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How Close Are We To Quantum Artificial Intelligence?

What Exactly Is Quantum Artificial Intelligence?

Although poorly understood at the moment, quantum artificial intelligence (QAI) is a promising technology that could be transformative to so many verticals, potentially enhancing — as well as accelerating — tried and tested artificial intelligence (AI) techniques.

QAI is a field of study that combines quantum computing with artificial intelligence (AI). It seeks to use the unique properties of quantum computers which leverage quantum mechanical effects (such as superposition and entanglement) to enhance the capabilities of AI systems.

However, quantum computing is still in its early stages, and there are many technical challenges that must be overcome before they can be used to implement QAI. Nonetheless, there is much excitement and research happening in this area, and QAI is seen as a promising area for future breakthroughs in AI. While some experiments have been conducted using quantum computing to enhance machine learning (ML) algorithms, these efforts are still in the initial trial stages of development and are not developed enough to be effective for world-world use cases.

How Quantum AI Works?

QAI works by exploiting the unique properties of quantum computers, such as quantum entanglement and superposition to carry out AI / ML tasks that would be hard or impossible to execute on classical computer systems.

We will now list the steps required to perform a QML task:

1) The first task is to encode the data into a quantum state, usually accomplished by using quantum circuits, which are a string of quantum gates that operate on qubits (a qubit is the basic unit of quantum information).

2) After the encoding has been done, the quantum computer can be utilized to teach a quantum model by calibrating the specifications of the quantum circuit so the outputs always give out the correct answer for any given input. The training operation is usually executed using a quantum version of gradient descent. A gradient descent requires calculating gradients of the output in terms of the circuit parameters and updating them when needed.

3) The last step in performing a QML task once the model has been trained is to make predictions on new data obtained. This demands encoding the new data into a quantum state and putting it through the trained quantum circuit to acquire the predicted output.

Although Quantum AI promises to revamp current ML techniques, the caveat to this is it is still an emergent technology that faces a plethora of challenges. One important roadblock is developing large-scale, error-corrected quantum computers that are able to perform these tasks with a high level of accuracy, while also being efficient at performing the tasks. This, however, doesn’t take away the fact that QAI is a promising technological discipline that has the capability to present us with new insights and possibilities in a whole range of fields.

Better Quantum AI Algorithms

One approach to improving QAI outcomes could be by coming up with better quantum algorithms, though this does require top-level expertise and a deep understanding of quantum mechanics, computer science and mathematical optimization.

The action required is easy: identifying the problem you want to solve using quantum algorithms. These could be in simulation, optimization, cryptography, or other areas.

Next is choosing the correct quantum algorithm for the given problem. Once you are clear about the specific algorithm you want to use for the particular problem, the next step is to optimize the quantum circuit to make it as efficient as possible. This requires decreasing the number of gates needed to implement the algorithm, which naturally lowers the number of qubits required, thus minimizing the amount of noise in the system.

After optimization has been achieved, error correction must be applied as quantum systems (computers) are prone to errors owing to noise and other anomalies. To lower error rates, you can apply surface code to safeguard the quantum state and enhance the fidelity of the quantum computation.

The next step is to experiment and iterate the quantum algorithm by running it on a real quantum computer or a quantum simulator. Once the algorithm has been put through it, you can analyze the results which allow for analysis to improve the overall performance of the algorithm.

One final step to refine the algorithm’s performance could be by collaborating with experts in the field. These should be specialists in computer science, quantum mechanics or mathematics. Here, their knowledge could give solid feedback on the quantum algorithm, which would lead to further experimentation, correction of the algorithm and ultimately improved performance over time.

Main Applications Of Quantum AI?

Here is where it gets interesting, as efficient QAI techniques have the power to transform many industries and fields.

We will now briefly discuss:

As already mentioned, Quantum AI can improve outcomes in ML by improving upon tried and tested ML techniques. This will lead to improved prediction rates and pattern recognition but can also be applied for unsupervised learning, clustering, and anomaly detection.

Chemistry and materials science is another discipline where QAI techniques can be used to simulate chemical reactions and predict the properties of new materials, leading in the long run to a revolution in drug discovery development and materials discovery.

With global warming an ongoing problem, QAI applied to climate modelling will be crucial in the future, as it can more accurately predict climate change than current techniques.

Quantum AI can also be useful for solving practical optimization problems such as those found in logistics and supply chains and for processes found in manufacturing.

Besides this, Quantum AI can be beneficial for the finance sector in improving financial data, identifying trends, making predictions, risk management assessment, and fraud detection.

Another area where QAI can play an important role is in cryptography by formulating more secure encryption algorithms, which are resistant to attacks from classical computers.

Finally, we have QAI for Artificial General Intelligence (AGI). Here QAI can be used to advance AGI systems that have the potential to think and learn like humans, bringing us to new and exciting discoveries in disciplines such as computer vision, language processing and robotics.

Could Quantum AI Change The Face of the World Forever?

The simple answer is yes, as rigorous QAI techniques applied to some of the fields above are life-changing in so many ways.

We must be aware, however, that we are still developing this technology and it may take many years of early-stage development to bring us to the next level. In spite of this, when we reach that point, QAI will definitely transform our world.

A prime example use case of this is the partnership between IonQ and Hyundai Motor in 2022 to leverage quantum machine learning to enhance the computation process for road sign image classification and simulation in a real-world test environment.

Featured image: Credit: Image by Gerd Altmann from Pixabay

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What: GenMat Webinar

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Jake Vikoren

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James Dargan

James Dargan is a writer and researcher at The Quantum Insider. His focus is on the QC startup ecosystem and he writes articles on the space that have a tone accessible to the average reader.

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The Future of Materials Discovery: Reducing R&D Costs significantly with GenMat’s AI and Machine Learning Tools

When: July 13, 2023 at 11:30am

What: GenMat Webinar

Picture of Jake Vikoren

Jake Vikoren

Company Speaker

Picture of Deep Prasad

Deep Prasad

Company Speaker

Picture of Araceli Venegas

Araceli Venegas

Company Speaker

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