QML stands for Quantum Machine Learning and refers to the execution of machine learning tasks on innovative hardware - a quantum computer. But what does this mean in detail? What benefits can this bring to your company, and how can you take the first step into this exciting world?
Sleek v2.0 public release is here
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What has changed in our latest release?
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All new features available for all public channel users
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Coding collaboration with over 200 users at once
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Categorizing Quantum Computing (QC), Machine Learning (ML), and Quantum Machine Learning (QML)
The applications of machine learning are diverse and include identifying structures, recognizing patterns, and predicting values. This is fundamentally based on statistics and linear algebra. Interestingly, quantum computing also shares these mathematical foundations. The idea of quantum computing, processing quantum information, has been around for decades. Now, we are finally seeing the first practically usable quantum computers. Quantum Machine Learning (QML) is essentially the execution of machine learning on a quantum computer.
Similar to many other challenges, it is crucial to tackle the right problem with the right tool. A current focus of research is to determine which types of problems are difficult for classical computers to solve but can be more easily handled by quantum computers. This involves complexity classes, discussing how the effort to solve increases as the problem size grows. It is considered problematic if the effort to solve doubles for each minor increase in the task. It also involves the relationship between input, search space, and output. Current considerations suggest that problems with small input (e.g., a set of objects), a large search space (all possible combinations), and a small output (a subset of objects) are well-suited for quantum computing.
Finally, literature indicates that a particular advantage can be gained with quantum data, such as in machine learning with data based on (quantum) physical experiments. This may involve materials research or predicting radiation.
Examples of Practical Applications
The potential of quantum computing is carefully examined in almost every industry to determine how this emerging technology can be beneficial. The following two examples illustrate its versatility.
In the aerospace industry, the focus is on developing materials that are extremely lightweight and exceptionally resilient. Simulations at the atomic level are required for this, a task that is ideal for quantum computing. With the help of quantum computers, researchers can simulate quantum physical phenomena that were previously challenging to explore. This leads to the development of materials that not only reduce the weight of airplanes and spacecraft but also increase efficiency and safety in the industry.
In the energy sector, the focus is on the intelligent use of renewable energies, such as wind and solar power. The goal is to optimize the energy grid, a huge and highly complex system with conditions derived from physics. Thanks to quantum computing, precise predictions about energy generation and demand can be made, while machine learning analyzes large datasets to predict energy needs. The combination of QC and ML enables more efficient control of energy flow in the network, leading to a more reliable and sustainable use of renewable energies.
Taking the First Steps
The application of QML in the corporate context is undoubtedly a complex task. Both the fundamental principles of quantum physics and machine learning, as well as the specific application domain, are challenging. But somewhere, it has to start, preferably with simple examples and pre-built algorithms, to get a sense of how QML works and how it could be integrated into your company's structure. Various platforms are available for this, such as PlanQK. On these platforms, pre-built algorithms can be explored, explanations understood, and experiments conducted.
It's an exciting time! We are starting to understand the theoretical benefits that quantum computing can offer, and now it's time to explore the real-world applications. This requires work on various fronts, from hardware development to software and algorithms to integration into existing corporate systems. While the opportunities are undoubtedly present, we must not underestimate the journey ahead. Nevertheless, we should engage with this technology today, as we should be well-prepared when the benefits of quantum computing come into full effect.
About
Dr. Sebastian Feld is an assistant professor at Delft University of Technology, Netherlands. He is part of the Quantum & Computer Engineering department, where he and his group are working on Quantum Machine Learning. The overall goal is to investigate how quantum technology might help creating near-term quantum applications, but also how machine learning techniques may assist with developing scalable quantum devices. Before, he was head of Quantum Applications and Research Laboratory (QAR-Lab) at LMU Munich. His main focus as a postdoctoral researcher was on optimization problems and the application of quantum-assisted artificial intelligence. He earned his doctorate from LMU Munich working on time series analysis. Feld is part of Anaqor’s scientific advisory council.