Intelligence Encyclopedia | 2022 In-Depth Guide to Quantum Artificial Intelligence

Quantum computing and artificial intelligence are both transformative technologies, and artificial intelligence is likely to require quantum computing to make significant progress. Although artificial intelligence uses classical computers to produce functional applications, it is limited by the computing power of classical computers. Quantum computing can provide computational boost to artificial intelligence, enabling it to solve more complex problems and AGI (artificial general intelligence).

What is quantum artificial intelligence?

Quantum artificial intelligence is the use of quantum computing to calculate machine learning algorithms. Thanks to the computational advantages of quantum computing, quantum artificial intelligence can help achieve results that cannot be achieved by classical computers.

What is quantum computing?

Quantum mechanics is a general model based on principles different from those observed in everyday life. Using quantum computing to process data requires establishing a quantum model of the data. Hybrid quantum classical models are also necessary for error correction in quantum computing and for the correct operation of quantum computers.

• Quantum Data: Quantum data can be viewed as packets of data contained within qubits used for computerization. However, observing and storing quantum data is challenging because properties such as superposition and entanglement make it valuable. Furthermore, quantum data are noisy, requiring the application of machine learning at the stage of correctly analyzing and interpreting these data.
• Hybrid Quantum Classical Model: When only using a quantum processor to generate quantum data, it is extremely likely to obtain meaningless data. Therefore, a hybrid model has emerged, driven by fast data processing mechanisms such as CPUs and GPUs commonly used in traditional computers.
• Quantum Algorithm: An algorithm is a series of steps that lead to the solution of a problem. In order to perform these steps on a device, a specific set of instructions for the device's design must be used. Compared to classical computing, quantum computing introduces a different instruction set that is based on a completely different execution philosophy. The purpose of quantum algorithms is to exploit quantum effects such as superposition and entanglement to arrive at solutions faster.

Why is it so important?

While artificial intelligence has made great strides over the past decade, it has yet to overcome technological limitations. Barriers to achieving AGI (Artificial General Intelligence) can be removed with the unique properties of quantum computing. Quantum computing can be used for rapid training of machine learning models and creation of optimization algorithms. The optimized and stable artificial intelligence provided by quantum computing can complete years of analysis in a short time and lead technological advancement. Neuromorphic cognitive models, adaptive machine learning or reasoning under uncertainty are some of the fundamental challenges facing artificial intelligence today. Quantum artificial intelligence is one of the most likely solutions for the next generation of artificial intelligence.

How does quantum artificial intelligence work?

Recently, Google partnered with the University of Waterloo, ​​X​​, and Volkswagen to launch TensorFlow Quantum (TFQ): an open source library for quantum machine learning. The purpose of TFQ is to provide the necessary tools to control and simulate natural or artificial quantum systems. TFQ is an example of a suite of tools that combines quantum modeling and machine learning techniques.

Source: Google

1. Convert quantum data to quantum data set: Quantum data can be represented as a multi-dimensional Arrays of numbers, called quantum tensors. TensorFlow processes these tensors to create datasets for further use.
2. Select a quantum neural network model: Select a quantum neural network model based on the understanding of quantum data structure. The aim is to perform quantum processing to extract information hidden in entangled states.
3. Sample or mean: The measurement of a quantum state extracts classical information from a classical distribution in the form of samples. These values ​​are obtained from the quantum state itself. TFQ provides a means to average multiple runs involving steps (1) and (2).
4. Evaluating Classical Neural Network Models - Since quantum data is now converted into classical data, deep learning techniques are used to learn the correlation between the data.

The other steps of evaluating the cost function, gradients, and updating parameters are classic steps in deep learning. These steps ensure the creation of effective models for unsupervised tasks.

What are the possibilities for applying quantum computing in artificial intelligence?

Researchers’ near-term realistic goal for quantum artificial intelligence is to create quantum algorithms that outperform classical algorithms and put them into practice.

• Quantum Algorithms for Learning: Develop quantum algorithms for quantum generalization of classical learning models. It can provide possible acceleration or other improvements during deep learning training. The contribution of quantum computing to classical machine learning can be achieved by quickly rendering optimal solution sets for artificial neural network weights.
• Quantum algorithm for decision-making problems: Classical decision-making problems are formulated based on decision trees. One way to arrive at a solution set is to create branches from certain points. However, this approach becomes less effective when each problem becomes too complex to be solved by continually cutting it in two. Quantum algorithms based on Hamiltonian time evolution can solve problems represented by multiple decision trees faster than random walks.
Quantum Search:
• Most search algorithms are designed for classical computation. Classical computing outperforms humans on search problems. Lov Grover, on the other hand, presented his Grover algorithm and said that a quantum computer could solve this problem faster than a classical computer. Artificial intelligence powered by quantum computing holds promise for near-term applications such as encryption.
Quantum Game Theory:
• Classical Game Theory is a modeling process widely used in artificial intelligence applications. The extension of this theory to quantum fields is quantum game theory. It can be a promising tool to overcome key issues in the implementation of quantum communications and quantum artificial intelligence.
What are the key milestones for quantum artificial intelligence?

Although quantum AI is an immature technology, improvements in quantum computing increase its potential. However, the quantum AI industry needs key milestones to become a more mature technology. These milestones can be summarized as:

Less error-prone and more powerful quantum computing systems

• Widely adopted open source modeling and training framework
• Huge and proficient Developer ecosystem
• Convincing AI applications where quantum computing outperforms classical computing
• These critical steps will enable further advancements in quantum AI.

（​​by Cem Dilmegani​​）

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