Complex issues may be solved more accurately and efficiently with quantum computing and machine learning.
Table of Contents
1. Best Practices to Implement Quantum Machine Learning
1.1. Operations and Manufacturing Industry
1. 2. Chemical and Biological Industry
2. Success Story- Quantum Machine Learning in Automotive Industry
3. Future Implementation of Quantum Machine Learning
Introduction
Quantum computing (QC) and machine learning (ML) are the two most hot technologies that are being adopted in the IT field. QC has the power of quantum physics to perform computation by providing an unprecedented level of scalability and accuracy; on the other hand, ML has deep learning capabilities and intelligent automation as leverage to scale out large data sets. Thus, the combination of these two applications, i.e., QC and ML, can create new opportunities that could solve complex problems with greater accuracy and efficiency than the traditional way of computing could.
In this article, we will dive into how to implement quantum machine learning (QML) and what the best practices are for AI technologists.
1. Best Practices to Implement Quantum Machine Learning
Here are a few best practices in various industries where Quantum machine learning can be implemented:
1.1. Operations and Manufacturing Industry
In the operations and manufacturing industries, a quantum computing process can have thousands of interdependent steps to optimize the problem related to manufacturing products. With so many possibilities, it takes a lot of computing to simulate the manufacturing process and requires minimizing the range of possibilities to adjust within computational limits. The parallelism of quantum computers would help unlock an unprecedented level of optimization in manufacturing.
1. 2. Chemical and Biological Industry
The chemical and biological industries must deal with complex products like drugs or resources that need quantum machine learning to discover and design drugs based on QML models. These models were the Q-RBFNN (Quantum Radial Basis Function Neural Network), hybrid QNN circuit model, and QFT-based hybrid QNN model (QFT-Quantum Fourier transform), which helps in predicting the compounds and chemical molecules that are needed to make new drugs. Furthermore, teams of scientists work together to make drugs against these bacteria or viruses.
1.3. Financial Industry
The financial industry has been using quantum machine learning technology to deal with uncertainty and is constrained in optimizing financial institutes for greater compliance, employing behavioral data, enhancing customer engagement, and a faster reaction to market volatility. Financial professionals who have used quantum computing in ML have witnessed promising results with capital markets, corporate finance, portfolio management, and encryption-related activities even during economic and financial crises. This implies that the arrival of quantum computing is potentially a game-changer.
2. Success Story- Quantum Machine Learning in Automotive Industry
The BMW Group is among the first automotive firms to take an interest in quantum computing. In 2021, BMW Group issued the Quantum Computing Challenge in association with AWS to crowdsource innovations around specific use cases, believing that quantum computing could benefit businesses by solving complex computing problems.
The objective was to determine if the image-trained machine learning system presently in use to detect fractures in produced elements might be improved. To properly train the model, high-resolution photographs of the manufactured components were required. In addition, the organization required a lot of them because those kinds of defects are quite uncommon. There is potential for improvement because obtaining and storing these photos requires time and memory.
BMW Group gave a statement that, “In light of the required human expertise to hand-tune algorithms, machine learning (ML) techniques promise a more general and scalable approach to quality control. Quantum computing may one day break through classical computational bottlenecks, providing faster and more efficient training with higher accuracy.”
After implementing the QML solution, the BMW Group has witnessed 97% accuracy by enhancing the classical algorithm by orchestrating quantum processing unit (QPU) calculations at a crucial part of the analysis. The Quantum model was trained on 40% of the whole dataset. In contrast, the Benchmark model was trained on 70%, which implies that the classical approach is more efficient and manages to provide accurate predictions without unnecessary inputs.
3. Future Implementation of Quantum Machine Learning
Quantum machine learning (QML) algorithms have the potential to solve maximum problems in a much faster time than the classical algorithm. According to IBM researcher Kristan Temme, there is strong evidence that QML is emerging at a significant speed in all industries. He quotes, “At this point, I’d say it’s a bit difficult to exactly pinpoint a given application that would be of value.”
There are also proven examples where QML has been an advantageous technology over classical computing. One such use case is in a weather forecasting application, where pattern recognition can be used in QML to create a great impact on speeding up the optimization processes by recognizing a huge number of satellite images to get the exact weather update.
To Sum up
Integration of quantum computing and machine learning made the lives of scientists, AI technologists, and IT professionals very easy, as it helped solve complex tasks that needed high-dimension feature space, multiclass classification, faster vector generation, and ML optimization. Researchers say that quantum machine learning is a growing field and will become popular as more people start using it. Thus, the areas of application of QML are still under process.
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