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What Happens When You Mix AI With Quantum Computers?


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AI and quantum computers are two fascinating technological topics. On the one hand, you have a mind that can do pretty much anything, and on the other, you have a computer that can perform calculations using the fabric of the universe itself. It’s mind-boggling.

To make matters more challenging, most people have no idea how these technologies will proceed in the future. Governments are putting money into them, believing they are humanity’s savior, but we have no idea how they will turn out in practice, making it harder to know how their combination will affect the world.

With that said, this post will attempt to find out. We’re going to take a look at what happens when you mix AI with quantum computing, and what sort of magic might unfold.

What Is AI?

AI science revolves around the engineering of machines and systems that can perform tasks that normally require human intelligence. It sounds simple enough, but it’s notoriously difficult. Researchers have been at it for more than seventy years, and only now are their efforts bearing fruit. AIs use algorithms that process large amounts of data to find patterns, insights, and solutions, but they don’t have a consciousness or subjectivity in the same way a person does.

What Are Quantum Computers?

Quantum computing is a new paradigm of computation that exploits the principles of quantum physics to manipulate information in ways that are impossible for classical computers. This aspect of their operation means they can do the impossible, like crack encryption codes and simulate realistic interactions between particles.

Quantum computers use quantum bits or qubits, which can exist in a superposition of two states (0 or 1) at the same time, and entanglement, which means that two or more qubits can share a quantum state and influence each other even when they are physically separated. It sounds ridiculously complicated, but it relies on physicists’ current understanding of how the universe works.

Quantum computers could solve intractable problems in seconds that might take a classic computer the age of the universe. For instance, quantum computers of sufficient power could factor large numbers in polynomial time (which is important for cryptography), simulate quantum systems (which is important for chemistry and physics), or search through unstructured databases, the critical technology for AI.

AI And Quantum Computing

AI and quantum computing are naturally synergistic technologies. Both work better in the presence of the other. AI can use quantum computing to enhance its capabilities and performance; quantum computing can use AI to improve its design and operation. You can imagine a feedback loop developing in the future where each system supports the other, generating increasingly powerful tools that chip away at the fabric of reality.

In this section, we take a look at some of the specific ways in which AI and quantum computing are natural bedfellows.

Quantum Machine Learning

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Regular machine learning is already powerful and generating tools, like Optimo and ChatGPT which accelerate work and learning. However, the application of quantum algorithms to machine learning problems such as classification, clustering and regression could dramatically improve processing times, letting these systems chomp through more data at a lower cost. The parallelism of quantum computers to process large datasets faster or with higher accuracy than classical methods is particularly beneficial in this regard.

Another function of quantum computers is their capacity to perform quantum k-means clustering. This technique lets them group data points into clusters based on their similarity using fewer resources than a classical computer.

Quantum support vector machines could also help researchers find more information in data they can extract for meaningful analysis. You can imagine this being used in business intelligence applications with multiple data inputs.

Lastly, researchers are envisioning quantum neural networks. These are models inspired by biological neural networks that consist of layers of interconnected nodes or neurons that process information. In a quantum computer, the basic “node,” would be qubits and it would be able to perform nonlinear functions or transformations of the data.

AI And Quantum Computers

At the same time, we could see the benefits of these new technologies flowing in the opposite direction: from AI to quantum computers. Engineering and technical challenges involved in constructing such devices mean that most quantum computers aren’t ready for primetime. However, artificial intelligence could help researchers overcome current problems and move forwards into a new paradigm.

For example, one branch of machine learning is called reinforcement learning, which involves an agent that learns from its own actions and rewards in an environment. Reinforcement learning can be used for tasks such as playing games, controlling robots or optimizing logistics. However, classical computers find it challenging because it requires a lot of trial-and-error which is a task they can only perform linearly.

Quantum computing could help overcome this by using quantum superposition to encode multiple actions in a single qubit. Energy usage would be considerably lower and a quantum agent could learn faster and more efficiently.

Another branch of machine learning called supervised learning, also stands to benefit from the quantum revolution. The approach, which involves an algorithm that learns from labeled data and makes predictions based on the data it collects, is mainly used for speech and image recognition.

Unfortunately, supervised learning can also be difficult for classical computers because it requires a lot of data and computational resources. Systems must populate and train complex models that can generalize well to new situations, which often takes trillions of iterations.

Quantum computers could get around this by using quantum interference to enhance the accuracy and robustness of the models. They could also use quantum parallelism to speed up the training process and reduce the amount of data needed. Machines could potentially learn new concepts in a single pass, similar to humans, instead of requiring millions of images for their training models.

Lastly, quantum computers have the potential to enhance unsupervised learning – the practice of finding hidden structures or patterns in unstructured data. Classical computers find this challenging because it requires creativity. However, quantum computing could help facilitate this task by using quantum entanglement to capture complex correlations, which may lead to better qualitative outcomes.

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