Quantum Computing

 What is quantum computing?

Quantum computing is a rapidly-emerging technology that harnesses the laws of quantum mechanics to solve problems too complex for classical computers. It is an area of computing focused on developing computer technology based on the principles of quantum theory (which explains the behavior of energy and material on the atomic and subatomic levels). Computers used today can only encode information in bits that take the value of 1 or 0—restricting their ability.

Quantum computing, on the other hand, uses quantum bits or qubits. It harnesses the unique ability of subatomic particles that allows them to exist in more than one state (i.e., a 1 and a 0 at the same time).

Why do we need quantum computers?

For some problems, supercomputers aren’t that super.

When scientists and engineers encounter difficult problems, they turn to supercomputers. These are very large classical computers, often with thousands of classical CPU and GPU cores. However, even supercomputers struggle to solve certain kinds of problems.

If a supercomputer gets stumped, that's probably because the big classical machine was asked to solve a problem with a high degree of complexity. When classical computers fail, it's often due to complexity

Complex problems are problems with lots of variables interacting in complicated ways. Modeling the behavior of individual atoms in a molecule is a complex problem, because of all the different electrons interacting with one another. Sorting out the ideal routes for a few hundred tankers in a global shipping network is complex too.

Why quantum computers are faster?

Let's look at example that shows how quantum computers can succeed where classical computers fail: 

A supercomputer might be great at difficult tasks like sorting through a big database of protein sequences. But it will struggle to see the subtle patterns in that data that determine how those proteins behave.

Proteins are long strings of amino acids that become useful biological machines when they fold into complex shapes. Figuring out how proteins will fold is a problem with important implications for biology and medicine.

A classical supercomputer might try to fold a protein with brute force, leveraging its many processors to check every possible way of bending the chemical chain before arriving at an answer. But as the protein sequences get longer and more complex, the supercomputer stalls. A chain of 100 amino acids could theoretically fold in any one of many trillions of ways. No computer has the working memory to handle all the possible combinations of individual folds.

Quantum algorithms take a new approach to these sorts of complex problems -- creating multidimensional spaces where the patterns linking individual data points emerge. In the case of a protein folding problem, that pattern might be the combination of folds requiring the least energy to produce. That combination of folds is the solution to the problem.

Classical computers can not create these computational spaces, so they can not find these patterns. In the case of proteins, there are already early quantum algorithms that can find folding patterns in entirely new, more efficient ways, without the laborious checking procedures of classical computers. As quantum hardware scales and these algorithms advance, they could tackle protein folding problems too complex for any supercomputer.

Applications of Quantum Computing

• Improving Cancer Treatment

If recognized in its early stages, some forms of cancer are treatable. This treatment can include various methods such as surgery, chemotherapy, and radiation therapy.

A major factor in treating cancer by radiation therapy is beam optimization. Radiation given to a patient kills the cells in the region, which it affects.

Often, along with the cancer cells, some surrounding parts of the healthy cells, too, are destroyed.

Various methods of radiation have been developed in the past decade that uses our classical computers.

However, in 2015, the researchers at the Roswell Park Cancer Institute proposed a new way to optimize the radiation beams that uses quantum annealing computers.

• Portfolio Optimization

Knowing which assets to invest in and which asset to divest is a challenge that cannot be solved using modern-day computing.

Ensuring that your portfolio is healthy will be the difference in the capital you attain in the future.

Portfolio optimization deals with selecting the best asset to invest in, which balances the risk with the expected returns.

A tough problem to solve for conventional computers, but quantum annealing can help answer these in a jiffy.

• Make AI More Human-like

Artificial intelligence is the science that allows machines to simulate human behavior.

Researchers have been trying to develop AI, which is more human-like. Machine learning and neural networks are the underlying technologies behind AI.

Neural networks use data sets based on matrices for computational purposes, which uses matrix algebra.

Quantum computing itself works in such a way that often matrices are used to determine the states of the qubits.

So essentially, any computational process performed on the neural networks would be similar to applying transformational quantum gates on qubits (a quantum gate is a basic circuit operating on a small number of qubits).

This makes quantum computers a perfect fit to implement AI.

Conclusion

The field of quantum computing is growing rapidly as many of today's leading computing groups, universities, colleges, and all the leading IT vendors are researching the topic. This pace is expected to increase as more research is turned into practical applications. Although practical machines lie years in the future, this formerly fanciful idea is gaining plausibility.

The current challenge is not to build a full quantum computer right away; instead to move away from the experiments in which we merely observe quantum phenomena to experiments in which we can control these phenomena. Systems in which information obeys the laws of quantum mechanics could far exceed the performance of any conventional computer. Therein lies the opportunity and the reward. No one can predict when we will build the first quantum computer; it could be this year, perhaps in the next 10 years, or centuries from now. Obviously, this mind-boggling level of computing power has enormous commercial, industrial, and scientific applications, but there are some significant technological and conceptual issue to resolve first.

But quantum computers will come.