For months, artificial intelligence and natural language processing have been dominating the headlines throughout the tech industry, but there is another important topic on the horizon that has long been promising to introduce a paradigm shift: quantum computing. More than just a new tool or program, quantum computing changes the way that computers work altogether.
The science behind quantum computing might seem complicated, but learning about some real-world applications can be a good way of demystifying the concept, as well as learning what kind of scope quantum computing has beyond the cryptography conversation. With an ultimate boost to the power of computation, quantum computing promises to revolutionize modeling and computation in a variety of fields, including the ones below.
From trading to general analysis, financial modeling is an incredibly difficult task for a variety of reasons. The theoretical background too much of financial analysis is still surrounded by open questions, meaning that researchers in the field of finance are still working on finding explanations for all sorts of market behavior.
One of the reasons that finding solutions to these questions is so difficult comes from the computational power necessary to run programs that test these theories. Equity premium models often use intraday trading data that updates every 5 minutes or even to the millisecond, which makes the resulting databases immense and difficult to work with. As a result, complicated models are difficult to test, which limits the extent to which these research questions can be explored.
By exponentially increasing computational power, quantum computing would allow much more complicated programs to run and, as a result, much more complicated models to be tested. In finance, this could be a chance to answer more questions about market behavior and market performance, and potentially grant firms and businesses more information about what makes good business work.
Similar to financial modeling, a lot of work done in healthcare depends on computational power. Drug development, for example, often employs simulation programs that determine the effects a drug should have on the body. Naturally, there are enormous quantities of variables to take into consideration, which can make these simulations incredibly computationally challenging.
With the help of quantum computing, we can expect more complicated and sophisticated simulations to help better develop life-saving and other useful drugs. The higher computational power would also help other medical processes work more smoothly, like Machine-Learning-assisted medical diagnosis. With quantum-powered AI tools, medical professionals would be able to run more accurate and more sophisticated automated diagnostic tests, which can lead to quicker diagnoses and more people helped and saved.
To broaden the scope of applications a bit, we can also consider quantum computing applications to the field of weather forecasting. It is perhaps less well-known that weather patterns are also incredibly complicated systems. Certain mathematical atmospheric models consider the weather to be a chaotic system, which makes accurate forecasting incredibly difficult.
Simulating the conditions that lead to weather development is, again, a very computationally taxing process, and that high computational cost limits the complexity of the models that we can run. Quantum computing would grant us the ability to make much more accurate weather predictions, which not only makes for more convenient weather apps, but also offers valuable help in extreme weather event prediction and in climate change research.
Quantum computing is not a new topic per se, but we are closer to having its computational power available for regular consumer and professional use than ever before. As these new computational heights start to be in our reach, it’s important that we become familiar with the potential that comes with them—both good and bad.
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