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.
Best Practices to Implement Quantum Machine Learning
Here are a few best practices in various industries where Quantum machine learning can be implemented:
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.
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.
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.
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