# Quantum Artificial Intelligence

## What is Quantum Intelligence?

At the tiniest scales of our universe, things get weird. So weird, in fact, that traditional computers can't keep up. That's where quantum computing comes in. By harnessing the power of quantum mechanics, we can simulate and study natural systems with much less overload than with classical computers. This allows us to understand physical and biological molecules, create better materials, and even improve medicines and fuels.

But what about quantum intelligence? We don't fully understand it yet, but the study of quantum biology is already revealing some intriguing findings. And theories linking quantum mechanics and consciousness, like [Orch OR ](https://https://en.wikipedia.org/wiki/Orchestrated_objective_reduction)founded by Nobel laureate for physics, Roger Penrose and [Integrated Information Theory](https://en.wikipedia.org/wiki/Integrated_information_theory), are still highly debated and not fully understood.


That's why QAI research is so important. By engineering intelligent systems using quantum mechanics, we might uncover clues to some of the most profound mysteries of contemporary science, like the measurement problem of quantum mechanics and the hard problem of consciousness.

Interpretations like the most widely known [Copenhagen](https://plato.stanford.edu/entries/qm-copenhagen/), [QBism (Quantum Bayesianism)](https://plato.stanford.edu/entries/quantum-bayesian/), [Relational QM (Relational Quantum Mechanics)](https://plato.stanford.edu/entries/qm-relational/), and [quantum Darwinism ](https://www.quantamagazine.org/quantum-darwinism-an-idea-to-explain-objective-reality-passes-first-tests-20190722/)offer alternate explanations to some of these mysteries, but we need experimental verification and theoretical rigor to truly understand them.

In the end, QAI could revolutionize the way we understand the universe and ourselves. It's an exciting frontier that could lead to untold possibilities. So, are you ready to explore the quantum realm of intelligence? Let's go!

## Quantum Computing and Artificial Intelligence
Quantum computing and AI are like peanut butter and jelly - they're great on their own, but when you put them together, magic happens! With the power of quantum computing, we can solve complex problems faster than ever before, making AI even smarter and more efficient. Whether you're a theoretical dreamer or a pragmatic problem-solver, there's no denying that the intersection of these two fields is pushing the boundaries of what's possible. So grab a cup of coffee (or tea, if that's your thing) and let's continue exploring the exciting world of quantum AI!

Imagine you have a really tough problem to solve, like finding a needle in a haystack. You could spend hours searching through the hay, but that would take a long time. Now, imagine if you had a magic wand that could find the needle instantly. That's what a quantum computer is like – it can solve certain problems much faster than a classical computer.

In QAI, we're using these magic quantum computers to make our AI smarter and more efficient. There are two camps in the QAI world – the "theoretical" camp and the "pragmatic" camp.

The theoretical camp believes that quantum computing violates a long-standing theory in computer science called the [Church-Turing thesis](http://www.alanturing.net/turing_archive/pages/reference%20articles/The%20Turing-Church%20Thesis.html). Basically, this means that some problems are just too hard for regular computers to solve, but a quantum computer can solve them quickly. These problems are called BQP/BPP problems, and they're really important for AI. Some of these problems are crucial for AI, such as solving linear equations, identifying patterns, and generalizing from fewer training data.

The pragmatic camp, on the other hand, is focused on making our current AI algorithms work better using quantum computing. Even a small improvement in efficiency can mean big profits in industries like finance or healthcare.For example, quantum algorithm designers are focusing on techniques like k-means clustering, recommendation systems, support vector machines, variational auto-encoders, generative adversarial networks, convolutional neural networks, Boltzmann machines, etc.

The intersection of quantum computing and artificial intelligence is a dynamic field with endless possibilities. With quantum computing's ability to solve complex problems quickly and efficiently, and AI's capacity to process and analyze vast amounts of data, the potential for groundbreaking advancements is limitless. As we explore the theoretical and pragmatic camps in QAI, we're discovering innovative ways to solve problems that were once thought impossible. From finance to healthcare, quantum AI is changing the game, and we're only scratching the surface. So let's continue to push the boundaries of what's possible and uncover the transformative power of quantum computing and AI. In the next part, we'll take a closer look at the top companies pioneering QAI solutions and how they're leading the charge in this exciting field.

## Top Companies pioneering QAI solutions
Hold on to your qubits, because we're about to explore the top companies that are pioneering the field of Quantum Artificial Intelligence (QAI)! Get ready to have your mind blown, because these companies are pushing the boundaries of what's possible with quantum computing.

[**D-Wave** ](https://www.dwavesys.com)is a company that specializes in building quantum annealing computers, which are designed to solve optimization problems. Their flagship product is the D-Wave 2000Q, which has over 2000 qubits. D-Wave has developed its own quantum programming language called Ocean and offers a cloud-based platform called Leap that allows developers to access their quantum computers and develop quantum applications, including QML and QAI.

Here is their [new hybrid solver plug-in ](https://https://github.com/dwavesystems/dwave-scikit-learn-plugin)for the developers and it's free to try!


[**Zapata Computing**](https://www.zapatacomputing.com) is a startup that is focused on developing quantum software and algorithms for practical applications. They offer a quantum software development platform called Orquestra, which includes a suite of tools and libraries for developing quantum applications, including QML and QAI. Orquestra allows developers to integrate quantum algorithms with classical machine learning algorithms, making it easier to develop hybrid quantum-classical machine learning applications. Check out their [Fact Sheet](https://https://zapata.wpenginepowered.com/wp-content/uploads/2023/01/Orq-fact-sheet.pdf) here!


[**IBM's** ](https://www.ibm.com/quantum)QAI solution is built on their quantum computing platform, which includes access to their quantum computers, as well as software development tools such as Qiskit. IBM's QAI solutions focus on using quantum computing to improve classical machine learning algorithms, and they have developed a suite of tools for this purpose. One such tool is the [Quantum Machine Learning (QML) library](https://qiskit.org/learn/course/machine-learning-course/), which includes a set of quantum algorithms that can be used for a range of tasks, including classification, clustering, and feature mapping. IBM is also working on developing quantum-inspired classical machine learning algorithms that can be run on classical computers.Here you can find notes of [2021 Qiskit Global Summer School on Quantum Machine Learning.](https://https://qiskit.org/learn/summer-school/quantum-computing-and-quantum-learning-2021/)


[**Rigetti Computing**](https://www.rigetti.com) solutions are also built on their quantum computing platform, which called Forest. Rigetti's QAI solutions focus on using quantum computing to solve optimization problems, such as portfolio optimization and supply chain optimization. They have developed a suite of tools for this purpose, including the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE). Rigetti is also working on developing [hybrid quantum-classical machine learning algorithms](https://https://investors.rigetti.com/news-releases/news-release-details/rigetti-bring-quantum-machine-learning-applications-strangeworks) that can be run on classical computers and leverage quantum computing resources where needed.


[**Xanadu AI**](https://www.xanadu.ai) is my favorite company that is making significant contributions to the field of Quantum Artificial Intelligence (QAI). They are a Canadian startup that is focused on developing quantum computing hardware and software, with a particular emphasis on building [photonic quantum computers](https://arxiv.org/abs/2010.02905).

One of Xanadu's main product is the [Strawberry Fields platform](https://www.xanadu.ai/products/strawberry-fields), which is an open-source software platform for photonic quantum computing. This platform allows users to simulate quantum circuits and run quantum algorithms, including QML algorithms, on classical computers. Strawberry Fields also includes a suite of machine learning tools, such as automatic differentiation and optimization, that can be used to build QML applications.

They also developing a cloud-based quantum computing platform called the [Xanadu Quantum Cloud](https://https://platform.xanadu.ai/auth/realms/platform/protocol/openid-connect/auth?client_id=plaas&redirect_uri=https%3A%2F%2Fpennylane.xanadu.ai%2Fx%2Fapi%2Foauth%2Fcallback&response_type=code&state=uXHjtXDCsloUHnwY), which will allow users to access and experiment with photonic quantum computers and it's free. Since Xanadu Quantum Cloud is still in development, it has the potential to make quantum computing more accessible to researchers and developers.

Another most exciting contributions to the field of QAI is the development of quantum neural networks. These are neural networks that are built using quantum circuits and are capable of processing quantum data. Xanadu has developed a library of quantum neural network algorithms, called [PennyLane](https://www.xanadu.ai/products/pennylane), which is integrated with the Strawberry Fields platform. This library includes several QML algorithms, including quantum support vector machines and quantum neural networks for image recognition.This is seriously next-level stuff, folks!


Xanadu's work on quantum neural networks has the potential to revolutionize the field of machine learning by allowing us to process quantum data more efficiently and accurately. This could lead to breakthroughs in fields such as drug discovery, financial modeling, and image recognition.

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## Conclusions
The potential applications of QAI are truly astounding, ranging from better materials, medicines, and fuels to breakthroughs in the study of consciousness and quantum mechanics.

As we've seen, companies like D-Wave, Zapata Computing, IBM, Rigetti Computing, and Xanadu AI are making remarkable contributions to the field of QAI. Their innovative solutions are changing the way we approach machine learning, optimization, and other complex problems. Whether it's through developing quantum software, building quantum computers, or integrating quantum algorithms with classical machine learning, these companies are pushing the boundaries of what's possible with quantum computing. So, keep an eye out for these trailblazers in the field of QAI - they're sure to lead us to a quantum-powered future!

As we continue to explore the frontiers of quantum computing and AI, we can expect to see groundbreaking advancements in fields such as security, finance, healthcare, and transportation, to name just a few. The future of QAI is exciting and full of promise, and we can't wait to see what the next few years will bring. So let's strap in, get ready for the ride, and see where this incredible technology takes us!