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The scene potential of Quantum algorithm Application: from text Classification, path Planning to Automobile parts selection

In the industrial application of quantum computing, the quantum computing cloud platform will undoubtedly play a key role, not only providing scalable quantum computing resources, enabling enterprises and research institutions to use quantum computing capabilities flexibly without purchasing expensive hardware. at the same time, it can also provide users with an environment for developing, testing and optimizing quantum algorithms, enabling them to quickly verify concepts, build models and solve complex problems. So as to accelerate the ability of quantum computing to solve practical problems.

In the previous article, we introduced SpinQit, a quantum software development framework independently designed and developed by Quantum Technology. Through SpinQit, developers can efficiently create, debug and run quantum applications. Generally speaking, in addition to quantum SDK, developers also need to develop corresponding quantum algorithms for different problems and scenarios in order to solve practical problems.

In order to effectively reduce the application threshold of quantum algorithms, quantum spin technology has launched three scenarios of text classification, automobile parts selection and path planning on the Taurus quantum computing cloud platform, corresponding to different problem models and scene categories, respectively, showing the potential uses and advantages of quantum computing in related scenarios. It is convenient for more enterprises and researchers to associate the relevant algorithm cases with the actual work scenarios.

On the platform, users can build the details of the problem through the interactive interface, and then run the quantum algorithm to solve it, and then see the process of solving the specific problem.

Assist text classification to speed up tasks exponentially using SWAP-test algorithm

Natural language processing has always been a thorny problem in the computer field. Because of the complexity and polysemy of natural language, it is often difficult for computers to accurately understand human language. Text classification is one of the most common and important task types in the field of natural language processing, which is widely used, such as intelligent question and answer, emotional analysis, content recommendation and so on. For example, in order to make accurate personalized recommendation to users, content service providers need to classify and analyze the reading content, combined with user portraits, and then complete personalized content recommendation.

In the past, the commonly used method is to transform the text data into high-dimensional feature vectors, and then use machine learning algorithms to process them. In view of this kind of scenario, the engineers of spin technology calculate and analyze the content tags with SWAP-test algorithm by deeply integrating the quantum algorithm, which is also an innovative method with great potential in processing text data.

Compared with the classical Euclidean distance algorithm (complexity O (N)), SWAP-test quantum algorithm makes use of the superposition of quantum states to get the distance (similarity) between two high-dimensional vectors only once, and its complexity is O (logN), so it can accelerate the calculation of text classification at the exponential level. This exponential acceleration is particularly useful for processing large text data sets, not only saving computing resources, but also improving processing speed.

Users can select training sample sets and test samples through the interface

In practical applications, SWAP-test quantum algorithm can play a role in a variety of natural language processing tasks. For example, in the task of news classification, we can quickly judge the similarity between a news article and the existing classification, so as to quickly classify it into the corresponding category. In emotion analysis, quantum algorithm can more accurately capture the emotional tendency in the text and help enterprises understand the real feedback of users on products and services.

In addition, the recommendation systems of e-commerce websites and streaming media platforms can also benefit from such quantum algorithms to more accurately recommend the content and goods that users are interested in based on the similarity of products or users.

Using SWAP-test algorithm to quickly judge the category of news articles

Optimization of production process by Grover search algorithm for Automotive parts selection

In the development and production process of today's mainstream automobile enterprises, the upgrading of automobile functions has greatly increased the number of parts needed. there are not only a variety of supply options for each part with the same function, but also among the parts options. there will also be a coupling or mutually exclusive relationship, which can not be matched at will. The different function combination schemes of different models further increase the complexity of the coordination between the parts, which makes the cost of the selection of automobile parts increase exponentially. For manufacturers and suppliers, how to choose the best parts configuration to meet the needs of different models is a complex optimization problem.

Take car company B as an example, when manufacturing S-series models, there are a variety of optional combinations of vehicle functions and parts. for example, the "four-wheel drive system" can be equipped with "double asynchronous motor" or "double DC motor". Among them, "double DC motor" must be used with a small "air spring". There are 692 kinds of similar parts available for functional configuration, and there are as many as 952 constraints for configuration options. With the increase of variables, the consumption of resources will also increase exponentially, which also urges automobile companies to select parts and find more effective ways to ensure production efficiency.

In fact, the task of parts selection is a common satisfiability problem (SAT problem): there is a set of variables and the constraints made up of these variables. To solve this kind of problem, that is, to judge whether the values of a group of variables meet these constraints. By using the Grover search algorithm, the engineers of spin technology have brought new ideas for the analysis of automobile design and assembly, and transformed the various limiting relations such as coupling and repulsion between automobile parts into Oracle (quantum black box) in quantum circuits.

Compared with the classical algorithm, the Grover search algorithm can make use of the superposition of quantum states and check whether the configuration combinations of different parts can meet all the constraints, and then quickly find the solution that meets the constraints among all possible values, which can not only achieve square acceleration, but also consume less resources, so that car companies and suppliers can efficiently complete parts selection and configuration. And then optimize the efficiency of automobile production and manufacturing.

Users can build different vehicle combinations through the interface.

On the Taurus quantum computing cloud platform, an application solution for the selection of automobile parts is provided, through the interface, in the functions of "driving range", "four-wheel drive system", "comfortable experience", "self-driving" and so on, build the combination of different vehicles, and then run the quantum algorithm, you can quickly get the best parts configuration scheme that meets the constraints.

Using the square level of Grover search algorithm to accelerate the efficiency of automobile parts selection

In addition to the selection of automobile parts, the quantum algorithm represented by Grover search algorithm has square acceleration effect on many search problems of disordered data. For example, in the model checking of hardware and software design, by expressing the design as a logical formula and using the SAT solver to check whether there are violations, potential design defects can be found in the early stage; in bioinformatics, problems such as gene network inference and protein structure prediction can be transformed into SAT problems.

Path Optimization of Patrol Inspection in Power Workshop using VQE algorithm to solve complex path Planning

In the industrial manufacturing industry, the equipment workshop needs regular inspection to ensure equipment operation and safety in production. In this kind of path planning, it is generally necessary to find a path with the shortest distance and the highest efficiency in order to save time and resources.

For example, in the chemical workshop of A plant, there are several key points that need daily inspection, and equipment anomalies can be found in time through the equipment inspection task. In the formulation of inspection tasks, the distance between inspection positions, the order of arriving at inspection positions and other factors will affect the efficiency of inspection tasks.

From the point of view of computational complexity, with the increase of the number of nodes on the path, the traditional enumeration method will lead to combinatorial explosion, so it is difficult to find the optimal solution in a reasonable time. For example, using the peak speed of a supercomputer to calculate the route planning of 30 fixed points at a time, it will take about 150 million years to get the optimal solution.

In fact, the task problem of this kind of route planning can be summarized as the "traveling Salesman problem (TSP problem)". For classical computers, it consumes a lot of resources. In order to solve this kind of problem, the engineers of measuring and spinning technology adopt VQE algorithm, which provides a new solution for workshop inspection route optimization.

By turning the problem into a quantum system and making the system evolve into different paths, the ground state is the result of the shortest path. The problem of consuming exponential resources on classical computers can be solved by using variational quantum algorithms (VQE) using polynomial resources on quantum computers.

Users can view the quantum circuit diagram through the interface.

Variational quantum algorithm (VQE) is an important algorithm in quantum computing, which is used to solve some combinatorial optimization problems. Especially in the case of using less resources, we can quickly solve the shortest path in the TSP problem.

On the Taurus quantum computing cloud platform, an application solution to the workshop inspection path optimization problem is provided: among the 20 optional points, the user selects 3-5 key points that must be inspected, and then runs the quantum algorithm to solve the shortest path. the execution process is a process of iterative optimization, and the user can see the change and optimization of the candidate path from the interface, and finally get the optimal path. It effectively reduces the resource consumption and time cost.

Using VQE algorithm to solve the problem of complex path Planning

In fact, all scenarios that can be modeled as the traveling Salesman problem can be solved by using the variational quantum algorithm VQE. For example, in the microelectronics manufacturing industry, variational quantum algorithm can be used to find the optimal processing path in PCB board drilling, integrated circuit layout design and micron / nanometer processing; in logistics and supply chain management, such as distribution route planning, freight route design, etc., variational quantum algorithm VQE can also be used to find the minimum transportation cost.

Explore broader potential application scenarios for the future

At present, the application case shown by Taurus quantum computing cloud platform is only the tip of the iceberg in the potential application field of quantum algorithm. In addition to the above three typical application scenarios, quantum computing has a broader application prospect in the future.

With the continuous progress and maturity of quantum computing technology, more fields will benefit from the acceleration effect of quantum algorithms. For example, in the field of finance, it will be possible to use quantum algorithms for portfolio optimization and risk management; in the field of health, quantum algorithms can be applied to molecular simulation, drug research and development; in the field of artificial intelligence, the application of quantum neural networks will accelerate the development of machine learning and deep learning.

In the future, through continuous innovation and application, the quantum computing cloud platform will continue to provide strong support for scientific research innovation, industrial upgrading and solving practical problems.

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