Quantum Computing and AI Can Pave the Way for Endless Possibilities for Data Science

Quantum computing refers to the use of quantum phenomena like superposition and entanglement for computation. It focuses on developing computer technology based on quantum theory principles, which explain the behavior of energy and material on atomic and sub-atomic levels. 

Classic computers that we use in our daily lives encode information in bits that are given values between 1 or 0. Quantum computing, however, uses quantum bits or qubits, harnessing subatomic particles’ ability to exist in more than one state, that is, 1 and 0, at the same time. This empowers quantum computers to handle operations at exponential speeds compared to conventional computers while also consuming lesser energy.

Bank holding company BBVA believes that using quantum algorithms in artificial intelligence techniques will boost machines’ learning abilities. 

According to Samuel Fernández Lorenzo, a quantum algorithm researcher who collaborates with BBVA’s New Digital Businesses area, “Quantum machine learning can be more efficient than classic machine learning, at least for certain models that are intrinsically hard to learn using conventional computers…We still have to find out to what extent do these models appear in practical applications.” [Source]

Quantum Computing and AI

Quantum computing and machine learning are not a match made in heaven. But in the world of humans, opposites do attract, and similarly, both these sciences requiring completely different backgrounds and skills are intrinsically related when it comes to driving futuristic technologies. Of course, the extent of influence that quantum computing will have on AI remains an open question. Still, most experts believe quantum computing will alter AI significantly in the times to come – probably even make it more humane and efficient. 

Machine learning and artificial intelligence have emerged as two key areas of research for applying quantum computing algorithms. The natural synergies between the two fields could change the future of data science in several ways, which are discussed in the next section.

Benefits of Integrating Quantum Computing With AI for Data Science

Various industries are sitting on increasing amounts of data, which holds invaluable insights for both businesses and science. However, complex datasets and multiple scenario modeling can prove challenging for traditional computing, and the application of quantum principles can help. Here are a few ways in which quantum computing and AI can improve data science.

1. Handling Increasing Amounts of Data

Machine learning and artificial intelligence feed on data, and quantum computers are designed to manage enormous amounts of data. With the ongoing improvements, programmers can now better manage qubits. Soon, they may be able to integrate quantum computing with artificial intelligence and machine learning to handle and crunch voluminous data at exponential speeds.

2. Building Better Data Models

Industries like finance and life sciences can no longer rely only on classical computing, as they require complex data models that these computers can’t generate. Quantum computers, on the other hand, possess exponential processing power to model extremely complicated situations. By using quantum computing for data modeling, it may also be possible to improve healthcare through better treatments and decrease the risk of financial fraud. 

3. Quantum Machine Learning

Supervised learning is employed in AI for most industrial applications like image recognition or forecasting. Quantum computing can add to the efficacy of the process by employing quantum computing algorithms to execute machine learning tasks. For example, SVMs are the most powerful machine learning algorithms. Still, additional layers of complexity often make them too taxing for classical computers to handle, but quantum computers can handle complicated algorithms and multiple datasets easily. Implementing a support vector machine suitable for quantum gates and quantum computing can help in classifying large and complex datasets. 

Further developments in the field can have exciting implications like:

• Developing a better understanding of nanoparticles
• Molecular modeling for medical research
• Space exploration
• Better security through a merger of QML, IoT, and blockchain
• Creation of new and sustainable materials through atomic mapping

The Need for Quantum Computing and AI in the Future

Machine-learning and quantum computing are two technologies that have fantastic potential on their own. In the near future, researchers aim to bring both the technologies together to achieve the quantum advantage for calculating complex algorithms much faster as compared to the best classical computers. Such a breakthrough could be a game-changer in the field of data science, leading to significant advances in both the scientific and business world. 

Professor Michael Hartmann of Heriot-Watt University, a leading researcher in artificial neural networks, intends to use quantum computing for developing the first dedicated neural network computer. This computer will loosely model the neural networks that power the processing capacity of the human brain. According to Professor Hartmann, quantum computing can lead to AI technology that will operate much faster than current technology. The reason for this thinking is that quantum computing uses sub-atomic particles that can exist in multiple states at the same time, which may enable machines to think much faster than traditional computers that use a binary system.

Unfortunately, many scientists are still not open to this amalgamation of technology. Some AI researchers point out examples of existing quantum computers struggling to solve simple problems that binary computers excel at. However, this limited view can lead to missing out on the benefits of both technologies. That being said, there are certainly some obvious concerns about the potential of quantum computing. As quantum states are highly sensitive to interference from the environment, this significantly increases the margin of error, which is also quite challenging to predict. Fortunately, research by a team at the Max Planck Institute for the Science of Light has directed that AI neural networks can be used to correct quantum errors. Various experiments and use cases also show that implementing multiple AI neural networks can significantly reduce the errors in quantum computing. These networks learn from each other to amplify the potential of quantum computing.

Parting Thoughts

The future of AI and Quantum Computing is interrelated, but there’s certainly a long way to go. The current state of research does not say that AI will solve all the issues in quantum computing or vice-versa. Still, it indicates that both technologies hold immense potential when used together. Researchers are also positive about some radical transformations in data science due to more accurate algorithms and the quantum advantage in dealing with complex and multiple datasets, which holds a lot of opportunity for both scientific and business worlds.