Quantum AI

Think of a world where difficult problems are solved in seconds, where difficult calculations that would require a supercomputer years to calculate, are done in seconds. This is the future driven by Quantum Computing and Artificial Intelligence: The Quantum AI.

Quantum Computing is an area of computer science focused on the development of technologies based on the rules of quantum physics to solve problems that are too complex for classical computing.

We can illustrate quantum computing with a coin example. In classical computing, this coin can only be either heads or tails, but in quantum computing, this coin can be both heads and tails at the same time, because of a quantum physics concept called superposition. Now imagine a hundred coins or qubits, each having multiple states at once. The number of outcomes grows very quickly, not gradually like in classical computing.

If we look at another example: to find the exit of a labyrinth the classical computer would systematically try each path one by one, taking a long time to find the exit, a quantum computer, however, could try all paths at once, finding the exit much faster. This ability to do parallel computations gives quantum computers an advantage over their classical counterparts in solving complex problems.

What can a quantum computer really achieve? Think of the most complex mathematical problems, or simulations of molecular interactions for drug development, or even forecasts about global climate changes. These tasks, which could take classical computers millions of years to finish, could possibly be solved by quantum computers in almost seconds. Quantum computers can help us find new ways of doing things, for example, they can help us create new materials and medicines by accurately simulating their behavior at a molecular level.

Now let’s talk around Generative Artificial Intelligence, which is the type of AI that can create using natural language new data that resembles a given dataset. It does this by learning the patterns and relationships in the data of human language (Large Language Model - LLM), and then use that knowledge to produce new, synthetic data that is similar to the original one.

Thus, what if we combine generative AI and quantum computing? Could we think of Quantum Neural Network (QNN) or Quantum AI as a possibility? QNN could use the superposition and parallel computations of quantum mechanics to be trained not on human language, like large language models (LLM) do today, but on the language of nature, on quantum, analyzing all the possible paths all at once.

If we train QNN to understand quantum physics, quantum chemistry, then it can help us predict properties of material, it can help us design molecules, design materials. Suppose we want to create a catalyst that can capture carbon from the air. We can train a model to generate some possible candidates for such materials. Then, we can use QNN to quickly identify the best one for carbon fixation. Essentially, QNN could work on the same principles that nature works, speaking the same language, helping us to solve the more complex problems.

Let's look at some examples to better understand the idea:

• Understanding of protein folding. Understanding how proteins, the building blocks of life, fold into three-dimensional shapes is a problem really complex. Misfolded proteins are linked to diseases like Alzheimer's and Parkinson's. A Quantum AI, with its ability to handle vast amounts of data simultaneously could find the way for new treatments and cures.
• Prediction of a traffic flow for a city, that would depend on a lot of data and variables to take into account. Classical neural network would have difficulties with such a task, but QNN excels in it. It could process and analyze all the options at once, predicting traffic patterns with remarkable accuracy. AIs could be trained to recognize patterns and make decisions faster than ever before.
• Prime factorization. Breaking down large numbers into their prime factors is virtually impossible for even the fastest classical computers when the numbers get large. This is the basis of RSA encryption, which secures our online communications. A sufficiently powerful quantum computer could factorize these large numbers efficiently, shaking the very foundations of modern cryptography.

These are just a few examples of the previously unsolvable problems that Quantum AI could conquer. The best has yet to come!

To learn more fascinating facts, you should check out this video:

Microsoft Corporate - How can we solve quantum problems today and in the future? ( eventbuilder.com )