Building and programming quantum hardware for the quantum annealing process

Abstract

The present invention relates to constructing and programming quantum hardware for quantum annealing process. Methods, systems, and apparatus for constructing and programming quantum hardware for quantum annealing process, including computer programs encoded on computer storage media.

Description

[0001] Description of the case

[0002] This application is a divisional application of Chinese invention patent application No. 201480074978.0, filed on December 31, 2014.

[0003] CROSS-REFERENCE TO RELATED APPLICATIONS

[0004] This application claims priority under 35 USC §119(e) to U.S. patent application serial number 61 / 985,348 filed on April 28, 2014 and U.S. patent application serial number 61 / 924,207 filed on January 6, 2014, the entire contents of which are incorporated herein by reference. Background Art

[0005] This specification relates to building and programming quantum hardware for quantum annealing processes. Summary of the invention

[0006] Artificial intelligence tasks can be transformed into machine learning optimization problems. In order to perform artificial intelligence tasks, quantum hardware, such as a quantum processor, is constructed and programmed to encode the solution to the corresponding machine optimization problem into an energy spectrum of a multi-body quantum Hamiltonian that characterizes the quantum hardware. For example, the solution is encoded in the ground state of the Hamiltonian. The quantum hardware performs adiabatic quantum calculation starting from a known ground state of a known initial Hamiltonian. Over time, as the known initial Hamiltonian evolves into a Hamiltonian for solving the problem, the known ground state evolves and remains at the instantaneous ground state of the evolving Hamiltonian. Without diagonalizing the Hamiltonian, the energy spectrum of the ground state of the Hamiltonian is obtained at the end of the evolution.

[0007] Sometimes quantum adiabatic computations become non-adiabatic due to excitations caused, for example, by thermal fluctuations. Instead of remaining in the instantaneous ground state, an evolving quantum state that initially starts at the ground state of the initial Hamiltonian can reach an excited state of the evolving Hamiltonian. Quantum hardware is built and programmed to suppress such excitations from the instantaneous ground state to a higher energy state during the early stages of the computation. In addition, quantum hardware is also built and programmed to assist in relaxation from a higher energy state to a lower energy state or ground state in the later stages of the computation. The robustness of finding the ground state of the Hamiltonian used to solve the problem is improved.

[0008] The details of one or more embodiments of the subject matter of this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter of this specification will become apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1A schematic perspective view of interacting qubits in a quantum processor.

[0010] Figure 2 is a schematic diagram showing the structure and interaction of two qubits in a quantum processor.

[0011] Figure 3 is a schematic diagram showing the effect of a quantum modulator on the quantum annealing process.

[0012] Figure 4 is a schematic diagram showing the interaction of the initial Hamiltonian, the problem Hamiltonian, and the Hamiltonian of the quantum regulator selected for the problem Hamiltonian during a quantum annealing process.

[0013] Figure 5 is a flow chart of an example process for determining the distribution of quantum regulators.

[0014] Figure 6 is a flow chart of an example process for performing an artificial intelligence task. DETAILED DESCRIPTION

[0015] Overview

[0016] Solutions to difficult combinatorial problems, such as NP-hard problems and machine learning problems, can be encoded in the ground state of a many-body quantum Hamiltonian system, also known as a quantum annealer or "QA". The QA of a system can sometimes be obtained by adiabatic quantum computation, where the QA is initialized to the ground state of a known and easily prepared initial Hamiltonian H. Over time, the QA is adiabatically guided to the problem Hamiltonian H that encodes the problem. p In theory, during adiabatic quantum calculations, QA can be maintained at i Gradually evolved to H p The Hamiltonian H total The instantaneous ground state of H total can be expressed as:

[0017] H total =(1-s)H i +sH p ,

[0018] where s is a time-dependent control parameter:

[0019] s=t / t T ,

[0020] And t T is the total time of the adiabatic quantum computation. If the system evolves slowly enough relative to the intrinsic energy range of the system, QA will deterministically reach the problem Hamiltonian H p The ground state.

[0021] In fact, quantum computing may not be completely adiabatic and QA may reach H during the computation process total This can lead to inaccurate results at the end of quantum computation. For example, in many hardware combinatorial optimization problems, such as decision problems, when the problem Hamiltonian demonstrates phase transitions at its computational complexity, H is relatively small relative to the intrinsic energy range of the system. total The gap size between the excited state and the ground state can be very small, such as exponentially small. In this scenario, the QA can undergo a quantum phase transition and can reach a large number of excited states, such as an exponentially large number of excited states. In addition, the QA can also be changed from H to H due to other factors such as quantum fluctuations caused by environmental interactions with the system and system defect errors including control errors and manufacturing errors. total In this specification, pushing QA from H i The ground state to H p The process of obtaining the ground state is called QA schedule or quantum annealing process.

[0022] The quantum hardware such as quantum processor in this specification includes quantum chip, except H i and H p In addition, the quantum chip defines a quantum regulator ("QG") so that the evolving Hamiltonian H total Change to H tot :

[0023] H tot =I(t)H i +G(t)H G +P(t)H p +H AG-B ,

[0024] Where I(t) and P(t) represent the initial Hamiltonian H i and the problem Hamiltonian H p The time dependence of G(t) is the Hamiltonian H associated with QG. G The time dependence of H AG-B is the interaction of the combined QA-QG system with its surroundings, often referred to as the bath. In general, it can be assumed that H AG-B is non-zero but constant and well characterized. In some embodiments:

[0025] H tot =(1-t)H i +t(1-t)H G +tH p +H AG-B

[0026] In general, QG can be viewed as a non-information-bearing degree of freedom that can be designed to manipulate the dissipative dynamics of the information-bearing degree of freedom. total In the example, the information-carrying degree of freedom is QA. Specifically, QG can be defined as an auxiliary subsystem with an independent tensor product structure. QG can be represented by H G To characterize, H G Characterizes both the interaction between the information-carrying degrees of freedom and QG and the free Hamiltonian of QG. QG can interact with QA consistently and in a non-perturbative manner. The interaction can also demonstrate strong non-Markovian effects.

[0027] The quantum hardware is constructed and programmed to allow QG to guide the quantum evolution of disordered quantum annealing hardware at finite temperature in a robust manner and improve adiabatic quantum computing processes. For example, QG can promote the pushing of QA towards quantum phase transitions while enabling QG to move from H to QA by effectively making it difficult for QA to reach excited states. total After the quantum phase transition, QA enters another phase where it is likely frozen in an excited state due to quantum localization. QG can adjust the energy level of QA to coordinate with the vibrational energy of the environment to promote QA's relaxation to a lower energy state or ground state. Such adjustment can increase the ground state fidelity, that is, the fidelity of QA in the ground state at the end of the calculation, and allow QA to avoid premature freezing in a suboptimal solution due to quantum localization.

[0028] In general, QA undergoes four phases during the quantum annealing process of this specification, including initialization, excitation, relaxation, and freezing, which will be explained in detail below. QG can be achieved by creating a bath with an average phonon energy of tot The mismatch between the average energy level intervals of the QG in the first two phases helps suppress QA excitation. In the third and fourth phases, the QG can enhance thermal fluctuations by creating an overlap between the spectral density of the system and the bath. The enhanced thermal fluctuations can allow the QA to have a tot The high relaxation rate of a higher energy state to a lower energy state or ground state. Specifically, due to quantum localization, QA can be frozen in a non-ground state. QG can allow QA to thaw from a non-ground state.

[0029] QG can be used to achieve universal adiabatic quantum computing when quantum interactions are limited due to natural or designed constraints of the quantum hardware. For example, a quantum chip can have designed constraints so that the Hamiltonian representing the interactions of the qubits on the quantum chip becomes a k-local stochastic Hamiltonian. In some embodiments, quantum hardware can be built and programmed to manipulate the structural and dynamic effects of environmental interactions and disorder, even without any control over the degrees of freedom of the environment.

[0030] In general, QG is problem-dependent. The quantum hardware of the present specification can be programmed to provide different QGs for different problem Hamiltonians. In some embodiments, quantum control strategies developed based on mean-field and microscopic methods can be used to generate a given Hamiltonian. p Determine QG. Additionally or alternatively, quantum control strategies can also implement random matrix theory and machine learning techniques to determine QG. The combined QA and QG can be adjusted and trained to generate a p The desired statistical distribution of the energy spectrum, such as Poisson distribution, Levy distribution, or Boltzmann distribution.

[0031] Example quantum hardware

[0032] like Figure 1 As shown, in a quantum processor, a programmable quantum chip 100 includes 4 by 4 unit cells 102 of eight qubits 104, which are connected by programmable inductive couplers as shown by the lines connecting different qubits. Each line can represent one or more couplers between a pair of qubits. The chip 100 can also include a larger number of unit cells 102, such as 8 by 8 or more.

[0033] Within each unit cell 102, such as within the cell encircled by a dashed circle and including qubit numbers q1 to q8, one or more of the eight qubits 104, for example the four qubits 104 labeled q1, q2, q5, q6, are logic qubits for use in computations performed by the quantum processor. Other qubits 104, for example the other four qubits labeled q3, q4, q7, q8, are control qubits to be programmed to perform the functions of the QG of the present specification. The control qubits do not participate in the computations that the logic qubits are configured to perform. In some embodiments, in each unit cell 102, half of the qubits are used as control qubits and the other half of the qubits are used as logic qubits. The size of the chip 100 implementing QG can be expanded by increasing the number of unit cells.

[0034] In some embodiments, the logic qubits and control qubits have the same construction. In other embodiments, the control qubits have a simpler or less elaborate construction than the logic qubits. Figure 2An example pair of coupled qubits 200, 202 in the same unit cell of a chip is shown. For example, qubits 200, 202 can correspond to two logic qubits, such as qubits q1, q5, to two control qubits, such as q3, q7, or to a logic qubit, such as qubit q1, and a control qubit, such as q7. In this example, each qubit is a superconducting qubit and includes two Josephson cells 204a, 204b or 208a, 208b connected in parallel. Each Josephson cell can include a Josephson junction 206 connected in parallel with a capacitor 207. Qubits 200, 202 are subjected to an external magnetic field B applied in a direction e3 perpendicular to the paper plane showing the figure, with symbol The B field is labeled as . An inductive coupler set 210 is connected between qubits 200 and 202 so that the qubits are coupled along the e3-e3 direction.

[0035] In some embodiments, two Josephson cells connected in parallel are used to construct both the logic qubit and the control qubit. However, the logic qubit can be constructed with higher precision than the control qubit. Therefore, the control qubit constructed with lower precision can perform the function of the QG at a reduced cost. In other embodiments, although the control qubit is constructed with a higher precision, the logic qubit can be constructed with a higher precision. Figure 2 The logic qubit is constructed by connecting two Josephson cells in parallel as shown, but the control qubit can be constructed using structures other than Josephson cells, such as quantum resonators. Figure 1 In the chip 100 of FIG. 1 , the control qubits in each unit cell interact with each other and with the logic qubit. These interactions act as QG to assist the QA represented by the logic qubit to reach H at the end of the quantum annealing process. p The ground state.

[0036] As an example, the Hamiltonian for QA can be written as:

[0037]

[0038] Among them, σ i x and σ i z are Pauli operators and each represents the spin of the i-th qubit in the x-direction or the z-direction, respectively. i and J ij is a parameter that can be programmed for different problems that are addressed by adjusting the coupling provided by the inductive coupler bank 210. i and J ij has a real value. N is the total number of logical qubits used for computation. Parameter J ijThe sparsity of is constrained by the hardware connectivity, i.e., Figure 1 The constraints on the connectivity of the qubits shown in . For unconnected qubits, the corresponding J ij is 0. Again, I(t) and P(t) represent the time dependence of the initial and problem Hamiltonians, respectively. In the simplified example, I(t) is equal to (1-s) and P(t) is equal to s, where s is equal to t / t T .

[0039] The additional M control qubits introduce an additional quantum control mechanism into chip 100, where M can be the same as or different from N. The control mechanism defines the SG or H total The auxiliary subsystem includes a tensor product structure and can be composed of the QG-QA interaction Hamiltonian H SG The auxiliary subsystem can be controlled by a global time-varying control knob G(t) and possibly by a set of macro-programmable environmental control parameters. In some embodiments, the control knob is s(1-s), where s=t / t T ; and the programmable environmental control parameter is temperature T.

[0040] Therefore, the Hamiltonian H for the combined QA-QG system in chip 100 is tot for:

[0041] H tot =I(t)H i +G(t)H G +P(t)H p

[0042] where I(t), G(t), and P(t) describe the general time dependence of the global control parameters. In this Hamiltonian, the initial Hamiltonian is:

[0043]

[0044] The Hamiltonian H p for:

[0045]

[0046] And the QG Hamiltonian H G for:

[0047] H G =H GF +H GA

[0048]

[0049]

[0050] Among them, H GF is the free Hamiltonian of QG, and H GA is the interaction Hamiltonian between QG and QA. Note that N G and N A are the total number of QG qubits and QA qubits, respectively. As an example, the total Hamiltonian can have the following explicit global time dependence:

[0051] H tot =(1-t / t T )H i -t / t T (1-t / t T )H G +(t / t T )H p

[0052] Programming quantum hardware

[0053] For a given problem and its corresponding Hamiltonian H p In particular, QG was determined to improve the ground state fidelity of QA without the need to convert H p Diagonalization. Ability to iterate over various QG implementations to improve knowledge about the results of the computation.

[0054] In some embodiments, QG is determined such that in the total Before the system undergoes a quantum phase transition, the QG Hamiltonian H QG The excitation of QA is suppressed. Specifically, QG is not resonant with the average phonon energy of the bath, which creates a mismatch between the average phonon energy and the average energy level spacing of the combined QA and QG, or reduces the undesired excitation of H tot After the system undergoes a quantum phase transition, the QG Hamiltonian H QG Enhanced from H tot The relaxation of any excited state of QA to the ground state. Specifically, H tot The average energy level spacing of the QG is resonant with the average phonon energy. The QG enhances thermal fluctuations by creating an overlap between the spectral density of the system and its bath. Thermal fluctuations can promote the QA to reach H with a high relaxation rate. tot ground state and prevents QA from being frozen in an excited state prematurely due to quantum localization.

[0055] Figure 3 An example of a desired QG function is shown in . total The energy levels E0, E1, E2, ..., E i (not shown) is plotted as a function of time t. At t=0, Htotal H i ; and at t = t T H total H p . From t = 0 to t = t T During the quantum annealing process, QA roughly experiences: an initial phase from t = 0 to t = t1, an excitation phase from t = t1 to t = t2, a relaxation phase from t = t2 to t = t3, and a relaxation phase from t = t3 to t = t T The freezing phase. Time t2 can be related to the total During the excited phase, as indicated by arrows 300, 302, QG increases the energy separation Δε between adjacent energy levels. i , such as Δε1=E2-E1 and Δε0=E1-E0, so that the increased energy interval is much larger than the average phonon energy. During the relaxation phase, as indicated by arrows 304, 306, 308, 310, QG adjusts the energy intervals Δε0, Δε1, ... to be comparable to the average phonon energy, thereby promoting the relaxation of QA from the excited state to the lower energy state or the ground state.

[0056] Figure 4 The three Hamiltonians H over time in different phases of the quantum annealing process are schematically shown in i , H p , and H QG The control parameters I(t), P(t), and G(t) control the shape of the corresponding Hamiltonian curve. In this example, I(t) and P(t) are linear and parabolic.

[0057] In addition, QG can be selected to allow H tot The QA of H evolves stably on the QA schedule and reaches a value similar to H p Ideally, at time t T The ground state fidelity of QA is 1 at time t. However, it is difficult to achieve a fidelity of 1 in a finite time period. T , H tot QA is in the combination of H p , H i , and H QG The evolution of QA can be expressed as:

[0058]

[0059] in is the state of QA at time 0, is at time t T The state of QA at this time, and ρA (t) is the density function of QA at other times. The probability is assigned to each state by, for example, using the probability mass function The evolution of QA can be further expressed as:

[0060]

[0061] where f G (k) is the probability mass function, k = 0, 1, ..., and corresponds to the quantum state level, and ∑ k f G (k) = 1. If the ground state fidelity is 1, then f G (0) = 1, and f G (k≠0)=0. As mentioned above, such a fidelity of unity is difficult to achieve. As an alternative, the desired QG can be chosen to provide an exponential distribution function:

[0062]

[0063] where λ G Defines the applicable p The probability mass function can be any probability distribution function. Examples include the Poisson distribution function, the Levy distribution function, and the Boltzmann distribution function.

[0064] To determine a QG with the desired function for the problem, the function includes the above reference Figure 3 and Figure 4 The functions described can use one or more techniques including, for example, open quantum system models, random matrix theory, quantum Monte Carlo, and machine learning. Figure 5 An example process 500 is shown for determining QG, which can be performed by a classical processor, such as a classical computer, or a quantum processor, or a combination thereof.

[0065] In process 500, regarding H totalThe information of the energy states (502) of the problem is usually unknown. Therefore, finding the global optimal QG is at least as difficult as solving the problem Hamiltonian itself. However, the application of QG in this context is highly robust to changes in its microscopic degrees of freedom. In some embodiments, the method is to introduce a mean field description of the QG, such as a random matrix theory model that relies on a few effective control parameters. Then, the macroscopic degrees of freedom of the QG can be pre-trained and trained using a mixture of unsupervised and supervised classical machine learning algorithms. In order to generate sufficient training data for the learning step, state-of-the-art classical (metaheuristic) solvers can be used to sample from the energy characteristic function properties of the problem Hamiltonian, which can include classical solvers such as path integral Monte Carlo, diffusion Monte Carlo, spin vector Monte Carlo, Markov chain Monte Carlo, simulated annealing, taboo search, large-scale neighborhood search, and cluster update. If available, training data can also be generated by quantum processors of previous generations, including quantum annealers and gate-model quantum computers. The assumed time at which the phase change occurs can be chosen to be approximately t = t T / 2, and can be further fine-tuned during the learning process to enhance the fidelity of the spectral properties of the QA using the Boltzmann distribution of the problem Hamiltonian that has been sampled by the chosen classical solver.

[0066] For a given problem Hamiltonian in the training data, some useful information related to the energy spectrum of the total Hamiltonian can be estimated, including: the approximate energy level E i , energy level interval Δε i , the distribution of energy level intervals, and the average energy level interval In some embodiments, the average energy level interval at time t is estimated as:

[0067]

[0068] where ε i (t) is H total is the energy of the i-th instantaneous eigenstate energy, and N is the total number of eigenstates.

[0069] Also in process 500, the total The average phonon energy of the bath in which the system characterized is located (504). Approximately, the average phonon energy can be taken as kT, where k is the Boltzmann constant and T is the temperature. The average phonon energy can also be calculated in a more precise manner. For example, an open quantum system model such as Lindblad form of dynamics can be selected for calculation. This selection can be based on calibration data of a quantum processor or quantum tomography. Under the open quantum system model, the energy at any given temperature T by Htotal The average phonon energy of the bath in which the characterized system is located can be defined as:

[0070]

[0071] Where J(ω) can be: Omhic spectral density, that is Super Omhic spectral density, i.e. The Drude-Lorentz spectral density is Or a flat spectral distribution, ie J(ω) = 1. In these equations, λ is the reorganization energy and γ is the bath frequency cutoff.

[0072] A probability mass function for the ground state fidelity of QA is selected (506). Based on the acquired information, the training data, the calculated average phonon energy, and the selected probability mass function, the process 500 determines (508) H p For example, the QG distribution can be represented by an exponential family of random matrices such as the Gaussian unitary ensemble selected using a random matrix theory model. Determine the average energy level spacing and QG or H QG The maximum energy eigenvalue and the minimum energy eigenvalue of are determined to allow QG to function as expected. Specifically, in the second phase of QA scheduling, for example, Figure 3 During the time t1 to t2 shown, the average energy level interval of QG is selected so that:

[0073]

[0074] in is the modified average energy level of QA and QA. This choice increases the H total The energy level spacing is such that H tot The energy level spacing of the combination is much larger than the average phonon energy. Therefore, possible excitations of QA to higher energy states caused by thermal fluctuations are suppressed. In addition, QG is also selected so that in the third phase of QA scheduling, for example, Figure 3 During the time period t2 to t3 shown, the average energy level interval of QG satisfies:

[0075]

[0076] This option allows H total The energy level spacing of is similar to thermal fluctuations. QAs can relax to lower energy states or ground states at a higher rate. The chosen exponential family can be parameterized with respect to controllable parameters of the quantum hardware, such as couplings between qubits.

[0077] In some embodiments, a deep neural network is used to represent the QG-QA system or the H tot The system is characterized, and stochastic gradient descent is used to train the QG distribution. As an example, for a given H of interest total Training is accomplished by selecting a statistically significant number of random matrices, such as 1000, from a parameterized exponential family that can generate path-integrated Monte Carlo outputs on average within the desired probability mass function of . In some embodiments, training can be performed from energy level intervals based on the desired average combination discussed above. Start with the selected initial QG distribution.

[0078] Figure 6 An example process 600 is shown in which a control system programs QA hardware, such as a quantum processor, for the QA hardware to perform an artificial intelligence task. The control system includes one or more classical computers, i.e., non-quantum computers, and also includes a quantum computer. The task is converted into a machine learning optimization problem, which is represented in a machine-readable form.

[0079] The control system receives (602) a machine-readable machine learning optimization problem. The control system encodes (606) the optimization problem as the designed H total The encoding is based on the structure of the QA hardware, such as the coupling between qubits. total An example is the Ising Hamiltonian H SG , and the encoding determines the parameter H i and J ij The value of H i and J ij The encoded information of is provided to the QA hardware, which receives (620) the information as initialization parameters for the hardware. In order to stabilize the QA during the quantum annealing process to be performed by the QA hardware, the control system, for example, by using Figure 5 The processor 500 selects a QG to further construct (608) the QG. The constructed QG includes h i G , J ij G , J ij GA The control parameters represented by the QA hardware are sent to the QA hardware to program the QA hardware.

[0080] The QA hardware receives (620) initialization parameters such as h i and J ij , and also receives (622) control parameters of QG, such as h i G , Jij G , J ij GA , and is programmed and initialized by the control system according to the received initialization parameters and the assumptions of the QG parameters. The QA hardware implements (624) a quantum annealing schedule to obtain the tot The eigenstates of the combined QA-QG system characterized. The solution to the machine learning optimization problem is encoded in these eigenstates. After a predetermined amount of time, the QA schedule ends and the QA hardware provides (626) an output represented by the eigenstates and their corresponding energy spectra. The output can be read by the control system or by another classical or quantum computer. The predetermined amount of time can be approximately However, shorter or longer time periods can be used. Shorter time periods can provide efficiency, while longer time periods can provide high ground state fidelity.

[0081] As described above, in the output provided by the QA hardware, the base state fidelity of the QA is typically less than 1. When the fidelity is less than 1, a single output provided by the QA hardware may not accurately encode the solution to the problem. In some embodiments, the QA hardware performs a QA schedule multiple times to provide multiple outputs using the same QG or different QGs provided by the control system, wherein the same QG or different QGs are selected from the same QG distribution determined for the problem, and the control system has a control system such as h i G , J ij G , J ij GA The plurality of outputs can be statistically analyzed and the problem can be solved or the artificial intelligence task can be performed based on the statistical results.

[0082] Specifically, in process 600, after the control system receives and stores (610) the output provided by the QA hardware, the control system determines (612) whether the QA hardware has completed a predetermined number of iterations of the QA schedule. If not, the control system returns to step 608 by constructing another QG, which can be the same as the QG previously used or can be a different QG selected from a previously determined QG distribution. The QA hardware receives (622) another set of control parameters of the QG and is reprogrammed by the control system based on the previously determined initialization parameters and the set of control parameters that encode the problem. The QA schedule is implemented again (624) and another output (626) is provided. If the QA hardware has completed a predetermined number of iterations of the QA schedule, the control system or another data processing system statistically processes (614) all of the outputs to provide a solution to the problem. The predetermined number of iterations can be 100 iterations or more, or 1000 iterations or more. In some embodiments, the number of iterations can be selected in conjunction with the length of the QA schedule so that the process 600 can be performed with high efficiency and provide a solution to the problem with high accuracy. For example, when the length of each QA schedule is relatively short, such as When the length of each QA schedule is relatively long, for example, In longer cases, the predetermined number of iterations can be selected to be relatively small, such as less than 1000 iterations.

[0083] The electronic embodiments described in this specification, i.e., non-quantum embodiments, subject matter, and digital functional operations, including structures disclosed in this specification and their structural equivalents, can be implemented in: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware, or a combination of one or more of them. The embodiments of the digital subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, which are encoded on a tangible non-transitory storage medium for execution or control of its operation by a digital processing device. The computer storage medium can be a machine-readable storage device, a machine-readable storage matrix, a random or serial access memory device, or a combination of one or more of them. Alternatively or additionally, the program instructions can be encoded on an artificially generated propagation signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode information for transmission to a suitable receiver device for execution by a data processing device.

[0084] The term "data processing equipment" refers to digital data processing hardware and covers all types of equipment, devices, and machines for processing data, including, for example, a programmable digital processor, a digital computer, or a plurality of digital processors or computers. The equipment can also be or further include special-purpose logic circuits, such as FPGAs (field programmable gate arrays) or ASICs (application-specific integrated circuits). In addition to hardware, the equipment can also optionally include code that creates an execution environment for a computer program, such as code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.

[0085] A computer program may also be referred to or described as a program, software, software application, module, software module, script, or code, and a computer program may be written in any form of programming language, including a compiled or interpreted language, or an illustrative or procedural language, and may be deployed in any form, including as an independent program or as a module, component, subroutine, or other unit suitable for use in a digital computing environment. A computer program may but need not correspond to a file in a file system. A program may be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple collaborative files (e.g., a file that stores one or more modules, subroutines, or a portion of a code). A computer program may be deployed to execute on a computer or on multiple computers located in one location or distributed across multiple locations and interconnected by a data communication network.

[0086] The processes and logic flows described in this specification can be performed by one or more programmable digital computers, which operate one or more quantum processors as appropriate, executing one or more computer programs to perform functions by operating on input data and generating outputs. The processes and logic flows can also be performed by dedicated logic circuits such as FPGAs or ASICs, and the devices can also be implemented as dedicated logic circuits such as FPGAs or ASICs. For a system of one or more digital computers, being "configured to" perform a specific operation or action means that the system has software, firmware, hardware, or a combination thereof installed thereon that runs to cause the system to perform the operation or action. For one or more computer programs, being configured to perform a specific operation or action means that one or more programs include instructions that, when executed by a digital data processing device, cause the device to perform the operation or action.

[0087] A digital computer suitable for executing a computer program can be based on a general purpose microprocessor or a special purpose microprocessor or both, or any other kind of central processing unit. Typically, the central processing unit will receive instructions and data from a read-only memory or a random access memory or both. The basic elements of a computer are: a central processing unit for implementing or executing instructions, and one or more memory devices for storing instructions and data. The central processing unit and the memory can be supplemented by a special logic circuit, or incorporated into the special logic circuit. Typically, a digital computer will also include one or more large capacity storage devices for storing data, such as a magnetic disk, a magneto-optical disk, or an optical disk, or the computer is operably coupled to receive data from or transmit data to the one or more large capacity storage devices, or both. However, a computer does not necessarily have such a device.

[0088] Computer-readable media suitable for storing computer program instructions and data include all forms of nonvolatile memory, media, and memory devices, including, for example: semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0089] The control of each system or part thereof described in this specification can be implemented in a computer program product including instructions, which are stored on one or more non-transitory machine-readable storage media and can be executed on one or more digital processing devices. The systems or parts thereof described in this specification can each be implemented as an apparatus, method, or electronic system, which may include one or more digital processing devices and memory storing executable instructions to perform the operations described in this specification.

[0090] Although this specification contains many specific implementation details, these details should not be interpreted as limitations on the scope of what may be claimed, but should be interpreted as descriptions of features specific to a particular embodiment. It is also possible to combine certain features described in this specification in the context of separate embodiments to be implemented in a single embodiment. Conversely, it is also possible to implement various features described in the context of a single embodiment separately in multiple embodiments or in any suitable sub-combination. In addition, although features may be described above as working in certain combinations, even initially claimed as such, one or more features from the claimed combination may be deleted from the combination in some cases, and the claimed combination may be directed to a sub-combination or a variation of the sub-combination.

[0091] Similarly, although operations are depicted in a particular order in the accompanying drawings, this should not be understood as requiring such operations to be performed in the particular order shown or in a sequential order, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing can be advantageous. In addition, the separation of various system components in the above-described embodiments should not be understood as requiring such separation in all embodiments, but it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged as multiple software products.

[0092] Specific embodiments of the subject matter have been described. Other embodiments fall within the scope of the appended claims. For example, the actions recited in the claims can be performed in a different order and still achieve the desired results. As an example, the processes depicted in the accompanying drawings do not necessarily require the particular order or sequence order shown to achieve the desired results. In some cases, multitasking and parallel processing can be advantageous.

Claims

1. A device for building and programming quantum hardware, comprising: The first pair of logical superconducting cells for computing; the first pair of control superconducting units to assist in computing; a first coupler, the first coupler being between the first logic superconducting unit and the second logic superconducting unit; a second coupler, the second coupler being between the first control superconducting unit and the second control superconducting unit; a third coupler, the third coupler being between the first logic superconducting unit and the second control superconducting unit; as well as A fourth coupler is provided between the second logic superconducting unit and the first control superconducting unit.

2. The device according to claim 1, wherein Each logical superconducting unit is configured to have a Pauli operator σ sj i A quantum state of , where i = x, y, or z.

3. The device according to claim 1, wherein: Each control superconducting unit is configured to have a Pauli operator σ ck i A quantum state of , where i = x, y, or z.

4. The device according to claim 1, wherein: When a magnetic field is applied along the z direction, the first coupler is s1 z σ s2 z represents the quantum operator σ of the first coupling to the first logical superconducting unit s1 z The quantum operator σ of the second logical superconducting unit s2 z coupling.

5. The device according to claim 1, wherein: When a magnetic field is applied along the z direction, the second coupler is c1 z σ c2 z The second coupling to the first control superconducting unit is represented by the quantum operator σ c1 z The second quantum operator σ controls the superconducting unit c2 z coupling.

6. The device according to claim 1, wherein: When a magnetic field is applied along the z direction, the third coupler is s1 x σ c2 z The third coupling represents the quantum operator σ of the first logical superconducting unit s1 z The second quantum operator σ controls the superconducting unit c2 z coupling.

7. The device according to claim 1, wherein: When a magnetic field is applied along the z direction, the fourth coupler is s2 x σ c1 z The fourth coupling represents the quantum operator σ of the second logical superconducting unit s2 z The quantum operator σ that controls the superconducting unit c1 z coupling.

8. The device according to claim 1, wherein: The first and second logic superconducting cells and the first and second control superconducting cells include superconducting qubits having binary quantum states.

9. The device according to claim 8, wherein: Each superconducting qubit is built using a Josephson junction and a capacitor connected in parallel.

10. The device according to claim 9, wherein: The logic superconducting cells are constructed with the same precision as the control superconducting cells.

11. The device according to claim 9, wherein: The control superconducting cells are constructed with lower precision than the logic superconducting cells.

12. The device according to claim 8, wherein: The superconducting qubits of the logic superconducting unit are constructed using Josephson junctions and capacitors connected in parallel, and wherein the superconducting qubits of the control superconducting unit are constructed using multi-junction Josephson cells, inductors, and capacitors connected in parallel and / or in series to construct a desired N-level controllable control system.

13. The apparatus according to claim 1, wherein: The first coupler, the second coupler, and the third coupler include inductive couplers.

14. The device according to claim 1, comprising a plurality of pairs of logic superconducting cells and a plurality of pairs of control superconducting cells, each pair of logic superconducting cells comprising the first logic superconducting cell and the second logic superconducting cell coupled by the first coupler, and different pairs of logic superconducting cells are coupled by additional couplers, each pair of control superconducting cells comprising the first control superconducting cell and the second control superconducting cell coupled by the second coupler, and different pairs of control superconducting cells are coupled by additional couplers, and each pair of logic superconducting cells is coupled with all pairs of control superconducting cells by couplers including the third coupler.

15. The device according to claim 14, wherein: The multiple pairs of logic superconducting units, the multiple pairs of control superconducting units and all couplers are configured such that the quantum Hamiltonian characterizing the multiple pairs of logic superconducting units, the multiple pairs of control superconducting units and all couplers is: Wherein, i represents the i-th pair of logic superconducting units and / or control superconducting units, h i and J ij has a real value associated with the coupling strength between one of the corresponding pair of logic superconducting units or control superconducting units and the other of the corresponding pair of logic superconducting units or control superconducting units, I(t), G(t), and P(t) are time-dependent control parameters, σ i x , σ i z and σ j z are Pauli operators and each represents the spin of the corresponding qubit along the x-direction or z-direction, respectively, N G and N A are the total number of quantum regulator (QG) qubits and quantum annealer (QA) qubits, respectively, and h i G , J ij G , J ij GA is the control parameter of the quantum regulator.

16. A method for building and programming quantum hardware, comprising: The first pair of logical superconducting units used for calculation is configured so that each logical superconducting unit has a sj i A quantum state of , where i = x, y, or z; The first pair of control superconducting units configured to assist in calculation without being used as operation units so that each control superconducting unit has a ck i A quantum state of , where i = x, y, or z; coupling the first logic superconducting unit and the second logic superconducting unit by applying a magnetic field along the z direction; coupling the first control superconducting unit and the second control superconducting unit by applying a magnetic field along the z direction; coupling the first logic superconducting unit and the second control superconducting unit by applying a magnetic field along the z direction; as well as The second logic superconducting unit is coupled with the first control superconducting unit by applying a magnetic field along the z direction.

17. The method of claim 16, wherein: When a magnetic field is applied to the first logic superconducting unit and the second logic superconducting unit, the magnetic field is given by σ s1 z σ s2 z represents the quantum operator σ of the first coupling to the first logical superconducting unit s1 z The quantum operator σ of the second logical superconducting unit s2 z coupling; When a magnetic field is applied to the first control superconducting unit and the second control superconducting unit, the magnetic field is given by σ c1 z σ c2 z The second coupling to the first control superconducting unit is represented by the quantum operator σ c1 z The second quantum operator σ controls the superconducting unit c2 z coupling; When a magnetic field is applied to the first logic superconducting unit and the second control superconducting unit, the magnetic field is s1 x σ c2 z The third coupling represents the quantum operator σ of the first logical superconducting unit s1 z The second quantum operator σ controls the superconducting unit c2 z coupling; and When a magnetic field is applied to the second logic superconducting unit and the first control superconducting unit, the magnetic field is given by σ s2 x σ c1 z The fourth coupling represents the quantum operator σ that controls the first superconducting unit. s2 z The quantum operator σ of the second logical superconducting unit c1 z coupling.

18. The method according to claim 16, wherein: The logic superconducting unit and the control superconducting unit include superconducting qubits having binary quantum states.

19. The method according to claim 16, wherein: The logic superconducting unit has the same precision as the control superconducting unit.

20. The method according to claim 16, wherein: The control superconducting cell has a lower precision than the logic superconducting cell.

21. The method according to claim 16, wherein: The logic superconducting unit, the control superconducting unit and all couplers are configured such that the quantum Hamiltonian characterizing the logic superconducting unit, the control superconducting unit and all couplers is: Wherein, i represents the i-th pair of logic superconducting units and / or control superconducting units, h i and J ij has a real value associated with the coupling strength between one of the corresponding pair of logic superconducting units or control superconducting units and the other of the corresponding pair of logic superconducting units or control superconducting units, I(t), G(t), and P(t) are time-dependent control parameters, σ i x , σ i z and σ j z are Pauli operators and each represents the spin of the corresponding qubit along the x-direction or z-direction, respectively, N G and N A are the total number of quantum regulator (QG) qubits and quantum annealer (QA) qubits, respectively, and h i G , J ij G , J ij GA is the control parameter of the quantum regulator.

22. A method for building and programming quantum hardware, comprising: The first logical superconducting cell and the second logical superconducting cell forming a first pair of logical superconducting cells for computing are configured so that each logical superconducting cell has a sj i A quantum state of , where i = x, y, or z; The first control superconducting unit and the second control superconducting unit of the first pair of control superconducting units for assisting calculation without being an operation unit are configured so that each control superconducting unit has a ck i A quantum state of , where i = x, y, or z; coupling the first logic superconducting unit and the second control superconducting unit by applying a magnetic field along the z direction; as well as The second logic superconducting unit and the first control superconducting unit are coupled by applying a magnetic field along the z direction.

23. The method of claim 22, wherein: When a magnetic field is applied to the first logic superconducting unit and the second control superconducting unit, the magnetic field is s1 x σ c2 z represents the quantum operator σ of the first coupling to the first logical superconducting unit s1 z The second quantum operator σ controls the superconducting unit c2 z coupling; and When a magnetic field is applied to the second logic superconducting unit and the first control superconducting unit, the magnetic field is given by σ s2 x σ c1 z The second coupling to the first control superconducting unit is represented by the quantum operator σ s2 z The quantum operator σ of the second logical superconducting unit c1 z coupling.

24. The method according to claim 22, wherein: The first and second logic superconducting cells and the first and second control superconducting cells include superconducting qubits having binary quantum states.

25. The method according to claim 22, wherein: The logic superconducting unit has the same precision as the control superconducting unit.

26. The method of claim 22, wherein: The control superconducting cell has a lower precision than the logic superconducting cell.

27. The method of claim 22, wherein: The logic superconducting unit, the control superconducting unit and all couplers are configured such that the quantum Hamiltonian characterizing the logic superconducting unit, the control superconducting unit and all couplers is: Wherein, i represents the i-th pair of logic superconducting units and / or control superconducting units, h i and J ij has a real value associated with the coupling strength between one of the corresponding pair of logic superconducting units or control superconducting units and the other of the corresponding pair of logic superconducting units or control superconducting units, I(t), G(t) and P(t) are time-dependent control parameters, σ i x , σ i z and σ j z are Pauli operators and each represents the spin of the corresponding qubit along the x-direction or z-direction, respectively, N G and N A are the total number of quantum regulator (QG) qubits and quantum annealer (QA) qubits, respectively, and h i G , J ij G , J ij GA is the control parameter of the quantum regulator.

28. An apparatus for building and programming quantum hardware, comprising: A first logical superconducting cell and a second logical superconducting cell of a first pair of logical superconducting cells for computing are formed so that each logical superconducting cell has a characteristic capable of crossing the Pauli operator σ sj i A quantum state of , where i = x, y, or z; A first control superconducting unit and a second control superconducting unit of a first pair of control superconducting units for assisting calculation without being an operation unit are formed so that each control superconducting unit has a ck i A quantum state of , where i = x, y, or z; a first coupler between the first logic superconducting unit and the second control superconducting unit, wherein the first coupler couples the first logic superconducting unit and the second control superconducting unit when a magnetic field is applied along the z direction; and A second coupler is provided between the second logic superconducting unit and the first control superconducting unit, wherein the second coupler couples the second logic superconducting unit and the first control superconducting unit when a magnetic field is applied along the z-direction.

29. The apparatus of claim 28, wherein: The first coupler is s1 x σ c2 z represents the quantum operator σ of the first coupling to the first logical superconducting unit s1 z The second quantum operator σ controls the superconducting unit c2 z coupling; and The second coupler is s2 x σ c1 z The second coupling to the first control superconducting unit is represented by the quantum operator σ s2 z The quantum operator σ of the second logical superconducting unit c1 z coupling.

30. The apparatus of claim 28, wherein: The first and second logic superconducting cells and the first and second control superconducting cells include superconducting qubits having binary quantum states.

31. The apparatus of claim 28, wherein: The logic superconducting unit has the same precision as the control superconducting unit.

32. The apparatus of claim 28, wherein: The logic superconducting cell has a lower precision than the control superconducting cell.

33. The apparatus of claim 28, wherein: The logic superconducting unit, the control superconducting unit and all couplers are configured such that the quantum Hamiltonian characterizing the logic superconducting unit, the control superconducting unit and all couplers is: Wherein, i represents the i-th pair of logic superconducting units and / or control superconducting units, h i and J ij has a real value associated with the coupling strength between one of the corresponding pair of logic superconducting units or control superconducting units and the other of the corresponding pair of logic superconducting units or control superconducting units, I(t), G(t), and P(t) are time-dependent control parameters, σ i x , σ i z and σ j z are Pauli operators and each represents the spin of the corresponding qubit along the x-direction or z-direction, respectively, N G and N A are the total number of quantum regulator (QG) qubits and quantum annealer (QA) qubits, respectively, and h i G , J ij G , J ij GA is the control parameter of the quantum regulator.

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