Quantum computing: the story of one of the greatest quantum technologies (Part 4)

Quantum computing: the story of one of the greatest quantum technologies (Part 4)

We began by exploring the field of quantum computing with a theoretical perspective, and after introducing it and exploring its scientific implications, we came to the real physical systems that were capable of doing so. In the fourth and final part, we will discuss the broad applications of this field and its prospect.


Although the power of quantum computing is impressive, it does not mean that the software on these computers will run billions of times faster. In fact, quantum computers are good for solving certain types of problems. Here are some of the most important applications that we can expect from the commercial generation of quantum computers.

Artificial intelligence

Machine learning is one of the hot research areas today. We are seeing different aspects of this field every day in voice recognition, image recognition and handwriting recognition. However, it is computationally difficult and costly to make it more practical. One of the important applications of quantum computing is artificial intelligence. AI is based on the principle of learning from the experience and the more accurate feedback that is gained, until the computer program reaches intelligence. This feedback comes from calculating the probabilities of possible choices; therefore, artificial intelligence is an ideal candidate for quantum computing that can revolutionize any industry, from the automotive to the medical industry. Lockheed Martin, for example, plans to use D-wave quantum computers to test self-driving software that is currently very sophisticated for classical computers. Google is also using a quantum computer to design software that can distinguish cars from other things. We have now reached the point where artificial intelligence is building more artificial intelligence and so its importance is rapidly increasing.

Computational Chemistry

There are many issues in material science that can lead to a lot of financial gain, for example, if we find the catalyst or the right process to develop a new material or a more efficient one. There is currently a great deal of effort in using classical computers to simulate chemical interactions, but in many cases problems cannot be solved classically. So Richard Feynman's basic idea must be put into practice, "Why not use a quantum computer to simulate quantum processes?" Finding optimal configurations for chemical reactions in quantum chemistry is so complicated that today's digital computers can only analyze the simplest molecules. Chemical reactions are, in essence, quantum-mechanical, but fully developed quantum computers will have no problem to calculate even the most complex processes. Now Google has made some progress in this area by simulating the energy of hydrogen molecules, which results in more efficient products, from solar cells to useful drugs and, in particular, fertilizer production. Since fertilizers account for 2% of global energy consumption, the results will have a profound impact on energy and the environment. Here are some important examples that solving them can be of great financial benefit:

  • Replacing the Haber process for producing ammonia for use in fertilizers
  • Finding new materials that can lead to room temperature superconductors
  • Finding a catalyst that can increase the efficiency of carbon decomposition
  • Developing a new battery chemistry that can significantly improve the performance of today's lithium-ion batteries

Another very important area that can make significant progress in quantum computing is the pharmaceutical industry. Currently, many drugs are developed by trial and error. This method is very costly and, if other efficient ways are used to simulate how a drug reacts, it will save considerable time and money.

Financial optimization

Finding an optimized combination for risk assessment and return-on investments are essential tasks that are performed every day in the financial industry. Monte Carlo simulations of these tasks are constantly running on classical computers and spend a lot of time. If we use quantum technology to accomplish these tasks, we can significantly improve the quality of our solutions and development time. Since many investments have billions of dollars in turnover, even a one percent improvement will be of great economic value.

Modern markets are one of the most sophisticated systems available today. While there are advanced mathematical and scientific tools to address this, it still suffers from one major difference among other scientific fields: "There are no controlled settings for performing the experiments." Investors and analysts have resorted to quantum computing to solve this problem. In fact, the inherent randomness of quantum computers corresponds to the random nature of financial markets.

Planning and managing resources

Many of the current optimizations in the industry can fall into the category of planning and resource management, for example the airlines planning manager have to be aware of the airline plan in order to deliver the best service at the least cost or the financial manager of a car company should find the optimum price of hundreds of car features to maximize customer satisfaction and profit simultaneously. Although classical calculations are used to perform these tasks, a quantum approach can make calculations much more efficient.


With the rise of cyber threats around the world and the increasing dependence of societies on digital systems, vulnerabilities are becoming more and more prevalent. As a result, the importance of cyber security is increasing day by day. Using some of the machine learning approaches mentioned above, a variety of techniques can be developed to combat cyber security threats and to detect them earlier and thus reduce the damage.

Crack the codes

Although Shor's algorithm and its ability to factor large numbers and break RSA encryption have received a lot of attention, it seems only temporary, as the world is turning to post-quantum encryption techniques that are not vulnerable to quantum computers. Much research has focused on the development of post-quantum encryption, so although we may have quantum computers that can factor very large numbers, it is unclear that we can use them at that time.

Much of the current online and digital security depends on the difficulty of factoring large numbers into prime numbers. Currently, this is done using classical digital computers, and the time required to break a password is costly and impractical. Quantum computing can do this factoring exponentially and more efficiently than classical digital computers, proving that these security techniques will soon be outdated. We should note that new cryptographic methods are being developed: In August 2015, the NSA introduced a list of quantum-resistant cryptographic methods that will be resistant to quantum computers, and in April 2016, the National Institute of Standards and Technology (NIST) Started a public survey that would take 4-6 years.

Weather Forecast

According to economists, nearly 30 percent of US GDP is directly or indirectly affected by the climate that affects food production, transportation, and retail trade. The ability to better predict the weather will bring great benefits. Scientists have long sought to simulate the equations governing such processes which involve too many variables and prolong classical simulations. Quantum physicist Seth Lloyd believes that such an analysis by a classical computer would take longer than real climate change. This prompted Lloyd and his colleagues at MIT to show that the governing equations have a hidden wave nature that can be solved by a quantum computer. On the other hand, quantum computers can help build better climate models. Based on these models, we predict future warming. They help us take the necessary steps to prevent a disaster.

Particle Physics

The ultimate use of such exciting new physics may be the study of another exciting new physics! Particle physics models are often extremely complex and require a lot of time for numerical simulations, which makes them ideal for quantum computing. Researchers from the University of Innsbruck and the Institute for Quantum Optics and Quantum Information (IQOQI) recently used a programmable quantum system to perform such simulations. This simulation showed excellent compatibility with real physics experiments.

Investors are scrambling to enter the realm of quantum computing. Not only the computer industry, but also banks, space companies and cyber security structures will take advantage of this computational revolution. Although quantum computing currently affects the above, this list is by no means complete and this is the most exciting part of the story. Like all new technologies, quantum computing will have unimaginable applications that will be clarified as hardware evolves and creates new opportunities.

Circuit, software and Error-free simulation

Correcting and debugging large software applications with millions of lines of code or with ASIC chips that have billions of transistors can be costly and difficult. There are billions or trillions of different states, and it is impossible for a classical computer to handle all of them in one simulation. Quantum computing can be very useful in this field by increasing accuracy and decreasing runtime.

Future Prospects

There is still much unknown in the field of quantum computing that makes it difficult to predict the future. The limitations of quantum computing have not been proven yet, but quantum computers have proven to be more efficient for some problems than classical computers, so it is reasonable to assume that much more will be discovered. As such, it is difficult to predict that current hardware designs will yield a scalable design. An important problem is that maintaining the coherence and manipulation of qubits are the two necessary components of all designs. The least promising of current technologies is the nuclear magnetic resonance, so this route is unlikely to lead to practical quantum computers. Atom traps designs are efficient in maintaining coherence and have succeeded in entangling many qubits simultaneously. On the contrary, quantum dot designs have the advantage of extremely fast cycle times, while superconductors have the advantage that they can be produced using well-understood methods.

The Canadian company D-Wave was the first company that sold quantum computers in 2011, although these computers were limited to certain types of mathematical problems. IBM, Google, Intel and Rigetti (startup in Berkeley, USA) have made practical quantum computers. Intel has given researchers a superconducting chip. Microsoft has invested heavily in building a quantum computer using unusual design that may make it more practical for commercial applications. China is also building a $ 10 billion national laboratory for quantum information science. IBM, Google and D-wave use small loops of superconducting wires. Microsoft has used another trick and is trying to use the elusive subatomic particles called Majorana fermions that keep the qubits longer in a quantum state. Many of these approaches require very special conditions, such as temperatures 180 times colder than the depth of the cosmos!

Quantum computing will not be available universally in the near future for two reasons: The first is computing power. Among the world's largest quantum computers ever built, Google holds the highest record with a 72 qubit computer. But Rigetti has promised a 128-qubit computer in the near future. The second reason is errors. Scientists can keep qubits in a quantum state for only a fraction of a second. In many cases this time is too short to run an entire algorithm. Errors enter the calculations as qubits become decoherent, so the calculations need to be corrected by adding more qubits, but this consumes a great deal of computational power which, in the first place, negates the advantage of using a quantum computer. Theoretically, Microsoft's design seems to be more accurate, though it has so far failed to produce even a single qubit computer.

Since quantum computers are difficult to scale and there are many common applications for equally efficient classical computers, quantum computers may never reach widespread consumer use. On the other hand, the design of quantum algorithms requires not only computer programming knowledge but also an understanding of quantum physics. Intermediate programmers, or even many professional ones, are unlikely to be trained in this field, so quantum computer engineering will be limited to physics-trained programmers. As the technology matures, quantum computers may become useful and used as expensive computing machines by research laboratories or large corporations. On the other hand, research in this area has not been sufficient and needs to be continued, so an unexpected breakthrough that makes it practical to use may be discovered.

Nevertheless, quantum computing promises the ability to solve problems that are not solvable for classical computers for all practical purposes. It has been proven that some quantum algorithms are more efficient than any classical alternative and some of these algorithms have been implemented on small-scale prototype hardware. However, we are still far from the practical realization of a quantum computer. The same quantum-mechanical features that have made quantum computers superior, making it difficult to design quantum algorithms and build practical hardware. We reviewed five major categories of hardware designs for quantum computers. Each of these designs have different strengths and weaknesses, and while many are promising, none have yet fully implemented large-scale quantum computers, and predicting which of these designs will make this possible first is hard.

While there is a great deal of interest in the development of quantum computing, there has also been some doubt and criticism. Quantum computers have proven to be difficult to manufacture and are highly susceptible to noise and error. Researchers estimate that more than 99 percent of the calculations performed by a quantum computer will be for error correction. Paul Davies has argued that even a 400-qubit computer would have problems with cosmic information of holographic principle. However, some researchers believe that quantum computer design is a digital issue, and scalable quantum computing will be made possible through variable design and error correction codes. They believe that building a quantum computer is just an engineering issue. However, due to the clear horizons that emerge behind the realization of a quantum computer, research can continue until this dream is realized. The future will determine who is right!



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