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Quantum computers have had a revolutionary impact on data science and artificial intelligence. In this article, we will go far beyond the basics.
We will look at the latest advances in quantum algorithms and their potential to solve complicated problems currently unimaginable using current technologies. We will also look at the challenges facing quantum computing and how to overcome them.
It’s a fascinating glimpse into the future, where the boundaries of technology are being pushed to novel limits, dramatically accelerating the development of artificial intelligence and data science.
Quantum computers involve specialized computers that solve mathematical problems and run quantum models, which are the principles of quantum theory. This powerful technology allows data scientists to build models related to complicated processes such as molecular formation, photosynthesis, and superconductivity.
Information is processed differently than in conventional computers, data transfer using qubits (quantum bits) rather than in binary form. Qubits are indispensable for delivering exponential computing power in quantum computers because they can exist in superposition—we explain this in more detail in the next section.
Using a wide range of algorithms, quantum computers can measure and observe enormous amounts of data. The necessary algorithms will be entered by the user, and the quantum computer will create a multidimensional environment that makes sense of the various data points to discover patterns and connections.
Quantum Computing: Significant Terminology
To better understand computer science, it is essential to understand four key concepts: qubits, superposition, entanglement, and quantum interference.
Qubits
Qubits, low for quantum bits, are the standard units of information used in quantum computers, much like classic computers apply binary bits. Qubits apply a principle known as superposition, allowing them to be in multiple states at once. Binary bits can only have the value 0 or 1, while qubits can have the value 0 or 1, be only part 0 or 1, or both 0 and 1.
While binary bits are typically silicon-based microprocessors, qubits can consist of photons, trapped ions, and atoms or quasiparticles, both real and artificial. For this reason, most quantum computers require extremely sophisticated cooling equipment to operate at very low temperatures.
Imposition
Superposition refers to quantum particles, which are a combination of all possible statesand these particles can change and move while the quantum computer observes and measures them individually. A good analogy for explaining superposition is the different times a coin is in the air when it is tossed.
This allows a quantum computer to evaluate each particle in multiple ways to find different outcomes. Instead of classic, sequential processing, quantum computers can perform a huge number of parallel calculations at once through superposition.
Entanglement
Quantum particles can correlate their measurements with each other, creating a network known as entanglement. During this engagement, a measurement from one qubit can be used in a calculation performed by other qubits. As a result, quantum computing can solve incredibly complicated problems and process huge amounts of data.
Quantum interference
During superposition, qubits can sometimes experience quantum interference, a chance that the qubits will become useless. Quantum computers have the means to try to reduce this interference to ensure that the results are as precise as possible. The more quantum interference, the less precise the results.
Quantum machine learning (QML) and quantum artificial intelligence (QAI) are two underserved but rapidly growing areas in data science. This is because machine learning algorithms are becoming too complicated for classic computers and require quantum computing capabilities to process them effectively. This is expected to ultimately lead to major advances in AI.
Quantum computers can be efficiently trained in the same way as neural networks, by adjusting physical control parameters to solve problems such as the strength of an electromagnetic field or the frequency of laser pulses.
An easy-to-understand apply case is an ML model that could be trained to classify content in documents, doing so by encoding the document in the physical state of the device so that it can be measured. With quantum computers 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 semanticallyproviding the user with practical information beyond their wildest imagination.
Quantum Machine Learning Research
Major players like Google, IBM, and Intel have invested heavily in quantum computers, but so far the technology has not been considered a viable and practical solution at the business level. However, research in this area is accelerating, and the technical challenges associated with quantum computers will certainly be solved by machine learning sooner rather than later.
IBM and the Massachusetts Institute of Technology (MIT) are proud to have discovered experimental research that showed it was possible to combine machine learning and quantum computing back in 2019. The study used a two-qubit quantum computer to show that quantum computing could improve supervised learning classification using a lab-generated dataset, paving the way for further research to determine the full potential of this technological partnership.
Quantum Machine Learning in Action
In this section, we will provide details of quantum computer projects launched by Google and IBM, allowing us to see the enormous potential of this technology.
- TensorFlow Quantum by Google (Inquiry) – In this project, Google aims to overcome the challenges transferring existing machine models to quantum architecturesTo speed up this process, TensorFlow Quantum is now open-source, allowing developers to build quantum machine learning models using a combination of Python and Google’s quantum computing framework. This means that research into quantum algorithms and machine learning applications has a more busy, better-equipped community, enabling further innovation.
- IBM Quantum Challenge – Combining classic software development with the development of quantum computing applications, IBM Quantum Challenge is an annual, multi-day event that focuses on quantum programming. The event, which attracts nearly 2,000 attendees, aims to educate programmers and researchers to ensure they are ready 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 groundbreaking application can generate entropy (total randomness) that can be measured. Not only is it a valuable cybersecurity breakthrough in terms of encrypting data, but it could also play a role in developing advanced AI systems that are capable of the unexpected.
Through this ongoing research and education, quantum computing can power machine learning models that can be applied to a variety of real-world scenarios. For example, in finance, activities such as investing in stocks and using AI signals for options trading will be supercharged by the predictive power of quantum AI. Similarly, the emergence of physical quantum computers will usher in a revolution in using kernel methods for linear classification of complicated data.
There are still significant steps that need to be taken before quantum machine learning becomes mainstream. Fortunately, tech giants like Google and IBM are providing open-source software and data science education resources to enable access to their quantum computing architectures, paving the way for novel experts in the field.
By accelerating the adoption of quantum computers, AI and ML are expected to take giant leaps forward, solving problems that classic computers cannot facilitate. Perhaps even global problems such as climate change.
While this research is still in its very early stages, the potential of this technology is quickly becoming apparent, and a novel chapter in the history of AI seems to be within reach.
Nahla Davies is a software developer and technical writer who, before turning to full-time technical writing, landed a stint as a lead software developer at an Inc. 5000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony, among other intriguing jobs.