Device for optimizing laboratory dispatching control

Abstract

The invention relates to a device (800) for optimizing laboratory scheduling control. A providing unit (801) provides a probe list including probe data. The probe data includes a probe identifier and a task. The task indicates an operation to be performed on the probe by the laboratory device. A formulating unit (802) formulates a scheduling problem based on the probe list such that optimization of the scheduling problem results in optimization of the task scheduling. An interface unit (803) is interfaced with the quantum computer (810) to optimize the scheduling problem with quantum computation and to receive quantum computation results indicative of the optimized scheduling problem. A determination unit (804) determines a schedule of the probe list on the basis of the received result. A control signal determination unit (805) determines a control signal for controlling the laboratory equipment (820) based on the determined schedule.

Description

Technical Field

[0001] The present invention relates to a device, method and computer program product for optimizing laboratory scheduling control. Further, the present invention relates to a quantum computer system, a laboratory system and a scheduling determination system comprising the device. Background Art

[0002] In modern laboratory environments, it is often necessary to perform multiple tests or analysis steps on a variety of different probes in multiple predetermined sequences. Furthermore, in modern laboratory environments, in order to reduce the cost of laboratory tests and analyses, while also increasing the efficiency of performing such tests and analyses, the higher the degree of automation, the better. For example, as the COVID pandemic has reasonably demonstrated, it is very important to analyze and test as many probes as possible as quickly as possible. However, even in an automated environment, it is difficult to determine a schedule for performing the corresponding test and analysis steps on the probes so that the laboratory equipment is optimally utilized and the test and analysis results are provided for each probe as quickly as possible. In particular, determining such a schedule is a very time-consuming and computationally resource-intensive task, which in many cases is still mainly based on the input of experienced users. Therefore, in an automated laboratory environment, it would be advantageous to schedule tasks less time-consuming, less computationally resource-intensive, and more objectively, in particular to more efficiently utilize existing laboratory equipment and to increase the level of laboratory automation. Summary of the invention

[0003] The object of the present invention is to provide an apparatus, method and computer program product to reduce time consumption, reduce computing resource intensity and more objectively optimize laboratory scheduling in an automated laboratory environment. In addition, the apparatus, method and computer program product can more efficiently utilize existing laboratory equipment and improve the level of laboratory automation. Further, the object of the present invention is to provide a system using the apparatus, method and / or computer program product to achieve such optimization of laboratory scheduling.

[0004] In a first aspect of the present invention, a device for optimizing laboratory scheduling control is proposed, wherein the device includes: a) a probe list providing unit, which is used to provide a probe list including probe data of multiple probes, wherein the probe data of the probe includes a probe identification and a task associated with the probe identification, wherein the task indicates one or more operations to be performed on the probe by laboratory equipment at a predetermined timing, b) a scheduling problem formulation unit, which is used to formulate a scheduling problem based on the probe list, wherein the scheduling problem is formulated so that optimization of the scheduling problem leads to optimization of the scheduling of tasks associated with the multiple probes, c) a quantum computer interface unit, which is used to interface with a quantum computer to optimize the scheduling problem using quantum computing and receive quantum computing results indicating the optimized scheduling problem from the quantum computer, d) a scheduling determination unit, which is used to determine the scheduling of the probe list based on the received quantum computing results, and e) a control signal determination unit, which is used to determine the control signal for controlling the laboratory equipment based on the determined schedule.

[0005] Since the device includes a quantum computer interface unit that allows interfacing with a quantum computer to optimize the scheduling problem using quantum computing and receiving a quantum computing result indicating the optimized scheduling problem from the quantum computer, and since the scheduling determination unit determines the scheduling based on the received quantum computing result, it is possible to optimize even complex scheduling problems with a large number of tasks and probes that need to be scheduled by utilizing the advantages of quantum computing, especially in optimization tasks. In addition, using a quantum computer to calculate at least a portion of the scheduling problem can reduce the computing time and necessary computing resources. In addition, since even very complex tasks can be solved by utilizing a quantum computer, the influence of experienced users can be reduced or even eliminated, thereby allowing objectivity in scheduling. Therefore, the device allows optimizing laboratory scheduling in a more time-saving manner and provides more objective results using fewer computing resources. In addition, since more complex problems can be solved and more objective results can be obtained, laboratory equipment can be used more efficiently and the level of automation is higher. In addition, since the computing time for rescheduling tasks is reduced, the downtime of laboratory equipment caused by rescheduling tasks can be reduced.

[0006] In general, the apparatus may be implemented in the form of software or hardware or a combination thereof, wherein hardware may refer to any known dedicated or general-purpose classical computer hardware. For example, the apparatus may be implemented as any known computing device, such as a PC. However, the apparatus may also be implemented as a cloud environment, a computing network, etc., so that at least a part of the apparatus may also be implemented as a network solution, and thus may be distributed on multiple computing devices.

[0007] The probe list providing unit is adapted to provide the probe list. In particular, the probe list providing unit can refer to a storage unit in which the probe list has been stored. However, the probe list providing unit can also include or refer to an input unit, which can be used by a user to indicate the corresponding probe list. In addition, the probe list providing unit can be implemented as or communicatively coupled to a scanning device, which is adapted to read the information provided on the probe itself. For example, a bar code can be provided for the probe, and the information related to the probe is stored on the bar code, and can be read by the corresponding scanning device.

[0008] Generally speaking, a schedule in a laboratory environment refers to a specification indicating which laboratory equipment must perform which laboratory operation on which probe in which time period or at which time. Therefore, a laboratory schedule defines the course of action to be performed by the laboratory equipment. The control performed based on the determined laboratory schedule causes the laboratory equipment to perform the predetermined laboratory schedule.

[0009] The probe list includes probe data of multiple probes. In general, a probe can refer to any type of material to which a test process or analysis should be performed by a laboratory device of a laboratory. For example, a probe can refer to a medical probe such as a tissue probe, a blood probe, etc. In addition, a probe can also refer to any other solid or fluid chemical, mixture or material. For example, a probe can also refer to an environmental probe, such as a soil, water or biomaterial sample. In particular, preferably, a probe refers to a sample of a new chemical or material, such as a new polymer, a medical preparation or a metal, to which a corresponding test process needs to be performed to determine the technical applicability. The corresponding probe can be provided in the form of any suitable sample container, which allows the sample to be accommodated and is configured to be used by the laboratory device, such as a tray or other accommodating device inserted into the laboratory device. In addition, in an embodiment, a task associated with a probe can also refer to first mixing the probe, for example, with a variety of other substances in a mixing step. Therefore, a probe can also refer to a sample that must be assembled in a first task before further tasks are performed on the probe.

[0010] The probe data of the probe includes a probe identification and at least one task associated with the probe identification. In general, the probe identification can refer to any identification that can clearly identify a specific probe. For example, the probe identification can refer to the identification number of the probe, which is also set on, for example, a probe container, particularly in the form of a barcode, a QR code or any other known computer-readable identification mark. However, the probe identification can also refer to the specific position of the probe, such as the position on a probe tray containing a plurality of probes. In this case, the probe identification can be, for example, a row and column in which the probe can be found in the probe tray. In addition, the probe identification can also refer to the RFID chip identification of the RFID chip used to mark the probe. At least one task associated with the probe identification and therefore with the probe indicates one or more operations that will be performed by the laboratory equipment on the probe according to a predetermined timing. In particular, a task can refer to one or more analysis steps or test processes to be performed on the probe, wherein one or more operations are necessary for performing an analysis or test process on the probe. These operations can refer to, for example, exhaustion operations, chemical addition operations, mixing operations, pipetting operations, measurement operations, heating or cooling operations, etc. For example, a task involving the analysis of a probe may include a first operation of exhausting the probe, then adding a specific chemical to the probe, then mixing the probe with the chemical, then pipetting and removing a portion of the mixed probe and heating the removed portion of the mixed probe, and then performing a predetermined measurement on the probe, such as an optical measurement. In general, each such operation belonging to a task must be completed within a corresponding timing. The corresponding timing may refer to the order of the operations, and further, if necessary, may also refer to a minimum or maximum time interval between specific operations. For example, a task may require waiting for a certain minimum time after an operation before performing the next operation, or performing an operation before a specific maximum time span expires. In general, the tasks provided as part of the probe data may be provided in any form that allows identification of one or more operations to be performed as part of the task performed by the laboratory device on the probe. For example, for known tasks, the tasks may also be provided in the form of an identification, wherein the identification allows the corresponding operations and the corresponding predetermined timings between the operations to be derived. For example, a plurality of known tasks with corresponding task identifications may be stored together with the corresponding one or more operations and the predetermined timings on a task storage device, which may be accessed to determine the corresponding operations of the tasks. However, the tasks may also be provided directly in the probe data by specifying one or more operations and the corresponding timings between the one or more operations. This is particularly advantageous when introducing new tasks (e.g., new analysis or testing procedures). The probe data may generally be provided in any known form to allow for the identification of probes and, therefore, the association of probes with corresponding tasks. For example, the probe data may refer to a list of probe identifications and associated tasks, or may refer to any other form, such as a table or matrix.

[0011] The scheduling problem formulation unit is configured to formulate a scheduling problem based on a probe list. In general, the scheduling problem formulation unit can be configured to automatically formulate a scheduling problem, for example, based on one or more predetermined rules or functions, which determine the expression of the scheduling problem based on the probe list. For example, the scheduling problem formulation unit can utilize a general mathematical expression of the scheduling problem, and can insert the information provided by the probe list into the general mathematical expression of the scheduling problem based on predetermined rules. However, the scheduling problem formulation unit can also be configured to allow the scheduling problem to be formulated during the user-machine interaction process, for example, by providing the user with different possible mathematical expressions of the scheduling problem and allowing the user to select the mathematical expression of the problem for inserting the information provided by the probe list. In addition, the scheduling problem formulation unit can also provide the automatically formulated scheduling problem to the user for verification or allow the user to modify the formulated scheduling problem.

[0012] In general, the scheduling problem is formulated so that the optimization result of the scheduling problem indicates the optimization of the scheduling of tasks associated with multiple probes, in particular the optimization of the scheduling of one or more operations of the tasks. Therefore, the scheduling problem can also be regarded as an optimization problem. In general, the scheduling problem can be formulated in any form or representation that allows the determination of the quantities describing the scheduling problem and the form of the interaction between these quantities. Preferably, the scheduling problem is provided in the form of a mathematical description of the scheduling problem. However, the scheduling problem can also be described in any other form of notation that allows, for example, the derivation of a mathematical representation of the scheduling problem.

[0013] In general, an optimization problem (i.e., a scheduling problem) refers to optimizing (i.e., minimizing or maximizing) the functional relationship between different quantities. The corresponding quantity may refer to a variable that may change during the problem optimization process. Further, these quantities may refer to a fixed quantity or a predetermined quantity that will not change during the problem optimization process, such as a quantity set by a constraint. In addition, these quantities may further refer to one or more quantities that are minimized or maximized during the problem optimization process, referred to as optimization quantities. In particular, the optimization of the optimization problem is performed for one or more variables, i.e., for one or more quantities that may change during the optimization process, so as to minimize or maximize the quantity to be optimized, respectively. Such a variable may refer to a quantitative variable, i.e., a quantity that can only take one value at a time, or may refer to a high-dimensional variable, in particular, a set of scalars that can only take one value at a time, such as a quantity or a matrix. In addition, a variable may also refer to a part of a higher-dimensional quantity, such as one or more scalars that may refer to a matrix or a vector. In general, an optimization problem (i.e., a scheduling problem) may be provided so that the corresponding variable of the optimization problem can take any value. However, in a preferred embodiment, the optimization problem is adapted so that one or more variables are binary variables, that is, can only take two values, for example, values ​​0 and 1, or 0 and -1, or 1 and -1. This is particularly advantageous for the part of the optimization problem calculated on a quantum computer. In a more general case, preferably, the optimization problem is adapted so that the variable can take any integer value. The optimization of the optimization problem (i.e., the scheduling problem) refers to minimizing or maximizing one or more optimization quantities. Preferably, at least one optimization quantity refers to the time of performing the operation of the task associated with the probe of the probe list, wherein the time is minimized in the optimization process of the scheduling problem. For example, a variable may refer to a time period in which a corresponding operation is performed on a specific probe. Therefore, the result of the optimization may refer to an indication of which operation should be performed on which probe in which time period to minimize the time required to perform all operations indicated by the tasks in the probe list. However, other or additional optimization quantities may also be defined. For example, the optimization quantity may also be the individual time required to perform the operation of the task associated with a specific probe, wherein the individual time of each probe or a selected subset of probes is minimized in the optimization process of the scheduling problem. Generally speaking, if multiple optimization quantities to be optimized are defined in the scheduling problem optimization process, it is impossible in most cases to optimize one quantity while also affecting another quantity, so that the respective optimization results are Pareto optimal solutions. Therefore, in this case, the scheduling problem can be formulated so that one or more Pareto optimal solutions for scheduling can be determined, wherein, based on the determined one or more Pareto optimal solutions, a corresponding scheduling plan can be selected, for example, by a user or according to a predetermined criterion. The quantum computer interface unit is adapted to interface with a quantum computer to optimize the scheduling problem using quantum computing.In general, quantum computing can be used to optimize and solve the scheduling problem, but can also be used to calculate only certain parts of the scheduling problem, which can then be used to optimize the scheduling problem, for example, on a classical computer. Preferably, the quantum computer interface unit is configured to send at least a part of the scheduling problem to the quantum computer to calculate at least a part of the scheduling problem using quantum computing.

[0014] Quantum computing can be defined as a method of computing that performs computing operations based on the controlled manipulation of quantum elements that follow quantum mechanical physics. Therefore, quantum computing allows the use of quantum effects, such as superposition and entanglement. This allows certain calculations to be performed more efficiently than classical digital computers. In contrast, classical computing on classical computing devices can be defined as a method of computing that uses transistor-based processors to perform computing operations. A quantum computer can be configured to perform quantum computing. In particular, a quantum computer may include one or more quantum elements that follow quantum mechanical physics, and a device for manipulating one or more quantum elements to perform computing operations (i.e., perform quantum computing). Therefore, a quantum computer can be a computer that performs computing operations based on quantum mechanical effects.

[0015] The quantum computer utilized may refer to any form of quantum computer. A quantum computer is typically adapted to perform quantum manipulations of corresponding quantum elements based on control signals to determine the solution to a problem. Quantum manipulation may refer to any manipulation performed directly or indirectly on an element of a quantum computer to implement a quantum mechanical description of the problem to be calculated (i.e., it can be described according to the rules of quantum mechanics rather than classical physics). In the case of a gate-based quantum computer, quantum manipulation is defined as a quantum operation involving a series of defined direct quantum manipulations of quantum elements. However, in the case of a quantum annealer, quantum manipulation refers to a more indirect manipulation. For example, after the initial state of a quantum element is prepared, the external field (e.g., a magnetic field) is slowly and continuously changed to cause the state of the quantum element to evolve to a final state in the external field.

[0016] In general, although a certain element of a quantum computer can be used to implement a quantum mechanical description of a problem, i.e., it can be described by the rules of quantum mechanics, the element itself does not necessarily have to refer to a quantum mechanical system, such as an atom or an ion. Preferably, quantum manipulation includes all operations that can directly or indirectly affect the state of a quantum element (i.e., a quantum bit) of a quantum computer. In particular, the quantum computer utilized may refer to an adiabatic quantum computing system, a quantum annealing system, and / or a gate-based computing system.

[0017] Further, the quantum computer interface unit is adapted to receive a quantum computation result indicating an optimized scheduling problem from the quantum computer. In particular, the result of the quantum computation refers to a result of computing at least a portion of the scheduling problem on the quantum computer. Therefore, the corresponding result directly indicates the optimized scheduling problem, for example, directly refers to a solution to the optimized scheduling problem, or can be used for computing the optimized scheduling problem, for example, performed on a classical computing device.

[0018] Then, the scheduling determination unit is adapted to determine the scheduling of the provided probe list based on the received quantum calculation result. For example, as described above, if the result of the quantum calculation only involves the calculation of a part of the scheduling problem, the scheduling determination unit can be adapted to perform or utilize the calculation of the scheduling problem based on the received result. In addition, if the received result already involves the optimized scheduling problem, the scheduling determination unit can be configured to interpret the optimized scheduling problem to determine the specific scheduling of the probe list. For example, the calculation result of the optimized scheduling problem can involve a matrix indicating which operation is performed on which probe in which time period. Then, the scheduling determination unit can be adapted to identify the corresponding probe identification, the corresponding operation and the corresponding time period in which the operation should be performed based on the received matrix (for example, according to the position in the matrix), and generate a list or table to determine which time period should be used for each probe of the probe list by which laboratory equipment to perform the corresponding operation of the corresponding task. Such a list or table can be regarded as the scheduling of the probe list. However, the scheduling probe list can also be provided in other digital or analog formats so as to derive the scheduling of each probe from the format.

[0019] Then, the control signal determination unit is adapted to determine the control signal for controlling the laboratory device based on the determined schedule. In general, the control signal can be provided in any format that can be interpreted by the laboratory device to execute the determined schedule. For example, the control signal can refer to a schedule representation that can be interpreted by the laboratory management system so as to control the corresponding laboratory device according to the schedule. However, the control signal can also directly include a signal that allows the control of the laboratory device, for example, allowing the start and / or stop of a mixer, a mobile robot system to place one or more probes in a specific position, increasing and / or decreasing the temperature in a heater, the vibration of a vibrating plate, etc.

[0020] In an embodiment, the quantum computer interface unit further includes a scheduling problem preparation unit for preparing at least a portion of the scheduling problem so that optimization of the scheduling problem can be performed using quantum computing. In general, the scheduling problem preparation unit can be part of the same hardware and / or software used to implement the device. However, the scheduling problem preparation unit can also be implemented in the form of hardware and / or software that is communicatively coupled to the quantum computer interface unit of the device, rather than itself being part of the same hardware and / or software of the quantum computer interface unit. For example, the problem list providing list, the scheduling problem formulation unit, the scheduling determination unit, and the control signal determination unit can be implemented as part of a first web service, wherein the quantum computer interface unit provides an interface to the quantum computer via the scheduling problem preparation unit provided as another web service.

[0021] Preferably, preparing at least a part of the scheduling problem so that the optimization of the scheduling problem can be performed using quantum computing includes: determining a control operation for controlling the quantum computer to perform at least a part of the quantum computing of the scheduling problem. In particular, the preparation includes transforming the scheduling problem into a quantum operation that can be prepared and performed on a specific quantum computer based on a corresponding control signal, thereby manipulating the quantum elements of the quantum computer according to the determined quantum operation. In addition, the preparation of at least a part of the scheduling problem can refer to any type of preparation and / or transformation of the scheduling problem, so that the scheduling problem can be performed using quantum computing. This embodiment is particularly preferred if the scheduling problem formulation unit is not adapted to directly provide a scheduling problem that can be calculated by a quantum computer. For example, preparation can refer to transforming the scheduling problem into a quantum mechanical description. In addition, since in most cases, when the variables of the calculated problem involve binary variables, the calculation on the quantum computer is easier to perform, the preparation can also involve transforming the scheduling problem or at least a part of the scheduling problem so that the variables of the problem or a part of the problem involve binary variables. Preferably, the preparation of the scheduling problem comprises determining control operations for controlling a quantum computer to perform quantum computations on at least a portion of the scheduling problem, and wherein the scheduling problem preparation unit is further adapted to send these control operations to the quantum computer for performing quantum computations on at least a portion of the scheduling problem.

[0022] In general, a control operation refers to a signal that causes a quantum computer to follow a predetermined rule or algorithm to perform a corresponding calculation indicated by the control operation. In particular, a control operation may refer to a signal that only initializes such a corresponding calculation, but may also refer to a signal that determines the complete calculation performed by the corresponding quantum computer. In addition, the control operation may be provided in a format that allows the control operation to be directly performed on the corresponding quantum computer, but may also be provided in a format that must first be converted into a format for controlling the corresponding quantum computer. For example, if the control operation specifies specific manipulations that should be performed on the quantum computer, the signals indicating these manipulations may be converted by a control unit specific to the corresponding quantum computer into signals interpretable by the quantum computer to perform the operation. In particular, for a quantum computer, it may be necessary to transform the specific manipulation indicated by the control operation into a corresponding specific control signal to control, for example, a laser unit or an electromagnetic field providing unit to provide a laser or an electromagnetic field, respectively, thereby allowing the quantum elements of the corresponding quantum computer to be subjected to the desired manipulation corresponding to the manipulation indicated by the control operation.

[0023] In an embodiment, the scheduling problem preparation unit is adapted to prepare a scheduling problem solvable on a quantum computer, the quantum computer being a quantum annealer or a gate-based quantum computer. In particular, preferably, the quantum computer is a quantum annealer.

[0024] In an embodiment, the scheduling problem is formulated so that the optimization of the scheduling problem refers to the optimization of the time required to perform the operations indicated by these tasks. The scheduling problem is formulated so that the optimization can refer to the optimization of the time required to perform the operations indicated by the tasks, with the advantage that the tasks can be completed in the shortest possible time. This allows more efficient use of laboratory equipment to analyze and / or test as many probes as possible.

[0025] In an embodiment, the device further comprises a constraint determination unit for determining the constraints of the scheduling based on the tasks and / or the laboratory equipment information based on the technical data indicating the laboratory equipment, wherein the scheduling problem is further formulated based on the determined constraints. In general, the constraints represent the constraints and restrictions provided by the specific laboratory equipment or the corresponding tasks themselves. For example, if the mixer of the laboratory equipment can only provide the positions of five probes in a certain time period, the scheduling optimization will preferably take into account the constraints, for example to avoid providing the mixer with more than five probes in a time period. In addition, as described in detail above, in most cases, the operation of the task must be performed in a fixed predetermined order and also in a fixed predetermined timing in order to obtain meaningful analysis or test results. Therefore, the task itself may also provide corresponding constraints that are advantageously considered during optimization. Then, the constraint determination unit is adapted to determine such constraints according to the tasks and / or laboratory equipment information. For example, it can be determined based on predetermined rules, which allow constraints to be derived according to the corresponding information provided by the tasks and laboratory equipment information. The identification of the task itself may be associated with, for example, a predetermined list of constraints, or the corresponding constraints may be derived from a list of operations associated with the task, for example by determining the order of the corresponding operations and the minimum / maximum time between the operations. Furthermore, the lab-device information may directly comprise the corresponding constraints, for example, may be directly provided so that the constraints are contained in the lab-device information. However, the lab-device information may also refer to, for example, a list of manufacturer identifications of lab-devices, and the constraint determination unit may then be adapted to determine the constraints based on the manufacturer identifications of the lab-devices, for example, by looking up technical details of the specific lab-device in a manual provided by the lab-device manufacturer. Preferably, the lab-device information further indicates whether one or more operations on one or more probes may be performed simultaneously or sequentially, wherein the constraints are further determined based on this information.

[0026] The scheduling problem is then further formulated based on the determined constraints. For example, if the laboratory equipment does not change for different schedules, then the constraints provided by the laboratory equipment can be directly provided as part of the scheduling problem, so that for a new set of tasks, only the constraints provided by the tasks need to be determined and integrated with the scheduling problem that already represents the constraints provided by the laboratory equipment. As described above, in order to formulate the scheduling problem, predetermined rules can be applied that indicate how to integrate the tasks and constraints into the scheduling problem, for example, into the mathematical form of the scheduling problem. When formulating the scheduling problem, these constraints can be viewed as weights or penalties. For example, higher weights can be given to terms that result in satisfying the constraints. On the other hand, penalties penalize solutions that do not satisfy the constraints, thereby increasing the likelihood of providing a solution that satisfies all constraints as the final schedule.

[0027] In a preferred embodiment, the probe list providing unit is further adapted to provide an updated probe list based on probe data received after determining the schedule of the original probe list, wherein the scheduling problem formulation unit is then adapted to reformulate the scheduling problem based on the updated probe list to determine the updated schedule. In many applications, probes are continuously provided to the laboratory, which then have to be inserted into the schedule as part of a new probe list. Therefore, it is advantageous if the schedule of the original probe list can be updated in the case of providing new probes to the laboratory. In particular, in this case, the original scheduling problem must be reformulated by the scheduling problem formulation unit as a new scheduling problem, which can then be solved again according to the embodiments described above. In this case, preferably, the reformulated scheduling problem takes into account the current state of the tasks associated with the original probe list. For example, preferably, the probe list providing unit is adapted to continuously update the probe list according to the current progress of the schedule, for example, by deleting tasks or task operations that have been performed by laboratory equipment from the probe list. Therefore, if new probes are added to the probe list, the generated updated probe list can relate to the current state of the schedule of the original probe list and take this state into account when performing further optimization, in particular formulating the scheduling problem. Generally speaking, since quantum computers are able to calculate optimization problems very quickly and thus also provide fast solutions for updated probe lists, the invention as described above is particularly suitable for constantly changing probe lists, which is a very efficient solution that allows full automation of laboratory equipment.

[0028] In another aspect of the present invention, a quantum computer system is proposed, wherein the quantum computer system comprises: a) an interface unit for receiving a scheduling problem from an apparatus as described above, the scheduling problem being formulated such that optimization of the scheduling problem leads to optimization of scheduling of tasks associated with a plurality of laboratory probes, wherein the tasks are to be performed by laboratory equipment on the plurality of laboratory probes, and b) a quantum computer adapted to perform quantum computation on the received scheduling problem, wherein the interface unit is further adapted to provide a quantum computation result of the scheduling problem to the apparatus for further processing.

[0029] In another aspect of the present invention, a laboratory system is proposed, wherein the laboratory system comprises: a) a laboratory device adapted to automatically perform one or more operations indicated by a task on one or more laboratory probes based on a control signal, and b) an apparatus as described above, wherein the apparatus provides the determined control signal to the laboratory device to execute the determined schedule.

[0030] In another aspect of the present invention, a scheduling determination system is proposed, wherein the scheduling determination system comprises a) the apparatus as described above, and b) the quantum computer system as described above, wherein the apparatus and the quantum computer system are connected via an interface unit.

[0031] In another aspect of the present invention, a method for optimizing laboratory scheduling control is proposed, wherein the method includes: a) providing a probe list including probe data of multiple probes, wherein the probe data of the probe includes a probe identification and a task associated with the probe identification, wherein the task indicates one or more operations performed by a laboratory device on the probe at a predetermined timing, b) formulating a scheduling problem based on the probe list, wherein the scheduling problem is formulated so that optimization of the scheduling problem leads to optimization of the scheduling of tasks associated with the multiple probes, c) sending the scheduling problem to the quantum computer via an interface for optimization using quantum computing, and receiving the optimization result of the scheduling problem from the quantum computer via the interface, d) determining the scheduling of the probe list based on the received quantum computing result, and e) determining a control signal for controlling the laboratory device based on the determined scheduling.

[0032] In another aspect of the present invention, a computer program product for optimizing laboratory scheduling control is proposed, wherein the computer program product comprises program code means for causing the apparatus as described above to execute the method as described above.

[0033] In another aspect of the invention, control signals for controlling laboratory equipment based on a determined schedule are proposed, wherein these control signals are generated using an apparatus or a method as described above.

[0034] In another aspect of the invention, use of an apparatus or a method as described above for controlling a laboratory device based on a determined schedule is proposed.

[0035] In another aspect of the present invention, a scheduling problem preparation device is proposed, wherein the scheduling problem preparation device includes: a) an interface for receiving a scheduling problem, wherein the scheduling problem is formulated so that optimization of the scheduling problem leads to optimization of scheduling of tasks associated with multiple probes, b) a processor configured to prepare at least a part of the scheduling problem so that optimization of the scheduling problem can be performed using quantum computing, and c) an interface for providing at least a part of the prepared scheduling problem to a quantum computer to calculate at least a part of the prepared scheduling problem.

[0036] It shall be understood that the apparatus as described above, the method as described above, the device as described above and the computer program product as described above have similar and / or identical preferred embodiments, in particular as defined in the dependent claims.

[0037] It shall be understood that a preferred embodiment of the present invention may also be any combination of the dependent claims or the above-described embodiments with the corresponding independent claim.

[0038] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In the attached picture:

[0040] Figure 1 Demonstrated state representation of qubits used in quantum computing devices,

[0041] Figure 2 A schematic example of a quantum computing device using qubits as computing units is shown.

[0042] Figure 3 Illustrative example methods for generating control signals to perform operations on a quantum computing device and for processing measurement signals from a quantum computing device are presented.

[0043] Figure 4 A schematic example of a hybrid system consisting of classical and quantum computing devices is shown,

[0044] Figure 5 A schematic example of a superconductor-based quantum computing device is shown,

[0045] Figure 6 A schematic example of a quantum computing device based on trapped ions is shown.

[0046] Figure 7 The overall structure of the scheduling system for optimizing laboratory scheduling control is schematically and exemplarily shown.

[0047] Figure 8 An embodiment of a device 800 for optimizing laboratory scheduling control is schematically and exemplarily shown.

[0048] Fig. 9 A flow chart of a method for optimizing laboratory scheduling control is schematically and exemplarily shown,

[0049] Fig.10 A high-throughput facility to which the present invention can be applied is shown schematically and exemplarily,

[0050] Fig.11 A schematic diagram showing the schedule determined for two probes, and

[0051] Fig.12 A long-term storage facility to which the present invention can be applied is shown schematically and exemplarily. DETAILED DESCRIPTION

[0052] The following article first briefly introduces the general basic principles of quantum computers and the computing performance of quantum computers. Further, the general principles can also be found in the following literature: "Quantum Computation and Quantum Information: 10th Anniversary Edition", author MA Nielsen and ILChuang (2010).

[0053] Classical computing devices use transistor-based processors. The state of each transistor has two controllable states, 1 or 0, representing a digital binary or bit. In order to perform operations on a classical computing device, a human-readable program code is converted into machine-readable instructions via a compiler. The machine-readable instructions are control signals for each transistor, such as voltage settings. The representation of the machine-readable instructions may include binary or hexadecimal representations. Based on such machine-readable instructions, operations are performed on the processor of the classical computing device.

[0054] Quantum computing is a relatively new computing method that uses quantum effects (such as superposition and entanglement) to perform certain calculations more efficiently than classical digital computers. Compared with digital computers that represent information in the form of bits (e.g., "1" or "0") as described above, quantum computing devices (i.e., quantum computers) use quantum bits (qubits / quantum bits) to represent information. Quantum computing devices are based on quantum elements that follow quantum mechanical physics, such as superconductors, ions, atoms, quantum dots, photons, particle spins, bosons, etc. These quantum elements can be manipulated in a controlled manner to perform operations.

[0055] Although qubits and their manipulation can be described in terms of their mathematical properties, each such qubit can be implemented in a physical quantum element in any of a variety of different ways. Examples of such quantum elements include superconducting materials, trapped ions, photons, optical cavities, single electrons trapped within quantum dots, point defects in solids (e.g., phosphorus donors in silicon or nitrogen-vacancy centers in diamond), molecules (e.g., alanine, vanadium complexes), or any medium that exhibits qubit behavior, including quantum states and transitions between them that can be controllably sensed or detected.

[0056] In general, for any given physical quantum element implementing a qubit, any of a variety of properties of the physical element may be selected to implement the qubit. For example, if an electron is chosen to implement a qubit, then the x, y, or z component of the electron spin degree of freedom may be selected as a property of such an electron to represent the state of such a qubit. For any particular degree of freedom, the physical quantum element may be controllably placed into a superposition or entangled state, and measurements may then be made on the selected degree of freedom to obtain a readout of the qubit value.

[0057] Compared to transistors in classical computing devices, each quantum element in a quantum computing device can adopt not only the ground state |1> or |0>, but also any superposition of this ground state, such as the state |X>. The state of each quantum element is represented by the state of a quantum bit (qubit), such as Figure 1 This is shown in the two-dimensional simplification of . To represent this state, Dirac notation is often used in quantum mechanics. In Dirac notation, the state in an n-dimensional complex vector space (such as Hilbert space) is represented by a braket symbol, for example, |X>. According to conventional terminology, the superposition of the "0" and "1" states in a quantum computing device can be represented as α|0>+β|1>. The states "0" and "1" or bits of a classical computing device are analogous to the ground states |0> and |1> or qubits of a quantum computing device, respectively. The value |α| 2 represents the probability that the qubit will be measured in the |0> state, while the value |β| 2 represents the probability that a qubit will be measured in the |1> state. If there is more than one qubit, two or more qubits may be entangled. Entanglement means that the state of one qubit depends on the state of at least one other qubit, and vice versa, wherein, further, in the entangled state, the individual qubits can no longer be considered separate qubits. In general, a register of N qubits in a quantum computer can be in a superposition of ground states at the same time, while a register of N classical bits can only be in a single ground state at the same time. Therefore, compared to classical computing devices on quantum computing devices, 2 N base states, thus achieving exponential inherent parallelism.

[0058] In order to perform operations on a quantum computing device, the computational methods used to solve a given problem can be converted into qubit manipulations, which can be converted into control signals for manipulating the qubits. The representation of machine-readable instructions can include common quantum mechanical representations of operations in Hilbert space. Depending on the specific implementation of the quantum computer, different representations of qubit states can be selected. Any state preparation on a quantum computing device can be represented by manipulations acting on qubit states. Manipulations can be converted into control signals to control the corresponding parts of the quantum computer, depending on the type of quantum computing device used. In this way, based on the manipulations acting on the qubit states, operations can be performed on the quantum equivalent of a classical processor (as part of a quantum computing device).

[0059] In a gate-based quantum computer system, the manipulation of a qubit state can typically be one or more qubit operations. A qubit operation can change the state of a qubit, for example, to Figure 1 The rotation of the vector |X> shown corresponds to a particular superposition. For example, in a superconducting quantum computer, this can be achieved by microwave pulses, or in a trapped ion quantum computer by irradiating the ions with a laser beam. Multi-qubit operations may generate entanglement between two or more qubits. For example, in a superconducting quantum computer, this can be achieved by connecting the qubits via an intermediate electrical coupling circuit, or in a trapped ion quantum computer by controlling the collective vibrations of the trapped ions.

[0060] In general, in order to prepare manipulations for solving a given problem on a quantum computer, the corresponding quantum mechanical representation of the problem can be converted into qubit manipulations, which are performed to prepare a solution to the given problem. After the predetermined solution is prepared, that is, after the operation is applied to the qubits of the quantum computer, a projection measurement of all individual qubits is performed, returning 0 or 1 for each qubit. On the quantum computing device, the measurement is achieved by applying a hardware-specific readout protocol (including control pulses in the case of a gate-based quantum computer, for example) for a series of readout manipulations and monitoring the response to the control pulses. For example, a superconducting qubit can be coupled to a hardware resonator. Measuring the shift in the resonator frequency allows the state of the qubit to be determined because the shift depends on the state of the coupled qubit. For example, in the case of trapped ions, optical readout can be used, for example, if the ion emits light, the state of the qubit is 1, and if the ion does not emit light, the state of the qubit is 0, and vice versa. In this way, qubits can be used to implement logic circuits or gates as in classical computing devices, especially on gate-based quantum computers.

[0061] exist Figure 2, a schematic example of a quantum computer is shown. Figure 2 The quantum computing device 100 shown in FIG. 1 comprises a quantum register 104 configured to perform quantum computation, a manipulation portion 106 configured to manipulate the quantum register (particularly quantum elements forming qubits), and a readout portion 108 configured to collect measurement signals from the quantum register 104 to read out the qubits after quantum mechanical computation. In particular, the manipulation portion 106 provides manipulation signals for manipulating the quantum register, wherein the manipulation signals are generated based on received control signals, which are determined based on corresponding operations that should be performed on the qubits. In some embodiments, a feedback loop between the manipulation portion 106 and the measurement portion 108 may be provided. In contrast to classical computing, in which one measurement cycle provides the state of a transistor, quantum computing includes performing multiple measurement cycles to provide a probability density or probability of a qubit state in the case of a gate-based quantum computer, or to determine the measurement value with the lowest energy in the case of a quantum annealer.

[0062] The quantum register 104 may be based on different quantum elements representing qubits. In some embodiments of a gate-based quantum computer, qubits may be implemented as quantum elements by photons. Such an optical quantum computing device may include a laser that generates photons that are provided to a waveguide. A beam splitter may be provided for manipulating the state of the photons based on a manipulation signal (such as a mechanical rotation applied to a mirror). In such an embodiment, the measurement portion 108 may be a photon detector, and the measurement signal may be a photon.

[0063] In other embodiments of gate-based quantum computers, qubits may be implemented by electronic states of ions trapped in a magnetic field. In this case, the manipulation portion 106 may utilize a laser, and the manipulation signal may provide a control laser pulse. Also, in this case, the readout portion 108 may be a photon detector combined with a readout laser pulse, and the measurement signal 102 may be a photon. Other qubit implementations may be based on superconductors as quantum elements, on semiconductor materials with anyons as quantum elements, etc.

[0064] Figure 3Schematic exemplary methods for generating control signals to perform manipulations on a quantum computer and for processing measurement signals from a quantum computing device are presented. In most embodiments of quantum computing devices known to date, control signals for the quantum computing device are prepared on a classical computing device, and measurement signals provided by the quantum computing device are further processed on the classical computing device. However, as quantum computing devices mature, other embodiments are also conceivable. In the following examples, a quantum computer refers to a gate-based quantum computer, wherein manipulation refers to operations on quantum elements of the quantum computer.

[0065] In order to generate control signals to perform operations on a quantum computing device, in step S10, a problem to be solved by means of a quantum computing device is preferably provided in the form of a mathematical description. For example, such a problem may include determining material properties based on a mathematical description of the electronic structure of the material. Other problems may include optimization problems and associated objective functions. In step S12, based on the problem to be solved, an operational description of the problem or sub-problem may be generated, wherein the operational description includes operations to be applied to the quantum bits of the quantum computer to solve the problem in quantum mechanical calculations. Further, the operational description may include a reference state that allows a representation of the initial quantum bit state to be generated on the quantum computer, and then further operations are applied on the quantum computer by manipulating the quantum bit state. Then, in step S14, based on the operational description, control signals may be generated to control the quantum computer, for example, by providing these control signals to a manipulation unit, which may then manipulate the quantum bit state based on these control signals. In step S16, the manipulation unit then applies manipulation operations to a single or multiple quantum bits of the quantum computer, wherein, based on these manipulation operations, the quantum bits perform quantum mechanical calculations. After the manipulation, in step S18, a measurement signal may be generated to determine the result of the quantum mechanical calculation. This step may include reading out (i.e. measuring) the qubit state after applying the manipulation operation to the initial qubit state. Then, in step S20, the measurement signal may be converted into a measurement quantity on a classical computer and, in the case of a subproblem, fed back into the problem to be solved. Finally, in step S22, the result of the problem calculation including the quantum mechanical calculation may be provided on the classical computing device.

[0066] Figure 4 A schematic example of a hybrid system including classical and quantum computing devices is shown. As shown in Ref. Figure 3 As described in the methods presented in , quantum computing devices are often used in conjunction with classical computing devices. Figure 4 As shown, the question preparation system can be implemented to perform e.g. Figure 3A classical computing device 110 of steps S10, S12, S20, S22 of the method shown. A control unit may then be provided as an interface between the classical computing device 110 and the quantum computer 100, wherein the control unit may also be, for example, a classical computing device that performs step S14. The control unit may then be communicatively coupled to a manipulation portion 106 that may control a manipulator of the quantum computing device. Furthermore, the manipulation portion 106 may be implemented as a classical computing device, for example, classical control hardware for controlling specific hardware components that perform quantum bit manipulation in a quantum computer. However, the manipulation portion 106 is generally considered to be part of a quantum computer, since it directly affects the quantum registers. The quantum computing device 100 is adapted to perform a quantum operation in step S16, in particular by manipulating the quantum bits of the quantum registers. Then, a measurement portion 108, which is also generally considered to be part of a quantum computing device, may perform step S18 by utilizing classical hardware. The measurement portion 108 may then be communicatively coupled to a preparation system 110 for further processing of the measurement signal.

[0067] Figure 5 A schematic example of a quantum computing device based on a superconductor is shown. A superconducting quantum computing device is one of the solid-state quantum computing technologies. Here, the quantum register 104 may include superconducting circuits 520, 522, 524 based on Josephson junctions. Then, depending on the amount of superconducting circuits selected to represent a quantum bit, the quantum bit may refer to a charge quantum bit, a flux quantum bit, a transport quantum bit, or a phase quantum bit, for example. Figure 5 A simplified illustration of a superconducting quantum computer involving charge qubits. For charge qubits, different states of the qubit are represented by an integer number of Cooper pairs on a superconducting island. In the case of gate-based quantum computing, quantum manipulation can be achieved by manipulating the qubits by microwave pulses. Resonators 512, 514, 516 can be used to manipulate the state of the qubit by applying microwaves, or to read out the state of the qubit by measuring the corresponding microwaves, wherein different resonators are typically used to manipulate the state of the qubit and to read out the qubit. In addition, resonator 518 can be used to apply microwaves that entangle the qubits. However, in addition to resonator 518, entanglement can also be achieved by inductive coupling or capacitive coupling of superconducting circuits or even by providing another qubit (here a superconducting circuit) between two qubits to be entangled.

[0068] At the operational level, such systems are maintained at extremely low temperatures, for example, tens of mK. The extreme cooling of the system keeps the superconducting material below its critical temperature and helps to avoid unnecessary state transitions. In order to maintain this low temperature, the quantum information processing system can be operated in a cryostat (such as a dilution refrigerator). In some embodiments, the control signal is generated in a higher temperature environment and transmitted to the quantum computer using a shielded impedance-controlled GHz transmission line (such as a coaxial cable). In some embodiments, a dispersion detection scheme is used to achieve state measurement of superconducting quantum bits. In order to read out or detect the state of any quantum bit, a probe signal (e.g., a traveling wave microwave) can be excited along a readout transmission line coupled to the quantum bit via a corresponding readout resonator. The frequency of the probe signal can be near the resonant frequency of the readout resonator. Depending on the internal quantum mechanical state of the quantum bit, the intensity or phase of the probe signal transmitted along the readout transmission line can be changed because the reflectivity of the readout resonator coupled to the quantum bit varies according to the state of the quantum bit. This allows state detection of the quantum bit, wherein during the readout of the quantum bit state, the state of the quantum bit collapses, that is, it is projected onto one of the ground states with a corresponding probability. By performing quantum mechanical calculations and readouts multiple times, for example, the corresponding probabilities can be determined. Further details of superconducting quantum devices are described in, for example, documents EP 3830867A1, EP 3449427 A1, US2020272925 A1, CN 212061223U and US 2019019099A1.

[0069] Figure 6 A schematic example of a quantum computing device based on ions in an ion trap is shown. Similar to a neutral atom trap, an ion trap with, for example, positively charged calcium ions can be used to implement a quantum computing device. Here, ions 626 are trapped in an oscillating electromagnetic field 624 within a high or ultra-high vacuum. The ions 626 are laser cooled and held in the oscillating electric field 624. For qubit manipulations (such as superposition or entanglement), lasers 628 of different frequencies can be used.

[0070] In general, based on the above quantum computer implementation, gate-type-based calculations can be performed on quantum computer hardware architectures. Gate-type-based calculations are based on quantum gates. Compared to classical gates, there are countless possible single-qubit quantum gates that can change the state vector of a qubit. Changing the state of a qubit state vector is often referred to as a single-qubit rotation, and may also be referred to herein as a state change or a single-qubit quantum gate operation. Rotation, state change, or single-qubit quantum gate operation can be represented mathematically by a unitary 2×2 matrix with complex elements. Rotation corresponds to the rotation of the qubit state within its Hilbert space, which can be conceptualized as a rotation of a vector on a Bloch sphere, where the Bloch sphere is often referred to as a geometric representation of the pure state space of a qubit. Multi-qubit gates change the quantum state of a set of qubits. For example, a two-qubit gate rotates the state of two qubits into a rotation of the two qubits in a four-dimensional Hilbert space, where, as is well known, the Hilbert space is an abstract vector space with an inner product structure that allows the measurement of lengths and angles. In addition, the Hilbert space is complete, that is, there are enough limits in the space to allow the use of calculus techniques.

[0071] The term "operational description" below refers to a representation of a problem that includes a sequence of quantum operations that should be applied during a quantum mechanical calculation of the problem on a gate-based quantum computer, or the manipulations performed on quantum elements during a quantum mechanical calculation of the problem on a quantum annealer. In the context of the present invention, the term "quantum operation" may include all types of quantum gates as described above and all manipulations known to quantum annealers, as described in more detail below. Further, in some applications, quantum operations may also include measurement manipulations. This allows the use of measurement feedback to implement algorithms. For example, in such an algorithm, a quantum computer may execute a quantum gate defined by a sequence of quantum operations, then measure only a subset (i.e., less than all) of the quantum bits or other computational elements (such as bosonic field states) in the quantum computer, and then decide which further quantum operations to perform next based on the results of one or more measurements. In particular, measurement feedback can be used to perform quantum error correction, but is not limited to being used to perform quantum error correction.

[0072] Not all quantum computers are gate-based quantum computers. Embodiments of the present invention are not limited to utilizing gate-based quantum computers. As an alternative example, embodiments of the present invention may also utilize, in whole or in part, a quantum computer implemented using a quantum annealing paradigm, which is an alternative to the gate-based quantum computing paradigm. More specifically, quantum annealing is a meta-heuristic method that finds the global minimum of a given objective function on a given set of candidate solutions (candidate states) by using a process of quantum fluctuations. In particular, quantum annealing is closely related to adiabatic quantum computing.

[0073] In general, the quantum annealing process also starts with providing or generating an initial Hamiltonian and a final Hamiltonian based on the computational problem to be solved using a classical computer, and providing the initial Hamiltonian, the final Hamiltonian and the annealing schedule as inputs to the quantum computer. In the case of an annealing process for solving an optimization problem, preferably, the Hamiltonian refers to the Ising Hamiltonian representing the optimization problem, wherein, in this case, the final Hamiltonian refers to the ground state of the Ising Hamiltonian or a good approximation thereof. Then, for example, the quantum computer is adapted to prepare an initial state that is relatively easy to prepare based on the initial Hamiltonian, such as a quantum mechanical superposition of all possible states (e.g., candidate states) with equal weights, by using a corresponding control unit that controls the manipulation part of the quantum computer. After the initial state is prepared on the quantum computer, the initial state is then evolved according to an annealing schedule that follows the time-dependent Schrodinger equation, which refers to the natural quantum mechanical evolution of the physical system of the quantum computer. More specifically, the state of the quantum computer undergoes time evolution under the time-dependent Hamiltonian, starting from the initial Hamiltonian and ending at the final Hamiltonian. If the evolution is slow enough, the system will remain close to the ground state of the instantaneous Hamiltonian. At the end of the time evolution, a set of quantum bits (i.e., quantum elements) on the quantum annealer is in a final state that is expected to be close to the ground state of the Ising Hamiltonian, which corresponds to the solution to the original problem (e.g., an optimization problem). The final state of the quantum computer can then be measured, thereby producing a result that can be used to solve the original problem. The measurement operation can be performed, for example, in any of the ways described above. The classical computer can then post-process the measurement results to produce an output representing the solution to the original computational problem. For example, the quantum annealer described above can be implemented on superconducting quantum computer hardware.

[0074] In addition, embodiments of the present invention may also utilize, in whole or in part, quantum computers implemented using a one-way quantum computing architecture (also known as a measurement-based quantum computing architecture). More specifically, a one-way or measurement-based quantum computer refers to a quantum computing method that first prepares an entangled resource state, typically a cluster state or a graph state, and then performs a single quantum bit measurement on it. It is "one-way" because the resource state is destroyed by the measurement. In such an architecture, the results of each individual measurement are random, but they are related in a way that the calculation can always succeed. In general, the basis selection for subsequent measurements needs to depend on the results of previous measurements, so all measurements cannot be performed simultaneously.

[0075] Figure 7 The overall structure of the scheduling system for optimizing laboratory scheduling control is schematically and exemplarily shown. Figure 8 A more detailed embodiment is shown. Figure 7Schematically showing a quantum computer, a job scheduler (e.g., referring to or including a device for optimizing laboratory scheduling control, such as referring to Figure 8 The principle structure and relationship between a probe list and a laboratory device are described in detail. In particular, the job scheduler is configured to formulate a scheduling problem, for example, based on the probe list to provide at least a part of the scheduling problem to the quantum computer for calculation. Then, the scheduling solution (especially the quantum computer calculation result indicating the scheduling solution) is provided to the job scheduler again, so that the job scheduler can determine the job scheduling based on the result of the quantum computer calculation and provide the corresponding scheduling control signal to the laboratory device to be controlled. In an embodiment where a new probe can also be added to the probe list during the scheduling of the original probe list previously determined, the laboratory device can be adapted to register such a new probe. For example, a barcode scanner of the laboratory device can notice that the probe has a new identifier indicated by the barcode, and then the newly registered probe can be provided to the job scheduler together with further information (for example, the task associated with the new probe). The job scheduler can then reformulate the scheduling problem and use the quantum computer again to determine the updated scheduling control provided to the laboratory device.

[0076] Figure 8 The embodiment of the device 800 for optimizing laboratory scheduling control is schematically and exemplarily shown in more detail. In particular, preferably, as Figure 7 The job scheduler shown includes an apparatus 800. The apparatus 800 includes a probe list providing unit 801, a scheduling problem formulating unit 802, a quantum computer interface unit 803, a scheduling determination unit 804, and a control signal determination unit 805. Optionally, the apparatus 800 may further include a scheduling problem preparation unit 806 as a part of the quantum computer interface unit 803. However, in other embodiments, the scheduling problem preparation unit 806 may also be an independent system, or may be omitted entirely. In general, the apparatus may be provided in any form of hardware and / or software. In particular, the apparatus 800 may include one or more processors configured to provide the functions defined by the units of the apparatus 800.

[0077] The probe list providing unit 801 is configured to provide a probe list including probe data of multiple probes. For example, the laboratory equipment 820 can generate a probe list by reading out the information of the identification system provided with the probe (for example, inserting the probe tray). For example, the probe inserted into the probe tray may include a barcode, a QR code or an RFID chip, which, when read out, can not only provide the identification of the probe, but also provide, for example, instructions for tasks to be performed on the probe. However, the probe list can also be provided by the user via the input unit input probe list. In particular, the probe list includes probe data of multiple probes, wherein the probe data includes probe identification (for example, probe identification number) and tasks associated with the probe identification. Tasks generally represent one or more operations that the laboratory equipment 820 performs on the corresponding probes at predetermined timing. For example, the task can indicate that the corresponding probe identified by the corresponding identification number should accept the PCR test process. This PCR test process as a task indicates multiple operations to be performed on the probe, such as adding chemicals to the probe, exhausting the probe, heating the probe, mixing the probe or measuring one or more characteristics of the probe. For each of the operations, if a corresponding control signal is provided, one or more laboratory equipment can be automatically used.

[0078] Then, the scheduling problem formulation unit 802 is adapted to formulate a scheduling problem based on the probe list. The scheduling problem is formulated so that the optimization of the scheduling problem leads to the optimization of the scheduling of tasks associated with multiple probes. For example, the scheduling problem formulation unit 802 can be adapted to formulate the scheduling problem using predetermined rules, preferably formulated in a mathematical expression. However, the scheduling problem formulation unit 802 can also be adapted to formulate the scheduling problem in a computer-guided user interaction process, wherein the scheduling problem formulation unit 802 can, for example, provide the user with multiple possibilities for formulating the scheduling problem based on the problem list, and then allow the user to select one or more parts of the scheduling problem to be used. The scheduling formulation unit 802 can be adapted to formulate the scheduling problem, for example, according to the following rules. First, all dependency constraints are added to the optimization problem statement, wherein the dependency constraints are implied by the various tasks associated with the probes and refer to the dependencies between the various operations of the tasks. For example, this can only include an order corresponding to the order of task operations, and can also include a specific waiting time between operations or tasks. Next, a known capacity constraint can be added to the optimization problem statement, wherein the capacity constraint refers to the known limited capacity of the equipment used for a certain task, for example, a robot can only transport one object at a time. In the last step, the optimization problem formulation is formulated so that the optimization of the optimization problem formulation related to the scheduling problem minimizes the time required to execute all the tasks of the probe list. For example, additional details and principles for formulating optimization problems based on a general task list can be found in the following literature, which details and principles can also be applied, for example, used as rules by the scheduling problem formulation unit. For example, in the patent application EP 3 859 470 A1, incorporated by reference, the objective problem of finding the best combination of parts forming a product is formulated, wherein the objective function is formulated as a quadratic unconstrained binary optimization based on the difference between the characteristics of the possible combinations of parts and the target characteristics. In the article "Job Shop Scheduling Solver based on Quantum Annealing" by D. Venturelli et al., Proceedings of the ICAPS-16 Workshop on Constraint Satisfaction Techniques for Planning and Scheduling (COPLAS), pages 25 to 34 (2016), a time-indexed quadratic unconstrained binary optimization is used to formulate the job shop scheduling problem to be solved on a quantum annealer. Furthermore, in the article “Traffic Flow Optimization Using a Quantum Annealer” by F. Neukart et al., ICT Frontiers, Vol. 4, p. 29 (2017), which is incorporated by reference, quadratic unconstrained binary optimization is used to formulate a traffic flow problem to be solved on a quantum annealer.

[0079] The quantum computer interface unit 803 is configured to provide an interface for interfacing with the quantum computer 810 to utilize the quantum computer to compute the scheduling problem. In addition, the quantum computer interface unit 803 may also be configured to receive a quantum computation result of the scheduling problem from the quantum computer 810, wherein the result indicates an optimized scheduling problem. In general, the quantum computing interface unit 803 may be directly configured to control the quantum computer 810 to perform the computation of at least a portion of the scheduling problem. For example, the quantum computer interface unit 803 may provide control operations to the quantum computer to directly control the manipulations performed by the quantum computer 810 on the quantum elements. However, alternatively, the scheduling problem preparation unit 806 may be provided as part of the quantum computer interface unit 803, or as a separate system that allows the preparation of the scheduling problem. In general, algorithms and methods for computing optimization problems on quantum computers are known.

[0080] For example, examples of algorithms, procedures, and methods for computing optimization problems on quantum computers, which can also be applied to scheduling problems, can be found in the following article. In the article “Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems” by A. Ajagekar et al., Computers and Chemical Engineering, Vol. 132, pp. 106630 (2020), incorporated by reference, a mixed integer linear programming model is used to formulate a job shop scheduling problem, and a hybrid solution strategy combining a decomposition-based algorithm with quantum computer solution techniques is used to globally optimize the job shop scheduling problem. The article "Digital Annealer for High-Speed ​​Solving of Combinatorial Optimization Problems and Its Applications" by S. Matsubara et al., 25th Asian and South Pacific Design Automation Conference (ASP-DAC), pp. 667-672 (2020), incorporated by reference, uses a digital annealer to solve combinatorial optimization problems based on the Ising formula of the energy function. And "Quantum algorithms for process parallel flexible job shop scheduling" by B. Denkena et al., CIRP Journal of Manufacturing Science and Technology, Vol. 33, pp. 100-114 (2021), incorporated by reference, describes a detailed algorithm for processing a job shop scheduling problem formulated as a quadratic unconstrained optimization problem using a quantum annealer. The scheduling problem or at least a part of the scheduling problem thus prepared is then calculated on the quantum computer 810, and the result is provided back to the device 800 via the quantum computer interface unit 803.

[0081] Then, the schedule determination unit 804 is adapted to determine the schedule of the probe list based on the received quantum computation result. For example, if the result of the quantum computation only involves the calculation of a part of the scheduling problem, the schedule determination unit 804 can be adapted to derive the optimized scheduling problem from the result of the quantum computation, thereby deriving the solution to the scheduling problem. However, if the result of the quantum computation directly indicates the solution to the scheduling problem, the schedule determination unit 804 can be adapted to convert the information provided by the solution to the scheduling problem into a scheduling format that can be directly utilized (e.g., by the control signal determination unit 805), which can be implemented as a laboratory management unit to provide corresponding control signals for controlling the laboratory equipment 820. For example, the schedule determination unit 804 can determine which laboratory equipment must perform which task on which probe in which time period based on the result of the quantum computation, in particular based on the result of the optimized scheduling problem. Based on the determined schedule, the control signal determination unit 805 is then configured to determine the control signal for controlling the laboratory equipment 810 according to the determined schedule. For example, the control signal determination unit 805 may be communicatively coupled to or may refer to an automated laboratory management system that allows direct control of the lab device 820, for example via wireless or wired signals. Then, the lab device 820 performs scheduling for all probes of the probe list based on the corresponding control signal.

[0082] Fig. 9A flow chart of a method for optimizing laboratory scheduling control is schematically and exemplarily shown. In particular, the method first receives a probe list including multiple probe identifiers and related tasks. In the next step, based on the received probe list, a scheduling problem is formulated to determine the scheduling of job execution (i.e., the operation of performing related tasks on the probes) so that the duration of the execution time objectively becomes the shortest. Preferably, the formulation of the scheduling problem takes into account all known constraints. Constraints can particularly refer to physical constraints of laboratory equipment, such as possible movements of laboratory equipment, possible number of probes that can be processed in parallel or sequentially by laboratory equipment, possible amount of fluid that can be processed by laboratory equipment, etc. In addition, constraints can refer to constraints provided by the task itself, for example, constraints provided by the operation that must be performed by the laboratory equipment to perform the task. For example, a certain task may require that operations be performed in a predetermined order and at a predetermined timing. Therefore, considering these constraints can not only obtain better solutions, but also avoid solutions that cannot be achieved by the laboratory equipment. In the next step, prepare the problem to be at least partially calculated on a quantum computer. For example, preparation can refer to transforming the scheduling problem into a statement based on binary variables, which is easier to calculate on a quantum computer. In the next step, the prepared scheduling problem is sent to the quantum computer for calculation. After computing the prepared scheduling problem on the quantum computer, the result of the quantum computation is received, and in a further step, a solution to the scheduling problem is determined by determining a scheduling control for controlling the laboratory device. In a final step, the scheduling control is provided to the laboratory device so as to perform scheduling on the probes in the probe list.

[0083] Some further details of a preferred embodiment of the invention will be described below.In a preferred application of the invention, the laboratory device refers to a high throughput device used in a high throughput facility. Fig.10 Schematic diagrams and exemplary embodiments of such a device are shown. Fig.10 The schematic diagram shows the possible equipment used in the high-throughput process, in particular a transport robot configured to transport the probe container from one processing station to another, a storage unit for storing the probe container, a capping machine, an oscillator, a pipette, a measurement storage unit, a measurement robot and a measurement instrument. Each of these units is associated with a processing station, at which the operation of the task can be performed on the probe. Furthermore, Fig.10 Information about the capacity of each unit is provided, for example, the transport robot has a capacity of 1 probe container, which means that the robot can transport one probe container at a time. Fig.11 The results of the schedule determined for the two probes of the exemplary system according to the above-mentioned scheduling planning method according to the present invention can be found in . The schedule specifically determines which device (ie, unit) of the high-throughput device performs which task for which probe.

[0084] Another preferred application of the present invention is to determine and control the scheduling of the formulation and / or synthesis robot. Such a robot can be configured to provide multiple functions required for the synthesis of molecular preparations, for example, the robot can be configured to add or remove substances from the probe container, oscillate or vibrate the probe, apply different measurement and analysis tools to the probe, etc. Then, the task performed on the probe can refer to synthesizing the substance based on a recipe or synthesis specification, which determines the operation performed on the base material probe. In general, such a robot can provide different restrictions for the synthesis scheduling, depending on the specific construction of the robot. For example, the frequency of each type of operation performed by the robot on the probe can be strictly limited, or these operations can be limited to certain possible operation sequences. In addition, the robot can also be limited to how many probes are operated at the same time. In addition, the construction of the robot can determine whether the robot can perform different operations in parallel or can only be performed sequentially. Therefore, for this preferred application, it is preferred that, for example as described above, the robot technology information indicating the corresponding constraints of a single robot is considered when formulating the scheduling problem.

[0085] In a further preferred application, the invention relates to determining and controlling the scheduling of long-term stability units. In this case, the task involves long-term analysis of probes that are stored for a long time (e.g., days, weeks or even years) between different test processes. For this scenario, an automated system allows managing the storage and retrieval of probes (e.g., probes provided in probe trays) based on a predetermined schedule. In this case, the above invention allows optimizing the scheduling of each probe in a long-term test process, in particular enabling efficient use of laboratory equipment used to perform the test process on the retrieved probes, in particular minimizing downtime. Fig.12 Examples of long-term stability facilities and possible associated equipment (ie, units) are shown, along with exemplary capacities for each station of the long-term stability units. Fig.12 The stations shown by way of example in are storage units, transport systems and measuring units, wherein only one probe tray can be transferred from the storage unit to the measuring unit at a time.

[0086] In a further preferred application, the present invention relates to determining and controlling the scheduling of a laboratory diagnostic process, such as a PCR process. In this case, the process can be performed using corresponding automated or semi-automated laboratory equipment dedicated to the corresponding task.

[0087] In general, one of the main goals of optimizing scheduling is to allow the operation of one or more tasks associated with one or more probes to be performed optimally while avoiding deadlock. In this way, the capabilities of an automated or semi-automated laboratory system can be fully utilized. However, in order to provide accurate results for each task, the timing of each operation associated with the task is preferably also considered, for example, the corresponding waiting time between different operations on the probe. For example, the device as described above is also referred to as an object scheduler hereinafter, and is preferably configured to control and organize tasks in a batch production or continuous production mode. In the case of batch production mode, a substantially fixed probe list will be provided to form the current batch, and the corresponding determined scheduling will be completed after optimization, and then a new batch of processing will be started using a new probe list. In the case of continuous production mode, the probe list can be changed, specifically, new probes can be added to the probe list, and the probes that have been processed can be deleted from the probe list. In this case, as described above, the scheduler is preferably adapted to update the probe list, and determine the scheduling of the updated probe list, so that the control of the automated process can continuously adapt to the probes added or deleted.

[0088] As input to the scheduler (e.g., via the probe list providing unit described above), a probe list including probe identifications and tasks associated with the respective probes is provided. The tasks indicate the operations to be performed on the probes by the respective lab-devices and the expected timing of the operations. The scheduler can then determine the schedule of the probe list, e.g., with reference to Figures 7 to 9 If the probes are processed in a continuous process, one or more new probes may be arbitrarily added during execution of the determined schedule, and the scheduler is preferably adapted to perform rescheduling based on the updated probe list.

[0089] In general, the scheduler (particularly the scheduling problem formulation unit as described above) is adapted to formulate a scheduling problem based on a provided probe list (particularly based on provided probe identifiers and associated tasks). In a preferred example, the scheduling problem can be formulated as an integer or mixed integer problem, whose objective function involves minimizing the time required to execute all tasks of the probe list.

[0090] Further, preferably, the scheduler (particularly the scheduling problem formulation unit) is adapted to consider constraints when formulating the scheduling problem. These constraints may refer to technical constraints (such as processing power) of laboratory equipment or task constraints (such as the necessary waiting time between different operations of the task). These constraints can be determined, for example, from a probe list (particularly the tasks provided from the probe list) and / or based on additional information (such as technical information of the laboratory equipment). In general, the scheduling problem can be formulated using categorical variables or continuous variables. However, it is preferred to use categorical variables to formulate the scheduling problem. If continuous variables are used, the scheduling problem is preferably transformed into categorical variables and then provided to the quantum computer. For example, the problem preparation unit as described above can be adapted to reformulate the scheduling problem accordingly, or determine a part of the scheduling problem that can be reformulated using categorical variables, that is, a sub-problem. Futjisu Annealer or D-Wave Annealer can be used as a quantum computer using categorical variables. Preferably, for example, a scheduler using a scheduling problem formulation unit or a problem preparation unit is adapted to use binary variables to formulate the scheduling problem.

[0091] Preferably, the above-mentioned constraints are also considered when formulating the scheduling problem. In a preferred example, the constraints are considered as part of the scheduling problem by providing the scheduling problem with additional conditions that must be met during optimization. In another preferred embodiment, the constraints are considered as part of the scheduling problem by adding the constraints as penalty terms to the objective function of the scheduling problem. In this case, the constraints are directly considered during the optimization process of the objective function. In addition, the advantage of this embodiment is that the constraints can be weighted individually. For example, different constraints can be weighted with different weights so that the importance of satisfying a certain constraint can also be reflected during the optimization process. An example of how to consider the constraints is provided below. In this example, at least one laboratory equipment resource allows two probes to be processed in parallel within the same time period. Therefore, this constraint can be provided as an additional condition in the scheduling problem, wherein the additional condition can be mathematically formulated as the sum of all probes processed by the corresponding resource within a certain time period, wherein for all solutions to the objective function, the sum must be less than or equal to two. In an alternative preferred example, the constraint can be formulated as a penalty term in the objective function. For example, mathematically, the constraint can be expressed as the sum of all probes processed by the corresponding resource within a certain time period minus two, and then added to the objective function of the scheduling problem. In addition, this term can also be used as a quadratic term to increase the penalty for solutions that deviate from the expected constraints. In addition, a weight can be provided to weight this penalty term during the objective function optimization process. Further constraints that are preferably considered when formulating the scheduling problem can be order constraints (such as which operation must be executed first), time constraints (such as waiting time between operations), task dependencies (such as tasks that can only be executed when other tasks have been completed), capacity constraints (such as the number of probes that can be processed in parallel within a certain time period), optional tasks (such as cleanup tasks or storage tasks, etc.).

[0092] Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0093] For the processes and methods disclosed herein, the operations performed in the processes and methods may be implemented in different orders. In addition, the operations outlined are provided only as examples, and some of the operations may be optional, may be combined into fewer steps and operations, may be supplemented with more operations, or may be expanded into more operations without departing from the essence of the disclosed embodiments.

[0094] In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality.

[0095] A single unit or device may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0096] The processes performed by one or several units or devices, such as providing a probe list, formulating a scheduling problem, interfacing with a quantum computer, determining a schedule, etc., may be performed by any other number of units or devices. These processes may be implemented as program code means of a computer program and / or dedicated hardware.

[0097] The computer program product may be stored / distributed on a suitable medium, such as an optical storage medium or a solid-state medium provided together with or as a part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

[0098] Any unit described herein may be a processing unit as part of a classical computing system. The processing unit may include a general-purpose processor, and may also include a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or any other dedicated circuit. Any memory may be a physical system memory, which may be volatile, non-volatile, or some combination of the two. The term "memory" may include any computer-readable storage medium, such as a non-volatile mass storage device. If the computing system is distributed, the processing and / or storage capabilities may also be distributed. The computing system may include multiple structures as "executable components". The term "executable component" is a structure that is fully understood in the computing field as a structure that can be software, hardware, or a combination thereof. For example, when implemented in software, a person of ordinary skill in the art will understand that the structure of the executable component may include software objects, routines, methods, etc. that can be executed on the computing system. This may include executable components in the computing system stack or on a computer-readable storage medium. The structure of the executable component may exist on a computer-readable medium so that when interpreted by one or more processors of the computing system (e.g., by a processor thread), the computing system performs a function. Such a structure may be directly computer-readable by a processor, for example, as in the case where the executable component is binary, or it may be constructed to be interpretable and / or compiled, for example, whether in a single level or multiple levels, thereby generating such binary that can be directly interpreted by a processor. In other cases, the structure may be hard-coded or hard-wired logic gates that are implemented exclusively or almost exclusively in hardware, such as in a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or any other dedicated circuit. Therefore, the term "executable component" is a term for a structure that is well known to those of ordinary skill in the field of computing, whether implemented in software, hardware, or a combination. Any embodiment herein is described with reference to actions performed by one or more processing units of a computing system. If such actions are implemented in software, one or more processors direct the operation of the computing system in response to having executed the computer-executable instructions that constitute the executable component. The computing system may also include a communication channel that allows the computing system to communicate with other computing systems, such as through a network. "Network" is defined as one or more data links that enable electronic data to be transmitted between computing systems and / or modules and / or other electronic devices. When information is transferred or provided to a computing system over a network or another communications connection (e.g., hardwired, wireless, or a combination of hardwired or wireless), the computing system properly views the connection as a transmission medium. Transmission media may include networks and / or data links that may be used to carry desired program code means in the form of computer-executable instructions or data structures and may be accessed by general purpose computing systems or special purpose computing systems or a combination thereof.Although not all computing systems require a user interface, in some embodiments, a computing system includes a user interface system for interacting with a user. The user interface serves as an input or output mechanism for the user, for example, via a display.

[0099] Those skilled in the art will appreciate that at least part of the present invention can be practiced in a network computing environment with various types of computing system configurations, including personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile phones, PDAs, pagers, routers, switches, data centers, wearable devices (such as glasses), etc. The present invention can also be practiced in a distributed system environment, where, for example, a local computing system and a remote computing system linked by a network by a hardwired data link, a wireless data link, or a combination of hardwired and wireless data links perform tasks together. In a distributed system environment, program modules can be located in both local and remote memory storage devices.

[0100] Those skilled in the art will also appreciate that at least part of the present invention can be practiced in a cloud computing environment. The cloud computing environment can be distributed, but this is not necessary. When the cloud computing environment is distributed, it can be distributed in multiple countries within an organization and / or have components across multiple organizations. In this specification and the appended claims, "cloud computing" is defined as a model for implementing on-demand network access to a shared pool of configurable computing resources (such as networks, servers, storage devices, applications and services). The definition of "cloud computing" is not limited to any of the other numerous advantages that can be obtained from this model when deployed. The computing system of the accompanying drawings includes various components or functional blocks that can implement various embodiments disclosed herein as explained. Various components or functional blocks can be implemented on a local computing system, or can be implemented on a distributed computing system that includes elements residing in the cloud or implementing various aspects of cloud computing. Various components or functional blocks can be implemented as software, hardware, or a combination of software and hardware. The computing system shown in the figure can include more or fewer components than those shown in the figure, and some of these components can be combined as needed according to the situation.

[0101] Any reference signs in the claims should not be construed as limiting the scope.

[0102] The present invention relates to an apparatus for optimizing laboratory scheduling control. A providing unit provides a probe list including probe data. The probe data includes a probe identification and a task. The task indicates an operation to be performed by a laboratory device on the probe. A formulating unit formulates a scheduling problem based on the probe list, so that optimization of the scheduling problem leads to optimization of task scheduling. An interface unit interfaces with a quantum computer to optimize the scheduling problem using quantum computing and receives a quantum computing result indicating an optimized scheduling problem. A determining unit determines a schedule of the probe list based on the received result. A control signal determining unit determines a control signal for controlling the laboratory device based on the determined schedule.

Claims

1. A device for optimizing laboratory scheduling control, wherein: The device (800) comprises: A probe list providing unit (801) is used to provide a probe list including probe data of a plurality of probes, wherein the probe data of the probes includes a probe identification and a task associated with the probe identification, wherein the task indicates one or more operations to be performed on the probe by a laboratory device at a predetermined timing, a scheduling problem formulation unit (802) for formulating a scheduling problem based on the probe list, wherein the scheduling problem is formulated so that optimization of the scheduling problem leads to optimization of the scheduling of tasks associated with the plurality of probes, a quantum computer interface unit (803) for interfacing with a quantum computer (810) to optimize the scheduling problem using quantum computing and receiving a quantum computing result indicating an optimized scheduling problem from the quantum computer (810), a schedule determination unit (804), which is used to determine the schedule of the probe list based on the received quantum computing result, and A control signal determination unit (805) is used to determine a control signal for controlling the laboratory device (820) based on the determined schedule.

2. The device according to claim 1, wherein: The quantum computer interface unit (803) further comprises a scheduling problem preparation unit (806) for preparing at least a part of the scheduling problem so that optimization of the scheduling problem can be performed using quantum computing.

3. The device according to claim 2, wherein: The preparation of the scheduling problem comprises determining control operations for controlling the quantum computer (810) to perform the quantum computation on at least a portion of the scheduling problem, and wherein the scheduling problem preparation unit (806) is further adapted to send the control operations to the quantum computer (810) for performing the quantum computation on at least a portion of the scheduling problem.

4. The device according to any one of claims 2 and 3, wherein: The scheduling problem preparation unit (806) is adapted to prepare a scheduling problem solvable on a quantum computer (810), which may be a quantum annealer or a gate-based quantum computer.

5. A device according to any one of the preceding claims, wherein: The scheduling problem is formulated such that optimization of the scheduling problem refers to optimization of the time required to perform the operations indicated by the tasks.

6. A device according to any one of the preceding claims, wherein: The apparatus further comprises a constraint determination unit for determining constraints of the scheduling based on the tasks and / or based on lab-device information indicating technical data of the lab-device (820), wherein the scheduling problem is further formulated based on the determined constraints.

7. The device according to claim 6, wherein: The lab-device information further indicates whether one or more operations on one or more probes may be performed simultaneously or sequentially, wherein the constraints are further determined based on the information.

8. A device according to any one of the preceding claims, wherein: The probe list providing unit (801) is further adapted to provide an updated probe list based on probe data received after determining the schedule of the original probe list, wherein the scheduling problem formulating unit (802) is then adapted to reformulate the scheduling problem based on the updated probe list to determine an updated schedule.

9. A quantum computer system comprising: An interface unit for receiving a scheduling problem from an apparatus according to any one of claims 1 to 8, the scheduling problem being formulated such that optimization of the scheduling problem results in optimization of the scheduling of tasks associated with a plurality of laboratory probes, wherein the tasks are to be performed by laboratory devices (820) on the plurality of laboratory probes, and a quantum computer (810) adapted to perform a quantum computation on the received scheduling problem, Wherein, the interface unit is further adapted to provide the quantum computation result of the scheduling problem to the device for further processing.

10. A laboratory system comprising: a lab-device (820) adapted to automatically perform one or more operations indicated by the task on one or more lab-probes based on the control signal, and The apparatus according to any one of claims 1 to 8, wherein the apparatus provides the determined control signal to the laboratory device (820) to execute the determined schedule.

11. A scheduling determination system, comprising: The device according to any one of claims 1 to 8, and The quantum computer system according to claim 9, wherein the device is connected to the quantum computer system via the interface unit.

12. A method for optimizing laboratory scheduling control, wherein: The method includes: providing a probe list including probe data of a plurality of probes, wherein the probe data of the probes includes a probe identifier and a task associated with the probe identifier, wherein the task indicates one or more operations to be performed by the lab device (820) on the probe at a predetermined timing, formulating a scheduling problem based on the probe list, wherein the scheduling problem is formulated such that optimization of the scheduling problem results in optimization of the scheduling of tasks associated with the plurality of probes, sending the scheduling problem to the quantum computer (810) via an interface for optimization using quantum computing, and receiving an optimization result of the scheduling problem from the quantum computer via the interface, determining a schedule for the probe list based on the received quantum computation results, and A control signal for controlling the lab-device (820) is determined based on the determined schedule.

13. A computer program product for optimizing laboratory scheduling control, wherein: The computer program product comprises program code means for causing an apparatus according to any one of claims 1 to 8 to perform a method according to claim 12 .

14. A scheduling problem preparation device, comprising: an interface for receiving a scheduling problem, wherein the scheduling problem is formulated such that optimization of the scheduling problem results in optimization of a schedule of tasks associated with the plurality of probes, a processor configured to prepare at least a portion of a scheduling problem such that optimization of the scheduling problem can be performed using quantum computing, An interface for providing at least a portion of the prepared scheduling problem to a quantum computer (810) for computing at least a portion of the prepared scheduling problem.

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