AI and Quantum: exploring the unknowable

Week after week we hear about the next step in AI from Large Language Models to generative AI, or the next big thing in quantum such as quantum superchemistry or advancements in quantum computing. So, one must wonder, what happens when these two disciplines cross paths? The answer – the unknowable becomes known.

Here we will explore how AI and quantum are coming together to revolutionise our understanding of both disciplines.

AI and quantum are two fields that are revolutionising the world of computing and information. AI is the science and engineering of creating intelligent machines that can learn from data and perform tasks that normally require human intelligence, such as reasoning, planning, decision-making, and problem-solving. Quantum is the science of the smallest particles of matter and energy, such as atoms, photons, and electrons, that obey the laws of quantum mechanics, such as superposition, entanglement, and interference.

These two fascinating technologies cross paths when quantum computers are applied to AI algorithms or when AI algorithms are used to control quantum systems – a feedback loop of advancement almost. Quantum computers, which are explained in more detail here, are devices that use qubits to perform parallel computations that are exponentially faster and more powerful than classical computers. AI algorithms are methods that use mathematical models and data to learn patterns and optimise outcomes that are otherwise too complex or hidden for humans or classical computers to detect effectively or within any reasonable timeframe.

Through the combination of AI and quantum, researchers and practitioners can achieve results that are not possible with either technology alone. For example, AI can assist quantum computers in the simulation of complex systems, such as molecules and materials, solve hard optimisation problems, such as scheduling and routing, improve decision-making and forecasting in various domains, such as finance and healthcare, and integrate multiple sets of data, such as text, images, or audio. Quantum computers can help AI learn from these large sets of data or find optimal solutions significantly faster. Additionally, they can be used to improve AI-generated outcomes, or design and implement novel quantum experiments that explore both fundamentals and theory work.

How AI is being applied to quantum

AI is being applied to quantum phenomena in various ways across many different sectors. Here are a few of the key uses of combining these technologies:

• Processing large sets of data – Quantum computers can utilise AI to analyse and extract insights from massive amounts of data that are beyond the reach of classical computers and maintain a challenge for quantum. For example, AI can massively help quantum computers to simulate complex systems such as molecules, materials, and quantum devices themselves, optimising design and performance in these areas.
• Solving complex problems even faster – Quantum computers are able to use AI to find optimal solutions to hard optimisation problems, such as scheduling, routing, cryptography, and machine learning – especially machine learning. A primary example of this would be the controlling of quantum systems such as quantum sensors or metrology.
• Integration of multiple large data sets – Quantum computers can use AI to combine and correlate data from multiple different sources and formats such as text, images, audio, and video. This could be via natural language processing, computer vision, speech recognition, or sentiment analysis. As it stands, AI is one of the only effective ways to synthesise these types of data at large into a format that computers (both quantum or classical) can readily understand.

How AI is already being used to power quantum advancements

Whilst this all seems very advanced and fresh on the mind, we are already seeing active applications of AI in the quantum sector, powering many up-and-coming advancements.

Quantum chemistry is seeing AI utilised to simulate complex structures and dynamics in previously impossible methods and detail at the atomic level. Such feats can enable considerably more advanced research into drug design, catalysis, battery technology, nanotechnology, and advanced materials.

Quantum metrology can also make good use of AI in its processes which aim to enhance the precision and accuracy of physical measurements. These techniques allow for time, frequency, distance, and temperature measurement to be considerably improved which can lead to advanced standards of measurement, navigation systems, clocks, and sensors.

Quantum cryptography is another area, that has seen a lot of attention in recent times, which AI can help improve. The rise of quantum computing has also paved the way for the potential malicious use of the technology to render previous security methods irrelevant. AI can be utilised alongside the computational power of quantum to ensure secure communications, connections, and data storage in the future. Combining the disciplines can lead to new security protocols for encryption, authentication, key distribution, and verification measures.

The sector that stands the tallest, however, is Quantum machine learning, something that could be a considerable breakthrough if fully achieved. The ability to enable powerful computers utilising quantum phenomena to learn and adapt could be revolutionary, and we are already seeing early signs of this. AI can allow quantum computers to actively learn from data and find patterns or solve problems that are otherwise too complex or hidden for humans or classical computers to detect.

The sky most certainly isn’t the limit when it comes to either of these technologies alone, and once combined only time will tell us where the true potential of AI and quantum lies. Will it be a feedback loop of success that pioneers the future, or will it be a fad that fizzles out?