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Learn about the state of Quantum computing

In the last quarter of 2018, The E.U. launched the Quantum Flagship megaproject with more than 1000 million euros in funding for a 10 – year period and more than 5000 researchers committed to the development of quantum technologies and to bring their capabilities into the market. Europe has finally made it to the race started by China and the U.S. Both countries want to dominate, or at least, to lead the so- called quantum race. The European Union finally rode on the most powerful research and development program of recent years.

This Quantum Flagship will build an European program network based on quantum technologies which will boost an ecosystem to provide the necessary knowledge, technologies and infrastructure for the development of this industry. The research areas are focused on quantum communication (QComm), quantum computing (QComp), quantum simulation (QSim), meteorology and quantum detection (QMS) and basic science (BSci).

Big technological companies doing research on quantum computing are immersed in a commercial race to see which will be the first to run a quantum computer. However, which are the main features of quantum computing and how can we benefit from it?

Current computers, either portable or big computers, are based on basic binary circuits, in other words, a “yes/no” response. Thus, programmers can create tasks to make that computer work with these sentences “if this/then that”. However, sometimes a computer with these characteristics cannot solve efficiently certain problems. For example, in many mathematical optimization problems, current computers take their time to evaluate individually every possible solution until optimal is found.

In contrast, quantum computers have a completely different concept as they do not use the “yes/no”. binary logic. By their nature, their basic circuits can respond to “yes/no/both (in the same proportion)”. With a quantum system, a developer can implement instructions “if this, then that/not-that/both” and here is what makes the difference. They can explore a great volume of data at the same time, offer very efficient solutions to very complex problems such as transport routes optimization.

Until now, quantum computing has not been developed yet, at least the way we are used to (a computer executing tasks), because one of the problems facing quantum computing is building multipurpose quantum computers. Compared to a normal computer, a quantum machine is extremely complex. The first models available, like the IBM´s recent version, are based on superconductive constituents (including Josephson effect devices), which need to work at a temperature below -273 ºC (almost absolute cero) The necessary cryogenics technology of the components to be able to read and handle steadily these qubits are extremely expensive and complex.

Despite the inherent technical difficulties to quantum-mechanic systems, extensive research has been carried out and confirmed promising applications of quantum computing as soon as the necessary hardware is ready. Artificial Intelligence is among them.

fraude

Supervised and unsupervised learning techniques for fraud detection.

Artificial intelligence is redefining fraud prevention techniques as it allows to obtain information based on experience. This information consists of transaction activities, behavior and trends. Before the use of artificial intelligence, the applied methods were based on rules that helped to analyze historical fraud patterns but could not prevent them. Although these models could identify fraud attempts, they did not provide information of the future.

Nevertheless, the technological sophistication of fraud crimes is higher and more precise and efficient attacks have grown during the last few years

As a result, leading companies dealing with potential fraud crimes, mainly in banking and insurance, must increase monitoring accuracy and acuity of customers potential risk for the institution. Decision-making on accepting or rejecting payments, limiting charges refund and reducing operational and reputational risks is much easier now.

Fraud prevention in the future will depend on a combination of supervised and unsupervised automatic learning techniques. Supervised automatic learning finds patters based on historical events, factors, trends, etc., and unsupervised automatic learning looks for relationships and variable links, a combination of both methodologies would help to prevent fraud in the following:

Detection in real time. The use of artificial intelligence enables the detection of attacks in real time, instead of weeks that usually takes to start receiving reverse requests of charges.

Thwarting the most sophisticated attempts of fraud. Fraud techniques get more and more sophisticated. Artificial intelligence would help to prevent and reduce these attacks.

Scoring in real time. Provide analysts a scoring for a better perspective to set the limits to maximize sales and minimize losses in real time.

Immediate transactions. Fraud prevention systems based on AI enable immediate transaction´s approval provided it is within the reverse charges threshold of the main debit and credit cards.

Reduction of false positives. False positives are reduced thanks to supervised and unsupervised automatic learning, whereas current techniques cannot efficiently detect them. Frequently, when a customer pays an unusual amount of money or from a new location, the card is blocked by the system as it interprets it wrongly as a suspicious activity. With artificial intelligence, it is possible to identify more precisely any change in customers expenditure habits.

Profitability in low margin products. AI has allowed insurance companies to continue their profitable business and attract new customers whose historical purchases are not part of the historical supervised learning of fraud systems

Supervised and unsupervised learning should be complemented with experts´ knowledge aiming at a mixed approach to focus their attention in more suspicious cases detected by AI.

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Edge Computing, are we going back to physical DPCs or is it the trendy buzzword?

In a future filled with data from thousands of millions of devices connected to the internet, faster and more reliable data processing will be crucial for industries development. In recent years, we have witnessed the Cloud computing consolidation thanks to its centralized and scalable nature.

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Data has significantly grown with the advent of network devices in the health sector such as medical clinical histories, diagnostic processes, and more particularly, medical imaging -the introduction of Real-World Evidence. Making correct use of this data could save a great number of lives and reduce sanitary costs.