Method for determining the best use of installation space

Abstract

The invention relates to a method for determining the best use of installation space, wherein the variants for a construction space usage are implemented in variables and based on the available construction space the variables are summarized as literals in several clauses, where an NP problem is formed with at least one clause with at least three literals, where at least one solution to the NP problem is sought using qubits and a quantum algorithm. The method proposed here for determining the best use of installation space enables a reduction of complexity up to quadratically and thus a significant reduction in computing time compared to classical methods.

Description

[0001] The invention relates to a method for determining the best use of installation space, as well as a computing device for carrying out such a method.

[0002] Lack of installation space is always a problem in vehicle development. Components are added or removed, especially during the development process. Currently, true optimization of installation space only occurs locally, not globally across the entire vehicle.

[0003] Based on this, the present invention is based on the object of at least partially overcoming the disadvantages known from the prior art. The features of the invention arise from the independent claims, for which advantageous embodiments are presented in the dependent claims. The features of the claims can be combined in any technically reasonable manner, whereby the explanations from the following description and features from the figures, which comprise additional embodiments of the invention, can also be consulted for this purpose.

[0004] The invention relates to a method for determining the best use of installation space, wherein the variants for a construction space usage are implemented in variables and based on the available construction space the variables are summarized as literals in several clauses, where an NP problem is formed with at least one clause with at least three literals, where at least one solution to the NP problem is sought using qubits and a quantum algorithm.

[0005] Unless explicitly stated otherwise, ordinal numbers used in the preceding and following descriptions serve only to clearly distinguish them and do not reflect the order or ranking of the designated components. An ordinal number greater than one does not necessarily imply that another such component must be present.

[0006] A quantum algorithm enables a reduction in complexity of up to a quadratic magnitude, thus significantly reducing computation time compared to classical solutions to such a difficult but solvable problem (NP problem). It has been determined that determining the best use of installation space is such an NP problem. Furthermore, it has been determined that a quantum algorithm can be used to solve such a problem of cost-effective and / or feasible variance in installation space utilization. Particularly in the context of the increasing electrification of motor vehicles—not only of the drive system, but also of conventionally used actuators and other functional units—the arrangement of components in the vehicle is becoming more arbitrary, gaining additional complexity but also degrees of freedom for better vehicle configuration.One example of this is the arrangement of a pump for a cooling circuit, which is conventionally driven by a combustion engine via a belt drive and therefore must be located close to the combustion engine. The pump, with its own (electric) drive motor, can be positioned anywhere, or multiple pumps can be arranged at more strategically advantageous locations within a vehicle. The same applies to a high-voltage battery, which is currently traditionally placed as a coherent component in the underbody of the vehicle, but is composed of individual modules, and the modules are also composed of individual cells. Thus, there is enormous (previously unused) freedom to utilize available installation space.

[0007] The experience of development engineers is often a hindrance in this regard, as implementation rules are not always questioned and / or are simply unknown. For example, the knowledge of engineers or manufacturers of individual components can be used to define the minimum boundary conditions for an arrangement, or to determine their technical consequences. This knowledge (which changes over time) is therefore available and / or is supplemented, corrected, and cleared of outdated regulations and experience (preferably with computer support, for example, using artificial intelligence such as neural networks). Nevertheless, a complex problem situation arises, which is currently being solved using simplified approaches, without an overview of the entire vehicle.However, with a quantum algorithm using qubits, such a (hard NP) problem can be solved overall, and at the same time in a sufficiently cost-efficient and time-efficient manner.

[0008] It is further proposed in an advantageous embodiment of the method that the quantum algorithm used is the Grover algorithm.

[0009] The Grover algorithm was published in the paper "A fast quantum mechanical algorithm for database search" by Lov K. Grover (Bell Labs, Murray Hill, NJ) in May 1996 (see https: / / web.archive.org / web / 20240117041155 / arxiv.org / abs / quant-ph / 9605043). It demonstrates that the complexity of a hard NP problem can be quadratically reduced using a quantum algorithm, thus significantly reducing computation time compared to classical solutions.

[0010] It is further proposed in an advantageous embodiment of the method that the NP problem is a knapsack problem and all of a possible plurality of solutions are determined, where the solutions determine the arrangement and their respective composition of the components.

[0011] Using a knapsack algorithm, the package can be continually improved with regard to installation space, even repeatedly during the development process. This allows for quick and optimal responses to concept decisions and similar issues. For this purpose, the vehicle or its installation space is divided into different installation space sections (e.g., front left, front right, rear left, rear right). For each installation space section, all possible components are compiled with their respective target values ​​(weight, volume, required infrastructure, and / or other). Certain components can be arranged in one of the several installation space sections if their position in the vehicle is not fixed to a specific area. The knapsack algorithm is then executed with a Grover search for each installation space section. The result is the best component list for each installation space section.In one embodiment, only duplicate components need to be removed. Alternatively, in a subsequent loop, the best use of the installation space is performed, taking into account all installation space sections with duplicate components.

[0012] A knapsack algorithm is described in the paper "Quantum Algorithm for Knapsack Problem by Usual Grover Iteration with Z-Axis-Rotation (180 degrees) on QCEngine" by Toru Fujimura (University of Tsukuba) published in 2023 in the Global Journal of Pure and Applied Mathematics, ISSN 0973-1768 Volume 19, Number 1 (2023), pp. 23-29 (see http: / / www.ripublication.com / gjpam.htm) or "Quantum-based algorithm and circuit design for bounded Knapsack optimization problem" by Wenjun Hou et al. published in August 2020 in Quantum Information and Computation. ISSN 1533-7146 Volume 20, Number 9&10, pp. 766-786 (see https: / / web.archive.org / web / 20221010195449 / https: / / www.rintonpress.com / journals / d oi / QIC20.9-10-4.html). It shows that the complexity of a difficult NP problem can be quadratically reduced using a quantum algorithm, thus enabling a significant reduction in computing time compared to classical solutions.

[0013] It is further proposed in an advantageous embodiment of the method that an auxiliary algorithm is executed by at least one classical bit, which, in order to fill the required amount of QBits for the current task with a plurality of possible combinations, carries out a randomization of such a number of possible combinations that exceeds the number of QBits used, where those randomized results determined by the auxiliary algorithm are processed together with the remaining combination possibilities using the number of qubits used.

[0014] The current problem is that quantum computers are equipped with very few qubits. For example, quantum computers with just over 400 qubits are currently in use, but there are also those with as many as 5,000 qubits. This is still far too few for complex problems. Another problem is the high error rate, which becomes increasingly noticeable with the increasing number of qubits. For example, a (pure) quantum computer can currently solve about one million variables with 100,000 boundary conditions, which corresponds to about 20 bits in classical design. Even if this is a temporary problem, it proposes an excellent approach for future applications, combining the strengths of both approaches (i.e., calculations using classical bits and those using qubits).

[0015] An effective approach for this is provided by a hybrid use of classical bits and qubits, as published in the paper “Quantum cooperative search algorithm for 3-SAT” by Sheng-Tzong Cheng et al., October 2006 in JCSS (see https: / / core.ac.uk / download / pdf / 82172598.pdf).

[0016] According to a further aspect, a computing device for carrying out the method according to an embodiment as described above is proposed, wherein the computing device comprises at least the following components: - a memory interface; - a first processor comprising a plurality of qubits; and - at least one second processor comprising classical bits, wherein the first processor is configured to execute the quantum algorithm.

[0017] The computing device is a so-called quantum computer or quantum processor (first processor) or system of quantum processors, whereby classical (binary) processors are still used here.

[0018] The computing device itself comprises a (volatile and / or non-volatile) data memory or simply a memory interface to such an (external) data memory. In one embodiment, both an internal data memory and an external data memory are used, preferably via a data connection to the Internet (for example, a so-called cloud). At least one of the conventional processors is configured to prepare the processed data for or after processing using the qubits.

[0019] It is further proposed in an advantageous embodiment of the computing device that the second processor executes an auxiliary algorithm for filling the required amount of QBits for a current task with a plurality of possible combinations, wherein by means of the auxiliary algorithm a randomization of such a number of possible combinations is carried out which exceeds the number of QBits of the first processor, where the randomized results obtained using the auxiliary algorithm are combined with the remaining Combination possibilities are processed using the QBits of the first processor. Preferably, another processor with classic bits is set up to process the results.

[0020] Here, it is proposed that at least one classical processor be configured to execute an auxiliary algorithm, as previously described with reference to the method for determining optimal space utilization. Even if quantum computers with a sufficient number of qubits will be available in the near future, it may be useful to compute certain subproblems of a task using classical bits, for example, in a problem that can be represented (at least with sufficient accuracy) purely polynomially.

[0021] The method proposed here for determining the best use of installation space enables a reduction of complexity up to quadratically and thus a significant reduction in computing time compared to classical methods. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited non-patent literature

[0000] Quantum Algorithm for Knapsack Problem by Usual Grover Iteration with Z-Axis-Rotation (180 degrees) on QCEngine” by Toru Fujimura (University of Tsukuba) was published in 2023 in the Global Journal of Pure and Applied Mathematics, ISSN 0973-1768, Volume 19, Number 1 (2023), pp. 23-29 (see http: / / www.ripublication.com / gjpam.htm), or “Quantum-based algorithm and circuit design for bounded Knapsack optimization problem” by Wenjun Hou et al. was published in August 2020 in Quantum Information and Computation. ISSN 1533-7146 Volume 20, Numbers 9&10, pp. 766-786 (see https: / / web.archive.org / web / 20221010195449 / https: / / www.rintonpress.com / journals / d oi / QIC20.9-10-4.html

[0012]

Claims

[1] Method for determining the best use of installation space, whereby the variants for a construction space usage are implemented in variables and based on the available construction space the variables are summarized as literals in several clauses, where an NP problem is formed with at least one clause with at least three literals, where at least one solution to the NP problem is sought using qubits and a quantum algorithm. [2] The method of claim 1, wherein the quantum algorithm used is the Grover algorithm. [3] A method according to claim 1 or claim 2, wherein the NP problem is a knapsack problem and all of a possible plurality of solutions are determined, the solutions determining the arrangement and their respective composition of the components. [4] Method according to one of the preceding claims, wherein an auxiliary algorithm is executed by at least one classical bit, which, in order to fill the required amount of QBits for the current task with a plurality of possible combinations, randomizes a number of possible combinations which exceeds the number of QBits used, wherein those randomized results determined on the basis of the auxiliary algorithm are processed together with the remaining possible combinations using the number of QBits used. [5] Computing device for carrying out the method according to one of the preceding claims, wherein the computing device comprises at least the following components: - a memory interface; - a first processor comprising a plurality of qubits; and - at least one second processor comprising classical bits, wherein the first processor is configured to execute the quantum algorithm. [6] Computing device according to claim 5, wherein the second processor for executing an auxiliary algorithm for filling the required amount of QBits for a current task with a plurality of possible combinations, wherein the auxiliary algorithm randomizes such a number of possible combinations that exceeds the number of QBits of the first processor, whereby those randomized results determined on the basis of the auxiliary algorithm are processed together with the remaining combination possibilities using the qubits of the first processor. Preferably, another processor with classic bits is set up to process the results.