Techpally Discern Quantum Computing Applications
Quantum Computing is now applied in any business that processes a lot of data. There are different ways quantum computing or computer can be used to process data, some of them are discussed below.
Quantum computer application “QAA” – Quantum Adiabatic Algorithm
The QAA is based on the Adiabatic Theorem of Quantum Mechanics from 1928. The Quantum Adiabatic Algorithm is realized on the hardware side by the Quantum Annealer from D-Wave and can already be used there today. For this reason, I classify it as “available at short notice”.On universal quantum computers, the Quantum Adiabatic Algorithm is an application of the Hamiltonian Simulation (see above).
Therefore, the QAA on NISQ quantum computers will initially only be used in a modified form, said chaktty.
An example of this is the QAOA:
Quantum computer application “QAOA” – Quantum Approximate Optimization Algorithm. Like the Quantum Variational Eigensolver, the Quantum Approximate Optimization Algorithm is a quantum-classical hybrid algorithm:
The qubits are set to represent a candidate for the solution of the optimization problem. With this candidate, the quantum computer determines the cost function of the problem. With a quantum computer, this should be efficiently feasible. Since this measurement is always a matter of probabilities, it is performed several times and the best value is used.
According to Techpally, Using a conventional computer, a new candidate for the solution of the problem is calculated based on the previous optimization process. The team around Edward Farhi also specifies how the solution candidates are determined. They are nothing more than a parameterization of the adiabatic path in the QAA. According to Businesspally’s boss, this is an indication that this solution approach leads to the goal.
Application in “Artificial Intelligence and Machine Learning“
I also dedicate an article on artificial intelligence and machine learning on quantum computer-info.de. Here I present the core statements of my article only briefly:
Two main topics of Artificial Intelligence and Machine Learning are examined in more detail in the context of quantum computer research:
Unsupervised Machine Learning: Searches for correlations and groups in raw data without any additional information.
The raw data is limited to the “essential”. Last but not least, Unsupervised Machine Learning determines what the “essential” in the context actually means.
Supervised Machine Learning: Known and already evaluated data are used to evaluate new unknown data.
Deep learning” with neural networks, which is on everyone’s lips, belongs to this category (e.g. for face or speech recognition. For Unsupervised Machine Learning, a series of quantum algorithms have been developed since 2008, which promise exponential acceleration.
According to businesspally, Many of these algorithms are based on the fact that linear algebra (the mathematics of pointers or vectors) is built into quantum computers quasi by nature. And they are exponentially efficient!
In the summer of 2018 the bang came: The young student Ewin Tang proved to the entire research community that some of these algorithms are much less spectacular than generally assumed. I won’t reveal more at this point. The application of the second focal point of artificial intelligence with quantum computers, supervised machine learning, has been studied in far less detail to date. Instead, research in recent years has specifically begun to develop strategies for quantum neural networks with NISQ quantum computers.
Among other things, the algorithms exploit the findings that have already led to the development of the QAOA and QAA algorithms (see above). The main argument for quantum neural networks is the following: Quantum computers are themselves something like “networks on steroids”. They possess possibilities that would be unthinkable for conventional network structures (like quantum entanglement). The conclusion suggests that this can create added value for deep learning.
Quantum computer application “IT consulting
Current quantum computers are not yet able to enable interesting applications. This could even change in the NISQ era and possibly very abruptly. There are indications that a kind of “Moore’s Law” could also apply to quantum computers xii, which for a long time accurately described the acceleration cycles of conventional computers. I have presented possible quantum algorithms for this in this article. Companies that rely on high-end algorithms usually cannot afford to miss a development with so much potential.
However, in order to make even the first steps in the quantum computing world, such special know-how is necessary that even companies with a lot of IT experience will not be able to get off the ground without support. For this purpose, it is imperative to deal with the current research on the respective quantum algorithms, which I have presented e.g. in this article.
The manufacturers of quantum computers are of course aware of this and are here accompanying you through your first steps. By the end of 2018, D-Wave was responsible for about 150 customer projects. In addition, two startups have made a name for themselves, specializing in sounding out the future market for software solutions independently of manufacturers and developing and bundling know-how in quantum computing: The Vancouver company 1QBit and QCWare from Silicon Valley.
I assume that both customers and North American companies will sooner or later realize that they are also dependent on IT consulting in Germany and Europe: For introductory events, to give an impression of the technology and the current possibilities at. As an interface between manufacturer and customer, which speaks both languages and is better available As an expert to implement projects directly and independently on the cloud offerings