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**Quantum Machine Learning (QML)**

**Introduction**

In 1935, Einstein, together with Boris Podolsky and Nathen Rosen, published a paper to illustrate the strange behavior of quantum mechanics, calling it "spooky action at a distance". Among the many peculiarities, the notion of quantum superposition challenges our imagination significantly. Furthermore, quantum entanglement becomes even stranger, because if one observes a quantum system, such as two electrons, which are in an entangled state, one can measure a property of an electron, such as its spin, and find out the spin of the electron. another electron - without even measuring it. Is it bizarre or surprising? I would say thatthey are both.

On the other hand, machine learning is one of the hottest topics in industry and business today, with most companies looking to integrate machine learning applications into their work processes and environments. The demand for this technology is only increasing and its use is improving every day, with many scientists and students dedicating themselves to research in this area.

Many of these complex concepts seem very difficult to understand at first, and are breathtaking for anyone. I can report from experience that after more than a year of study, it takes countless attempts, experimentation and perseverance to understand the concepts behind the mechanics of quantum machine learning. But, I think it's always better to experiment, especially in the quantum world.

This article will discuss quantum machine learning, which combines quantum computing and machine learning. Let's explore how it works, its application, and some of the advantages and disadvantages associated with it. This article will help you understand this new topic and plan appropriate learning and application strategies.

Before moving on to the next section, it's important to remember the words of Amrita Manzari, Head of AI and Quantum Machine Learning at QSI: "Start slow and then step up." Therefore, it is important to follow one step at a time and, if necessary, read and reread until this fundamental knowledge is fixed.

**What is ComputingQuantum?**

Quantum computing is an area of study that seeks to use quantum systems, such as atoms and subatomic particles, to process information faster and more efficiently than classical computers. Qubits, which can simultaneously represent states 0 and 1, allow the execution of many taskssimultaneously, thanks to the principles of quantum physics.

An important visual tool for understanding and working with qubits is the Bloch sphere, which symbolizes all possible combinations of qubit spin values in three-dimensional space (Figure 1). Each specific quantum state of the qubit is represented by a point on the Bloch sphere, making its states and transitions easier to visualize.

Figure 1 - Bloch sphere

Source: The Author

To illustrate how quantum computing works, we can imagine a game where you have to guess the color combination of a safe. On a classic computer, you would have to try each color combination one at a time, which would take a lot of time and effort. But on a quantum computer, you could create a superposition of all possible combinations and test them out.simultaneously, greatly speeding up the process of figuring out the right combination.

Therefore, quantum computing has the potential to revolutionize areas such as cryptography, molecular simulation and artificial intelligence, and research is already underway to improve the technology. However, there are still many challenges to be overcome before quantum computers become widely accessible and available.

Figure 2: Example of a quantum computer (Chinese optical quantum computer Jiuzhang 2.0 can solve a problem 1024 times faster than a classic computer.)

Fonte: CHAO-YANG LU/UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA

**Differences between Classical Programming and Machine Learning**

Traditional programming and machine learning are two different ways of solving problems. In traditional programming, a programmer creates the program, using rules and logic (Figure 3). It's a manual process. Machine learning, on the other hand, is automated. It uses training and test data, algorithms to learn patterns, and loss functions to test accuracy (Figure 4).

One of the main differences is how they handle unexpected data. Traditional programs follow predefined instructions. They can't deal with data that wasn't anticipated. Machine learning algorithms, on the other hand, are capable of adapting to new data.

Furthermore, traditional programming uses data to perform a task logically, while machine learning uses data to predict complex patterns in large data sets.

Figure 3 - Conventional Program

Source: Author

Figure 4 - Machine Learning Program

Source: Author

Both have advantages and disadvantages. Traditional programming is good for simple tasks, while machine learning is better for complex tasks. But machine learning is harder to understand and debug, and needs a lot of training data.

Therefore, as we could see that machine learning is totally different from classical programming, now let's compare machine learning with quantum machine learning in the next section.

**Quantum Machine Learning?**

After Having gained knowledge about classical programming, machine learning and quantum computing, we can now explore the field of QML. Figure 5 illustrates the areas covered by QML, which is the combination of quantum computing and machine learning.

Figure 5 - QT x ML

Source: Adapted by the Author [x]

QML uses quantum computers to learn faster and more efficiently, enabling training of models on larger and more complex datasets. It can help solve many types of tasks, such as image recognition and language translation, as well as processing large volumes of data and solving complex problems.

By using the concepts of coherence, superposition and entanglement (they will be presented in a future article), quantum computers are able to process information more efficiently than classical computing. Quantum algorithms are procedures performed on a quantum computer to solve a specific problem, such as searching databases, factoring large numbers and optimization, the latter being used to accelerate machine learning algorithms.

In QML, quantum algorithms are developed to solve typical machine learning problems using the efficiency of quantum computing. This can be done by adapting classical algorithms oryour subroutines to run on a quantum computer.

Therefore, we can conclude that machine learning techniques can be used to generate quantum processes and that quantum computing concepts can be applied to improve machine learning algorithms. In the next section, we will explore some of the most interesting and promising applications of Quantum Computing.

**Quantum Computing Applications**

After going through several concepts that were necessary to understand Quantum Computing and Quantum Machine Learning, we will start to deal with their applications, which have the potential to revolutionize several areas. As presented, quantum computing is an emerging technology that uses quantum principles to process information much faster and more efficiently than classical computers.

One of the main applications of quantum computing is in cryptography, where it can be used to develop more robust and inviolable security systems. This is due to the fact that quantum cryptography uses the properties of quantum mechanics to guarantee the privacy and security of transmitted information.

Another area where quantum computing could have a big impact is in artificial intelligence, as it can speed up the training process of machine learning algorithms, allowing machines to learn faster and deal with much larger data sets than they can. that is possible with conventional computers. This happens because quantum computing can perform calculations exponentially faster than classical computing, in addition to solving problems that are practically impossible for conventional computers, such as factoring large prime numbers.

Also noteworthy is the use of quantum computing for optimization, an important area in several disciplines such as engineering, physics and finance. The ability of quantum computing to solve optimization problems more efficiently than classical computing is remarkable. An example is Grover's algorithm, which can find the minimum of a quadratic function in sub-exponential time, which proves useful in optimization problems.

**Conclusion**

In this article, we explored how quantum computing can improve machine learning, with significant impact in areas such as cryptography, artificial intelligence and optimization. However, there are still many hurdles to overcome before quantum computers become widely accessible. It is crucial to proceed with caution and gradual progress in the adoption of this complex technology. It should be noted that QML requires perseverance and experimentation, but with time and dedication, it can transform today's industries and businesses, creating innovative learning and application strategies. I hope you enjoyed and understood the article and I look forward to seeing you all in the next one.

**References:**

[1] CAMPBELL, Charlie. Quantum Computers Could Solve Countless Problems—And Create a Lot of New Ones, Time, 2023. . Accessed March 2, 2023.

[2] MICROSOFT. What is Quantum Computing?. Available from: https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-quantum-computing/#introduction. Accessed March 1, 2023.

[3] RESEARCH BRIEFS. Quantum Computing Vs. Classical Computing In One, 2021. Available at: https://www.cbinsights.com/research/quantum-computing-classical-computing-comparison-infographic/. Accessed March 1, 2023.

[4] AREL, Ryan. classic vs. quantum computing: What are the differences?, TechTarget, 2022. Available at: https://www.techtarget.com/searchdatacenter/tip/Classical-vs-quantum-computing-What-are-the-differences. Accessed March 2, 2023.

[5] INSIGHTSOFWATER. Machine Learning vs. Traditional Programming, 2019. Available at: https://insightsoftware.com/blog/machine-learning-vs-traditional-programming/. Accessed March 2, 2023.

[6] MANZARI, Amrita. Quantum Machine Learning: a roadmap for technologists, QSI. Available at: https://quantumstrategyinstitute.com/2022/02/28/quantum-machine-learning-a-roadmap-for-technologists/. Accessed on: March 2, 2023.

[7] SHARMA, Divyansh. Applications of Quantum Computing, AIM, 2022. Available at: https://www.analyticsvidhya.com/blog/2022/09/applications-of-quantum-computing/?utm_source=related_WP&utm_medium=https://www.analyticsvidhya.com /blog/2023/03/what-is-quantum-machine-learning/. Accessed on: March 1, 2023.

[8] NAYAK, Clethan. Microsoft has demonstrated the underlying physics required to create a new kind of qubit, Microsoft, 2022. Available at: https://www.microsoft.com/en-us/research/blog/microsoft-has-demonstrated-the-underlying- physics-required-to-create-a-new-kind-of-qubit/. Accessed on: March 2, 2023.

[9] CHOI, Charles. Two of World’s Biggest Quantum Computers Made in China Quantum computers Zuchongzi and Jiuzhang 2.0 may both display "quantum primacy" over classical computers, IEEE Spectum, 2021. Available at: https://spectrum.ieee.org/quantum-computing-china. Accessed on: March 2, 2023.

Presidente da I2AI, Coordenador da Comissão de Tecnologia da Informação Quântica pela ABNT, Speaker, Pesquisador e Coronel do Exército Brasileiro. É um profissional extremamente qualificado e experiente em diferentes áreas. Possui mestrado em Governança, Tecnologia e Inovação, pela Universidade Católica de Brasília, bem como especializações em Ciências Militares, Bases Geo-Históricas, Engenharia de Sistemas, Ciência de Dados, Inteligência Artificial e Educação a Distância, além de vários MBAs, como em Administração Imobiliária, Gestão Pública Federal, Inteligência Artificial, Gestão de Projetos e Nova Lei de Licitações.

Além disso, é membro da Sociedade Brasileira de Computação, bem como do Grupo de Excelência em Processo Prospectivo e Construção de Cenários - CRA-SP.

At the crossroads of innovation, where technology meets strategy, artificial intelligence emerges as the ultimate catalyst for redefining leadership in the digital age

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