QUANTUM SOLVER FOR MULTI-OBJECTIVE FUNCTION BLACK-BOX OPTIMIZATION

Systems and techniques that facilitate multi-objective function optimization are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory that can execute the computer executable components stored in memory. The computer executable components can comprise an initialization component that initializes circuit parameters for an ansatz quantum circuit in a quantum computer; a measurement component that measures a plurality of bitstrings from a state of the quantum circuit; and an optimization component that determines a subset of bitstrings comprising feasible Pareto-efficient elements of the plurality of bitstrings and a hypervolume based on the subset of bitstrings and updates the circuit parameters to increase indices of the hypervolume.

BACKGROUND

The subject disclosure relates to black-box optimization, and more specifically to a quantum solver for multi-objective function black-box optimization.

SUMMARY

According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise an initialization component that initializes circuit parameters for an ansatz quantum circuit in a quantum computer; a measurement component that measures a plurality of bitstrings from a state of the quantum circuit; and an optimization component that determines a subset of bitstrings comprising feasible Pareto-efficient elements of the plurality of bitstrings and a hypervolume based on the subset of bitstrings and updates the circuit parameters to increase indices of the hypervolume. An advantage of such a system is that it can generate Pareto fronts in fewer cycles, thus decreasing the workload of a quantum processor.

According to another embodiment, a computer-implemented method can comprise initializing, by a system operatively coupled to a processor, circuit parameters for an ansatz quantum circuit in a quantum computer; measuring, by the system, a plurality of bitstrings from a state of the quantum circuit; determining, by the system, a subset of bitstrings comprising feasible Pareto-efficient elements of the plurality of bitstrings; determining, by the system, a hypervolume based on the subset of bitstrings; and updating, by the system, the circuit parameters to increase indices of the hypervolume. An advantage of such a method is that it can generate Pareto fronts in fewer cycles, thus decreasing the workload of a quantum processor.

According to another embodiment, a computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to initialize, by the processor, circuit parameters for an ansatz quantum circuit in a quantum computer; measure, by the processor, a plurality of bitstrings from a state of the quantum circuit; determining, by the processor, a subset of bitstrings comprising feasible Pareto-efficient elements of the plurality of bitstrings; determining, by the processor, a hypervolume based on the subset of bitstrings; and update, by the processor, the circuit parameters to increase indices of the hypervolume. An advantage of such a computer program product is that it can generate Pareto fronts in fewer cycles, thus decreasing the workload of a quantum processor.

DETAILED DESCRIPTION

Quantum computing is generally the use of quantum-mechanical phenomena for the purpose of performing computing and information processing functions. Quantum computing can be viewed in contrast to classical computing, which generally operates on binary values with transistors. That is, while classical computers can operate on bit values that are either 0 or 1, quantum computers operate on quantum bits (qubits) that comprise superpositions of both 0 and 1, can entangle multiple quantum bits, and use interference.

Quantum computing provides opportunities to solve complex multi-objective function problems that may not be possible with classical computing. However, when solving multi-objective function problems, optimization of one function or criteria may negatively impact optimization of another function or criteria. For example, consider the task of determining a distribution of material formulations to create materials that are both strong and easily adaptable to various shapes. Optimization for adaptability may negative impact strength and vice versa. These types of problems can be framed as multi-objective optimization challenges but are typically intricate and time consuming. Furthermore, there are cases in which the decision variables are binary, making them combinatorial problems, and introducing complex constraints. While these types of problems are in high demand across numerous industries, there is no established method or system for solving them efficiently using classical computing.

In one or more embodiments, the present disclosure can be implemented in the form of systems, computer-implemented methods, and/or computer program products that can address the above identified issues by initializing circuit parameters for an ansatz quantum circuit in a quantum computer, measuring a plurality of bitstrings from a state of the quantum circuit, determining a plurality of bitstrings from a state of the quantum circuit, determining a subset of bitstrings comprising feasible Pareto-efficient elements, determining a hypervolume based on the subset of bitstrings and updating the circuit parameters to increase indices of the hypervolume. By determining the Pareto front, parameters that produce the most Pareto-efficient elements can be identified, and solutions that balance all objectives of the multiple objective functions are achieved.

In an embodiment, the system can initialize a quantum circuit |φ (θ) with random circuit parameters θ. This allows for the quantum circuit to be utilized as a sampler, wherein the quantum state produced by the circuit parameters can be converted into bitstrings that described the distribution of the multiple objective functions. The quantum state can be measured to obtain a collection of observed bitstrings: X:|φ(θ)≈Σx∈Xa(x)|x . A subset of bitstrings comprising Pareto efficient solutions X* can be created from X. The Pareto front S can be updated such that (S ∪X*)*. The hypervolume indices can then be determined using the formulation Ind=HV(F(X*),z) of X* with a given reference point z. The system can then check if a convergence criterion or criteria has been met. This criteria can comprise a set number of iterations, the size of set X*, based on a set number of iterations without a change in the size of X*, or another criteria set by an entity. If the convergence criteria are not met, the circuit parameters θ can be updated using a classical randomization technique and the process repeated to search for parameters that produce an increased number of Pareto efficient solutions.

As referenced herein, an “entity” can comprise a human, a client, a user, a computing device, a software application, an agent, a machine learning (ML) model, an artificial intelligence (AI) model, and/or another entity.

FIGS. 1 and 2 illustrates block diagram of example, non-limiting systems 102 and 202 that can facilitate black-box optimization in accordance with one or more embodiments described herein. Aspects of systems (e.g., system 102 and the like), apparatuses or processes in various embodiments of the present invention can constitute one or more machine-executable components embodied within one or more machines (e.g., embodied in one or more computer readable mediums (or media) associated with one or more machines). Such components, when executed by the one or more machines, e.g., computers, computing devices, virtual machines, etc. can cause the machines to perform the operations described. System 102 can comprise initialization component 110, measurement component 112, optimization component 114 processor 106, memory 108 and quantum system 101. System 202 can further comprise iteration component 216.

In various embodiments, quantum optimization solver systems 102 and 202 can comprise a processor 106 (e.g., a computer processing unit, microprocessor) and a computer-readable memory 108 that is operably connected to the processor 106. The memory 108 can store computer-executable instructions which, upon execution by the processor, can cause the processor 106 and/or other components of the quantum optimization solver systems 102 and 202 (e.g., initialization component 110, measurement component 112, optimization component 114, iteration component 216, processor 106, memory 108 and/or quantum system 101) to perform one or more acts. In various embodiments, the memory 108 can store computer-executable components (e.g., initialization component 110, measurement component 112, optimization component 114, iteration component 216 processor 106, memory 108 and/or quantum system 101), the processor 106 can execute the computer-executable components.

In one or more embodiments, initialization component 110 can initialize circuit parameters for an ansatz quantum circuit in a quantum computer (e.g., quantum system 101). For example, an optimization problem can comprise multiple objection functions F (L dimensional) that are to be minimized simultaneously. At the same time, there are various constraints that have to be met, and the set of binary vectors that satisfy these constraints are referred to as C. Accordingly, this problem can be defined as

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In some cases, both F and C may be black-box function. When dealing with conflicting functions, decisions are made in order to balance these multiple objectives. Therefore, a set of Pareto optimal solutions is desirable. These solutions are a set of choices representing the best trade-offs between different objectives, forming the Pareto front. To form this Pareto front, a quantum computer (e.g., quantum system 101) is utilized as a sampler for distributions that classical methods are unable to represent efficiently. Using an ansatz quantum circuit, the quantum state |φ(θ) produced by the circuit parameters θ embodies probability distributions comprising Pareto-optimal solutions while ensuring high likelihood of the existence of such solutions. The initialization component 110 can initialize the quantum circuit with parameters that are chosen at random by a classical method. In one or more embodiments, the ansatz quantum circuit can comprise alternating rotational and entanglement layers.

In one or more embodiments, measurement component 112 can sample bitstrings from the quantum state produced by the parameterized quantum circuit initialized by initialization component 110. For example, measurement component 112 can measure a set of bitstrings X⊆N from the quantum state |φ(θ) as |φ(θ)≈ΣXx∈Xa(x)|x. Given the set of bitstrings X, measurement component 112 can extract a subset X*comprising the feasible and Pareto efficient elements using the formula X*={x∈X: {y∈X:y>x, y≠x}=∅}. Once subset X* has been determined, the Pareto front S can be updated such that S←(S∪X*)*.

In one or more embodiments, optimization component 114 can determine a hypervolume based on the subset of bitstrings. For example, optimization component 114 can determine a hypervolume wherein indices of the hypervolume are determined by Ind=HV(F(X*), z) of X*, with a given reference point z. This hypervolume then represents the observed feasible Pareto solutions concerning the reference point z. Optimization component 114 can then utilize a randomization optimization method to update the circuit parameters to increase the indices of the hypervolume.

In some embodiments, one or more forms of Bayesian optimization can be utilized to update or tune the circuit parameters. Bayesian optimization can comprise sequential cycles or optimization that achieve global optimization of black-box functions. For example, a Gaussian Process-Expected Improvement (e.g., GP-EI) optimization method. This employs a Gaussian process as a surrogate model, in combination with the Expected Improvement as the acquisition function. Because GP-EI uses a Gaussian process, it not only predicts the function value, but also incorporates the uncertainty in the objective function evaluation. This enables the careful selection of the next evaluation point. In another example, a Sequential Model-based Algorithm Configuration (e.g., SMAC) approach can be utilized. SMAC utilizes a random forest as the surrogate model, while adopting Expected Improvement as the aquation function. In a further example, a Tree-Structured Parzen Estimator (e.g., TPE) approach can be utilized. TPE utilizes kernel density estimation for each hyperparameter. TPE aims to discern the distribution of favorable hyperparameter settings from less optimal ones. This allows TPE to manage continuous, discrete and categorical parameters. In another embodiment, Covariance Matrix Adaptation Evolution Strategy (e.g., CMA-ES) can be employed to update the parameters. CMA-ES evenly and effective samples the vicinity of the optimal solution, which allows for construction of the Pareto front. In another embodiment, a Nondominated Sorting Genetic algorithm (e.g., NSGA-II) can be employed. NGSA-II creates a population of candidate solutions or parameters that evolve toward an optimal solution in order to solve multi-objective functions.

Turning to FIG. 2, in one or more embodiments, iteration component 216 can determine how many iterations of optimization should take place in order to continue to generate the Pareto front. For example, after optimization component 114 updates the parameter 0, iteration component 216 can determine whether to end the Pareto front generation or to continue Pareto front generation. If the criteria are not met, measurement component 112 can measure bitstrings based on the quantum state produced by the updated parameters and additional Pareto efficient bitstrings can be added to the Pareto front S. The optimization component can then determine and updated hypervolume and update parameters again. In an embodiment, the performance criteria can comprise a set number of iterations, a Pareto front that comprises at least a set number of elements, a set number of iterations in a row that do not add new Pareto elements to the Pareto front, and/or another performance-based criteria set by an entity.

FIG. 3 illustrates an algorithm for a quantum black-box solver that identifies Pareto efficient elements in accordance with one or more embodiments described herein.

In some embodiments, the objective functions F can be scaled or adjusted such that their values are within a range defined by an entity such as [0,1] or [−1,0]. The input for such a black-box solver can comprise a quantum circuit |φ (θ)), an empty Pareto front S, reference point z and a randomization method for optimization such as TPE, CMA-EST, Bayesian Optimization, NSGA-II, GP-EI, SMAC and the like. In one or more embodiments, the reference point z can be set to the maximum value of the range defined by the entity (e.g., [0,1] or [−1,0]). At step 1, the quantum circuit can be initialized with randomly selected values. At step 2, the quantum state produced by the quantum circuit can be measured to obtain a collection of observed bitstrings X:|φ(θ)≈Σx∈Xa(x)|x. At step 3, a subset X* can be created from X, wherein X*comprises Pareto efficient elements. At step 4, the Pareto front S can be updated to include X* using S←(S∪X*)*. At step 5, the hypervolume can be determined using the formulation Ind=HV(F(X*),z) of X* with a given reference point z. In some embodiments, the hypervolume calculation can be narrowed down to include only the top-ranked bitstrings with the highest probability of occurring in the sampling, wherein the entity can specify the number of candidate bitstrings to consider for the calculation. At step 6, the performance or stopping criteria can be checked. At step 7, if the stopping criteria are met, then the Pareto front can be output to an entity. If the stopping criteria are not met, then the parameters θ can be updated using the desired randomization method and the steps 2 through 7 can be repeated with the updated parameters.

FIGS. 4 and 5 illustrate graphs comparing the performance of the multi-objective function solver described herein with alternative methods and/or system in accordance with one or more embodiments described herein. Graph 400 illustrates performance results of a multi-objective function with a problem size of 20 and 3 objective functions. As shown, the y-axis of graph 400 shows the size of the hypervolume and the x-axis shows the number of optimization iterations performed. As illustrated, the method described herein achieves the same result as the optimal value in less than 2000 iterations while none of the other methods shown achieve comparable results with 5000 iterations. Graph 500 illustrates performance results of a multi-objective function with a problem size of 20 and 5 objective functions. As shown, the y-axis of graph 500 shows the size of the hypervolume, and the x-axis shows the number of optimization iterations performed. As illustrated, the method described herein achieves the same result as the optimal value in less than 2000 iterations while none of the other methods shown achieve comparable results with 5000 iterations. Accordingly, the methods and/or systems described herein allow for better results and do so in less iterations, thereby reducing the workload of a processor and/or quantum computer associated with the methods and/or systems.

FIG. 6 illustrates a graph comparing the performance of the function solver described herein with alternative methods and/or system in accordance with one or more embodiments described herein. Graph 600 illustrates the performance results of a single objective function with a problem size of 15. The y-axis of graph 600 shows the objective function value that is maximized, and the x-axis shows the number of optimization iterations performed. As shown, the methods and/or systems described herein offer performance increases in single function optimization as well as multi-objective function optimization.

FIG. 7 illustrates a flow diagram of an example, non-limiting, computer-implemented method 700 that can generate Pareto fronts for multi-objective problems in accordance with one or more embodiments described herein.

At 702, method 700 can comprise initializing, by a system (e.g., system 100/200 and/or initialization component 110) circuit parameters for an ansatz quantum circuit. For example, as described above in reference to FIGS. 1-3, initialization component 110 can randomly choose initial circuit parameters for a quantum circuit, wherein the quantum circuit is utilized as a sampler in order to generate a Pareto front.

At 704, method 700 can comprise measuring, by the system (e.g., system 100/200 and/or measurement component 112) a plurality of bitstrings of the quantum state. For example, as described above in reference to FIGS. 1-3, the quantum state of the parameterized circuit represents the probability distribution of the multi-objective function.

At 706, method 700 can comprise determining, by the system (e.g., system 100/200 and/or measurement component 112), a subset of bitstrings comprising Pareto-efficient elements. For example, as describe above in reference to FIGS. 1-3, measurement component 112 can measure a set of bitstrings X⊆N from the quantum state |φ(θ) as |φ(θ)≈Σx∈Xa(x). Given the set of bitstrings X, measurement component 112 can extract a subset X* comprising the feasible and Pareto efficient elements, using the formula X*={x∈X:{y∈X:y>x, y≠x}=∅}. Once subset X* has been determined the Pareto front S can be updated such that S←(S∪X*)*.

At 708, method 700 can comprise determining, by the system (e.g., system 100/200 and/or optimization component 114), a hypervolume based on the subset of bitstrings. For example, as describe in relation to FIGS. 1-3, optimization component 114 can determine a hypervolume wherein indices of the hypervolume are determined by Ind=HV(F(X*),z) of X*, with a given reference point z. This hypervolume then represents the observed feasible Pareto solutions concerning the reference point z.

At 710, method 700 can comprise updating, by the system (e.g., system 100/200 and/or optimization component 114), the circuit parameters to increase indices of the hypervolume. For example, as described above in reference to FIGS. 1-3, optimization component 114 can utilize one or more classical randomized techniques to update the circuit parameters in order to increase the indices of the hypervolume, and thus identify additional Pareto-efficient elements.

FIG. 8 illustrates a flow diagram of an example, non-limiting, computer-implemented method 800 that can generate Pareto fronts for multi-objective problems in accordance with one or more embodiments described herein.

At 802, method 800 can comprise initializing, by a system (e.g., system 100/200 and/or initialization component 110) circuit parameters for an ansatz quantum circuit. For example, as described above in reference to FIGS. 1-3, initialization component 110 can randomly choose initial circuit parameters for a quantum circuit, wherein the quantum circuit is utilized as a sampler in order to generate a Pareto front.

At 804, method 800 can comprise measuring, by the system (e.g., system 100/200 and/or measurement component 112) a plurality of bitstrings of the quantum state. For example, as described above in reference to FIGS. 1-3, the quantum state of the parameterized circuit represents the probability distribution of the multi-objective function.

At 806, method 800 can comprise determining, by the system (e.g., system 100/200 and/or measurement component 112), a subset of bitstrings comprising Pareto-efficient elements. For example, as describe above in reference to FIGS. 1-3, measurement component 112 can measure a set of bitstrings X⊆N from the quantum state |φ(θ) as |φ(θ)≈Σx∈Xa(x). Given the set of bitstrings X, measurement component 112 can extract a subset X* comprising the feasible and Pareto efficient elements, using the formula X*={x∈X:{y∈X:y>x, y≠x}=∅}. Once subset X* has been determined the Pareto front S can be updated such that S←(S∪X*)*.

At 808, method 800 can comprise determining, by the system (e.g., system 100/200 and/or optimization component 114), a hypervolume based on the subset of bitstrings. For example, as describe in relation to FIGS. 1-3, optimization component 114 can determine a hypervolume wherein indices of the hypervolume are determined by Ind=HV(F(X*),z) of X*, with a given reference point z. This hypervolume then represents the observed feasible Pareto solutions concerning the reference point z.

At 810, method 800 can comprise updating, by the system (e.g., system 100/200 and/or optimization component 114), the circuit parameters to increase indices of the hypervolume. For example, as described above in reference to FIGS. 1-3, optimization component 114 can utilize one or more classical randomized techniques to update the circuit parameters in order to increase the indices of the hypervolume, and thus identify additional Pareto-efficient elements.

At 812, method 800 can comprise determining, by the system (e.g., system 100/200 and/or iteration component 216), whether convergence criteria has been met by the updated parameters and or the generated Pareto front. For example, as described above in reference to FIGS. 1-3, iteration component 216 can determine whether a set number of update iterations have passed and/or whether the Pareto front comprises a defined number of elements. In response to a “YES” determination, method 800 can end at step 814. In response to a “NO” determination, method 800 can return to step 804 and repeat updating of the parameters.

A practical application of quantum optimization solver system 102 is that is allows the generation of more accurate Pareto fronts in fewer quantum processor iterations in comparison to other methods and/or systems. This therefore provides a technical improvement by decreasing the overall number of iterations that a quantum system and/or quantum processor operates while generating a Pareto front, thereby decreasing the workload of such a quantum system and/or quantum processor (e.g., quantum system 101).

It is to be appreciated that quantum optimization solver system 102 can utilize various combination of electrical components, mechanical components, and circuitry that cannot be replicated in the mind of a human or performed by a human as the various operations that can be executed by quantum optimization solver system 102 and/or components thereof as described herein are operations that are greater than the capability of a human mind. For instance, the amount of data processed, the speed of processing such data, or the types of data processed by quantum optimization solver system 102 over a certain period of time can be greater, faster, or different than the amount, speed, or data type that can be processed by a human mind over the same period of time. In another example, a human mind is not capable of performing quantum operations. According to several embodiments, quantum optimization solver system 102 can also be fully operational towards performing one or more other functions (e.g., fully powered on, fully executed, and/or another function) while also performing the various operations described herein. It should be appreciated that such simultaneous multi-operational execution is beyond the capability of a human mind. It should be appreciated that quantum optimization solver system 102 can include information that is impossible to obtain manually by an entity, such as a human user. For example, the type, amount, and/or variety of information included in quantum optimization solver system 102, such as quantum states of qubits, can be more complex than information obtained manually by an entity, such as a human user.

Turning generally to FIG. 9, one or more embodiments described herein can include one or more devices, systems and/or apparatuses that can facilitate executing one or more quantum operations to facilitate output of one or more quantum results. For example, FIG. 9 illustrates a block diagram of an example, non-limiting system 900 that can complete the execution of a quantum job.

The quantum system 901 (e.g., quantum computer system, superconducting quantum computer system and/or the like) can employ quantum algorithms and/or quantum circuitry, including computing components and/or devices, to perform quantum operations and/or functions on input data to produce results that can be output to an entity. The quantum circuitry can comprise quantum bits (qubits), such as multi-bit qubits, physical circuit level components, high level components and/or functions. The quantum circuitry can comprise physical pulses that can be structured (e.g., arranged and/or designed) to perform desired quantum functions and/or computations on data (e.g., input data and/or intermediate data derived from input data) to produce one or more quantum results as an output. The quantum results, e.g., quantum measurement 911, can be responsive to the quantum job request 904 and associated input data and can be based at least in part on the input data, quantum functions and/or quantum computations.

In one or more embodiments, the quantum system 901 can comprise one or more quantum components, such as a quantum operation component 903, a quantum processor 906 and a quantum logic circuit 909 comprising one or more qubits (e.g., qubits 907A, 907B and/or 907C), also referred to herein as qubit devices 907A, 907B and 907C. The quantum processor 906 can be any suitable processor, such as being capable of controlling qubit coherence and the like. The quantum processor 906 can generate one or more instructions for controlling the one or more processes of the quantum operation component 903.

The quantum operation component 903 that can obtain (e.g., download, receive, search for and/or the like) a quantum job request 904 requesting execution of one or more quantum programs. The quantum operation component 903 can determine one or more quantum logic circuits, such as the quantum logic circuit 909, for executing the quantum program. The request 904 can be provided in any suitable format, such as a text format, binary format and/or another suitable format. In one or more embodiments, the request 904 can be received by a component other than a component of the quantum system 901, such as a by a component of a classical system coupled to and/or in communication with the quantum system 901.

The quantum operation component 903 can perform one or more quantum processes, calculations and/or measurements for operating one or more quantum circuits on the one or more qubits 907A, 907B and/or 907C. For example, the quantum operation component 903 can operate one or more qubit effectors, such as qubit oscillators, harmonic oscillators, pulse generators and/or the like to cause one or more pulses to stimulate and/or manipulate the state(s) of the one or more qubits 907A, 907B and/or 907C comprised by the quantum system 901. That is, the quantum operation component 903, such as in combination with the quantum processor 906, can execute operation of a quantum logic circuit on one or more qubits of the circuit (e.g., qubit 907A, 907B and/or 907C). The quantum operation component 903 can output one or more quantum job results, such as one or more quantum measurements 999, in response to the quantum job request 904.

It will be appreciated that the following description(s) refer(s) to the operation of a single quantum program from a single quantum job request. However, it also will be appreciated that one or more of the processes described herein can be scalable, such as execution of one or more quantum programs and/or quantum job requests in parallel with one another.

In one or more embodiments, the non-limiting system 900 can be a hybrid system and thus can include both one or more classical systems, such as a quantum program implementation system, and one or more quantum systems, such as the quantum system 901. In one or more other embodiments, the quantum system 901 can be separate from, but function in combination with, a classical system.

In such case, one or more communications between one or more components of the non-limiting system 900 and a classical system can be facilitated by wired and/or wireless means including, but not limited to, employing a cellular network, a wide area network (WAN) (e.g., the Internet), and/or a local area network (LAN). Suitable wired or wireless technologies for facilitating the communications can include, without being limited to, wireless fidelity (Wi-Fi), global system for mobile communications (GSM), universal mobile telecommunications system (UMTS), worldwide interoperability for microwave access (WiMAX), enhanced general packet radio service (enhanced GPRS), third generation partnership project (3GPP) long term evolution (LTE), third generation partnership project 2 (3GPP2) ultra mobile broadband (UMB), high speed packet access (HSPA), Zigbee and other 802.XX wireless technologies and/or legacy telecommunication technologies, BLUETOOTH®, Session Initiation Protocol (SIP), ZIGBEE®, RF4CE protocol, WirelessHART protocol, 6LoWPAN (Ipv6 over Low power Wireless Area Networks), Z-Wave, an ANT, an ultra-wideband (UWB) standard protocol and/or other proprietary and/or non-proprietary communication protocols.

Computing environment 1000 contains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as translation of an original source code based on a configuration of a target system by the quantum black box solver code 1080. In addition to block 1080, computing environment 1000 includes, for example, computer 1001, wide area network (WAN) 1002, end user device (EUD) 1003, remote server 1004, public cloud 1005, and private cloud 1006. In this embodiment, computer 1001 includes processor set 1010 (including processing circuitry 1020 and cache 1021), communication fabric 1011, volatile memory 1010, persistent storage 1013 (including operating system 1022 and block 1080, as identified above), peripheral device set 1012 (including user interface (UI), device set 1023, storage 1024, and Internet of Things (IoT) sensor set 1025), and network module 1015. Remote server 1004 includes remote database 1030. Public cloud 1005 includes gateway 1040, cloud orchestration module 1041, host physical machine set 1042, virtual machine set 1043, and container set 1044.

COMPUTER 1001 can take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as remote database 1030. As is well understood in the art of computer technology, and depending upon the technology, performance of a computer-implemented method can be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of computing environment 1000, detailed discussion is focused on a single computer, specifically computer 1001, to keep the presentation as simple as possible. Computer 1001 can be located in a cloud, even though it is not shown in a cloud in FIG. 10. On the other hand, computer 1001 is not required to be in a cloud except to any extent as can be affirmatively indicated.

PROCESSOR SET 1010 includes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitry 1020 can be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitry 1020 can implement multiple processor threads and/or multiple processor cores. Cache 1021 is memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set 1010. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitry. Alternatively, some, or all, of the cache for the processor set can be located “off chip.” In some computing environments, processor set 1010 can be designed for working with qubits and performing quantum computing.

Computer readable program instructions are typically loaded onto computer 1001 to cause a series of operational steps to be performed by processor set 1010 of computer 1001 and thereby effect a computer-implemented method, such that the instructions thus executed will instantiate the methods specified in flowcharts and/or narrative descriptions of computer-implemented methods included in this document (collectively referred to as “the inventive methods”). These computer readable program instructions are stored in various types of computer readable storage media, such as cache 1021 and the other storage media discussed below. The program instructions, and associated data, are accessed by processor set 1010 to control and direct performance of the inventive methods. In computing environment 1000, at least some of the instructions for performing the inventive methods can be stored in block 1080 in persistent storage 1013.

VOLATILE MEMORY 1010 is any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, the volatile memory is characterized by random access, but this is not required unless affirmatively indicated. In computer 1001, the volatile memory 1010 is located in a single package and is internal to computer 1001, but, alternatively or additionally, the volatile memory can be distributed over multiple packages and/or located externally with respect to computer 1001.

END USER DEVICE (EUD) 1003 is any computer system that is used and controlled by an end user (for example, a customer of an enterprise that operates computer 1001) and can take any of the forms discussed above in connection with computer 1001. EUD 1003 typically receives helpful and useful data from the operations of computer 1001. For example, in a hypothetical case where computer 1001 is designed to provide a recommendation to an end user, this recommendation would typically be communicated from network module 1015 of computer 1001 through WAN 1002 to EUD 1003. In this way, EUD 1003 can display, or otherwise present, the recommendation to an end user. In some embodiments, EUD 1003 can be a client device, such as thin client, heavy client, mainframe computer and/or desktop computer.

REMOTE SERVER 1004 is any computer system that serves at least some data and/or functionality to computer 1001. Remote server 1004 can be controlled and used by the same entity that operates computer 1001. Remote server 1004 represents the machine(s) that collect and store helpful and useful data for use by other computers, such as computer 1001. For example, in a hypothetical case where computer 1001 is designed and programmed to provide a recommendation based on historical data, then this historical data can be provided to computer 1001 from remote database 1030 of remote server 1004.

PUBLIC CLOUD 1005 is any computer system available for use by multiple entities that provides on-demand availability of computer system resources and/or other computer capabilities, especially data storage (cloud storage) and computing power, without direct active management by the scale. The direct and active management of the computing resources of public cloud 1005 is performed by the computer hardware and/or software of cloud orchestration module 1041. The computing resources provided by public cloud 1005 are typically implemented by virtual computing environments that run on various computers making up the computers of host physical machine set 1042, which is the universe of physical computers in and/or available to public cloud 1005. The virtual computing environments (VCEs) typically take the form of virtual machines from virtual machine set 1043 and/or containers from container set 1044. It is understood that these VCEs can be stored as images and can be transferred among and between the various physical machine hosts, either as images or after instantiation of the VCE. Cloud orchestration module 1041 manages the transfer and storage of images, deploys new instantiations of VCEs and manages active instantiations of VCE deployments. Gateway 1040 is the collection of computer software, hardware and firmware allowing public cloud 1005 to communicate through WAN 1002.

PRIVATE CLOUD 1006 is similar to public cloud 1005, except that the computing resources are only available for use by a single enterprise. While private cloud 1006 is depicted as being in communication with WAN 1002, in other embodiments a private cloud can be disconnected from the internet entirely and only accessible through a local/private network. A hybrid cloud is a composition of multiple clouds of different types (for example, private, community or public cloud types), often respectively implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technology that enables orchestration, management, and/or data/application portability between the multiple constituent clouds. In this embodiment, public cloud 1005 and private cloud 1006 are both part of a larger hybrid cloud. The embodiments described herein can be directed to one or more of a system, a method, an apparatus and/or a computer program product at any possible technical detail level of integration. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the one or more embodiments described herein. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a superconducting storage device and/or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium can also include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon and/or any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves and/or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide and/or other transmission media (e.g., light pulses passing through a fiber-optic cable), and/or electrical signals transmitted through a wire.

Aspects of the one or more embodiments described herein are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to one or more embodiments described herein. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. These computer readable program instructions can be provided to a processor of a general-purpose computer, special purpose computer and/or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, can create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein can comprise an article of manufacture including instructions which can implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus and/or other device to cause a series of operational acts to be performed on the computer, other programmable apparatus and/or other device to produce a computer-implemented process, such that the instructions which execute on the computer, other programmable apparatus and/or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality and/or operation of possible implementations of systems, computer-implementable methods and/or computer program products according to one or more embodiments described herein. In this regard, each block in the flowchart or block diagrams can represent a module, segment and/or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function. In one or more alternative implementations, the functions noted in the blocks can occur out of the order noted in the Figures. For example, two blocks shown in succession can be executed substantially concurrently, and/or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and/or combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that can perform the specified functions and/or acts and/or carry out one or more combinations of special purpose hardware and/or computer instructions.

While the subject matter has been described above in the general context of computer-executable instructions of a computer program product that runs on a computer and/or computers, those skilled in the art will recognize that the one or more embodiments herein also can be implemented at least partially in parallel with one or more other program modules. Generally, program modules include routines, programs, components and/or data structures that perform particular tasks and/or implement particular abstract data types. Moreover, the aforedescribed computer-implemented methods can be practiced with other computer system configurations, including single-processor and/or multiprocessor computer systems, mini-computing devices, mainframe computers, as well as computers, hand-held computing devices (e.g., PDA, phone), and/or microprocessor-based or programmable consumer and/or industrial electronics. The illustrated aspects can also be practiced in distributed computing environments in which tasks are performed by remote processing devices that are linked through a communications network. However, one or more, if not all aspects of the one or more embodiments described herein can be practiced on stand-alone computers. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.