Quantum Machine Learning (QML) | Beyond The AI
Quantum Machine Learning will be Beyond The AI? According to Microsoft’s definition, machine learning is the process of using mathematical models to help a computer learn without direct instructions. It is considered a subset of artificial intelligence and algorithms are used to identify patterns in the data. It was the German theoretical physicist Max Planck who introduced the Latin word quantum, which means “how much?”, to physics in 1900 with the meaning of “the smallest amount that can exist”.
We generate about 2.5 Exabytes of data every day, which is enough to fill 2.5 million laptops every day maybe 5 million tablets. This is a cluster that encompasses all sectors, and of course finance is one of them. For example, the New York Stock Exchange alone generates 1 Terabyte of data per day. Machine learning algorithms, which evolve and become more complex as data science grows in importance, are the most powerful weapon we have in the face of exponentially increasing crazy amounts of data.
Quantum Machine Learning | Beyond The AI
However, big data has come to the point where even the most powerful algorithms running on classical computers (such as XGBoost) are no longer able to analyze the data we express in Exabytes. At this point, the most important feature of quantum computers, which make use of the unique principles of quantum mechanics such as superposition and entanglement, comes to our rescue: They can process at an unfathomable speed.
According to Google, this speed is large enough to solve a problem in 3.5 minutes that the fastest classical computers can solve in 10,000 years. To prepare your business or organization on the way to this speed, follow the roadmap below.
Learn one of the open-ended software languages.
Today, there are numerous open-ended and free programming languages available, such as Python, R, C, and even Julia, licensed by the Massachusetts Institute of Technology and characterized by its speed. The most important feature of these languages is that they contain a large number of library “libraries” created by software developers.
Therefore, it is possible to perform almost all kinds of quantitative analysis by using only libraries efficiently with very little coding knowledge. For example, “caret”, which stands for Classification and Regression Training and is one of the most frequently used R libraries, has dozens of ready-made functions defined for data analysis and forecasting. Similarly, in Python, we can give an example of a library called “scikit-learn”.
Choosing Python from among these languages can be very useful, because almost all of the platforms where the quantum machine learning algorithms we will see in the third step can be written are based on Python Quantum machine programming language.
Get to know the algorithms.
First of all, it is necessary to correctly identify the problem that is tried to be solved. Depending on the nature of the problem, you can choose the appropriate algorithms from a multitude of methods, such as supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing. For example, supervised machine learning algorithms such as RandomForest can work quite well for classification problems that are frequently used in financial markets, while algorithms such as Support Vector Regression (SVR) can be used for regression problems.
Explore quantum machine learning platforms for Quantum Machine Learning!
Well-known machine learning algorithms are designed to run on classical computers. It is important to remember that quantum computers are still in prototype form and it will take time for them to become commercially widespread. So methods that work with “Bits” need to be transformed to work with “Qubit”.
One of the platforms created for this is TensorFlow. TensorFlow, an open-source and free machine learning library developed by Google in 2015, focuses exclusively on deep neural networks “Deep Neural Networks.” The platform was developed in 2020 and houses TensorFlow Quantum (TFQ), a quantum machine learning library focused on quantum data and hybrid quantum-classical models. It also has a pretty detailed white paper on how to use the platform and how to write algorithms.
It also has a pretty detailed white paper on how to use the platform and how to write algorithms. Similarly, Qikkit, a Python-based platform created by IBM where every conceivable work on quantum computing can be done, and Azure, developed by Microsoft, are also very popular.
Build your team for Quantum Machine Learning!
Quantum machine learning in finance is an area where many different disciplines need to work together. So create a dedicated quantum team that will work in full coordination with research departments in your businesses and organizations, and invite professionals from quantum physics, machine learning, mathematics, and economics/finance to join the team.
Develop pilot projects by working with start-ups.
The number of start-ups operating in the field of quantum computing in our country is almost non-existent, but there are many reputable enterprises working in this field in the world. These include Multiverse and Zapata, a quantum computing company working with BBVA, a global Spanish bank.
Although quantum machine learning is still in its infancy in the world, it is a critically important field. Companies that adopt this technology and adapt it to the way they do business will take a few steps forward. The way to achieve this is to establish a qualified team in addition to the technical equipment and to produce projects.
What is Quantum Machine Learning (QML)?
The promise of QML is to offer exponential speedups compared to classical machine learning algorithms, allowing us to solve more complex problems that are currently intractable.
At its core, QML is about using quantum computers to perform machine learning tasks. In a classical computer, data is processed in binary form, with each bit either being a 0 or 1. In contrast, a quantum computer uses quantum bits (qubits) that can represent a 0 and a 1 simultaneously, a phenomenon known as superposition, what a s. This allows for massive parallel and same time processing and the ability to explore many possible solutions at once.
QML and algorithms
One of the most significant challenges in QML is developing algorithms that are compatible with the limited resources of today’s quantum computers. While quantum computers have made tremendous progress in recent years, they are still relatively small and noisy compared to classical computers. This means that QML algorithms need to be designed to work with limited qubit resources and be robust to noise.
Despite these challenges, there have been some exciting developments in QML recently. For example, researchers have demonstrated the use of quantum algorithms for tasks such as pattern recognition and data clustering. Additionally, some quantum-inspired classical algorithms have been shown to outperform classical machine learning techniques.
Conclusion for Quantum Machine Learning
QML has the potential to revolutionize many areas of science and industry. For example, it could lead to breakthroughs in drug discovery, financial modeling, and even quantum physics itself. However, there are still many technical and practical challenges that need to be overcome before QML becomes a practical tool for researchers and businesses.
In conclusion, QML is an exciting and rapidly evolving field that brings together two of the most transformative technologies of our time. While there are still many obstacles to overcome, the potential benefits of QML are too great to ignore. We can expect to see significant progress and breakthroughs in QML in the coming years, which could transform the way we approach many important problems and issues.