least one If the request includes an indication that the corresponding generated solution of It will be appreciated that training is performed in this case.

[0179] In one or more embodiments, training involves at least one corresponding generated solution and at least one and performing a procedure based on a machine learning protocol using another computational task. and updating variable parameters of the quantum computer simulator accordingly. nothing.

[0180] Referring now to Figure 5, a physics-inspired model that includes at least one variable parameter is Remote access to a computing platform including a computer simulator An embodiment of a flow chart illustrating an embodiment of a method for enabling access is shown. More precisely, Figure 5 shows the physics-inspired computer simulations carried out over a data network. This section explains how to use the emulator.

[0181] A physics-inspired computer simulator may be any suitable physics-inspired computer simulator. The system may be a computer, such as the system 100 disclosed in FIG. 1 or the system disclosed in FIG. Any physics-inspired system described herein with respect to the illustrated system 2000 It will be understood that in one or more embodiments, the computer The parameters of the computer simulator, inspired by the [translate], are stored in memory.

[0182] According to process step 502, a request including at least one computational task is received.

[0183] It will be appreciated that the request may be received according to various embodiments. In an embodiment, the request is received from a computing system communicating over a data network. In one or more embodiments, the request is received by a data network interface. by a digital computer operably connected to the communication interface via a will be sent.

[0184] The digital computer can be any suitable digital computer, for example For example, with respect to the system 100 disclosed in FIG. 1 or the system 2000 disclosed in FIG. It should be understood that any digital computer described herein is a computer.

[0185] Still referring to FIG. 5, according to process step 504, the received request is translated. In practice, the received request is based on at least one physics-inspired computer simulation. It will be understood that the instructions are converted into instructions suitable for the emulator.

[0186] In one or more embodiments, the conversion is performed using a control unit of a computing system. Various alternative embodiments may be provided for converting the received request. It will be understood that.

[0187] Still referring to FIG. 5, according to process step 506, the instructions include: The data is then transmitted to a computer simulator inspired by physics.

[0188] In one or more embodiments, the instructions include using a control unit to In one or more embodiments, the instructions are transmitted to a computer simulator inspired by It is stored in memory.

[0189] Still referring to FIG. 5, according to process step 508, the instructions include: performed using a computer simulator inspired by physics, and The solutions are generated by a physics-inspired computer simulator.

[0190] Still referring to FIG. 5, according to processing step 510, at least one corresponding raw The resulting solution is received.

[0191] At least one corresponding generated solution may be received according to various embodiments. It will be appreciated that in one or more embodiments, at least one corresponding generated The solution is received using a control unit of the computing system. In an embodiment, at least one corresponding generated solution is stored in a memory.

[0192] Still referring to FIG. 5, according to processing step 512, at least one corresponding raw The resulting solution is provided to a digital computer.

[0193] At least one corresponding generated solution is generated by a digital computer according to various embodiments. It will be appreciated that in one or more embodiments, at least one The two corresponding generated solutions are the communication interface and data The data is provided to a digital computer using a data network.

[0194] One or more implementations of the methods and computing systems disclosed herein It will be appreciated that this is a highly advantageous embodiment for a number of reasons.

[0195] More precisely, the methods and computing systems disclosed herein An advantage of one or more embodiments is that they are based on physics trained using real computational tasks. The goal is to provide access to computer simulators inspired by For example, a simulator of a quantum device, which is relatively cheaper than access to the quantum device. It is a price.

[0196] One or more implementations of the methods and computing systems disclosed herein Another advantage of these forms is that they are compatible with physics-inspired computers such as quantum computers. The aim is to make it possible to imitate

[0197] One or more implementations of the methods and computing systems disclosed herein Another advantage of these forms is that they allow the user to perform computational tasks on the The goal is to enable improvements to computer simulators inspired by the

[0198] Section 1 A processing unit remotely accesses the computing platform over a network. A computing system for enabling access to a computer The platform must be based on at least one physics-inspired model that includes variable parameters. the computing system includes a computer simulator a) a communications interface for receiving requests provided by a processing unit, The requirement is to develop at least one physics-inspired computer system that contains variable parameters. a communication interface including at least one computational task for processing using a simulator; Face and, b) a communication interface and at least one physics-inspired system with variable parameters; a control unit operably connected to the computer simulator, The unit converts the received request into at least one physics-inspired computer simulation. and converting the instructions into instructions for the transmitting the at least one computational task to a computer simulator; receiving at least one corresponding solution; and a control unit for executing a control unit; c) a control unit and at least one physics-inspired computer simulator; a memory operatively connected to at least one computing task; The data set contained in the received request and at least one physics-inspired computer The variable parameters of the data simulator and at least one corresponding solution received. a memory for storing one or more of the following: a computing system including:

[0199] Section 2 10. The computing system of claim 1, The computing platform must include at least one physics-inspired computing platform. further comprising a computer, Additionally, the claim requires at least one physics-inspired computer simulator; At least one physics-inspired computer and at least one of Further including the selection, Additionally, the control unit is operable to at least one physics-inspired computer. Further connected to Noh, Furthermore, the selection of requirements must be based on at least one physics-inspired computer If so, the control unit converts the received request into at least one physics-inspired and converting the instructions into instructions for the computer based on at least one physics and transmitting the idea to a computer to perform at least one computational task. Further used in Additionally, the control unit receives at least one physics-inspired computer-generated signal. further used to receive at least one corresponding solution, Computing system.

[0200] Section 3 10. The computing system of claim 1, operatively connected to at least one physics-inspired computer simulator. the training unit further comprises at least one physics-inspired computer Computing System, a training unit for training computer simulators Tem.

[0201] Section 4 3. The computing system of claim 2, wherein the computing system operatively connected to at least one physics-inspired computer simulator. the training unit includes at least one physics-inspired computer A computing system that is a training unit for training a data simulator.

[0202] Section 5 5. The computing system of claim 4, At least one type of physics-inspired computer simulator is based on physics. Inspired by computers, Additionally, the training unit will be able to transmit instructions to a physics-inspired computer with fewer and at least one computer-derived physics-inspired At least one physics-inspired computer simulator with corresponding solutions Used to train Computing system.

[0203] Section 6 In the computing system of claim 1, the communication interface includes a plurality of The request is received, and the computing platform further processes the received request according to the criteria. a queuing unit for queuing the plurality of received requests. Operating system.

[0204] Section 7 10. The computing system of claim 5, comprising: at least one computing task; An indication that at least one corresponding solution is usable for training purposes is given by If the request includes a physics-inspired computer-transmitted response, the training unit and at least one corresponding instruction obtained from a physics-inspired computer. The solution is used to train at least one physics-inspired computer simulator. , a computing system.

[0205] Section 8 10. The computing system of claim 1, The remotely accessed processing device operates over a data network to a communication interface. A computing system including operably connected digital computers.

[0206] Section 9 2. The computing system of claim 2, wherein the computing system is a physics-inspired computing system. instructions for the data and at least one physics-inspired computer simulator The instructions for a computing system are identical.

[0207] Section 10 10. The computing system of claim 1, wherein the computing platform The form is a computing system, including a distributed computing system.

[0208] Section 11 2. The computing system of claim 2, wherein the computing system is a physics-inspired computing system. Data includes non-classical computing systems.

[0209] Section 12 12. The computing system of claim 11, wherein the non-classical computer comprises: NISQ device, quantum computer, superconducting quantum computer, ion trap quantum computer quantum annealing, optical quantum computers, spin-based quantum dot computers and photonic-based quantum computers. computing system.

[0210] Section 13 10. The computing system of claim 1, further comprising at least one physics-inspired The computer simulator for obtaining the above-mentioned equations includes a computer-implemented method for: a) mimicking the output of a physics-inspired computer for a given input; and b) updating at least one of the variable parameters using a training unit; and improving the corresponding performance by a computing system including:

[0211] Section 14 In the computing system according to any one of claims 3 to 4, The processors are Tensor Processing Units (TPUs), Graphical Processing Units (GPUs), and Field Field Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs) ).

[0212] Section 15 10. The computing system of claim 1, further comprising at least one physics-inspired The resulting computer simulator can be used to simulate various computing Gu system.

[0213] Section 16 A computer to enable remote access to the computing platform A data implementation method, wherein the computing platform includes a variable parameter. , including at least one physics-inspired computer simulator, the method comprising: a) receiving a request at a communication interface, the request including at least At least one physics-inspired computer simulator is available for processing. and b) Computing at least one computational task of the received request into at least one physics-inspired and translating the obtained instructions into instructions suitable for a computer simulator. c) providing instructions to at least one physics-inspired computer simulator; And, d) receiving at least one corresponding generated solution resulting from the execution of the instructions; To believe and e) providing at least one corresponding generated solution; and 11. A computer-implemented method comprising:

[0214] Section 17 17. The computer-implemented method of claim 16, The computing system includes at least one physics-inspired computer and , training for training at least one physics-inspired computer simulator; a unit; Additionally, the received request may be used to process at least one computational task. A physics-inspired computer and at least one physics-inspired computer system and further including an indication of at least one selection of the Furthermore, at least one computational task in the request involves a physics-inspired computer and At least one physics-inspired computer simulator are converted into instructions suitable for Furthermore, the provision of instructions may be based on physics-inspired computers and / or computer-based systems, depending on the selection. with at least one physics-inspired computer simulator Executed in Computer-implemented methods.

[0215] Section 18 17. The computer-implemented method of claim 16, wherein the request is received using a data network. and received from a digital computer operably connected to the communication interface; Further, at least one corresponding generated solution is provided to a digital computer. Computer-implemented methods.

[0216] Section 19 18. The computer-implemented method of claim 17, At least one corresponding generated solution is based on at least one physics-inspired concept. obtained from the computer, Furthermore, the at least one corresponding generated solution and the at least one computational task are used. and training at least one physics-inspired computer simulator using the Including, Computer-implemented methods.

[0217] Section 20 20. The computer-implemented method of claim 19, wherein at least one computational task and at least one an indication that at least one corresponding generated solution can be used for training purposes, If the request includes, training is performed, a computer-implemented method.

[0218] Section 21 20. The computer-implemented method of claim 19, wherein the training comprises: a) using at least one corresponding generated solution and at least one computational task; and performing a procedure based on a machine learning protocol. b) correspondingly, variable parameters of a physics-inspired computer simulator; and updating 11. A computer-implemented method comprising:

[0219] Section 22 17. The computer-implemented method of claim 16, wherein the computer-implemented method comprises: and storing at least one corresponding generated solution. method.

Claims

1. A processing unit remotely accesses the computing platform over a network. A computing system for enabling access to said computer The platform is based on at least one physics-inspired system that includes variable parameters. and a computer simulator that obtains the a) a communications interface for receiving requests provided by a processing device, The request includes at least one physics-inspired computer program that includes a variable parameter. A communication interface including at least one computational task for processing using a data simulator. With the interface, b) the communication interface and the at least one physical a control unit operably connected to a computer simulator inspired by The control unit then converts the received request into the at least one physics-inspired converting the obtained instructions into instructions for a computer simulator; and and transmitting the at least one of the plurality of images to a physics-inspired computer simulator. performing two computational tasks and receiving at least one corresponding solution. a control unit for performing the c) connecting said control unit with said at least one physics-inspired computer simulation system; a memory operatively connected to the at least one a computation task, a data set included in the received request, and the at least one object the variable parameters of a science-inspired computer simulator and the received and at least one corresponding solution. and, a computing system including:

2. 10. The computing system of claim 1, The computing platform includes at least one physics-inspired computer. further comprising a computer, Further, the request may include the at least one physics-inspired computer simulation. and at least one of said at least one physics-inspired computer. further including a choice about one of Further, the control unit may include a controller for controlling the at least one physics-inspired computer. further operatively connected to Further, said selecting of said requests may be performed by said at least one physics-inspired computer. If the selection is for a data item, the control unit forwards the received request to the at least and converting the instructions into instructions for a single physics-inspired computer. , transmitting the at least one physics-inspired computer to the at least one and further used to perform two computational tasks, Further, the control unit may include a controller for controlling the at least one physics-inspired computer. further used to receive at least one corresponding solution from Computing system.

3. 10. The computing system of claim 1, wherein the computing system the system is operable to said at least one physics-inspired computer simulator. and a training unit connected to the at least one object, the training unit A training unit for training computer simulators inspired by physics. computing system.

4. 3. The computing system of claim 2, wherein the computing system the system is operable to said at least one physics-inspired computer simulator. and a training unit connected to the at least one object, the training unit A training unit for training computer simulators inspired by physics. computing system.

5. 5. The computing system of claim 4, The at least one type of physics-inspired computer simulator may include: Supporting computers inspired by science, Further, the training unit may include a computer-generated image of the physics-inspired computer. instructions and obtained from said physics-inspired computer and computing the at least one physics-inspired computational model using at least one corresponding solution. used to train computer simulators, Computing system.

6. 2. The computing system of claim 1, wherein the communication interface comprises: A plurality of requests is received, and the computing platform and further comprising a queuing unit for queuing the received plurality of requests. Computing system.

7. 6. The computing system of claim 5, wherein the at least one computing task an indication that the problem and the at least one corresponding solution are usable for training purposes; the training unit may then execute the physics-inspired computer and the corresponding instructions transmitted to the physics-inspired computer and the instructions obtained from the physics-inspired computer. and the at least one corresponding solution, A computing system for training the obtained computer simulator.

8. 10. The computing system of claim 1, wherein the computing system The processing device remotely accessing the system communicates with the communication interface via a data network. a computing system including a digital computer operably connected to the interface; Tem.

9. 3. The computing system of claim 2, wherein the physics-inspired computing said instructions for a computer and said at least one physics-inspired computer system. The instructions for the simulator are the same as those for the computing system.

10. 10. The computing system of claim 1, wherein the computing platform The platform is a computing system, including a distributed computing system. 。

11. 3. The computing system of claim 2, wherein the physics-inspired computing A computer is a computing system, including non-classical computers.

12. 12. The computing system of claim 11, wherein the non-classical computer The data includes NISQ devices, quantum computers, superconducting quantum computers, and ion trap quantum Computers, quantum annealing, optical quantum computers, spin-based quantum dot computers and photonic-based quantum computers. , computing systems.

13. 10. The computing system of claim 1, wherein the at least one physics The computer simulator inspired by the present invention includes a computer-implemented method, the method comprising: a) mimicking the output of a physics-inspired computer for a given input; b) updating at least one of the variable parameters using the training unit; and thereby improving the corresponding performance; a computing system including:

14. 5. The computing system according to claim 3, wherein the training The units are the Tensor Processing Unit (TPU), the Graphical Processing Unit (GPU), Field Programmable Gate Arrays (FPGAs) and Application Specific Integrated Circuits (ASICs) SIC).

15. 10. The computing system of claim 1, wherein the at least one physics Inspired by this, computer simulators are used to simulate various computer-based systems, including neural networks. Operating system.

16. A computer to enable remote access to the computing platform In a data implementation method, the computing platform and at least one physics-inspired computer simulator, teeth, a) receiving a request at a communication interface, said request comprising at least At least one physics-inspired computer simulator is used to process the data. contains at least one computational task; and b) computing the at least one computational task of the received request converting it into instructions suitable for a physics-inspired computer simulator; and c) transmitting said instructions to said at least one physics-inspired computer simulator. and d) at least one corresponding generated solution resulting from execution of said instructions; receiving the e) providing said at least one corresponding generated solution; and 11. A computer-implemented method comprising:

17. 17. The computer-implemented method of claim 16, The computing system includes at least one physics-inspired computer and a computer program for training said at least one physics-inspired computer simulator. and a training unit for Further, the received request is sent to a processor for use in processing the at least one computational task. and a computer system for generating a plurality of physics-inspired images. and a computer simulator; Further, the at least one computational task of the request may be a physics-inspired computation. and said at least one physics-inspired computer simulator. and Further, the providing of the instructions may include providing the physics-inspired computer in response to the indication of selection. and said at least one physics-inspired computer simulator. Executed in at least one of Computer-implemented methods.

18. 17. The computer-implemented method of claim 16, wherein the request is received via a data network. from a digital computer operatively connected to said communications interface using and wherein the at least one corresponding generated solution is transmitted to the digital computer. The computer-implemented method is provided in accordance with the present invention.

19. 18. The computer-implemented method of claim 17, The at least one corresponding generated solution is based on the at least one physics-inspired obtained from the computer Furthermore, the at least one corresponding generated solution and the at least one computational task and generating said at least one physics-inspired computer simulator using including training, Computer-implemented methods.

20. 20. The computer-implemented method of claim 19, wherein the at least one computational task and the at least one corresponding generated solution are usable for training purposes. If the request includes an indication that the training is performed, the computer-implemented method is

21. 20. The computer-implemented method of claim 19, wherein the training comprises: a) the at least one corresponding generated solution and the at least one computational task; and performing a procedure based on a machine learning protocol using b) responsively controlling the variable performance of the physics-inspired computer simulator; Update the parameters and 11. A computer-implemented method comprising:

22. 17. The computer-implemented method of claim 16, wherein the computer-implemented method comprises: storing the instructions and the at least one corresponding generated solution. Computer implementation method.

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