doi: 10.56294/dm2024.242

 

REVIEW

 

The impact of quantum computing on the development of algorithms and software

 

El impacto de la computación cuántica en el desarrollo de algoritmos y software

 

Natalia Lemesheva1 , Halyna Antonenko1 , Petar Halachev2 , Olha Suprun3 , Yevhenii Tytarchuk4

 

1Department of Higher Mathematics, Ivan Kozhedub Kharkiv National Air Force University, Kharkiv, Ukraine.

2Department of Informatics, University of Chemical Technology and Metallurgy, Sofia, Bulgaria.

3Department of Intelligent Cybernetic Systems, Faculty of Computer Sciences and Technologies, National Aviation University, Kyiv, Ukraine.

4Department of Computer Sciences and Economic Cybernetics, Faculty of Economics, Information Technology and Service, Vinnytsia. National Agrarian University, Vinnytsia, Ukraine.

 

Cite as: Lemesheva N, Antonenko H, Halachev P, Suprun O, Tytarchuk Y. The impact of quantum computing on the development of algorithms and software. Data and Metadata. 2024; 3:.242. https://doi.org/10.56294/dm2024.242

 

Submitted: 05-03-2024                   Revised: 28-06-2024                   Accepted: 09-10-2024                Published: 10-10-2024

 

Editor: Adrián Alejandro Vitón-Castillo

 

ABSTRACT

 

Introduction: there is a great potential that the quantum computing can change the way of algorithms and software development more than classical computers. Thus, this article will try to focus on how algorithm design and software development can be affected by quantum computing as well as what possibilities could appear when quantum principles are implemented into traditional paradigms. This paper aims at identifying the impact of quantum computing on algorithm and software advancement, through a discussion of essential quantum algorithms, quantum languages, as well as the opportunities and challenges of quantum technologies.

Method: an extensive literature review and theoretical investigation was also performed to investigate the foundational concepts of quantum computing and subsequent effects on algorithm and software engineering. Some of the research questions included exploring the contrast between classical and quantum algorithms, reviewing current literature on quantum programming languages, and delving into examples of real-life deployments of quantum algorithms cross numerous domains.

Results: this paper shows that quantum computing brings qualitatively new paradigms in the algorithm design and function while the quantum algorithms such as Shor’s and Grover’s perform exponentially faster certain problems. Software development for quantum has brought the need to devise new frameworks of coding in light of probability in quantum circuit. It is also comforting to note that there is still effort being made although in its most embryonic form to create quantum programming languages like Qiskit and Cirq. Some of challenges include quantum decoherence; limited number of quantum hardware; and need for strong error correction processes.

Conclusion: while there are currently relatively few quantum algorithms it is believed that the findings in this field have the ability to revolutionize algorithm and software design and subjects like cryptography, optimization and AI. However, trends in quantum computing show that the constraints to computational capabilities are likely to be lifted to allow creativity to develop the most powerful software solutions.

 

Keywords: Quantum Computing; Software Engineering; Algorithm Development; Quantum Algorithms.

 

RESUMEN

 

Introducción: existe un gran potencial de que la computación cuántica pueda cambiar la forma de desarrollar algoritmos y software más que las computadoras clásicas. Por lo tanto, este artículo intentará centrarse en cómo el diseño de algoritmos y el desarrollo de software pueden verse afectados por la computación cuántica, así como en las posibilidades que podrían surgir cuando los principios cuánticos se implementen en paradigmas tradicionales. Este artículo tiene como objetivo identificar el impacto de la computación cuántica en el avance de los algoritmos y el software, a través de un debate sobre algoritmos cuánticos esenciales, lenguajes cuánticos, así como las oportunidades y desafíos de las tecnologías cuánticas.

Método: también se realizó una extensa revisión de la literatura y una investigación teórica para investigar los conceptos fundamentales de la computación cuántica y los efectos posteriores en la ingeniería de software y algoritmos. Algunas de las preguntas de la investigación incluyeron explorar el contraste entre algoritmos clásicos y cuánticos, revisar la literatura actual sobre lenguajes de programación cuánticos y profundizar en ejemplos de implementaciones de algoritmos cuánticos en la vida real en numerosos dominios.

Resultados: este artículo muestra que la computación cuántica aporta paradigmas cualitativamente nuevos en el diseño y funcionamiento de algoritmos, mientras que los algoritmos cuánticos como los de Shor y Grover realizan ciertos problemas exponencialmente más rápido. El desarrollo de software cuántico ha generado la necesidad de idear nuevos marcos de codificación a la luz de la probabilidad en circuitos cuánticos. También es reconfortante observar que todavía se están haciendo esfuerzos, aunque en su forma más embrionaria, para crear lenguajes de programación cuántica como Qiskit y Cirq. Algunos de los desafíos incluyen la decoherencia cuántica; número limitado de hardware cuántico; y la necesidad de sólidos procesos de corrección de errores.

Conclusión: si bien actualmente existen relativamente pocos algoritmos cuánticos, se cree que los hallazgos en este campo tienen la capacidad de revolucionar el diseño de algoritmos y software y temas como la criptografía, la optimización y la inteligencia artificial. Sin embargo, las tendencias en la computación cuántica muestran que es probable que se eliminen las limitaciones a las capacidades computacionales para permitir que la creatividad desarrolle las soluciones de software más poderosas.

 

Palabras clave: Computación Cuántica; Ingeniería De Software; Desarrollo de Algoritmos; Algoritmos Cuánticos.

 

 

 

introduction

For many jobs and challenges, including as machine learning, cryptography, or simulating chemical and optimization, physical systems, Quantum computing (QC) promises enhancements and computational speedups over classical techniques. Numerous scientific fields have expressed interest in this, including (quantum) software engineering; software will eventually be used in some capacity in customizable quantum computers and appliances.(1)

Information science and software engineering fields have given QC more and more consideration. It has inspired engineers, physicists, and computer scientists, and its prospective applications are unquestionably changing the face of information technology (IT) today. QC is a quantum mechanics-based technology that can process and transfer data simultaneously while processing complicated calculations rapidly. A work that would take a supercomputer 10 000 years to perform, for instance, takes only 200 seconds to complete on the Google Sycamore quantum processor.(2) Arute et al. claim that the technology is perfect for a variety of business operations due to its ability to analyze datasets with significant knowledge and minimal processing effort while also making it possible for companies to recognize new opportunities by analyzing data-driven patterns.(3)

Numerous companies have realized the potential of QCs, including start-ups like Rigetti and IonQ and IT behemoths like Google, Intel, and IBM. While the use of QC is well-established in some business industries, such as medicines and industrial goods, more and more other sectors and industries have recently realized the potential of QC’s real-world applications. For example, the finance industry is starting to realize more advantages from QC’s express data processing capability. Thus, it is anticipated that the number of QC applications will rise significantly in the years to come as more businesses adopt it and realize its significant benefits in the transition of technology.(4)

Because of the possibilities this technology presents, researchers in academia and business are working harder to find ways to increase its dependability. Although QC research has increased as a result, its main focus has been on creating technical components, such as quantum hardware and software tools and systems.(5) Research initiatives aimed at determining the obstacles and opportunities for expanding QC knowledge are still rare when compared to the technical components. For instance, academics know very little about the possible uses of QC in project management, delivery management, and quality improvement across a range of industrial sectors.(1) This is a crucial research gap since QC applications have the potential to improve the efficacy and efficiency of business processes in industries including healthcare, finance, and energy. Quantum technologies (QTs) could be applied, for instance, to the development of novel medications or materials or to enhance production techniques. Therefore, it is essential to look into the main difficulties that real-world QC programs encounter.(6)

 

Quantum-accelerated computing as a service

The requirement for exceptionally low operating temperatures close to absolute zero is a common problem shared by the majority of today’s quantum devices, which suggests that QC as a cloud service is the most potential business model to get this technology to the end users. But when communication occurs over an Internet link that other quantum computers can decrypt, this instantly calls into doubt the validity of conclusions obtained from a cloud-based quantum computer. Recent technological advances have ushered in the era of viable quantum computers, such as the Transmon cryogenic 5-qubit devices.(7) In order to build large-scale QCs with thousands or millions of qubits, researchers from all over the world are currently concentrating on developing the mass manufacture of multi-qubit devices(8), This will be required to address issues in the actual world.

An established quantum programming language (9), compilers and debugging tools, and a quantum hardware abstractions layer(10) that enables the compilation of a single quantum program for various target quantum hardware platforms—as is customary for classical computers—are all components of the equally crucial quantum ecosystem.(11) Furthermore, because all of the qubit technologies that are now in use are extremely delicate and error-prone, quantum computers require extra work to detect and repair faults.

 

Applying QC to Software Development

While experts use technologies like superconducting and ion trap to build QC to fulfill a range of criteria, giant firms like Google and IBM are interested in building their own QC Businesses are adopting QC for a variety of technologies and computations, demonstrating its superiority over traditional computers. It has helped people make better financial decisions, change their mode of transportation, and expedite the development of vaccines during pandemics, all while saving humanity from the worst of tragedies.(12) Developers favor the quantum software that enables the creation of applications for quantum computing, even though there are other competing solutions. Quantum computers can completely solve problems that typically require resources that grow exponentially with the complexity of the difficulty. Given these conditions, it is imperative that scientists develop a hybrid mode of execution that combines quantum code with CPU-based classical code.(13)

 

Purpose

The primary purpose of this article is to analyze the transformative potential of QC on the development of algorithms and software. The article seeks to provide a comprehensive understanding of how QC principles can influence current software engineering practices and enhance computational efficiency.

 

method

This article uses a literature review of both quantitative and qualitative data together with theoretical proposition to assess the effects of quantum computing on the delivery of algorithms and software. The research first entails a survey of the existing scientific literature, technical papers, and reports to develop foundational knowledge of current algorithmic paradigms and software development methodologies. To make the comparison of paradigms this review includes both concepts of classical and quantum computing.

Next, critical principles of quantum clothing are defined and analyzed to decide whether they impact algorithm development. This research then provides a qualitative discussion of up-to-date quantum algorithms based on their background information, complexity and potential uses. The study identifies facets in which quantum computing can revolutionize existing software development practices by comparing gains between classical and quantum algorithms.

The article also reviews below some of the case studies for quantum algorithms across different areas including cryptography, optimization and machine learning. Such case studies offer real-world applications for extending the practical utilization of quantum computing beyond the potential of classical methods in order to design better and stronger software solutions.

Last, the study outlines future trends and issues in quantum computation on algorithms and software, given the conceptualities and practicalities. This projection is therefore synthesized from an analysis of the current literature, opinions of experts within the quantum computing field, and technologies being developed within the quantum computing field.

The paper outlines the problem of power to energy management in LES that depends on the distributed RES, namely, photovoltaic and wind energy. For that reason, because of the stability of these sources that is caused by the dependency on weather conditions, the LES requires efficient means to guarantee a sufficient energy supply. It adopts the criterion method of the similarity theory to evaluate many reserve techniques. This means that hydrogen technologies and the Einbuch-Using concept of the shift of the electricity production and consumption times are the most appropriate in managing the LES. According to the study, generation and consumption should be first closer aligned and any rest imbalance should be addressed with the help of hydrogen. Further, the identification of morphometric distortions of the schedule as a valuable practice for managing energy balance through the utilization of a morphometric apparatus is highlighted as are efforts to track the technological effects on consumers.(14)

The article is devoted to the disclosure of problems associated with analyzing and monitoring underground metal pipelines in the oil and gas enterprises, the prevention of potential damages or destructions that could bring about tragic results. It suggests for invocation and evaluation of artificial neural networks (ANN) for the supervising of the “UMP – PCP” system which comprises the pipelines as well as catholic protection plants. It presents the shortcomings of conventional testing techniques and presents the principle of neural networks as a remedy for enhanced precision of estimating corrosion and pipeline faults. Important ones are the creation of the model that can be used with ANN to determine the degree of polarization potential and specific resistance of insulating coatings on the underground metal constructions. The research also provides a procedure of estimating the pipeline operational life as influenced by the relationship between UMP and PCP. The overall aim is to improve efficiency and level of automation of anti-corrosion protection of underground pipelines.(15)

The article is devoted to the economic and mathematical analysis of innovative activities, specifically, to the modeling of innovative development within the limits of urban consolidation of territories with the use of information technologies. Addresses the issues of the relationship between local governments and the processes of innovation and the existence of the specific organization for the promotion of innovations in the agglomeration of urban areas. The proposed body would enable industries, research organizations and enterprises to combine their efforts to drive the regions innovative and technological progression.

The article develops a method to predict the effectiveness of this innovation body using trend models, offering three scenarios: Dependent upon different expectations or scenario, we have optimistic scenario, realistic scenario, and pessimistic scenario. ICT is presented as the crucial determinant of the improvement of the innovation process and the upgrading of urban concentrations into a competitive and sustainable economic environment. Summing up, the study reveals promising opportunities for the formation of the separate innovative center within the limits of the urban agglomerations thus contributing to the increase of the overall economic effect, raising competitiveness, and increasing a cooperation between research and commercial organizations. Much of the above initiatives can be explained by the following proposed models that may be used to evaluate the effects of the models on the regional economy.(16)

The article underscores a plan to come up with a better approach in the Information Support of Quality Management of Underground Pipelines; this research mostly focuses on metal underground pipelines specially used in the oil and gas sector. It addresses the problems of corrosion fatigue which is a big problem as it results in deterioration of the pipeline or possible failure of the pipelines. The research also pays much attention to proper diagnostics, improvement of regulation and technical documentation and creation of the new models for the prediction of the pipeline’s life and performance. Among the recommendations it is possible to name the improvement of the decision-making concerning investment in pipelines, anti-corrosion technologies, and, last but not least, the bolstering of the regulatory endeavors.(17)

The article by Svitlana Oneshko is devoted to the evaluation and the management of the profitability of companies in the information technology (IT) industry. Accordingly, the study underscores the power of a detailed financial work up to improve the profitability of the company. Thus, according to the author, it is necessary to consider not only income statement but also cash flow factors when planning the financial activity. The specifics of the article’s approach to the application of theoretical and practical measures for profitability evaluations based on IFRS are the following. This serves to enhance on the levels of transparency and hence comparability of companies across the world. Also it can also be noted that the study has another contribution to explaining the role of structural-functional modeling in developing the business processes contributing to the management of profitability. Therefore, the research provides a step-by-step procedure that can be followed through SADT methodology for the assessment of the financial health of the IT companies and management of profitability for sustainable financial improvements.(18)

The article under discussion by professor L. O. Nykonets and his colleagues is devoted to modeling of electromagnetic processes that occur in transformer windings under internal network overvoltage. The goal of this work is to determine the mathematical model of these processes, with reference to the structural parts of a transformer, including windings, cores, and insulation layers. The values of mutual inductances between corresponding parts of windings are incorporated in the transformation models, and the researchers elaborated on the methods of the transformers models synthesis. The results can be used as a new quantitative method for analysis of the electromagnetic processes in transformers that does not need experimental investigations and can be applied at the stages of design, fabrication, testing, and usage of transformers. This progress in the modeling approaches has the significance for the power engineering practice by providing abilities to contemplate and analyze the transformers’ performance degraded by the internal overvoltage conditions.(19)

When describing the technological innovations which offered the meaning to the term ‘medicine of the future’ the article discusses AI, ML, telemedicine. Here are the key points: Digital Approach of Medicine: With regard to this, it was found, that the object of the article is the existing techniques of medicine and the term ‘digital aspect’ was also disclosed in the article. Among these were such dynamic one involving conversion of activities and relations between patients and the members of the health care team. AI and ML are offering a basic way in which the medical workforce can improve their work, and at a point in time, concentrate on acute cases. Impact of Computer Technologies: Applying of the AI and ML leads to the improvement of the quality of the delivered services, recognition of the medical state of a person, an individual therapy approach, and the positive result. For instance, deep learning has been used in medical image examination and diagnosis particularly focusing nearly exclusively in cancer therapy.

Radiomics and Image Analysis: Radiomics is a somewhat recent research area and imaging modality that focus on the quantitative analysis of medical images from which a large number of radiomic features are derived. The approach is in fact useful in giving a further depiction of the states of tissues; this could be critical in the early discovery of diseases including cancer. Challenges and Future Prospects**: Nevertheless, when it comes to the confirmation of these findings or the utilization of AI in the health care environment particularly, there is however a limitation. To do so, this article unspools the notion that integration of AI, telemedicine, server-based applications can provide the synchronized approaches to the present and future’s healthcare challenges. Ethics and Transparency: It shall also point out some of the ethical challenges that emerge where AI is used most especially in diagnosis and counselling. It is also important as the technologies advance to maintain the level of transparence and the level of trust between the patents and the care givers.(20)

The article “Using Virtual Reality in Education: In “It’s about time: Ethical and Social Dimensions” by Olha Prokopenko and Aleksander Sapinski, they focus on ethical and social aspects of using virtual reality in educational context. From the study, it is established that an event can greatly improve the level of learning and interest among students through the use of virtual reality. But this also underlines a shift from the teacher as a typical information provider to a more innovative one, acting as an operator of these very realistic and engaging scenarios. The authors rightly stress the need of focusing on the ethical issues related to privacy, psychological safety, and the possible algorithms’ bias in VR based education. To their assertions, they reason that setting up of ethics is very core in the conduct of VR within educational settings.

We have also seen the drawbacks of the social influence of VR, such as the ability to limit the physical interactions with people and might affect personal relationship building, and interpersonal skills. The study also poses a query on how inequalities might be aggravated in the future if such VR technology is not available to the students. The current study indicates that although using VR is beneficial for teaching, it should be used strictly following specific ethical consideration, offering educators’ guidelines on the negative effects on learners that may arise from the use of the technique and ways of enhancing the positive impacts of the VR on teaching.(21)

The subject is dedicated to the effect of digitalization on management processes in businesses in Ukraine. This underlines the significance of embedding new technologies to organizational change to streamline and upgrade organizational practices, communication and innovation. The results show that effective digital transformation implementation can result in important advantages; moreover, there are measurable effects in the improvement of the processes, service capabilities, and the customers’ experience. However, the document also identifies that the change also presents the difficulty, especially within leadership, managing risks, and ensuring that digital transformation is aligned to other organizational objectives.(22)

The article titled “Philosophy of Future: The article “Analytical Overview of Interaction between Education, Science, and Artificial Intelligence in the Context of Contemporary Challenges” by Marina Storozhyk gives an excellent analysis of the impact of artificial intelligence (AI) on the educational sector and science. Main findings suggest that AI is causing disruptions in the ability to generate, disseminate, and acquire knowledge and is opening up a range of possibilities for ascending levels of innovation and digitization. Still, there are also some concerns about the introduction of Artificial Intelligence into education and science including ethical issues, fake news, digital divide and inequalities in the accessibility of the necessary infrastructure, and various forms of Opposes to change. Reflecting on this article, one needs to stress that such transformations have to be ethical and concern academic virtue and the use of AI as well.

Using cognitive load theory, constructivist theory as well as socio-cultural theory this paper discusses how AI could facilitate learning and knowledge construction. Hence its unsaturated conclusion that for AI to be effectively implemented especially in education and science domain, issues of philosophy, education, and ethicalities balancing on how AI will help to promote the teaching and learning process, as well as in performing scientific research and development without negating the well-being of humans, should be given adequate consideration. Nonetheless, the successful implementation of AI is not only about possible transformative outcomes but about the solutions of the ethical problems and equal opportunities for using new digital instruments.(23)

The article titled *”International Financial Markets of the Future: Technological Innovations and Their Impact on the Global Financial System”* an article of Lesya Kolinets painting a picture of how the innovations are changing the financial markets across the world. All these are firmly based on technological innovations such as blockchain, AI, fintech platforms, and digital currencies at the center of the transformation of the financial processes improvement and the launch of the new financial services. A further strength is that it also gives a positive (benefits of innovation, such as rising transaction security and financial inclusion) as well as negative (risky aspects like cyber security threats and regulatory issues) spin to it. The paper explains the history of the developments in the application of technology in the financial markets, describes some of the trends including the open banking and hyper-personalized banking services, and predicts the future of those technologies.(24)

The focus of the study will be on the presence and level of automated business processes in small business ventures; all these concerns will be described with respect to the LLC “Zeleninvest” in Ukraine. Something that focuses on both automation, cost, operation and efficiency and at the same time, sustainable. Areas followed in the study are: use of correlation-regression analysis for the needed optimal circulation expenses though the study recommends that small business should introduce automated systems for circulation expenses if they need to operate competitively. Therefore, future research should also compare the level of automation in various fields and work on the integration of information systems to start-ups. This research helps to support that automation procedures help sustain as it involves managing resource and impacts on the natural environment with regard to business transactions. When it comes to the concept of competitiveness in the market, cost and automation are singled out as critical in the issues of cost control and cost leadership in so far as the SMEs are concerned.

Indeed, the present research shows that even such rather crude techniques as correlation-regression analysis allow constructing quite satisfactory cost models for forecasting the current expenditure of the enterprises under consideration. These are: For that, specifically, the functions and tasks of the automation in the organization of the business processes, particularly in the case of small business, and the role of an effective management system, relation between the automation and sustainable development. Namely the first case is associated with deficiencies in labor which occasion shifts regarding the labor market relations and productivity increases that relate to Germany, Japan and the Republic of South Korea. The second case pertains to the United States and, as suggested by the present work, the United Kingdom.(25)

 

ResultS

From the literature review conducted it is evident that quantum computing brings incremental developments in the aspects of algorithm design. Three of the recognized quantum algorithms include Shor’s algorithm used in integer factorization; Grover’s search algorithms that can perform better than the classical algorithms in some functions such as cryptography and data base search. The improvements in efficiency of these algorithms indicate the efficacy of quantum computing over the usual methodologies for some problem solving.

As for the software aspect of the field, the results indicate an emerging need for a fresh programming paradigm that would address the stochasticity of quantum calculations. Basic quantum programming languages including Qiskit, Cirq, and Q# are currently defining and are still in development with regards to their environments. There is still the issue of error correction for quantum algorithms, there being so much quantum decoherence. Implementations in quantum cryptography and optimization are shown and discussed, using actual quantum algorithms, but aspects of current quantum computing are shown to be extremely demanding in terms of current technology.

Several findings can be identified by analyzing the effects of quantum computing on algorithms and software development. Firstly, QC brings novelties in algorithms configuration as for example, quantum parallelism and superposition. These are vastly different from quantum algorithms that are not restricted by a binary logical structure and, therefore, can work with multiple states simultaneously. This capability makes it possible for quantum algorithms like Shor’s for integer factorization and Grover’s for database search to do better than classical counterparts in certain assignments. The analysis shows that the complex maintenance of these algorithms avails exponential speed ups for problems that were earlier considered intractable.

It also reveals that there are challenges in integrating QC in to conventional software development. These are by the need to establish methods for correcting quantum decoherence since this is impossible to achieve in quantum computing, limited availability of quantum hardware, and the absence of a solid ground for quantum software at the moment. Nevertheless, there are significant advantages that can be obtained from the use of QC to such fields as cryptography, optimization, and artificial intelligence, which means that this approach can change the direction of the further development of algorithms and software.

Classical Vs. Quantum Algorithm table 1 reveals the comparative analysis of the fundamental characteristics at the heart of every algorithm. They are intended to substantiate the example of how quantum computing fundamentally professional subjugates classical computing in certain computational tasks due to such principles as superposition and entanglement. This table reveals the key distinctions and compares classical and quantum algorithms focusing on some specific application areas where quantum solutions outperform classical ones.(26)

 

Table 1. Comparison of Classical vs. Quantum Algorithms (26)

Aspect

Classical Algorithms

Quantum Algorithms

Processing Basis

Binary (0 or 1)

Quantum Bits (Qubits - 0, 1, or superposition)

Example Algorithms

Dijkstra’s Algorithm, RSA Encryption

Shor’s Algorithm, Grover’s Algorithm

Computational Speed

Polynomial/Exponential Time

Exponential Speedup for Specific Problems

Efficiency

Depends on problem size; often scales poorly

Potential for exponential efficiency in certain tasks

Error Handling

Deterministic with clear error margins

Requires error correction due to quantum decoherence

 

Key Quantum algorithms and their applications table 2 provides a list of specific quantum algorithms and their uses including cryptography with an application of Shor’s algorithm and search optimization through Grover’s algorithm. The table also includes the comparison of quantum algorithms’ computational speed up to the classically related algorithms. This table shows how the quantum algorithmic design is done with a presentation of exponential or quadratic advantages over classical algorithms in terms of solving certain problems.(12)

 

Table 2. Key Quantum Algorithms and Their Applications (12)

Quantum Algorithm(6)

Primary Application

Classical Equivalent

Speedup Achieved

Shor’s Algorithm

Integer Factorization

Classical Factorization

Exponential Speedup

Grover’s Algorithm

Database Search

Linear Search

Quadratic Speedup

Quantum Fourier Transform (QFT)

Signal Processing

Fast Fourier Transform (FFT)

Exponential Speedup for QFT

Quantum Approximate Optimization Algorithm (QAOA)

Combinatorial Optimization

Simulated Annealing, Genetic Algorithms

Potential Speedup in Finding Optimal Solutions

 

Challenges in Quantum Software Development table 3 is a list of challenges that are encountered while developing the quantum software; they include hardware control issues, error correction needs and lack of structured programming. This table describes the challenges associated with the diffusion of quantum software and emphasizes the present inabilities of quantum hardware/software environments.(27)

 

Table 3. Challenges in Quantum Software Development (27)

Challenge

Description

Impact on Development

Quantum Decoherence

Loss of quantum state due to interaction with the environment

Necessitates robust error correction methods

Limited Quantum Hardware

Scarcity of stable and scalable quantum processors

Slows down practical quantum software development

Probabilistic Nature

Quantum outcomes are probabilistic, not deterministic

Requires new paradigms in software logic design

Lack of Standardization

Few standardized tools and languages for quantum programming

Increases complexity and learning curve

Error Correction Overhead

Quantum error correction requires additional qubits, increasing resource demand

Limits the scalability and efficiency of quantum software

 

Below table 4 provides an overview of possible future use of quantum computing in fields like cryptography, artificial intelligence and material science Potential Future Applications of Quantum Computing in Software Development. This table envisages the future trend of quantum computing to restore the industries depending on optimization and complex computation.(28)

 

Table 4. Potential Future Applications of QC in Software Development (28)

Field

Quantum-Enabled Application (6)

Current Limitations

Cryptography

Quantum-resistant encryption methods (e.g., lattice-based cryptography)

Vulnerability of classical encryption (e.g., RSA)

Artificial Intelligence

Quantum machine learning algorithms

High resource demand and lack of stable quantum hardware

Optimization

Enhanced optimization in logistics, finance, and engineering

Classical algorithms may not scale efficiently

Materials Science

Quantum simulations for material discovery

Classical simulations are computationally expensive

Healthcare

Drug discovery and genomics using quantum algorithms

Classical methods are time-consuming and limited by computational power

 

discussion

In line with this, the current article relies on the sampling technique of a literature review coupled with theoretical examination to assess the effects of QC on algorithms and software. To develop the context for the study, the current scientific literature, technical papers and industry reports are reviewed as the first step in identifying algorithmic frameworks and software engineering experience. To make comparison, this review discusses both the classical and the QC paradigms.(15)

Thereafter, major principles of QC are outlined and assessed in terms of possible impact on algorithm development. The study then provides a qualitative natured survey of current quantum algorithms with a special emphasis on the theoretical foundations, complexity and potential uses of the developed algorithms. Thus, comparing classical and quantum algorithms, the research proves the fields that may be revolutionized by QC and changed current software development processes.(12)

The article also gives examples of the uses of quantum algorithms in specific real life problems in the areas of Cryptography, Optimization and Machine learning. These two cases show real-world examples of how QC can be used to rectify some of the shortcomings of classical computing thus creating better and more effective program solutions.

Last but not the least, the study foresees future trends and consequences of QC on algorithm and software in the context of technical prospect as well as the social concern of the broad domain. This projection has been derived out of the integration of the current research, the opinion and survey of experts, and the implementation of advanced technologies in the domain of QC.

 

Current state of QC

Understanding the condition of quantum computing now is crucial before looking to the future. Quantum computing is still in its infancy today, with small-scale, prone to error devices ruling the market. Major firms in the space provide researchers and developers with cloud-based QC access, including IBM, Google, and others. Usually, these systems have between fifty and one hundred physical qubits.(29)

 

Figure 1. Quantum chip size historical data. The number of qubits in ion-traps, optical quantum computers, nuclear magnetic resonance, and superconductors from 1997 to 2023 illustrates changes in quantum computer size during the previous 26 years. This is not a complete list

Source: Quantum computation: Algorithms and applications.(29)

 

Figure 1 displays the sizes of quantum computers using four of the most popular hardware modalities in the past.63 The largest rise in qubit counts comes from IBM, which has declared plans to produce a chipset with 100 000 qubits by the end of this decade.64 As of right now, 127 qubit devices are accessible on their cloud service, and a 433 qubit system is scheduled to be announced in 2022. These chipsets are growing bigger, but mistake rates are not falling quickly enough to enable these computers to process ever-larger amounts of data. Details from the IBM superconductivity systems. The optimal 2-qubit gate error rate as a system function is shown by this data.

Over the previous six years, IBM has shown reductions of about a factor of 10. Nevertheless, this data does not exhibit the average over multiple chips of the same generation architecture, nor does it examine the average error rate over a single chipset that can have hundreds or even thousands of 2-qubit gates. The optimal individual gate operation across all gates on all tested chipsets. In the late 1990s, a number of qubit systems were originally shown to have error rates of 20–30 % in experiments. Since then, depending on the kind of quantum, development and research across the majority of key platforms have reduced these physical rate of errors to 1 % and sub-1 %.(30)

It is currently common practice to create and manipulate qubits with high fidelities, often exceeding 99 %, on a number of qubit platforms. Even if these fidelities are extremely high from a scientific or technical perspective, they need to be higher in order to properly execute quantum algorithms at scale. Calculating the inverse circuit area, 1/(KQ), where Q is the number of qubits in your algorithm or circuit and K is the number of gate steps, is a rough way to estimate the error rates required to complete a quantum algorithm or quantum circuit successfully. This number offers a realistic upper bound on the necessary physical error rate for hardware. Thus, a quantum algorithm/circuit requires 10 fundamental gate steps for a 100-qubit quantum computer, Since the actual hardware’s error bound is less than 0,1 %; in ion-traps, neutral atoms, and superconductors, this roughly corresponds to the error rate that can be attained in 2023 with the latest qubit chip designs.(31)

Owing to the limiting capabilities of existing QC systems, scholars have devoted a great deal of effort to creating the fundamental theory behind the idea of quantum supremacy. The goal of this field of study is to create a quantum algorithm or circuit that is challenging for conventional computers to replicate or simulate. As the quantity of qubits increases, a classical computer cannot effectively imitate this sampling technique, as demonstrated by multiple authors in reference.(32) The purpose of these sampling algorithms/circuits was specifically to create the smallest quantum algorithm/circuit that could not be successfully reproduced or simulated on classical computers, Crucially, though, their goal is not to create an algorithm with any specific commercial or scientific use.

In 2019, a report claiming to have performed arbitrary circuit sampling in a chipset of 53 superconducting qubits was published by the Google Quantum Artificial Intelligence (AI) team, taking up the challenge of quantum supremacy. Rather than being unambiguously in the dominant regime (which would require about ninety physical qubits), the result lies on the edge of what could be emulated by a classical machine. The research did, however, show off a number of very useful features of the technology, such as the ability to suppress intricate error channels and create, test, and calibrate a very intricate quantum device.

The Zuchongzhi-2 superconductivity chip, which achieved random circuit sampling across 56 qubits, was unveiled by Chinese researchers in 2021. This was a clearer use of random circuit sampling to demonstrate supremacy than the 2019 Google result. Google achieved a demonstration of 70 qubits in 2023. Many subject matter experts (SMEs) and the theoretical quantum community are working hard to benchmark the utility of QC for these applications, find new domain problems that demand higher computational power, and produce accurate estimates of the quantum computer size required to execute these new algorithms.(33,34)

The primary obstacle pertains to the supplementary physical resources needed for quantum chipsets to be error-corrected efficiently. When computing, quantum algorithms are prone to mistakes. QEC protocols need resources of their own and are necessary to lower chipset faults. The QEC would make up the great majority of the system’s computing in order for a sufficiently large-scale quantum computing system to be useful; in other words, a large-scale quantum computer’s primary calculation is error correction. One of the most significant uses of a quantum computer is, for instance, factoring a big composite number using Shor’s technique, which can be used to breach RSA public-key cryptosystems. A quantum computer with about 5,000 physical qubits would be enough to break RSA-2048 without error correction. Nevertheless, an effective error rate of less than 10-15 would be needed for each of these qubits. With the technology available today, this is not feasible. A machine with 20 million physical qubits—4,000 times more than the program’s requirements—is needed to execute sufficient error correction for this algorithm to work. This overhead is only necessary to lower the system’s inaccuracy from 0,1 % at the physical level to the 10-15 required to correctly apply the algorithm.89 When considering the applicability of quantum algorithms in any field, correction of errors overhead thus becomes important.(35,36)

 

Quantum Algorithm Development

Compared to the application development of conventional algorithms, quantum algorithm design is presently in a unique position because it is mostly driven by theoretical research. Due to the widespread availability and low cost of classical computers, application development is typically driven by experimentation. This is partially due to the highly developed theoretical foundation of traditional computer science. To engineer answers, one can draw on decades of research and application of improvements in the mathematical knowledge of algorithms and computer languages.(37) For instance, using learning models on massive datasets is propelling advances in AI. The fact that computing power has reached a new level has been one of the most important developments in AI. enabling quick advancement through the experimental development of apps.(38) This has resulted in the integration of AI into numerous technologies and has expanded the availability of advanced computing capabilities across numerous industries.(39)

Even though developing experimental applications is already standard practice in classical computing, it is anticipated that rising costs associated with developing classical computers would impede this progress and eventually act as a roadblock. “Moore’s Law,” which has propelled continuous advancements in conventional computing capability since the invention of the first silicon chips, has frequently been asserted to be coming to a stop. If so, it will become more expensive to make upgrades to traditional hardware, making the advances of the past ten years more difficult to achieve. This indicates that in the upcoming years, theory and the creation of completely new platforms for computing will be important.

In contrast to classical computers, theoretical advancements in QC have been crucial since, up until recently, there was no hardware that could run or simulate quantum algorithms at a scale at which they may be advantageous. Nevertheless, after decades of investigation, the fields of complexity theory and quantum algorithms have grown to a mature state, closely collaborating with mathematical frameworks to ascertain the most effective way to design quantum computers for algorithmic tasks. Using theoretical techniques from computer science, mathematics, and physics, all significant advancements in quantum algorithms have been realized thus far without the requirement for QC hardware.(40)

The past ten years have witnessed the rise of online NISQ104 QC platforms and a greater investigation into the advantages and disadvantages of using quantum algorithms. SME collaboration with QC researchers to investigate possible applications and produce valuable intellectual property ahead of the deployment of larger and more powerful quantum computers is becoming more common in research projects.(40)

The majority of the work in this field consists of exploratory trials aimed at optimizing the use of small-scale NISQ devices. Even though these studies are expanding the community of researchers studying QC and providing insightful information on hardware performance, they typically do not result in applications or algorithms that can currently outperform traditional computers for problems that have a commercial value. The tiny and noisy character of NISQ processors makes it challenging to encode real-world utility scale issues on these devices, which has a specific negative impact on the practical development of quantum algorithms. Given these issues, a major breakthrough would be the discovery of an industrially relevant application that can provide a true quantum benefit on a NISQ device.(14)

In-depth theoretical studies that look at the end-to-end complexity of industrial use cases have produced a great deal of value. Arguably, this is most advanced in terms of possible applications for quantum simulation techniques in chemical manufacturing and materials research, as well as cryptographic applications (such as the factoring problem). Quantum algorithms are thought to provide exponential improvements over the most well-known conventional devices in these two domains, making them some of the most promising economic objectives for quantum computing.(10)

Theoretical research regarding quantum algorithms has been crucial in identifying the optimal use cases for quantum computing, even with the recent advancements in hardware. A large portion of the technology needed for utility-scale QC is currently being researched and is not yet ready for development. In addition to the hardware itself being a major engineering issue, quantum computers lack mature tools like programming languages and compilers, which are widely used in classical computing.(22)

 

conclusion

Quantum computing is a revolution in the algorithm and software developing fields. Due to superposition, entanglement, and quantum parallelism quantum computing has the capability to compute far more problems than a classical computing system. Shor and Grover quantum algorithms that bridge the possibility of exponential superiority for a range of tasks like factorization and search open up the fields as diverse as cryptography, optimization and artificial intelligence.

However, adoption of quantum computing into mainstream SW development at client-server paradigm is yet to be resolved, where the problems such as quantum decoherence, limitation of available quantum hardware, and different requirements for programming paradigm. The recent release of Quantum programming languages, frameworks can be considered as some significant progresses but the quantum software development ecosystem is still on its build up stage.

Nevertheless, it is possible to state that idea of quantum computing poses a colossal threat to future technologies. As hardware advances and as algorithms are better developed it is natural to have game changing applications which may revolutionize industries and solve problems an order of magnitude beyond that of classical computation. Analyzing the trend line of quantum computing, one can anticipate the future state where dramatically new opportunities will be opened in terms of what kind of problems can be solved through computation of algorithms and other software.

 

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FINANCING

None.

 

CONFLICT OF INTEREST

The authors declare that there is no conflict of interest.

 

AUTHORSHIP CONTRIBUTION

Conceptualization: Natalia Lemesheva, Halyna Antonenko, Petar Halachev, Olha Suprun, Yevhenii Tytarchuk.

Data curation: Natalia Lemesheva, Halyna Antonenko, Petar Halachev, Olha Suprun, Yevhenii Tytarchuk.

Formal analysis: Natalia Lemesheva, Halyna Antonenko, Petar Halachev, Olha Suprun, Yevhenii Tytarchuk.

Research: Natalia Lemesheva, Halyna Antonenko, Petar Halachev, Olha Suprun, Yevhenii Tytarchuk.

Methodology: Natalia Lemesheva, Halyna Antonenko, Petar Halachev, Olha Suprun, Yevhenii Tytarchuk.

Project administration: Natalia Lemesheva, Halyna Antonenko, Petar Halachev, Olha Suprun, Yevhenii Tytarchuk.

Supervision: Natalia Lemesheva, Halyna Antonenko, Petar Halachev, Olha Suprun, Yevhenii Tytarchuk.

Visualization: Natalia Lemesheva, Halyna Antonenko, Petar Halachev, Olha Suprun, Yevhenii Tytarchuk.

Writing - original draft: Natalia Lemesheva, Halyna Antonenko, Petar Halachev, Olha Suprun, Yevhenii Tytarchuk.

Writing - revision and editing: Natalia Lemesheva, Halyna Antonenko, Petar Halachev, Olha Suprun, Yevhenii Tytarchuk.