Q. A. I: Current State, Trends, and Prospects
Quantum Computing and Artificial Intelligence are both emergent technologies that are expected to, and in some cases already are, drastically change our way of life. The ability of AI to process large sets of data and perform complex pattern recognition tasks combined with the unparalleled computational power of Quantum Computers is an exciting prospect that promises not only a giant leap in processing capacity but also the possibility of tackling computational problems that were simply not possible before.
In the past few years, we have already seen a huge increase in AI being applied to virtually every imaginable sector, from finance and military to aerospace, agriculture, healthcare and manufacturing.
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Although Quantum Computing has taken a bit longer to enter mainstream use, huge strides have been made in just the last few years with near term quantum systems already achieving quantum supremacy, and commercially available platforms already in existence. With these technologies becoming more and more common, one of the biggest questions that is being asked is: how do we combine the efficiency of AI and power of QC and what can we do with it? This has led to the development of a new regime of technologies, Quantum-Artificial-Intelligence (Q-AI).
Although the merger of these disruptive technologies is still largely in the conceptual domain, there are already a number of examples where it is being applied to solve real-life problems. The main aim of Q-AI is to develop or improve quantum algorithms. In many cases machine learning is used in optimization problems (and these are applied almost everywhere!). So improving and enhancing the capability of AI to deal with these optimization problems is very valuable.
This is where quantum computing steps in, the algorithms deployed on quantum systems are intrinsically different from those of classical computers and means that certain problems can be more efficiently dealt with. This means that optimization problems can be better addressed. Furthermore, the unique hardware used to create quantum computers can also be important for developing specialized computation platforms that drive AI development such as in the case of GPUs. Using quantum computers to enhance AI can obviously have serious implications for industry.
Similarly, a lot of interest exists in using machine learning techniques to evaluate and enhance quantum computers. This is important because conventional QC are noisy (they operate based on the physics of Quantum mechanics and can be difficult to predict and control which can lead to computation errors). To deal with this obvious problem, machine learning can be used to evaluate data produced by the qubits and assess whether the qubits are operating property. This is very important for the near-term quantum computers commercially available because they generally only have a small number of qubits and therefore don’t produce too much data, making error correction difficult. In this way machine learning can be used for error correction, and greatly enhance the current capacities of Quantum Computers.
One of the most common applications of Quantum Computers is in simulating molecular systems or chemical interactions. This is because these computational problems are quite complex and require enormous computational resources. When combined with AI, quantum computing sees a new paradigm for modelling and simulating biological problems. These problems are enormously valuable for various industries, most notably pharmaceuticals. Here Q-AI is already being used for important problems such as molecular interactions and protein folding which are used for drug design and drug performance predication. This is probably one of areas we are seeing the fastest adoption of Q-AI with established companies as a well as serval newly created start-ups dedicated to this application .
Another area that is seeing the combined efforts of Quantum Computing and machine learning is in the aerospace industry. A typical aircraft can have literally hundreds of thousands of sensors onboard. These are used to help navigate the plane considering the various parameters such as wind speed and direction, weather parameters, etc. Analyzing and processing such a large amount of data in real-time is an unbelievably expensive
computational task and very difficult for classical computers to deal with. This does however make for an ideal problem for Q-AI; not only is there a lot of data to process, but the chaotic and probabilistic and chaotic nature of the parameters is ideal of Quantum Computing to deal with. This data can also be used in manufacturing optimization, in this way Q-AI is potentially valuable for improving how planes are built and flown. This application has already attracted significant investment from companies such as Airbus and Lockheed Martin .
Although these developments are quite exciting, at this stage it is still difficult to predict just how fast we will see large scale adoption of Q-AI. This is because, being as novel as it is, corporations need to invest in not only new infrastructure but also the human capacity with entirely new skill sets required to run these systems.
Although we have not yet scratched the surface of the full potential of Q-AI, we are seeing increase in creative research directions as well as interest from industry. This is a good indication that the combined efforts of Quantum Computing and Artificial intelligence are probably going to play a
significant role in future technological developments and economic culture.