quantum computing

Quantum Computing's Significance

quantum computer

In the following decade, quantum computing is expected to change a variety of industries, such as medical, machine learning, artificial intelligence, cryptography, and finance. Investors, governments, and businesses that seek eventual quantum dominance are the primary sources of funding for quantum computing advancements.

In 2019, the government of the United States created the 'National Quantum Initiative,' which aims to expand the quantum computing area. In addition, the government budgeted $1.2 billion for quantum realm promotion. Similarly, China is investing $10 billion on the construction of the 'National Laboratory for Quantum Information Sciences' in an effort to realize its quantum ambitions.

As quantum computing moves closer to becoming a reality, it is imperative to comprehend its significance.

Today, quantum computing is chosen for the following reasons:

the pursuit of solutions to growing complex issues

The issues encountered by humans today are far more complex than what can be solved by technological technology. Due to the complexity of these issues, it would require millennia for today's supercomputers to solve them.

Modern cybersecurity issues, optimization issues, stock profile management, aerospace and molecular research issues are a few examples. The modeling of proteins provides a second instance. During the COVID-19 pandemic, the scientific community attempted to identify a computational tool that could rapidly model and deactivate a single protein. This worldwide health problem would have been averted if such a tool had been accessible.

Currently, energy consumption is also a crucial factor. As a result of the exponential growth of the world population, energy consumption has increased substantially. This has produced the challenging problem of 'energy source optimization,' which cannot be solved by present computers. With the advent of quantum computing, it is now possible to solve such difficult issues.

the economic potential of quantum computing

Let's examine an illustration of an application that assists farmers, agricultural businesses, and allied industries.

Around fifty percent of the world's food supply is dependent on ammonia fertilizers, which is a well-established fact. This ammonia is created via the chemical process known as the Haber-Bosch process, which needs high temperatures and pressure. The process's physical limits are quite challenging to overcome, as they result in substantial energy consumption, which is one of the major issues.

Quantum computers have a role in this scenario. It has been established that our planet can create ammonia fertilizer at standard temperature and pressure using an enzyme called nitrogenase. However, this enzyme is produced using a complex catalytic method that modern computers cannot manage. Utilizing molecular modeling, the nitrogenase pathway is mapped by traversing roughly one thousand carbon atoms. Thus, it restricts the industrial production of nitrogenase, which has an effect on the total industrial output of ammonia-based fertilizers.

By constructing molecular models of nitrogenase, quantum computers can assist in this situation. Computing allows for the construction of similar molecules to the enzyme, which aids in the production of inexpensive and energy-efficient ammonia.

Ammonia-based fertilizers would be readily accessible and reasonably priced if quantum computers were utilized. The method would also lessen the load of energy consumption often observed during the nitrogenase production process.

helps with non-linear problems

Classical computing is ideally suited for linear problems in which sequential processes are the primary focus. Such computing systems are built on the foundations of linear mathematics, which examines linear equations and transformation features.

However, nature is intrinsically nonlinear and fraught with uncertainty. Classical systems are ineffective in addressing such nonlinear issues. Nevertheless, quantum computers are able to process nonlinear data. Optimization of traffic equilibrium, probability of moon landing, etc., are examples of such nonlinear issues.

managing a tremendous increase in data volume

Daily, a great amount of data is generated in our digital age and big data environment. With the advent of the internet of everything, each IoT device, wearable, gadget, and sensor is coupled to a computing network, contributing to the created data. Daily, computing devices generate approximately 2.5 quintillion bytes of data, as reported by Domo.

Modern computers and supercomputers are susceptible to errors when processing such a vast amount of data, which hinders performance. In addition, computational activities such as assessing the effects of medications on a molecular scale are difficult for traditional computers to perform. Quantum computers are better suited for such jobs due to their ability to process vast quantities of data more quickly.