The Idea of Quantum Computing at D-Wave

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Table of Contents

Introduction
Potential of the Idea
Scalability of the Idea
Scalability Challenges
Latest Innovations in BA Management
References
Footnotes

Introduction

The idea that the current paper discusses is the utilization of quantum computing for the decision-making and problem-solving processes. The difference between personal computers and their innovative counterparts was addressed in detail by the CEO of D-Wave named Alan Baratz. He proposed to pay more attention to the possible startups that could utilize software to resolve fundamental physics and business problems at the same time. This approach was found to be rather useful because it broadened the horizons for conventional developers who only worked with non-quantum computers.¹ Owing to Baratz’s efforts, quantum computing quickly became the most important area of development for D-Wave, as the CEO also realized that it could be crucial to utilize the advantages offered by the quantum medium.

The most important point about the idea of quantum computing at D-Wave is that the company quickly realized the marketing potential of it and took over the quantum computing market. The essential advantage of quantum computing is the presence of dedicated cloud services that make it possible to store and pull data from the online database at unbelievable speeds.² The strength of cloud-based initiatives makes quantum computing practically unreachable because each effort exerted by teams interested in this innovative technology allows them to overcome the skepticism and advance both in terms of hardware and software.

Potential of the Idea

The first reason why the quantum computing technology has potential is the ability to translate common physics into hardware and software solutions that allow researchers to combine particles and achieve incredible results where such particles may turn into anything of interest to the developers. The benefit of quantum computing, in this case, is that particles may assume the superposition when not observed, allowing the developers to increase the number of potential combinations of particles.

The second reason that validates the potential of quantum computing is the advent of qubits that can store much more values than their conventional counterparts (bits). All 16 combinations within the qubit can be effectively accessed at any moment, while also creating a link between two qubits that react to one another’s transformations in real-time.³ Therefore, the limited capacity of conventional cloud-based services comes to an end with quantum computing and qubits.

The utmost reason to perceive quantum computing as a technology of the future is the concept of quantum teleportation that creates room for quicker communication among data qubits. The experience of D-Wave shows that qubit manipulation may also be helpful, as the developers get multiple chances to acquire a single output while having numerous input points. Regardless, a decision-making system based on quantum computing uses this single output to compile all possible answers.

Scalability of the Idea

The first way to scale the idea of quantum computing would be to develop improved materials modeling and allocate more resources to the issue of materials simulation. Even though the technology is mostly implemented to resolve physics-related inquiries, its business potential may also be related to the possibility of simulating the outcomes of complex decisions on a long-term scale.⁴ Compared to classical computing, it would be harder to deploy its quantum counterpart, but the possible increases in speed and accuracy of operations would become irreplaceable.

One more attempt to scale quantum computing would be to optimize the analytical capabilities of the organization and pay more attention to the financial side of the implementation process. The sequential background of classical computing would not support an increased number of variables, which means that quantum computing could be scaled through resource optimization.⁵ The parallelism inherent in this technology might be used for many business areas such as task scheduling, planning, or managing resources.

The last option available to the researchers interested in scaling quantum computing would be a stronger focus on cloud services and database optimization. The increasing demand could be managed with the help of nonrelational database capabilities such as better indexing and higher speed of query processing.⁶ Any unstructured data sets available to quantum computing users would be evaluated by tech specialists, who would also disclose the potential improvement areas and see if the innovation could be scaled further.

Scalability Challenges

The first challenge affecting the scalability of quantum computing is the presence of computational issues that stem from the fact that the area of utilizing qubits is still rather underresearched. Even though there are more opportunities for data processing and analysis, it would be harder to scale quantum computing in the case where simulations are incredibly costly and put a strain on the organization. Therefore, computational issues decrease the validity of potential solutions to long-standing business problems that could not be addressed by conventional computing tools.

Another problem is the so-called quantum supremacy that would make it harder for the organization to reduce the rate of errors. There will be no chance to scale technology effectively when the environment does not respond to the essential needs of end-users. An incredibly high rate of error is going to avert many organizations from investing in quantum computing, as it would be perceived as a risky asset.

Overall, the scalability of quantum computing could be crucially affected by the high cost of deployment and maintenance of this innovative technology. The sources of noise and high temperatures would have to be removed completely; otherwise, the whole system would become ineffective and unstable. The team is going to spend an extremely high amount of resources on developing the technology to an extent where it would become less expensive and more beneficial in terms of its price-output ratio.

Latest Innovations in BA Management

The current field of study is BA management, and the most probable position after graduation is either business or data analyst. The first possible innovation that could influence my professional career is the advent of numerous business management applications that strengthened the impact of cloud on management and allowed for improved scalability for the same price.⁷ Therefore, an increased level of flexibility and cost-effectiveness makes it evident that the cloud is a technology of the future.

Another crucial innovation that I might be able to benefit from is business intelligence. It could be helpful in terms of generating complex data reports and forecast spreadsheets. The main advantage of business intelligence is that it can be integrated into existing infrastructure harmlessly. Even small- and medium-sized enterprises are currently making the best use of business intelligence, which means that it does not affect the company’s budget drastically.⁸

The last technology that I might have to watch out for as a business analyst would be content management systems. The prevailing nature of data collected by corporations across the world makes it safe to say that static websites have become obsolete and have to become a part of the legacy software unit. Even though it would be costly to hire a specialist capable of setting up the given content management system properly, the ultimate outcomes are generally positive and cannot be overlooked.

References

Covers, O., & Doeland, M. (2020). How the financial sector can anticipate the threats of quantum computing to keep payments safe and secure. Journal of Payments Strategy & Systems, 14(2), 147-156.

Cusumano, M. A. (2018). The business of quantum computing. Communications of the ACM, 61(10), 20-22.

Lardinois, F. (2020). Hear how three startups are approaching quantum computing differently at TC Disrupt 2020. Web.

Liang, T. P., & Liu, Y. H. (2018). Research landscape of business intelligence and big data analytics: A bibliometrics study. Expert Systems with Applications, 111, 2-10.

Mohseni, M., Read, P., Neven, H., Boixo, S., Denchev, V., Babbush, R.,… & Martinis, J. (2017). Commercialize quantum technologies in five years. Nature, 543(7644), 171-174.

Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence & analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37-58.

Footnotes

Lardinois (2020).
Ibid.
Cusumano, (2018).
Covers & Doeland (2020).
Mohseni et al. (2017).
Lardinois (2020).
Liang & Liu (2018).
Rikhardsson & Yigitbasioglu (2018).

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