Data science has become the hottest job of the century, but that does not mean it is for all. Not everyone can become data scientists as it requires continuous learning. Every other day, we witness innovation in the domain, thereby keeping up with the pace is demanding. Consequently, you can aspire to other rising technologies that will democratise rapidly in the coming years. Today, due to the lockdown, one has ample time to explore innovative technologies that will become popular as we move ahead.

Here is the list of technologies that you can explore:-

Quantum Computing

A combination of maths, physics and computer science, quantum computing has started seeing the light of the sun. Due to the rise of the data science landscape, the need for processing a colossal amount of data is increasing as organisations are rapidly collecting data. However, due to the dearth of required computational power, firms are struggling to process gathered information, thereby creating data silos. And since Moore’s law is starting to fail, we have to move away from dated hardware methodologies.

In 2019, Google claimed supremacy in quantum computing by benchmarking computation in 200 seconds, which would have taken the current world’s fastest computer 10,000 years. The technology proposed in the 1980s by Paul Benioff took decades for researchers to make a breakthrough. Now that everyone sees value in quantum computing, it is poised to rise in the coming years. Getting your hand on such technology during its nascent stage can open up new opportunities in the future.

Start exploring by enrolling in this course for free.

Blockchain

Blockchain has been at the forefront of the rising technologies for quite some time. Earlier deemed as bitcoin and other cryptocurrencies, today, it is used in a wide range of use cases to simplify the authenticity while keeping the security in check. Undoubtedly, it will revolutionise the finance industry, the state-of-the-art blockchain will be widely used in digital identity, supply chain management, healthcare, among others. According to a report, global blockchain technology will expand to reach $176 billion by 2025 and exceed $3.1 trillion by 2030. Blockchain in the future might become the go-to technology for almost all enterprise operations. Consequently, one should pursue a career in blockchain and innovate to streamline business processes.

Blockchain course here.

AR/VR

Augmented reality and virtual reality has the potential to revolutionise the whole world by providing immersive experiences in almost every use case. As remote working is becoming the new normal, the demand for VR and AR has doubled in the last two months as per Hemanth Satyanarayan, chief executive Imaginate. Among other technologies, mixed reality would be the next big thing. And according to Digi-Capital AR/VR market will reach around $65 billion revenue by 2024. The COVID-19 crisis has made humans realise how important these technologies are, however, to democratise companies will have to overcome numerous barriers associated with the AR/VR landscape. The requirement of high-performance computation, intuitive 3D interface design, and more, have been the reason why VR and AR have witnessed a slower growth. But, companies like Facebook is showing promise in making breakthroughs in the field to enable mass adoption of the technology.

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The pace of improvement in quantum computing mirrors the fast advances made in AI and machine learning. It is normal to ask whether quantum technologies could boost learning algorithms: this field of inquiry is called quantum-improved machine learning.

Quantum computers are gadgets that work dependent on principles from quantum physics. The computers that we at present use are constructed utilizing transistors and the information is stored as double 0 and 1. Quantum computers are manufactured utilizing subatomic particles called quantum bits, qubits for short, which can be in numerous states simultaneously. The principal advantage of quantum computers is that they can perform exceptionally complex tasks at supersonic velocities. In this way, they take care of issues that are not presently feasible.

The most significant advantage of quantum computers is the speed at which it can take care of complex issues. While they’re lightning speedy at what they do, they don’t give abilities to take care of issues from undecidable or NP-Hard problem classes. There is a problem set that quantum computing will have the option to explain, anyway, it’s not applicable for all computing problems.

Ordinarily, the issue set that quantum computers are acceptable at solving includes number or data crunching with an immense amount of inputs, for example, “complex optimisation problems and communication systems analysis problems” — calculations that would normally take supercomputers days, years, even billions of years to brute force.

The application that is routinely mentioned as an instance that quantum computers will have the option to immediately solve is solid RSA encryption. A recent report by the Microsoft Quantum Team recommends this could well be the situation, figuring that it’d be feasible with around a 2330 qubit quantum computer.

Streamlining applications leading the pack makes sense well since they’re at present to a great extent illuminated utilizing brute force and raw computing power. If quantum computers can rapidly observe all the potential solutions, an ideal solution can become obvious all the more rapidly. Streamlining stands apart on the grounds that it’s significantly more natural and simpler to get a hold on.

The community of people who can fuse optimization and robust optimization is a whole lot bigger. The machine learning community, the coinciding between the innovation and the requirements are technical; they’re just pertinent to analysts. What’s more, there’s a much smaller network of statisticians on the planet than there are of developers.

Specifically, the unpredictability of fusing quantum computing into the machine learning workflow presents an impediment. For machine learning professionals and analysts, it’s very easy to make sense of how to program the system. Fitting that into a machine learning workflow is all the more challenging since machine learning programs are getting very complex. However, teams in the past have published a lot of research on the most proficient method to consolidate it in a training workflow that makes sense.

Undoubtedly, ML experts at present need another person to deal with the quantum computing part: Machine learning experts are searching for another person to do the legwork of building the systems up to the expansions and demonstrating that it can fit.

In any case, the intersection of these two fields goes much further than that, and it’s not simply AI applications that can benefit. There is a “meeting area where quantum computers perform machine learning algorithms and customary machine learning strategies are utilized to survey the quantum computers. This region of research is creating at such bursting speeds that it has produced a whole new field called Quantum Machine Learning.

This interdisciplinary field is incredibly new, however. Recent work has created quantum algorithms that could go about as the building blocks of machine learning programs, yet the hardware and programming difficulties are as yet significant and the development of fully functional quantum computers is still far off.

The future of AI sped along by quantum computing looks splendid, with real-time human-imitable practices right around an inescapable result. Quantum computing will be capable of taking care of complex AI issues and acquiring multiple solutions for complex issues all the while. This will bring about artificial intelligence all the more effectively performing complex tasks in human-like ways. Likewise, robots that can settle on optimised decisions in real-time in practical circumstances will be conceivable once we can utilize quantum computers dependent on Artificial Intelligence.

How away will this future be? Indeed, considering just a bunch of the world’s top organizations and colleges as of now are growing (genuinely immense) quantum computers that right now do not have the processing power required, having a multitude of robots mirroring humans running about is presumably a reasonable way off,  which may comfort a few people, and disappoint others. Building only one, however? Perhaps not so far away.

Quantum computing and machine learning are incredibly well matched. The features the innovation has and the requirements of the field are extremely close. For machine learning, it’s important for what you have to do. It’s difficult to reproduce that with a traditional computer and you get it locally from the quantum computer. So those features can’t be unintentional. It’s simply that it will require some time for the people to locate the correct techniques for integrating it and afterwards for the innovation to embed into that space productively.

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Physicists have applied the ability of machine learning algorithms to learn from experience to one of the biggest challenges currently facing quantum computing: quantum error correction, which is used to design noise-tolerant quantum computing protocols. In a new study, they have demonstrated that a type of neural network called a Boltzmann machine can be trained to model the errors in a quantum computing protocol and then devise and implement the best method for correcting the errors.

The physicists, Giacomo Torlai and Roger G. Melko at the University of Waterloo and the Perimeter Institute for Theoretical Physics, have published a paper on the new machine learning algorithm in a recent issue of Physical Review Letters.

“The idea behind neural decoding is to circumvent the process of constructing a decoding algorithm for a specific code realization (given some approximations on the noise), and let a neural network learn how to perform the recovery directly from raw data, obtained by simple measurements on the code,” Torlai told Phys.org. “With the recent advances in quantum technologies and a wave of quantum devices becoming available in the near term, neural decoders will be able to accommodate the different architectures, as well as different noise sources.”

As the researchers explain, a Boltzmann machine is one of the simplest kinds of stochastic artificial neural networks, and it can be used to analyze a wide variety of data. Neural networks typically extract features and patterns from raw data, which in this case is a data set containing the possible errors that can afflict quantum states.

Once the new algorithm, which the physicists call a neural decoder, is trained on this data, it is able to construct an accurate model of the probability distribution of the errors. With this information, the neural decoder can generate the appropriate error chains that can then be used to recover the correct quantum states.

The researchers tested the neural decoder on quantum topological codes that are commonly used in quantum computing, and demonstrated that the algorithm is relatively simple to implement. Another advantage of the new algorithm is that it does not depend on the specific geometry, structure, or dimension of the data, which allows it to be generalized to a wide variety of problems.

In the future, the physicists plan to explore different ways to improve the algorithm’s performance, such as by stacking multiple Boltzmann machines on top of one another to build a network with a deeper structure. The researchers also plan to apply the neural decoder to more complex, realistic codes.

“So far, neural decoders have been tested on simple codes typically used for benchmarks,” Torlai said. “A first direction would be to perform error correction on codes for which an efficient decoder is yet to be found, for instance Low Density Parity Check codes. On the long term I believe neural decoding will play an important role when dealing with larger quantum systems (hundreds of qubits). The ability to compress high-dimensional objects into low-dimensional representations, from which stems the success of machine learning, will allow to faithfully capture the complex distribution relating the errors arising in the system with the measurements outcomes.”

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