With the exponential growth in computing power, quantum computing is getting ready for its close up. Quantum computers are ideally suited to solving complex problems, which are hard for classical computers but are easy to factor on a quantum computer. Such an advancement creates a world of opportunities, across almost every aspect of modern life.
In fact, Google has recently made headlines proclaiming the achievement of quantum supremacy, where its computers can perform a task that a conventional computer can’t. IBM is also making noise about their supercomputers, which are blazingly fast. However, we often wonder what these things actually do and what are its real-world applications?
In this article, we are going to talk about some of the top quantum computing applications in the real world.
Artificial Intelligence & Machine Learning
Artificial intelligence and machine learning is a prominent area right now because the industry is witnessing significant deployments at the consumer level of many different platforms. Some of the widespread applications we see every day are in voice, image and handwriting recognition. However, with the increase in applications, it becomes a difficult and computationally expensive task, especially if a good accuracy is required. And, that’s where quantum computing can help and can process through the test problem in very less time, which would have taken classical computers thousands of years to complete.
IBM, once said, one of the most promising quantum computing applications will be in the field of computational chemistry. It is believed that the number of quantum states, even in a molecule of caffeine, is astoundingly large, and therefore difficult for conventional computing memory to process that. The ability for quantum computers to focus on the existence of both 1 and 0 simultaneously could provide immense power to the machine to successfully map the molecules which, in turn, potentially opens opportunities for pharmaceutical research. Some of the critical problems that could be solved via quantum computing are — replacing Haber–Bosch process to produce ammonia to use in fertilisers; optimising new materials to achieve a room-temperature superconductor; searching for a catalyst to improve the efficiency of carbon sequestration, and developing a new battery to improve the performance over today’s lithium-ion batteries.
Drug Design & Development
Designing and developing a drug is the most challenging problem in computational chemistry. Usually, drugs are being developed via the trial and error method, which is not only very expensive but also a risky and challenging task to complete. Researchers believe quantum computing can be an effective way of simulating how a drug will react, which, in turn, can save a ton of money and time. These advancements in machine learning and optimisation could enhance the efficiency dramatically, with biomedical and chemical simulations could help companies carry more drug discoveries and be able to uncover new medical treatments in record time.
Cybersecurity & Cryptography
The online security space currently depends on the difficulty of factoring large numbers into primes. Although this can be presently achieved by classical digital computers to disintegrate every possible factor, the amount of time it takes to crack the code is an expensive and impractical task. Cybersecurity is indeed becoming an essential concern with threats around the world. And with our increasing dependency upon digital systems, we are becoming vulnerable towards these threats. Quantum machine learning can help in developing various techniques to combat cybersecurity threats and can also be used to mitigate the damage that they may do. There are also promising quantum encryption methods being developed using the one-way nature of quantum entanglement.
For a finance industry to find the right mix for fruitful investments based on expected returns, the risk associated, and other factors are important to survive in the market. To achieve that, the technique of ‘Monte Carlo’ simulations are continually being run on conventional computers, which, in turn, consume an enormous amount of computer time. However, by applying quantum technology to perform these massive and complex calculations, companies can not only improve the quality of the solutions but also reduce the time to develop them. Because financial leaders are in a business of handling billions of dollars, even a tiny improvement in the expected return can be worth a lot for them. Algorithmic trading is another potential application where the machine uses complex algorithms to automatically trigger share dealings analysing the market variables, which is an advantage, especially for high-volume transactions.
Improved data analysis and robust modelling will indeed enable a wide range of industries to optimise their logistics and scheduling workflows associated with their supply-chain management. The operating models need to continuously calculate and recalculate optimal routes of traffic management, fleet operations, air traffic control, freight and distribution, and that could have a severe impact on applications. Usually, to do these tasks, conventional computing is used; however, some of them could turn into more complex for an ideal computing solution, whereas a quantum approach may be able to do it. Two common quantum approaches that can be used to solve such problems are — quantum annealing and universal quantum computers. Quantum annealing is an advanced optimisation technique that is expected to surpass traditional computers. In contrast, universal quantum computers are capable of solving all types of computational problems, not yet commercially available.
Currently, the process of analysing weather conditions by traditional computers can sometimes take longer than the weather itself does to change. But a quantum computer’s ability to crunch vast amounts of data, in a short period, could indeed lead to enhancing weather system modelling allowing scientists to predict the changing weather patterns in no time and with excellent accuracy — something which can be essential for the current time when the world is going under a climate change. Weather forecasting includes several variables to consider, such as air pressure, temperature and air density, which makes it difficult for it to be predicted accurately. Application of quantum machine learning can help in improving pattern recognition, which, in turn, will make it easier for scientists to predict extreme weather events and potentially save thousands of lives a year. With quantum computers, meteorologists will also be able to generate and analyse more detailed climate models, which will provide greater insight into climate change and ways to mitigate it.
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