As modern civilization is continuously developing, there is a need to discover new paths or ways to exploit physical quantities like energies, forces as well as materials. The idea of a computing machine was started from the early thinking of the famous Charles Babbage, who discovered the world’s first mechanical computer. Babbage’s machine was not typically a computer, which is seen today, but a different engine used for calculating mathematical problems of polynomial functions. Charles Babbage was credited as the father of the computer. Technological development in the field of computer science and technology can be seen as the culmination of technological advancements year after year. Since the discovery of Integrated Circuit (IC) technology, computers are continuously growing smaller and smaller despite high computational capabilities. Although computers of modern times are very advanced, but the basic working principle remains the same. All the instructions given by the user are converted into binary bits i.e., 0 or 1 for the computational purpose. As all computers have to use binary bits, they possess computational limitations. To overcome limitations scientists of various organizations like Google, IBM, Intel as well as Apple tend to use Quantum Mechanical principles to build new types of computing machines. In recent times Google claimed that they have achieved quantum supremacy by creating a quantum processor named Sycamore. IBM also developed its first quantum computing machine. These two discoveries open new frontier in science and computing. In this report, the learner wants to investigate various blocks of quantum computing.      

                                                           

Literature review 

  Quantum computers use various phenomena related to quantum mechanics to achieve more computational supremacy than their conventional counterparts. Conventional computers use binary number concepts or hexadecimal numbers for all computational purposes (Boyer et al., 2017). As per Boolean algebra, binary numbers are represented as 0 or 1, i.e., absolute truth or absolute false. Users give their instructions in conventional languages like English, French etc, which cannot be interpreted by computers. Computers convert these languages into machine languages consisting of a set of binary or hexadecimal numbers for executing the instructions (Debnath et al., 2016). As binary or hexadecimal numbers provide to possibility, therefore computers execute instructions by keeping two possible situations in their mind. Quantum computers differ in this aspect. In the realm of Quantum Mechanics, no distinct possibilities are present. It employs a set of possible probabilities for computational purposes, which further gives them superiority over their modern counterparts (Dumitrescu et al., 2018). They use concepts of qubits or quantum bits which is equivalent as bits in conventional computers.

The root of quantum computing lay in the year of 1960 when conjugate coding was developed by Stephen Wiesner. The concept of qubits first saw daylight after Alexander Holevo published a paper, that showed that more information can be carried by using n number of qubits than n number of conventional bits. This is known as the Holevo’s Theorem (Figgatt et al., 2017). In the year of 1976, Polish physicist as well as mathematician, Roman Stanislaw proved that the classical Shannon Capacity Theorem cannot be successfully applied in the case of information-carrying qubits (Gambetta et al., 2017). He also gave a proposed model of qubit information theory, which is a more generalized form of Shannon’s Information theory, i.e., Shannon’s theorem can be derived from qubit information theory whereas vice versa is not possible. The world’s first model regarding quantum computing was given by Paul Benioff, who showed that Turing machines can be operated if laws of Quantum Mechanics like Schrodinger equations is applied to them. This paper laid the main foundation stone for quantum computing research. Toffoli gates were invented by Tommaso Toffoli. These gates are analogous to conventional Boolean logic gates. In this decade research of quantum computing was at its peak.

A first universal model of quantum computers was given by David Deutsch (Guo et al., 2019). Any simulation regarding quantum computing can be easily done by using this aforesaid computer. World’s first realization of physical quantum computer was proposed by Yoshihisa Yamamoto in 1988. It can realize famous CNOT gate of Richard Feynman (Hadzihasanovic et al., 2018). Fredkin gate was also realized using quantum mechanics principles by scientist Gerard J. Milburn in 1988. In 90’s various researches on quantum computing realization was done. Even an Oracle program was written to run a quantum computer. But there is no physical quantum computer or processor is present until Google as well as IBM’s discovery was made. In 2019, Google claimed that they made the world’s first Quantum Processor named Sycamore, which can execute a task in only 200 seconds, which can be accomplished by using modern conventional computers in 10,000 years (Harvey., 2019). They simulated quantum computing in a supercomputer named Summit for conducting the aforesaid test. Summit is the most powerful supercomputer present now. After publishing this test result in the famous science journal Nature, Google claimed that they had achieved supremacy in the quantum computing field. This is a 54-qubit processor as per Google’s information. Later,  IBM started its famous program named IBM Q Experience, which gives access to its quantum computing processors to the public for further development. Researchers and companies like Google, IBM, and Apple as well as institutions like MIT are constantly developing quantum computing to get out of the prototypes and to construct commercial quantum computers. According to various literature, quantum computers are faster as well as more secure in terms of security. They are nearly impossible to breach by contemporary hacking technologies. The state of flow of possibilities rather than distinct possibilities, gives extra advantage to them. It can be further illustrated by the adjacent example. It is known to all that in modern communication, information is sent either in the form of a stream of binary bits ( a sequence of 0 and 1’s) or as light ( in the case of optical fibers using Wavelength Division Multiplexing or WDM technology) . Information can be hacked from the channel carrying information or the receiver itself. But in the case of qubits or quantum bits, this scenario differs completely (Linke et al., 2017). As information is in the quantum state, therefore, they are not like a sequence of 0s and 1’s but they are like a stream of probabilities of 0 and 1’s. So, from this state of probabilities, source information can be recovered without any legitimate private key. Now if a hacker tries to measure the aforesaid quantum state, then the whole system of the receiver will shut down. Even if  this quantum state of the receiving station can be hacked, from these states original states of information cannot be recovered. Some researchers also said that there are no stand procedures present to standardize the information carried by qubits. Enormous research is needed in this purpose.

Previous studies also showed various strengths as well as vulnerabilities of quantum computing than its conventional counterparts, which are the following:

In the conventional computing, information is stored in the form of binary data i.e., 0 and 1. Quantum computing use qubits for storage of information. Qubit actually not distinct like binary counterparts. They can be understood as flow of possibilities of 0 as well as 1’s (Pant et al., 2019).

In conventional computing, there is a possibility of copying the desired information before sending it through the channel. But in the case of quantum computing, it is impossible. Quantum computing never allows the sending as well as distribution of data simultaneously (Romero et al., 2018).

The direction of information flow in conventional computing method is unidirectional i.e., information can be sent by the sender to receiver through a communication channel but the reverse procedure cannot be done. Quantum computing is more advanced in this context. Quantum computing allows users to send it to the receiver or  to receive it from the sender. It can be said that quantum computing is analogous to full duplex communication.

Noise is nothing but an unwanted electrical signal. For effective communication, it is very necessary to maintain noise in a certain limit concerning the original information signal. For this purpose signal-to-noise ratio is measured. The information that is sent by conventional methods is very much prone to noise, whereas quantum computing methods create a channel immune to noise for information transmission.

It can be found from previous studies that although quantum computers are not present in reality, there are several fields of their applications, which are the following:

To make secure aero planes, quantum computing can be used. Many complex calculations are needed to understand the data of jet simulation software completely. It is a very challenging task for conventional computers, whereas quantum counterparts will resolve easily (Rudolph., 2017).

Another important application of quantum computing method lies in discovery of distant stars and planets in less time period. Astronomical calculations are very complex in nature. Quantum computers with their huge analytical and aggregating power can solve complex mathematical calculations in a flash of time. .

Modern banking systems can be more secure by using quantum cryptography. As there is no distinct states present in quantum computing, therefore it is nearly impossible for hackers to breach passwords from the channel as well as devices (Saffman., 2016).

The Discovery of new drugs and the detection of new unknown diseases require numerous mathematical calculations in a short period. This purpose can be solved better by quantum computers than conventional ones. From previous literature, it can be found that by using quantum simulations, the chance of detection of critical diseases like cancer can be increased. If cancer of patients is detected earlier, then the mortality rate will dramatically reduce.

Quantum computing can also increase the Gross Domestic Product or GDP of a country. If various data collected from individuals is analyzed by quantum computers, then the spending behavior of customers can be improved rapidly. This further creates the country’s economic and GDP growth (Wang et al., 2016).

Various gaps in the Literature

Though several kinds of literature indicate the enormous advantages of quantum computer research, there are some gaps are present, which are the following:

Although quantum devices were made all of them are in the prototype stage. Therefore no excusive manufacturing technology is present to build quantum computers of macroscopic structure. No research was conducted on this aspect.

 It was known to all that it is nearly impossible to simulate large quantum computers using conventional technology. The existing systems can contain only 100 number of qubits or quantum bits. These systems are too small to incorporate quantum simulation systems for various research and industrial purposes. Even if classical methods are applied the behavior of quantum systems cannot be understood properly.

Although organizations like Google made quantum processors to achieve quantum supremacy, these processors are all in the prototype stage. Making these prototypes in real quantum form is very challenging as engineers face several engineering problems like designing, realization as well as testing (Watson et al., 2018).

  

Conclusion:

It can be concluded that Quantum computers are enormously efficient than their traditional or conventional counterparts. They perform huge tasks in 200 seconds, whereas executing the same task in a normal computer requires 10,000 years of computation. Therefore it can be easily understood that it can change this world forever. It can be used in several fields of science as well as engineering like medicine, cryptography as well security systems. Modern banking systems can be more secure by using quantum cryptography. As there are no distinct states present in quantum computing, therefore it is nearly impossible for hackers to breach passwords from the channel as well as devices. The Discovery of new drugs and the detection of new unknown diseases require numerous mathematical calculations in a short period. This purpose can be solved better by quantum computers than conventional ones. For this purpose, researchers as well as organizations are constantly developing quantum computing to get out of the prototypes and to construct commercial quantum computers.  

 

References:

 

Boyer, M., Brodutch, A. and Mor, T., 2017. Entanglement and deterministic quantum computing with one qubit. Physical Review A, 95(2), p.022330.

Debnath, S., Linke, N.M., Figgatt, C., Landsman, K.A., Wright, K. and Monroe, C., 2016. Demonstration of a small programmable quantum computer with atomic qubits. Nature, 536(7614), pp.63-66.

Dumitrescu, E.F., McCaskey, A.J., Hagen, G., Jansen, G.R., Morris, T.D., Papenbrock, T., Pooser, R.C., Dean, D.J. and Lougovski, P., 2018. Cloud quantum computing of an atomic nucleus. Physical review letters, 120(21), p.210501.

Figgatt, C., Maslov, D., Landsman, K.A., Linke, N.M., Debnath, S. and Monroe, C., 2017. Complete 3-qubit grover search on a programmable quantum computer. Nature communications, 8(1), pp.1-9.

Gambetta, J.M., Chow, J.M. and Steffen, M., 2017. Building logical qubits in a superconducting quantum computing system. npj Quantum Information, 3(1), pp.1-7.

Guo, C., Liu, Y., Xiong, M., Xue, S., Fu, X., Huang, A., Qiang, X., Xu, P., Liu, J., Zheng, S. and Huang, H.L., 2019. General-purpose quantum circuit simulator with projected entangled-pair states and the quantum supremacy frontier. Physical review letters, 123(19), p.190501.

Hadzihasanovic, A., Ng, K.F. and Wang, Q., 2018, July. Two complete axiomatisations of pure-state qubit quantum computing. In Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science (pp. 502-511).

Harvey, S.P., 2019. Developing Singlet-Triplet Qubits in Gallium Arsenide as a Platform for Quantum Computing (Doctoral dissertation).

Linke, N.M., Maslov, D., Roetteler, M., Debnath, S., Figgatt, C., Landsman, K.A., Wright, K. and Monroe, C., 2017. Experimental comparison of two quantum computing architectures. Proceedings of the National Academy of Sciences, 114(13), pp.3305-3310.

Pant, M., Krovi, H., Towsley, D., Tassiulas, L., Jiang, L., Basu, P., Englund, D. and Guha, S., 2019. Routing entanglement in the quantum internet. npj Quantum Information, 5(1), pp.1-9.

Romero, J., Babbush, R., McClean, J.R., Hempel, C., Love, P.J. and Aspuru-Guzik, A., 2018. Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz. Quantum Science and Technology, 4(1), p.014008.

Rudolph, T., 2017. Why I am optimistic about the silicon-photonic route to quantum computing. APL Photonics, 2(3), p.030901.

Saffman, M., 2016. Quantum computing with atomic qubits and Rydberg interactions: progress and challenges. Journal of Physics B: Atomic, Molecular and Optical Physics, 49(20), p.202001.

Wang, X.L., Chen, L.K., Li, W., Huang, H.L., Liu, C., Chen, C., Luo, Y.H., Su, Z.E., Wu, D., Li, Z.D. and Lu, H., 2016. Experimental ten-photon entanglement. Physical review letters, 117(21), p.210502.

Watson, T.F., Philips, S.G.J., Kawakami, E., Ward, D.R., Scarlino, P., Veldhorst, M., Savage, D.E., Lagally, M.G., Friesen, M., Coppersmith, S.N. and Eriksson, M.A., 2018. A programmable two-qubit quantum processor in silicon. Nature, 555(7698), pp.633-637.

 

 

About the Author - Jane Austin

Jane Austin is a 24-year-old programmer specializing in Java and Python. With a strong foundation in these programming languages, her experience includes working on diverse projects that demonstrate her adaptability and proficiency in creating robust and scalable software systems. Jane is passionate about leveraging technology to address complex challenges and is continuously expanding her knowledge to stay updated with the latest advancements in the field of programming and software development.