April 15, 2024

Breaking down quantum computing: implications for data science and artificial intelligence

Breaking down quantum computing: implications for data science and artificial intelligence
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Quantum computing has had a transformative impact on data science and artificial intelligence, and in this article we’ll go far beyond the basics.

We will explore cutting-edge advances in quantum algorithms and their potential to solve complex problems, currently unimaginable with current technologies. Additionally, we will also discuss the challenges that lie ahead for quantum computing and how they can be overcome.

This is a fascinating glimpse into a future where the limits of technology are pushed to new frontiers, vastly accelerating the capabilities of artificial intelligence and data science.

Quantum computing involves specialized computers that solve mathematical problems and run quantum models that are principles of quantum theory. This powerful technology allows data scientists to build models related to complex processes such as molecular formations, photosynthesis, and superconductivity.

Information is processed differently to normal computers, transferring data using qubits (quantum bits) rather than in binary form. Qubits are vital in terms of delivering exponential computational power in quantum computing as they can remain in superposition; We will explain this in more detail in the next section.

Using a wide range of algorithms, quantum computers can measure and observe large amounts of data. The user will enter the necessary algorithms and the quantum computer will create a multidimensional environment that makes sense of the various data points to discover patterns and connections.

Quantum computing: important terminology

To better understand computer science, it is important to understand four key terms; qubits, superposition, entanglement and quantum interference.


Qubits, short for quantum bits, are the standard units of information used in quantum computing, similar to how traditional computing uses binary bits. Qubits use a principle known as superposition so that they can be in multiple states at the same time. Binary bits can only be 0 or 1, while Qubits can be 0 or 1, only a part of 0 or 1, or both 0 and 1.

While binary bits are typically silicon-based microchips, qubits can consist of photons, trapped ions, and atoms or quasiparticles, both real and artificial. Because of this, most quantum computers require extremely sophisticated cooling equipment to operate at very cold temperatures.


Superposition refers to quantum particles that are a combination of all possible states, and these particles can change and move as the quantum computer observes and measures them individually. A good analogy to explain overlap is the different times a coin is in the air when it is tossed.

This allows the quantum computer to evaluate each particle in many ways to find different results. Instead of traditional sequential processing, quantum computing can run a large number of parallel calculations at once thanks to superposition.


Quantum particles can correlate with each other in terms of their measurements, creating a network known as entanglement. During this engagement, the measurement of one qubit can be used in calculations performed by other qubits. As a result, quantum computing can solve extremely complex problems and process large amounts of data.

Quantum interference

During superposition, qubits can sometimes experience quantum interference, increasing the likelihood that the qubits will become unusable. Quantum computers have measures in place to try to reduce this interference and ensure that the results are as accurate as possible. The more quantum interference, the less precise the results will be.

Quantum machine learning (QML) and quantum artificial intelligence (QAI) are two underrated but rapidly growing fields within data science. This is because machine learning algorithms are becoming too complex for traditional computers and require the capabilities of quantum computing to process them effectively. Over time, this is expected to lead to major advances in artificial intelligence.

Quantum computers can be effectively trained in the same way as neural networks, adapting physical control parameters to solve problems, such as the intensity of an electromagnetic field or the frequency of laser pulses.

An easy-to-understand use case is a machine learning model that could be trained to classify content within documents, doing so by encoding the document into the physical state of the device so it can be measured. With quantum computing and AI, data science workflows will be measured in milliseconds as quantum AI models will be able to process petabytes of data and compare documents semantically, providing the user with actionable insights beyond their imagination. wild.

Quantum Machine Learning Research

Big players like Google, IBM and Intel have invested heavily in quantum computing, but so far the technology is not yet considered a viable and practical solution at the enterprise level. However, research in this field is accelerating and the technical challenges involved in quantum computing will surely be solved with machine learning sooner rather than later.

IBM and the Massachusetts Institute of Technology (MIT) can be credited with discovering experimental research that showed it was possible to combine machine learning and quantum computing in 2019. In one study, a two-dimensional quantum computer was used. qubits to demonstrate that quantum computing could boost supervised classification learning using a data set generated in the lab. This has paved the way for future research that defines the full potential of this technological partnership.

Quantum machine learning in action

In this section we will provide details of the quantum computing projects launched by Google and IBM, giving an idea of ​​the enormous potential of the technology.

  • Google TensorFlow Quantum (TFQ) – In this project, Google aims to overcome the challenges of transferring existing machine models to quantum architectures. To accelerate this, TensorFlow Quantum is now open source, allowing developers to build quantum machine learning models using a combination of Python and Google’s quantum computing frameworks. This means that research into quantum algorithms and machine learning applications has a more active and better equipped community, enabling future innovations.
  • IBM’s quantum challenge – Bridging the gap between traditional software development and quantum computing application development, IBM’s Quantum Challenge is an annual multi-day event focusing on quantum programming. The event, attended by nearly 2,000 participants, aims to educate developers and researchers to ensure they are prepared for the quantum computing revolution.
  • Cambridge Quantum Computing (CQC) and IBM – CQC and IBM launched a cloud-based quantum random number generator (QRNG) in September 2021. This innovative application can generate entropy (complete randomness) that can be measured. Not only is this a valuable advancement for cybersecurity in terms of data encryption, but it can also play a role in developing advanced artificial intelligence systems that are capable of the unexpected.

Thanks to this ongoing research and education, quantum computing could power machine learning models that can be applied to various real-world scenarios. For example, in finance, activities such as investing in stocks and using AI signals for options trading will be boosted by the predictive power of quantum AI. Furthermore, the advent of physical quantum computers will bring about a revolution in terms of the use of kernel methods for linear classification of complex data.

There are still important steps to be taken before quantum machine learning can be introduced into the mainstream. Fortunately, tech giants like Google and IBM are providing open source software and data science educational resources to enable access to their quantum computing architecture, paving the way for new experts in the field.

By accelerating the adoption of quantum computing, AI and machine learning are expected to take giant steps and solve problems that traditional computing cannot facilitate. Possibly even global issues like climate change.

Although this research is still in its early stages, the potential of the technology is quickly becoming evident and a new chapter of artificial intelligence is within our reach.

Nahla Davies is a software developer and technology writer. Before dedicating her full-time job to technical writing, she managed, among other interesting things, to work as a lead programmer at an Inc. 5,000 experiential brand organization whose clients include Samsung, Time Warner, Netflix, and Sony.

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