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Quantum AI

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[Stanford University - Andrew Brodhead]


- Overview

Quantum artificial intelligence (AI) is an emerging field that studies quantum computing. Quantum computing uses qubits, or tiny particles, to perform calculations quickly. It operates non-linearly, outperforming conventional computers in speed, efficiency, and accuracy.

Quantum AI can be used in many applications, including: 

  • Healthcare: Quantum simulations can speed up drug discovery and analysis.
  • Cybersecurity and finance: Quantum AI can handle high-dimensional data.
  • Machine learning: Quantum AI Deep Learning engines can help machine learning algorithms mine complex and unstructured data.


Some other applications of quantum AI include:

  • Trading: Quantum AI is a trading platform for digital currencies that uses automated processes and complex algorithms.
  • Quantum error correction: Google Quantum AI simulates the braiding of anyons and encodes logical qubit states for quantum error correction.


Quantum AI research presents the opportunity for exponential speedup in solving certain classes of problems. Quantum algorithms have the potential to outperform classical algorithms in tasks such as optimization, factorization, and machine learning, unlocking new possibilities for innovation and discovery.

- Quantum AI

Quantum AI is here to pave the way for the next chapter in our quest for digital intelligence. AI is a transformative technology that requires quantum computing to achieve major improvements. 

While AI can be used with conventional computers, it is limited by conventional computing power. Quantum computing can enhance the ability of AI to solve more complex problems, allowing it to solve more complex problems. 

Quantum computing may lead to more efficient and stable AI that can perform years of analysis in a few hours, leading to technological advances. Today's AI has trouble with problems such as neuromorphic cognitive models, adaptive machine learning, and reasoning under uncertainty. These will be the most likely remedies for future quantum AI.

One way to effectively evaluate and improve AI systems is to mimic real-world data. We can use quantum AI with the help of this technology to make fake molecules, cancer cells or MRI images that closely resemble the real thing. This allows us to build better machine learning models that can be applied to real-world data and problems.


- The Objectives of Quantum AI Research

Artificial intelligence (AI) has made huge strides in recent years, but computational limitations have yet to be overcome. The unique properties of quantum computing can help us overcome the barriers to AGI (Artificial General Intelligence). The ability to quickly train machine learning models and develop optimization algorithms can be harnessed from quantum computing.

We are approaching the limits of what a typical computer can handle, and the amount of data continues to grow. Moore's Law, which states that the number of transistors on an integrated circuit doubles every two years, has proven extremely durable since its introduction in 1965. Leading companies in the field are racing to be the first to develop a viable quantum computer that will be more powerful than today's computers and can solve increasingly complex problems.

Quantum technologies have reached a point where widespread implementation requires the involvement of multiple disciplines. The goals of this Quantum AI are to investigate the potential use of AI and related technologies for quantum applications, and to inform the computational intelligence community about current advances in quantum information technology. 

Over the past few decades, many quantum information and processing systems have been created and verified in laboratory, field, and commercial settings. The results demonstrate the feasibility of practical applications in areas related to quantum AI. This includes data security, optimization, finance, high precision sensors, simulation and computer applications. 

The research and development of quantum technology has received strong support from enterprises and governments. However, a lot of work still needs to be done to bring quantum technology-based gadgets and systems into consumers' homes. Additionally, many challenges present opportunities to contribute knowledge, technology, and engineering from outside the field of AI.  


- Quantum AI Research

The research of quantum AI is at the intersection of quantum information science (QIS), artificial intelligence, soft computing, computational intelligence, machine learning, deep learning, optimization and other disciplines, involving many important contents of recent quantum computing and quantum computing. It touches on many important parts of near-term quantum computing and noisy intermediate-scale quantum (NISQ) devices.

Research in quantum AI is based on theory, modeling, and significant research into hybrid classical-quantum algorithms using classical simulations, IBM Q services, PennyLane, Google Cirq, D-Wave quantum annealers, and others. 

So far, the research of quantum AI provides us with the building blocks to achieve quantum advantage, which can solve problems in combinatorial optimization, soft computing, deep learning and machine learning faster than traditional classical computing. 

Addressing these issues is important to making quantum computing usable for noise-resistant, large-scale applications. This makes it easier to see the big picture and facilitates cutting-edge research across the quantum stack, making it an essential part of any QIS effort. 


- The Research Areas of Quantum AI

In recent years, we have seen research being done to improve artificial intelligence systems based on quantum computing ideas. This emerging quantum AI research field focuses on the research of quantum computing, which is characterized by combining certain principles of quantum mechanics such as standing waves, interference, qubits, coherence, state superposition, and interference concepts with machine learning. 

Computational intelligence and soft computing methods, such as artificial neural networks, fuzzy systems, evolutionary computing, swarm intelligence, and hybrid soft computing methods. This research area showcases a wide range of research projects integrating artificial intelligence with quantum computing systems. 

The Research areas relevant to quantum AI include but are not limited to the following topics:

  • Quantum machine learning,
  • Quantum-inspired soft computing,
  • Hybrid classical-quantum neural network models,
  • Qubit- and qutrit-based quantum-inspired neural network models,
  • Quantum optimization,
  • Hybrid classical-quantum algorithms
  • Variational quantum algorithms
  • Quantum metaheuristics


- The Generative Models of Quantum AI

The generative models of quantum AI algorithms can not only answer questions, but also generate output of images, music, video or other types of information. 

Consider if you have lots of photos of faces in profile but not enough images of faces in front. If you want security detection software to recognize double facial recognition on the front of a person's face, you can use these generative models to generate more frontal views very accurately. Adding a quantum processing unit to a conventional framework has the potential to improve image quality. 

Traditional machine learning methods are only as good as the data you feed them. If you try to train a traditional face detection algorithm on a small set of images, it won't work. 

However, you can use quantum-enhanced generative models to expand this dataset by including more pictures (number and diversity), which will greatly improve the detection model.


- How Does Quantum AI Work?

According to Google, how quantum AI works involves four main steps: 

  • The first step is to convert quantum data into conventional data. Afterwards, this data is converted into a multidimensional array of numbers as a quantum tensor. Quantum AI transforms these tensors to create datasets that can be used further.
  • Selecting quantum neural network models based on quantum data structures. The goal is to use quantum processing to extract information hidden in entangled states.
  • Quantized quantum states extract information from classical distributions in the form of samples. These values come from the quantum state itself. Quantum AI provides programs that combine many runs, each with its own steps (1) and (2).
  • Deep learning algorithms identify relationships between data after transforming it into classical data.


The final stages of deep learning with cost functions, gradients, and parameter updates are standard approaches in the field. These stages ensure that a good model is produced for unsupervised activities.


[Honolulu, Hawaii - Civil Engineering Discoveries]

- Better Algorithms of Quantum AI

The flexibility of quantum AI in representing and processing so many states makes it extremely useful in solving a variety of problems. 

The first application we're seeing is materials science, where simulating small molecules requires computation. For example, larger devices will be able to create drugs and optimize logistics by determining the most efficient path among any number of future alternatives.

Most current industrial applications of AI are based on supervised learning for problems such as image recognition and consumption prediction. 

Based on the various QML concepts that have been proposed, we can predict - in some cases potentially exponential - speedups for some of the most popular algorithms in the field, including "support vector machines" and certain types of neural networks.

Dimensionality reduction techniques are another type of dimensionality reduction. These techniques represent our raw data in a more compact form while preserving most of the features. At this stage, the scientist pointed out how quantum computing would play a role in determining certain global features in data sets rather than individual details.


- Research Topics of Quantum AI

The link between quantum mechanics and AI also yields benefits in both directions. Now, scientists are using traditional machine learning to improve our understanding of quantum physics. 

The accuracy and efficiency of AI algorithms can be used to fine-tune quantum circuits, which can help eliminate errors and save time in most problem domains of quantum research. Machine learning is also helping physicists detect quantum entanglement and identify new phases of matter. 

Research areas related to quantum AI include but are not limited to the following topics:

  • Quantum machine learning
  • Quantum-inspired soft computing
  • Hybrid classical-quantum neural network model
  • Quantum inspired neural network model based on Qubit and qutrit
  • Quantum optimization
  • Hybrid classical-quantum algorithms
  • Variational Quantum Algorithms
  • Quantum meta heuristics


[More to come ...]

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