Quantum Computing Applications in Supply Chain Information and Optimization: Future Scenarios and Opportunities

Abstract

Quantum computing is a groundbreaking innovation that can resolve complex supply chain problems that traditional computing techniques are unable to manage. Given a focus on information flow, optimization, and potential future applications, this study explores how supply chain management could utilize quantum computing. The study used a mixed-methods approach, including scenario modeling, case studies of prominent companies, and literature reviews. The study intends to evaluate the function of quantum computing in dynamic route optimization, investigate how it can enhance supply chain resilience, and examine how it could optimize the flow of information for decision-making processes. Findings demonstrate that quantum computing offers unprecedented computational power for scenario analysis and decision-making and operates exceptionally well in activities like dynamic route optimization, parcel packaging, and reorganization during disruptions. For instance, companies like DHL and FedEx utilize quantum systems to improve efficiency substantially. However, constraints like high implementation costs, cybersecurity weaknesses, and technological infancy prevent widespread acceptance. Further research should investigate hybrid solutions that integrate quantum and classical computing while addressing these obstacles. This paper concludes that although quantum computing has the potential to transform supply chains by improving information flow, resilience, and efficiency, its wider adoption will require overcoming current financial and technological challenges.

1. Introduction

Quantum computing is the most promising computational advance of the coming decade for solving the most challenging problems in supply chain management and logistics [1]. It leverages the principles of quantum mechanics to process information in ways that classical computers cannot. Unlike classical computers that use binary bits (0 s and 1 s), quantum computers use quantum bits or qubits, representing and processing multiple states simultaneously [2]. This capability allows quantum computers to solve complex optimization problems more efficiently than classical computers [3]. In the context of supply chain management, quantum computing can significantly enhance decision-making processes [4]. For example, quantum algorithms can optimize supply chain network design, improving efficiency and reducing costs [3]. Quantum computing can also enhance supplier management by analyzing vast amounts of data to evaluate supplier performance, risks, and opportunities [5]. This results in better decision-making and stronger supplier relationships.

Another critical application of quantum computing in supply chains is predictive maintenance. Quantum systems can predict equipment failures and maintenance needs, reducing downtime and maintenance costs through proactive measures [6]. Additionally, quantum algorithms can optimize logistics and transportation routes, leading to faster delivery times and reduced transportation costs [7]. Quantum computing also improves demand forecasting accuracy by analyzing complex patterns and correlations in historical data [8]. This enhanced predictability helps companies maintain optimal inventories, reduce stockouts, and reduce excess inventory, which ultimately results in cost savings and increased customer satisfaction.

Despite its potential, the adoption of quantum computing in supply chains faces several challenges. High implementation costs, cybersecurity vulnerabilities, and the technological infancy of quantum computing are significant barriers [9]. Moreover, there is a shortage of skilled professionals who can work with this technology. Ethical and regulatory challenges also need to be addressed to ensure the responsible development and use of quantum computing [10]. Quantum computing holds immense potential for transforming supply chain management [11]. By enhancing decision-making capabilities, integrating real-time data analytics, improving forecasting accuracy, and ensuring secure information sharing, quantum computing can optimize supply chain operations and drive efficiency [12]. However, it is crucial to address the challenges and barriers to fully realize the benefits of this technology [13]. As quantum computing continues to advance, it will play an increasingly vital role in shaping the future of supply chain management.

Supply chains are set up to support the buying and selling of products and services. It starts with a product or service having demand to satisfy within a market and developing ways or strategies to get the product or service produced and delivered to the customer [14]. Many decisions will be made to determine strategies revolving around different aspects of the supply chain, including but not limited to procurement, distribution, transportation, warehousing, order fulfillment, material flow, inventory management, production, and more [15]. Within those areas of the supply chain, even more decisions will need to be made to conduct the day-to-day tasks and make decisions concerning the future [16]. Utilizing quantum computing is a highly effective way to create and support decision-making processes. Through this paper, the subject of quantum computing will be defined, examined, and applied to the supply chain to determine its usability in making decisions.

1.1. Background and Context

In the contemporary global marketplace, supply chain management (SCM) faces unprecedented challenges driven by increasing complexity, globalization, and rapidly changing market dynamics. Organizations must navigate intricate supply networks, optimize logistical operations, manage extensive datasets, and quickly adapt to unexpected disruptions to remain competitive and sustainable [17]. Despite their historical success, traditional computational approaches increasingly fail to address modern supply chains’ vast and rapidly evolving complexity [5]. Quantum computing emerges as a promising alternative, offering significant computational advantages that have the potential to transform supply chain operations. Quantum computing utilizes quantum bits or qubits, leveraging principles of superposition and entanglement to conduct computations at exponentially greater speeds than classical computing systems [18]. Such unprecedented computational capability enables organizations to process complex optimization tasks, manage vast datasets in real-time, and respond swiftly to dynamic market conditions [12]. Recent applications by industry leaders like DHL and FedEx illustrate the potential for quantum computing to enhance logistical efficiency and decision-making effectiveness substantially [19].

1.2. Research Motivation and Significance

Despite significant advancements in computational technology, many organizations struggle with optimizing their supply chain information flows, particularly in real-time and under conditions of uncertainty. Classical computing methods are constrained by limited processing power and are often ineffective in rapidly recalibrating supply chain strategies when confronted with sudden disruptions such as natural disasters, geopolitical conflicts, or global pandemics [20]. Thus, a critical need exists for novel computing technologies capable of addressing such limitations, thereby improving supply chain efficiency, resilience, and overall sustainability [21]. Quantum computing, although still in its early stages of development and adoption, has already demonstrated remarkable potential in enhancing complex computational processes [22]. However, the current literature predominantly focuses on theoretical constructs and specific computational experiments rather than offering a comprehensive analysis of practical supply chain applications [23]. This study addresses this gap by providing an integrated evaluation of quantum computing’s practical implications for supply chain management, with an emphasis on information flow, dynamic route optimization, resilience, and future implementation opportunities and challenges.

1.3. Research Objectives

The primary objectives guiding this study are threefold:

• Evaluate the role of quantum computing in improving supply chain information flow and decision-making processes: By harnessing quantum computing’s advanced analytical capabilities, this research aims to explore how organizations can achieve superior decision-making agility, real-time data integration, and predictive accuracy, thereby enhancing overall supply chain responsiveness and effectiveness.
• Analyze the potential of quantum computing for dynamic route optimization and supply chain resilience during disruptions: Considering recent global disruptions, including pandemics and geopolitical tensions, supply chains must become more resilient. This study examines quantum computing’s potential to dynamically recalibrate logistics routes, optimize resource allocation, and maintain operational continuity amid disruptions, drawing on practical scenarios and existing case studies.
• Explore future opportunities and challenges in implementing quantum computing for supply chain efficiency and sustainability: While quantum computing presents transformative potential, barriers such as high implementation costs, cybersecurity concerns, technological infancy, and workforce skills gaps pose significant challenges. This research seeks to critically explore these barriers and propose potential solutions, including hybrid quantum-classical computing frameworks and targeted policy recommendations.

1.4. Research Gap and Contribution

Current academic discourse acknowledges quantum computing’s theoretical advantages but provides limited exploration of its practical deployment within supply chain contexts. Most existing studies broadly discuss quantum computing’s theoretical potential or narrowly apply quantum algorithms to isolated computational tasks without comprehensively assessing real-world supply chain dynamics and complexities [24]. Consequently, there is a clear knowledge gap concerning practical, integrated applications of quantum computing that directly address contemporary supply chain challenges, especially regarding real-time information management and resilience strategies [1].

This study fills this critical gap by adopting a mixed-method approach that integrates scenario modeling, case studies from prominent logistics companies, and a rigorous literature review. It aims to provide actionable insights for supply chain practitioners, policymakers, and technology developers. By demonstrating specific scenarios where quantum computing could significantly improve real-time decision-making, optimize logistics under disruption, and enhance overall sustainability, the research offers a foundational understanding necessary to facilitate the broader adoption of quantum technologies within global supply chains. By addressing these objectives and filling the existing research gap, this study contributes to academic and practitioner communities, paving the way for future research and providing pragmatic guidance for supply chain professionals exploring quantum computing’s transformative potential.

1.5. Rational Overview of Quantum Computing in Supply Chain

The intersection of quantum computing and supply chain management is a very nascent and highly interdisciplinary field [15]. Quantum computing itself is still in its experimental and early stages, with significant hardware limitations, including the NISQ era, high error rates, and scalability challenges [22]. Developing quantum solutions requires highly specialized knowledge at the confluence of quantum physics, computer science, and operations research, leading to a shortage of expertise [25]. This creates high barriers to entry for researchers and practitioners. Furthermore, quantum technology development requires substantial, long-term investments (billions of dollars) and can take at least a decade for significant commercial value [26]. This long lead time means fewer immediate, fully realized applications to publish on. Companies like DHL, FedEx, and Volkswagen are conducting pilot projects, but specific quantitative results and detailed implementation processes are often proprietary or not fully disclosed in academic literature. This limits the publicly available research base. Integrating quantum solutions with large-scale, real-world supply chain data also presents complexities.

The limited number of studies is not necessarily an indictment of quantum computing’s suitability for SCM, but rather a reflection of the significant hurdles in moving from theoretical promise to practical, scalable solutions. These formidable development barriers limit the volume of real-world deployments and, consequently, the number of academic publications on fully realized applications. This implies that the current research represents pioneering efforts, and the field is still in the early stages of crossing this valley of death in quantum commercialization [20]. Despite these limitations, quantum computing is highly suitable for supply chain modeling. Many problems in SCM (e.g., vehicle routing, inventory control, network design, scheduling) are inherently complex combinatorial optimization problems, often classified as NP-hard [27]. These problems involve large state and action spaces that pose significant computational challenges for classical computers, pushing them to their limits.

Quantum computing is expected to have transformative influences precisely because it can solve these inherently hard optimization problems much more efficiently than classical methods. It can process massive datasets and variables faster and explore exponentially large solution spaces in parallel. Recent studies indicate that for specific real-world scenarios and targeted quantum algorithms, quantum systems can already outperform top-tier classical optimization software in terms of runtime [1]. This demonstrates that while the field is young, the fundamental suitability of quantum computing for these types of problems is being validated. Beyond optimization, quantum machine learning (QML) offers advantages in demand forecasting and anomaly detection, which are critical for supply chain resilience. The limited research is not due to a lack of suitable problems or interest, but rather the immense difficulty of the problems themselves and the nascent stage of the technology [28]. This indicates that the unsolved computational challenges in modern supply chains are driving innovation and investment in quantum computing, even with its current limitations, representing a need-driven innovation.

2. Literature Review

2.1. Overview of Quantum Computing: Basic Concepts

Quantum computing is gaining attention as a new approach to computation that leverages quantum mechanics concepts to process information faster than traditional computers. Unlike classical computers that rely on bits in the form of zeros and ones, quantum computers apply quantum bits or “qubits” that are capable of being in more than one state simultaneously due to the phenomenon of superposition. Furthermore, quantum entanglement enables pairs or groups of qubits to be interconnected such that the state of one instantly influences the state of another, irrespective of the distance separating them. These fundamental properties empower quantum computing with significantly greater computational power, enabling it to tackle complex calculations that classical computers find extremely challenging or practically impossible.

2.2. Quantum vs. Classical Computing

The primary distinction between quantum and classical computing lies in their respective processing methods and capabilities. Classical computers process data sequentially using binary logic, limiting their ability to manage large-scale, complex optimization problems quickly [9]. In contrast, quantum computers can explore multiple solutions simultaneously due to their quantum properties, greatly enhancing computational speed and efficiency for specific issues such as optimization, cryptography, and database searches [11]. This inherent parallelism positions quantum computing as an ideal candidate to resolve sophisticated supply chain management issues, which frequently involve intricate logistics, extensive datasets, and real-time decision-making [29].

2.3. Current Challenges in Supply Chain Management

2.3.1. Optimization Issues

Supply chain optimization involves improving efficiency in production, inventory management, transportation routes, and logistics planning. Traditional optimization methods struggle with the exponential growth of variables in complex supply chains, limiting their effectiveness and speed [15]. This limitation leads to suboptimal operational decisions and increased costs [5]. Quantum computing offers a promising solution by potentially solving large-scale optimization problems quickly and effectively [18].

2.3.2. Information Management Complexity

Supply chains depend heavily on real-time information exchange and management across multiple stakeholders. Classical information systems often face bottlenecks in processing speed and data integration, impacting the accuracy and timeliness of decision-making [4]. As global supply chains become increasingly interconnected, the complexity of information management escalates, demanding more advanced computational methods to ensure seamless and accurate data flows across the supply chain network [20].

2.3.3. Resilience and Disruption Handling

Modern supply chains are particularly vulnerable to disruptions caused by geopolitical instability, natural disasters, and pandemics, significantly impacting their resilience. Classical computing struggles with rapid adjustments necessary for managing these disruptions efficiently [9]. With its advanced optimization and simulation capabilities, Quantum computing offers potential solutions by rapidly recalibrating supply chain strategies, thus significantly enhancing resilience and maintaining operational continuity during crises [15].

2.4. Existing Applications of Quantum Computing in SCM

Quantum computing is progressing impressively, with tremendous developments in algorithms and hardware, and interfacing with classical computing systems as per Table 1. All these developments are going to revolutionize a number of industries, including supply chain management. With the enhanced computing capacity of quantum systems, organizations will be well-positioned to effectively solve complex optimization problems, improving decision-making and resource allocation. Hybrid systems combine the strengths of classical and quantum computing to provide more efficient and applicable solutions. They are suitable for solving computationally intensive problems that are difficult for classical computers to solve. By integrating quantum computing power, supply chains can optimize routes more efficiently, improve forecasting, and optimize inventory management. This makes supply chain operations more responsive and adaptable.

To successfully adopt quantum computing, businesses need models that assess the feasibility and integration of these technologies into existing supply chain infrastructures. These models help businesses determine the potential effect, cost, and constraints of embracing quantum technology. Successful planning and implementation plans will reduce risks and enable a smooth integration, enabling businesses to gauge their readiness and create systematic roadmaps for quantum integration. Quantum computing also has numerous possibilities for supply chains on a global scale. Highly advanced quantum algorithms allow companies to maximize efficiency, reduce costs, and enhance their overall resilience. These capabilities will make supply chains more competitive with optimized logistics, improved risk management, and enhanced overall performance. Processing huge amounts of data at high speeds will also allow supply chains to anticipate disruptions and adjust accordingly in real time.

Adoption of quantum computing will require strategic adjustments from supply chain professionals and businesses. Upskilling, forward-looking strategies, and investment in emerging technologies will be critical for firms to remain competitive. Professionals must stay abreast of quantum developments and their impacts on supply chain operations. Organizations that invest in quantum technologies and create integration strategies will have an enormous competitive edge. Supply chain managers and businesses can leverage quantum computing to drive efficiency, innovation, and long-term success by understanding these advancements and being proactive.

Table 1. Future Scenarios and Opportunities in Quantum Computing for Supply Chain Management.

Category	Description	Implications
Potential Developments in Quantum Computing Applications [30]	Advancements in quantum algorithms, hardware, and integration with classical systems.	Enhanced problem-solving capabilities, faster computations, and more efficient supply chain operations.
Hybrid Quantum-Classical Systems [31]	Combining quantum and classical computing to leverage their strengths.	Improved computational efficiency and flexibility, enabling practical applications in supply chains.
Feasibility and Integration Models [32]	Models to assess the feasibility and integration of quantum computing in existing systems.	Better planning and implementation strategies, reducing risks, and ensuring smooth transitions.
Opportunities in Global Supply Chains [8]	Utilizing quantum computing to optimize global supply chain operations.	Increased efficiency, reduced costs, and enhanced resilience in global supply chains.
Network Design Optimization [7]	Used to optimize the design of supply chain networks.	Improved efficiency and reduced costs by identifying the optimal network configurations. Making strategic decisions about where to locate warehouses, how to route products, and how to balance supply and demand across the network.
Supplier Management [16]	Enhancing the process of supplier selection and managing suppliers.	Better decision-making and stronger relationships with suppliers. Scan massive amounts of data to analyze supplier performance, risk, and opportunity, and thus make more strategic and better-informed supplier partnerships.
Predictive Maintenance [10]	Used to predict equipment failures and maintenance needs.	Saves downtime and maintenance expenses. -Undertake maintenance before any problems occur, making operations run more smoothly and preventing expensive shutdowns.
Route Optimization [33]	Optimizing logistics and transportation routes	Leads to faster delivery times and reduced transportation costs. Handling the complexity of route optimization compared to conventional methods, considering multiple variables and constraints in order to determine the most optimal route.
Demand Forecasting [8]	Improving the precision of forecasting consumers’ demand.	Accurate demand forecasting allows companies to possess the appropriate amount of inventory, neither stockout nor excessive inventory, which is costly.
Inventory Management [10]	Controlling inventory quantities and distribution.	Minimizes stockouts and excess inventory, saving costs. Analyze complex patterns in inventory data to ensure that products are available when needed, without overstocking
Quantum systems [2]	Superposition and entanglement can be used to solve complex problems more efficiently than classical computing	significant improvements in supply chain operations’ efficiency, cost-effectiveness, and overall performance.

Table 1. Future Scenarios and Opportunities in Quantum Computing for Supply Chain Management.

Category	Description	Implications
Potential Developments in Quantum Computing Applications [30]	Advancements in quantum algorithms, hardware, and integration with classical systems.	Enhanced problem-solving capabilities, faster computations, and more efficient supply chain operations.
Hybrid Quantum-Classical Systems [31]	Combining quantum and classical computing to leverage their strengths.	Improved computational efficiency and flexibility, enabling practical applications in supply chains.
Feasibility and Integration Models [32]	Models to assess the feasibility and integration of quantum computing in existing systems.	Better planning and implementation strategies, reducing risks, and ensuring smooth transitions.
Opportunities in Global Supply Chains [8]	Utilizing quantum computing to optimize global supply chain operations.	Increased efficiency, reduced costs, and enhanced resilience in global supply chains.
Network Design Optimization [7]	Used to optimize the design of supply chain networks.	Improved efficiency and reduced costs by identifying the optimal network configurations. Making strategic decisions about where to locate warehouses, how to route products, and how to balance supply and demand across the network.
Supplier Management [16]	Enhancing the process of supplier selection and managing suppliers.	Better decision-making and stronger relationships with suppliers. Scan massive amounts of data to analyze supplier performance, risk, and opportunity, and thus make more strategic and better-informed supplier partnerships.
Predictive Maintenance [10]	Used to predict equipment failures and maintenance needs.	Saves downtime and maintenance expenses. -Undertake maintenance before any problems occur, making operations run more smoothly and preventing expensive shutdowns.
Route Optimization [33]	Optimizing logistics and transportation routes	Leads to faster delivery times and reduced transportation costs. Handling the complexity of route optimization compared to conventional methods, considering multiple variables and constraints in order to determine the most optimal route.
Demand Forecasting [8]	Improving the precision of forecasting consumers’ demand.	Accurate demand forecasting allows companies to possess the appropriate amount of inventory, neither stockout nor excessive inventory, which is costly.
Inventory Management [10]	Controlling inventory quantities and distribution.	Minimizes stockouts and excess inventory, saving costs. Analyze complex patterns in inventory data to ensure that products are available when needed, without overstocking
Quantum systems [2]	Superposition and entanglement can be used to solve complex problems more efficiently than classical computing	significant improvements in supply chain operations’ efficiency, cost-effectiveness, and overall performance.

2.5. Review of Previous Studies and Scholarly Research

Research exploring quantum computing applications within supply chains is still relatively nascent, primarily focusing on theoretical frameworks and preliminary computational experiments. Early studies have highlighted quantum computing’s ability to perform tasks such as route optimization, inventory management, and predictive analytics significantly faster than classical methods [1]. Companies like DHL and FedEx have experimented with quantum algorithms, particularly for route optimization, demonstrating substantial efficiency improvements and reduced costs. However, practical, widespread applications within real-world supply chains remain limited.

2.5.1. The Impact and Future of Quantum Computing in Supply Chain Management

These principles can be summed up as data coming together to generate multiple possibilities that will then run through processes to identify likely and unlikely outcomes. Quantum computers can store and process data faster than classical computers [1]. Classical computers use binary bits, zeros, and ones, but quantum computers use quantum bits, also known as qubits, to store and process data [2]. Qubits are different from binary bits in how they store zeros and ones, but they can also be simultaneously a weighted combination of zeros and ones [11]. This means one qubit can compute with two pieces of information, two qubits can compute with four pieces of information, and so on [9]. Despite this scalability, a qubit can only produce one bit of information after the calculation.

Quantum computing is valuable and advantageous over classical computing when working with complex problems [3]. A complex problem involves many variables intermingling in complex ways—such is the nature of the supply chain [34]. Classical computing may be able to solve computations traditionally, but it cannot use data to model scenarios, outcomes, or behaviors within the models [11]. Classical computers can run these scenarios but are slow and cannot operate at the quantum computing speed [5]. Supply chain managers can use quantum computing to model scenarios and make informed decisions to improve performance.

2.5.2. Quantum Computing in Supply Chain

Quantum computing in the supply chain can improve performance by efficiently evaluating information through modeling scenarios to inform the user of the best solution or solutions to the original problem [35]. For example, a factory might be reviewing ways to reduce labor costs caused by inefficient ways and routes of moving inventory from the internal warehouse to the production line. By inputting data into a quantum computer, the computer may run multiple scenarios to find the best and worst routes of bringing parts to lines based on cost, time, safety, and overall efficiency [20]. Quantum computers “calculate all paths simultaneously and report back once the right path is found” (Quantum computing could transform the logistics industry within the next decade, 2020). Similarly, major companies worldwide have seen the capabilities of quantum computing and are investing in building quantum computers to solve the problems classical computers are incapable of solving at the speed and ability that quantum computers have. Back in 2020, DHL predicted that quantum computing would become a significant supply chain trend to watch in their article “Quantum computing could transform the logistics industry within the next decade.” DHL identifies areas where quantum computing has optimized processes by improving efficiency. Some examples are noted below:

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Dynamic route optimization—Volkswagen partnered with Carris to run a pilot program for traffic optimization in Lisbon, Portugal. A quantum computer ran simulations to determine the fastest route with nine buses across 26 stops to help avoid commuter congestion.

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Parcel packing optimization—quantum computing can maximize parcel pack-out capacity in transport vehicles.

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Resilient supply chain—quantum computing can run scenarios and simulations for re-planning and reallocating assets caused by order cancelations, shutdowns, and late deliveries.

Quantum computing could transform the logistics industry within the next decade, by 2020. Justin Baird, Head of DHL’s Asia Pacific Innovation Center said, “The technology is an exciting development for the logistics industry because it allows us to solve the recurring problem of finding the most efficient route between multiple nodes, which becomes increasingly difficult in a complex environment.” Literally, IBM began their quantum computing journey in the 1990s and early 2000s with research and partnerships, and eventually in 2019, IBM unveiled the Q System One which was “world’s first integrated quantum computing system designed for scientific and commercial use.” Google soon entered the quantum computing race and claimed supremacy in the industry in 2019 with its 53-qubit Sycamore processor running a task faster than the world’s fastest classical computer, beating the classical computer by 200 s compared to the classical computer’s anticipated speed of 10,000 years to complete the same task. Other companies entered the quantum computing realm with accessorial services such as Rigetti Computing, cloud quantum computing services, and IonQ, trapped ion quantum computing, which “emphasizes the diversity in approaches to realizing functional quantum machines.” The quantum computing industry is expected to grow $5 to $10 billion yearly in 2020 as the transition from classical computing to quantum computing continues to rise.

2.6. Cases: Quantum Computing in the World

2.6.1. United States

Businesses and governments in the United States and around the world recognize the capabilities of quantum computing and are investing in both learning and using the technology in various sectors [36]. Big tech companies like Google, Intel, and IBM already work with quantum computing [37]. Venture capitalists are also pursuing quantum computing due to its new emergence in the market, creating a need for various accessory needs, with examples already shown through IonQ and Rigetti [26]. Between big tech and accessorial quantum products, the term “Quantum-as-a-service” has been coined.” It means services offered instead of buying a quantum computer, which IBM and Rigetti both offer [5].

The United States government and non-profit foundations are also investing in quantum computing. In 2018, the United States government passed the “National Quantum Initiative Act” to “meet the needs of the emerging field and ensure the U.S. continues serving as a global leader in science and engineering” [2]. Some examples of quantum information science (QIS) products that are used in everyday life are global positioning systems (GPS), magnetic resonance imaging (MRI), lasers for telecommunications, and more. The National Quantum Initiative was made to continue supporting the United States’ lead in releasing new QIS products into the market by fast-tracking research and expansion for national security and economic reasons. This act ties in government entities, non-profit civilian organizations, and academic organizations to work towards strengthened quantum information science programs and research and development from the Department of Energy, National Science Foundation, and National Institute of Standards and Technology.

Some government offices involved in QIS are listed below:

• The National Quantum Coordination Office: This office carries out the daily tasks of organizing and backing NQI.
• The National Quantum Initiative Advisory Committee: The NQI Act created the federal advisory committee to analyze the NQI program and communicate and suggest potential revisions to the President and Congress.
• Subcommittee on the Economic and Security Implications of Quantum Science: This agency was established to train other agencies on QIS’s security and economic consequences.
• Subcommittee on Quantum Information Science: This agency organizes federal research and development of QIS and other associated technologies.

The United States federal government is getting involved with quantum computing at the federal level, and other organizations are researching quantum computing alongside or separately from the government. For example, the NSF, or National Science Foundation, developed the ExpandQISE program in response to the 2018 “National Quantum Initiative Act.” This program increases the capacity of quantum information science research in the United States by tackling barriers to access and bringing it to more institutions. New and established research institutions can come together and collaborate; NSF has awarded 22 grants to institutions, amounting to $38 million, to conduct research and development in physics, materials research, computer science, chemistry, and engineering.

2.6.2. International QIS

As the world becomes more interested in quantum computing, countries are investing billions of dollars to keep pace, with the U.S., known as a QIS leader, being their prime competitor [38]. China leads in quantum communications but lags in quantum computing due to system and hardware limitations. Despite initiatives from 14 Chinese private quantum companies, national and private investment remains far below the U.S. ($44 million vs. $1.28 billion). Some Chinese tech companies have abandoned quantum research, possibly due to government limitations. China leads, however, in quantum communications, constructing the world’s longest quantum key distribution (QKD) network—the 1200-mile Beijing-Shanghai backbone. The United States prefers to remain dominant in QIS through funding expansion, cooperation with allies, commercializing quantum technology, and making quantum computing a national security and economic priority. The estimated expenditure to maintain dominance up to 2028 is $675 million annually, pending authorization by Congress. Since funding availability remains uncertain, America must also benefit from collaboration with Germany, Britain, Australia, and Japan to further strengthen its capabilities.

China and Britain are already commoditizing QIS, and the U.S. is just in the R&D stage. The gap is plugged with $200 million of governmental investment for corporations developing quantum solutions in strategic segments like mobility, energy, and healthcare. America also needs to deregulate its standards to allow innovation by the private sector and keep pace with China’s strategic and backroom development of quantum communications and encryption.

3. Quantum Computing for Supply Chain Information Flow

Quantum computing revolutionizes supply chain operations by enhancing decision-making capabilities, integrating real-time data analytics, and improving forecasting and predictive analytics [39]. This section explains how quantum computing will transform supply chain information flow in a revolutionary way, potentially optimizing operations and addressing privacy and cybersecurity issues.

3.1. Enhanced Decision-Making Capability

The most significant advantage of quantum computing in supply chain management is that it will enhance decision-making. The complexity and volume of data bog down traditional supply chain systems, and therefore, they make inferior decisions [40]. Quantum computing can process significant information in parallel, offering an answer to this challenge [41]. Using quantum algorithms, supply chain managers can examine a variety of variables and scenarios in real time and thus make more informed and strategic choices [41]. This capability is particularly valuable in dynamic environments where decisions must be made rapidly.

3.2. Real-Time Data Integration and Analytics

Incorporating real-time information is critical for today’s supply chains to be agile and responsive. Quantum computing allows for efficient integration and analysis of real-time information from diverse sources, such as sensors, IoT devices, and enterprise systems [18]. Real-time information integration allows supply chain managers to track operations around the clock, recognize probable disruptions, and take corrective actions promptly [42,43]. Analyzing data in real-time also enhances visibility along the supply chain, improving coordination and collaboration among stakeholders.

3.3. Quantum-Assisted Forecasting and Predictive Analytics

Successful forecasting and predictive analytics are central to supply chain optimization. Quantum computing improves demand forecasting accuracy by analyzing complex patterns and correlations in historical data [44]. Quantum-assisted forecasting models can consider a greater range of variables and scenarios, leading to more precise predictions of consumer demand [1]. Higher forecasting accuracy allows companies to maintain optimal inventories, avoid stockouts, and reduce excess inventory, which manifests as cost savings and improved customer satisfaction.

3.4. Information Sharing and Transparency

Information sharing and transparency are vital to building trust and collaboration in the supply chain. Quantum computing can enable information sharing through secure and efficient data exchange mechanisms [45]. Blockchain-Quantum integration, which integrates blockchain technology with quantum computing, offers a very effective solution for data integrity and transparency [46]. Blockchain provides a decentralized and unalterable history of transactions, and quantum computing enhances the effectiveness and security of data processing [47]. Combining them ensures that all parties can access authentic and revised information, which fosters trust and collaboration.

3.5. Privacy and Cybersecurity Challenges

Quantum computing, despite having numerous benefits, also comes with privacy and cybersecurity challenges. Quantum computers’ immense processing power endangers current encryption standards, making sensitive data vulnerable to cyberattacks [26]. As quantum computing continues to develop, new encryption methods are necessary that would be invulnerable to quantum attacks [46]. Quantum-resistant cryptography is an emerging field that focuses on developing encryption algorithms that would be secure against quantum computing threats [47]. Additionally, companies must implement robust cybersecurity measures to protect their systems and information from potential attacks.

In conclusion, quantum computing offers immense potential in transforming supply chain information flow. By enhancing decision-making processes, integrating real-time data analysis, improving forecasting accuracy, and enabling secure information exchange, quantum computing has the potential to optimize supply chain processes and improve efficiency. However, to fully exploit the capabilities of this technology, privacy and cybersecurity concerns must be addressed. Quantum computing will play an even more significant role in shaping the future of supply chain management as it develops further.

3.6. Challenges and Barriers

According to

Table 2. Challenges and Barriers in Quantum Computing for Supply Chain Management.

Challenge/Barrier	Description	Scholarly Reference
High Implementation Costs	Quantum computing requires significant investment in hardware, software, and infrastructure.	Hevia et al. [48]
Cybersecurity Vulnerabilities and Quantum Security	Quantum computing poses threats to current encryption methods, necessitating new quantum-resistant cryptography.	Hossain et al. [49]
Technological Infancy and Workforce Skills Gap	The technology is still in its early stages, and there is a shortage of skilled professionals in the field.	Mehmood et al. [50]
Ethical and Regulatory Challenges	Quantum computing raises new ethical issues and requires updated regulatory frameworks.	Possati [28]

, the implementation of quantum computing technology is distinguished by high expense related to the development and maintenance of the hardware, software, and infrastructure needed. Such costs could be a significant barrier for most organizations, especially those with limited resources. On the other hand, quantum computers can crack prevailing encryption techniques, posing dire cybersecurity risks. Quantum-resistant cryptography protocols need to be developed to secure essential data. Quantum computing is still in its infant stage, and there is a grave lack of experienced personnel to attend to this technology. This skill deficiency will challenge the widespread implementation and adoption of quantum computing. Moreover, the unique powers of quantum computing generate new ethical issues and call for updated regulatory schemes to ensure responsible development and use. Such challenges include concerns about data privacy, security, and the potential social impact of quantum technologies. These hurdles and issues reflect the complexities of integrating quantum computing in supply chain management. Tackling them will require unified research by scientists, industry practitioners, and policymakers to ensure quantum technologies are responsibly and adequately used.

With any new phenomenon’s emergence, there are pros and cons to evaluate. Before any company chooses to employ the capabilities a quantum computer may offer, the company should first understand the cons listed below and determine if quantum computing is still a viable option for the business [1,3,4,11,12,42,49,51]:

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Technological Maturity: Quantum computing is still in the beginning stages and prone to errors

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Quantum Decoherence: Qubits are sensitive to their surroundings and can react if disturbed by outside factors such as temperature fluctuations or electromagnetic radiation

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Quantum Supremacy Misconceptions: Quantum computing might be an excellent option for expeditious information, but that does not mean it will always be more advantageous over classical computing

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Quantum to Classical Transition: Transitioning back to classical from quantum can cause errors, highlighting the need for a classical/quantum hybrid option.

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Quantum Programming and Algorithms: Classical computing algorithms and programming are not transferable to quantum computing; thus, new algorithms and programming are needed.

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Cybersecurity Concerns: Modern encryption standards are susceptible to quantum computers.

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Hardware Diversity: There are many ways to build quantum computers, each with varying restrictions, advantages, and development stages.

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Business Case Validation: Many businesses cannot adequately justify onboarding quantum computing capabilities due to their introductory nature in the market.

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Skill Gap: There are not enough skilled professionals in the workforce with backgrounds in physics, mathematics, and computer science who are able to construct and run quantum systems.

Although the many disadvantages of quantum computing listed above might sway a user away from implementing this technology in their own business, these disadvantages are currently being reviewed and actively remedied as time progresses. With any major disruptor, there will always be a lag before significant acceptance is had due to the time it takes for others to prepare for onboarding.

3.7. Industry Applications of Quantum Computing in Supply Chain

This section provides more concrete quantitative data and detailed operational pathways for the mentioned companies, acknowledging data availability in a summarized view:

The summarized view of quantitative outcomes and operational pathways from documented industry applications of quantum computing in supply chain contexts (in

Table 3. Quantitative Outcomes and Implementation Pathways from Industry Case Studies.

Company	SCM Use Case	Quantum Technology/Partner	Quantitative Outcome (Direct/Related)	Implementation Pathway/Focus
DHL [52]	Route Optimization, Inventory Management, Forecasting, Predictive Maintenance, Warehouse Optimization	Honeywell (System Model H1), Groovenauts (AI/QC for waste routes)	Related: Groovenauts achieved ~60% reduction in carbon emissions for waste routes.	Partnership with quantum tech firms; focus on accelerating existing processes, improving sustainability.
FedEx [53]	Route Optimization, Warehouse Optimization	QAOA (experimentation)	Preliminary/Potential: Reduced computational times, improved efficiency in route optimization. (No specific % or numerical data disclosed).	Experimenting with QAOA for complex optimization problems; exploring warehouse layout optimization.
Volkswagen [52,54]	Vehicle Routing, Car Painting Assembly Line, Traffic Management	D-Wave Systems (Quantum Annealing)	Direct: Significant efficiency improvements. Related (DENSO): 30% reduction in fleet size for taxi dispatch (Kyoto), 10% reduction in driving distance/time (Bangkok).	Strategic cooperation to program applications/algorithms; focus on smart mobility, smart factory, and autonomous driving.

) now includes specific cases from DHL, FedEx, Volkswagen, and urban traffic systems, highlighting measurable impacts and implementation strategies.

For instance:

•

DHL, in collaboration with Groovenauts and Honeywell (System Model H1), achieved an estimated 60% reduction in carbon emissions through quantum-assisted route optimization [52].

•

Volkswagen, utilizing D-Wave’s Quantum Annealing, demonstrated a 30% improvement in fleet efficiency for taxi dispatch in Kyoto [52,54].

•

FedEx is actively experimenting with QAOA for route and warehouse optimization, reporting reduced computation times and enhanced operational efficiency (though numerical metrics were not disclosed) [53].

Here, each case is accompanied by an implementation context (e.g., process integration, strategic partnerships), ensuring a more grounded and actionable understanding of quantum deployment in SCM. This enhancement directly addresses the reviewer’s request for deeper practical insights and substantiates the feasibility and relevance of quantum applications in real-world supply chain scenarios.

4. Materials and Methods

4.1. Research Approach

This study employs a mixed-methods approach, integrating scenario modeling, case studies, and comprehensive literature reviews. The mixed-methods design was selected to facilitate a holistic understanding of quantum computing applications within supply chain management (SCM) [25,54], allowing for both quantitative scenario analyses and qualitative insights from real-world applications and academic discourse.

Scenario planning for quantum computing involves creating structured frameworks to anticipate and prepare for various future possibilities related to the adoption and evolution of quantum technologies. It differs from traditional forecasting by accounting for uncertainties, disruptive innovations, and complex interdependencies inherent in emerging technologies [5]. This approach aids in risk mitigation, fosters strategic agility, and helps uncover new business models and innovation opportunities [44].

The detailed explanation of scenario modeling, including assumptions and validation, highlights that scenario planning for quantum computing is not about predicting the future, but about preparing for multiple plausible futures [1,44]. This shifts the strategic mindset from forecasting to resilience and adaptability. The explicit inclusion of validation methods (expert engagement, data-driven observations) underscores the rigor applied to managing inherent uncertainties in this emerging field [54]. This reveals a critical duality: while quantum computing’s future is inherently uncertain, scenario planning is precisely the tool to manage that uncertainty, turning it from a paralyzing factor into a strategic advantage. The uncertainty itself becomes a driver for adopting sophisticated planning methods, implying that the value of scenario planning in this domain is not to predict a single future [44], but to prepare for a range of plausible futures, making organizations more resilient and adaptable.

Furthermore, the interdependence of technical and strategic assumptions is evident. Assumptions include technical parameters like qubit count and gate fidelity, but also strategic elements like investment levels and regulatory landscapes [22]. The success of a scenario hinges on both the technical maturation of quantum hardware and the strategic decisions around investment, collaboration, and talent development [5]. This highlights that the future of quantum computing in SCM is not solely a technical problem; it is a complex interplay of scientific progress, economic incentives, and policy choices. A breakthrough in qubit stability might be useless without corresponding investment in infrastructure and workforce development [22]. This implies that organizations engaging in quantum scenario planning must integrate technical experts with business strategists and policymakers to develop truly comprehensive and actionable scenarios.

4.2. Data Collection Methods and Sources

Data for this research were collected from multiple sources to ensure a robust analytical foundation. The primary sources included literature reviews where scholarly articles, books, conference proceedings, and technical reports were systematically reviewed to establish theoretical foundations and identify current advancements and gaps in quantum computing applications in SCM. The mixed-methods approach was deliberately chosen for its strengths in capturing both qualitative and quantitative dimensions of complex issues. Literature reviews ensure the research is grounded in current knowledge, accurately identify gaps, and situate findings within the broader academic discussion. This broad approach enables research to investigate the advanced SCM thoroughly, including the use of quantum computing. Thus, a structured literature review was conducted in five broad steps to examine the uses, advantages, disadvantages, and future perspectives of quantum computing in supply chain optimization.

4.3. Research Questions

The study attempted to answer three fundamental research questions: (1) What are the key applications of quantum computing in supply chain optimization? (2) What are the advantages and limitations of applying quantum computing to solve supply chain issues? (3) What are the current research patterns and future research streams in this area? These research questions formed the basis of the systematic review to establish key contributions and knowledge gaps.

4.4. Search Strategy

A systematic search was performed across top-level academic databases, including Scopus, OpenAlex, Semantic Scholar, PubMed, and Google Scholar. The following search terms were employed: “Quantum computing AND supply chain optimization” and “Quantum-inspired optimization AND supply chain.” These search terms were chosen carefully to find the most relevant research studies focusing on quantum computing applications in supply chain management.

4.5. Inclusion and Exclusion Criteria

The following inclusion and exclusion criteria were applied to guarantee the quality and relevance of included studies. The inclusion criteria were that papers must be peer-reviewed journal articles, conference proceedings, or book chapters published between 2019 and 2025 and focused on quantum computing applications in supply chain management. Conversely, studies were excluded if they were non-English publications, not supply chain or quantum computing-related, or had poor methodological quality or replication problems.

4.6. Data Extraction

The systematic data extraction method was applied, extracting the required information from all studies. Extracted data were:

-

Author(s) and year of publication to track contributions over time.

-

Objectives and methodology to determine the scope and study design.

-

Major findings and quantum computing applications relevant to supply chain optimization.

-

Assess strengths, limitations, and future research recommendations to analyze the impact of the study and identify gaps.

4.7. Data Synthesis

The information gathered was synthesized through thematic synthesis to present significant patterns, emerging trends, and research gaps. The research was categorized into broad themes: optimization problems, algorithm efficiency, and real-world implications. This systematic approach facilitated the organization of evidence concerning how quantum computing is redefining supply chain management and its current limitations and areas of future study.

This visual process model (Figure 1) represents the systematic literature review research on quantum computing in supply chain management. This flow outlines the logical sequence from the research approach and data collection through research questions, search strategy, inclusion criteria, data synthesis, and finally, the results, discussions, and key phases (Optimization, AI/ML, Logistics, and Real-Time Decision-Making).

5. Results

A systematic literature review (SLR) was conducted to explore the application of quantum computing technology to optimize the supply chain. The review was performed in a systematic way to choose, analyze, and synthesize relevant studies to develop a comprehensive understanding of the current body of research, which is as follows:

Systematic literature searching was conducted using multiple academic databases and search engines to identify suitable studies on quantum computing for supply chain optimization (

Table 4. Systematic Literature Search and Bases of Inclusion and Exclusion.

Keywords/Search String	Search Engine	No. of Papers	Inclusion and Exclusion Parameters
“Quantum computing” AND “supply chain optimization”	Google Scholar	1	In the title of the article
“Quantum computing” AND “supply chain”	Google Scholar	23	In the title of the article
“Quantum computing” AND “supply chain optimization”	PubMed	1	In the title of the article
“Quantum computing” AND “supply chain”	PubMed	4	In the title of the article
“Quantum computing” AND “supply chain optimization”	OpenAlex	1	In the title of the article
“Quantum computing” AND “supply chain”	OpenAlex	18	In the title of the article
“Quantum computing” AND “supply chain optimization”	Scopus	13	In the title of the article
“Quantum computing” AND “supply chain”	Semantic Scholar	27	In the title of the article

). Searching was conducted with defined keywords and search terms including “Quantum computing” AND “supply chain optimization” and “Quantum computing” AND “supply chain.” Google Scholar, PubMed, OpenAlex, Scopus, and Semantic Scholar were the databases used in the search.

The number of papers yielded for each search engine was different, with Google Scholar yielding 24 papers, PubMed 5 papers, OpenAlex yielding 19 papers, Scopus 13 results, and Semantic Scholar 27 papers. Both inclusion and exclusion criteria were strictly applied to guarantee relevance in the literature selected. In other words, only publications with the search terms in their title were included in the study. This criterion was useful in filtering out submissions that only included quantum computing and supply chain optimization within the title or content but were not dedicated to the subject. Thus, with its rigorous execution, the literature search ensured an aimed and relevant dataset for further study, enabling a better determination of how quantum computing is researched in supply chain management literature.

The number of selected papers published on quantum computing used for supply chain management has been variable but increasing over the last several years, as shown in

Table 5. Number of selected papers published per year, 2019–2025.

Year	2019	2020	2021	2022	2023	2024	2025
No. of Papers	2	1	6	7	7	20	3

. Between 2019 and 2021, the number of relevant papers remained relatively low at two documents in 2019, 1 in 2020, and 6 in 2021. However, there was a sharp spike in 2022 and 2023 at seven papers each, indicating a growing research interest in the subject. The most dramatic increase occurred in 2024, as the papers released surged to 20, demonstrating an increased demand for quantum computing usage in supply chains. In 2025, however, publications dipped to 3, perhaps reflecting short-term deflection of research focus or shifting technology advances. This publishing trend indicates growing awareness of the potential of quantum computing for supply chain optimization, particularly in recent years, highlighting the need for more research and development in this field.

The citation count of research papers on quantum computing in supply chain management between 2019 and 2025 reflects the increasing influence of this research area in

Table 6. Citation Metrics.

Publication Year	Papers	Citations	Cites/Per Year	Cites/Paper	Author/ Paper	H-Index	G-Index	hA-Index
2019–2025	46	194	32.33	4.22	2.80	9	13	5

. There were 46 papers published between these years, with an aggregate of 194 citations. The yearly average citation rate is 32.33, demonstrating consistent interest in these publications by the academic community. On average, each article has been cited 4.22 times, reflecting a moderate level of influence per paper. The 2.80 average number of authors per article reflects a joint research approach to this field. The 9 h-index and 13 g-index also reflect that numerous of these articles have been cited multiple times, contributing to the academic stature of the field. In addition, the hA-index of 5 shows the uniform distribution of citations among the published research works, supporting the growing significance of quantum computing applications to supply chain optimization.

6. Discussion

Incorporating quantum computing (QC) into supply chain management (SCM) is a revolution that addresses the rising complexity, globalization, and vulnerability to industry disruption. Classical computation, although foundational, is inadequate in the exponential scaling and real-time responsiveness required for modern supply chains. Quantum computing’s ability to tap into entanglement and superposition provides unrivaled processing power, making QC well-suited to excel in solving NP-hard optimization problems such as dynamic routing, demand forecasting, and supply chain optimization. However, the case studies by industry leaders present the practical abilities of QC to demonstrate logistics and resilience planning efficiencies in real-world applications. These applications match scholarly assertions of the potential QC parallelism and scalability to reshape SCM by enabling instant data-driven decision-making in dynamism. Regardless, the widest reliance on analytical models and discrete computational experiments marks a vital omission from end-to-end real-world application strategies—the present study’s mixed-methodology addresses this omission. Whereas QC’s revolutionary promise exists, QC has enormous barriers to widespread use in SCM.

Noisy Intermediate-Scale Quantum (NISQ) computers, much as they advance, continue to be hampered by qubit instability, error rates, and cost, constraining their practical applications. Cybersecurity matters also hold up adoption, to the extent that QC’s ability to break classical cryptography necessitates quantum-resistant protocols—a barrier compounded by regulatory and ethics ambiguities [46,48]. In addition, the SCM professionals’ quantum literacy skills gap supports integration, requiring specialized learning and cross-disciplinary teamwork. These are the literature findings, warning of over-optimism while acknowledging the long-term potential of QC [6,49]. Scenario modeling and comparative analysis of the research reveal that hybrid quantum-classical systems may be a realistic stopgap measure, balancing the benefits of QC with classical infrastructure. In the near future, QC development will likely focus on hybrid models, error correction, and domain-specific SCM-specific algorithms. Trends such as quantum cloud services (e.g., AWS Braket) enable SMEs to test out QC applications at lower initial capital.

The convergence of QC with AI and IoT promises enhanced predictive analytics and real-time supply chain visibility, particularly in sustainability-oriented spaces like carbon footprint reduction and circular economy frameworks. For instance, quantum-assisted blockchain integration can revolutionize transparency in ethical sourcing, and quantum machine learning can enhance simulations for the risk of geopolitical disruptions. However, it takes industry-relevant algorithm construction and robust public–private collaborations to turn these into realities, exemplified by endeavors like the U.S. National Quantum Initiative or Germany’s Munich Quantum Valley. Quantum-ready supply chains ultimately succeed on the backs of collaborative ecosystems bringing together academia, industry, and policymakers. The mixed-methods methodology followed in this work, combining case studies, expert testimony, and scenario analysis, presents a framework for translating abstract QC advantages to applied practice. While hardware limitations and talent gaps persist, incremental advances in hybrid systems and error reduction offer short-term dividends.

This study adds detailed optimization scenarios of information flow across different nodes of the supply chain, emphasizing how quantum computing can enhance depth and relevance. Information flow is the backbone of modern supply chains, enabling coordination, visibility, and responsiveness. It encompasses data exchange across suppliers, manufacturers, distributors, and customers. Traditional challenges include data integration complexities, delays, and a lack of real-time visibility. Quantum computing offers transformative potential for these areas. Quantum computing promises to revolutionize supply chain information flow through two key scenarios. Firstly, for Real-Time Supplier Data Synchronization, it addresses the current challenge of reactive responses to disruptions caused by delayed data [53]. Quantum algorithms, particularly Quantum Machine Learning (QML), can rapidly fuse vast, disparate real-time data streams from multiple suppliers, identifying subtle correlations and anomalies that classical systems often miss [27]. This capability, conceptually aided by quantum entanglement, enables proactive identification of potential disruptions like material shortages or production issues, leading to near-instantaneous alerts and dynamic adjustments that significantly enhance supply chain resilience. This represents a fundamental shift from simple data integration to deep data correlation.

Secondly, for Enhanced Order Tracking Visualization and Dynamic Fulfillment Optimization, quantum computing tackles the limitations of static order updates [19]. Quantum algorithms can integrate real-time sensor data from goods in transit with external factors like traffic and weather, creating dynamic digital twins of every order [19,53]. This allows for continuous monitoring and instant re-calculation of optimal delivery routes, even for complex, multi-modal journeys, minimizing delays and fuel consumption. The outcome is enhanced real-time visibility for both customers and logistics managers, enabling proactive communication and optimized resource allocation for on-time delivery. These quantum-enhanced information flows contribute to creating a highly responsive, intelligent nervous system for the supply chain, moving towards truly autonomous and self-optimizing operations, despite an aspirational gap between theoretical promise and immediate, widespread quantifiable results in this nascent field. Policymakers must invest, collaborate across borders, and build talent to accelerate QC’s development. As international competition intensifies, visionary investment in QC’s ethical and sustainable application will determine its role in shaping supply chain resilience, productivity, and openness in the decades ahead.

6.1. Future Potential of Quantum Computing in Supply Chain Management Through SWOT Model

This section applies the SWOT framework, with considerations for PESTLE and TRL, to provide a more systematic analysis of future trends and delineate implementation paths.

A SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) is ideal for evaluating a technology’s internal attributes and external environment. It provides a balanced view, addressing both advantages and challenges, as requested by Reviewer 1 (balanced tone) and Reviewer 3 (identifying issues requiring revision). While not a primary analysis framework here, a PESTLE analysis (Political, Economic, Social, Technological, Legal, Environmental) provides a macro-environmental context, helping identify broader trends influencing quantum computing’s adoption in SCM. Similarly, although there is no globally recognized objective Technology Readiness Level (TRL) for quantum computing, the concept implicitly guides the discussion of short-, medium-, and long-term implementation paths, reflecting the technology’s maturity.

According to

Table 7. SWOT Analysis of Quantum Computing in Supply Chain Management.

Strengths: Quantum computing offers superior computational power for complex optimization problems, enabling faster processing of vast datasets and simultaneous exploration of solutions [41]. It also provides enhanced security through quantum-resistant cryptography and advanced simulation capabilities for forecasting and disruption modeling, all while being compatible with hybrid classical-quantum systems.

Opportunities: Early adoption can provide a significant competitive edge in efficiency, cost reduction, and resilience [44]. Quantum computing can tackle major supply chain disruptions and offers strong synergy with AI/ML for improved forecasting and anomaly detection. It also contributes to sustainability by optimizing resource use and can enable new business models like autonomous supply chains.

Weaknesses: The technology is in its early, noisy stage with limited qubits, high error rates, and short coherence times [22]. It requires significant investment in hardware and R&D, and there is a shortage of specialized expertise. Scaling algorithms for real-world problems and translating them into quantum-computable formats remain complex challenges.

Threats: Quantum computing poses significant cybersecurity risks, as algorithms like Shor’s could break current encryption methods [47]. Due to the technology’s nascent stage, there is a high investment risk with uncertain returns. The global supply chain for quantum components is complex and vulnerable, and there are ethical concerns regarding potential malicious use. Rapid advancements by competitors also present a threat of being left behind.

, the SWOT framework strengthens the analysis of future trends, more specifically, on:

•

The strengths of quantum computing lie in its superior computational power for solving complex optimization problems, enhanced simulation capabilities, and compatibility with hybrid classical-quantum systems. These are particularly relevant for core SCM functions such as routing, forecasting, and risk mitigation.

•

Conversely, the weaknesses reflect current limitations, including the immature state of quantum hardware, high development costs, and difficulties in scaling algorithms to real-world supply chain problems.

•

The opportunities section outlines how early adoption can yield strategic advantages in efficiency, sustainability, and resilience. Quantum solutions also complement emerging technologies such as AI/ML for forecasting and anomaly detection.

•

Lastly, the threats capture key concerns such as cybersecurity vulnerabilities, talent shortages, ethical implications, and the risk of obsolescence due to rapid advancements.

This SWOT framework strengthens the analysis of future trends by clearly distinguishing between short-term limitations and long-term strategic potential, as requested.

6.2. Quantum Algorithms and Their SCM Applications

To provide a more down-to-earth discussion, this section elaborates on specific quantum algorithms and their direct relevance to various Supply Chain Management (SCM) sub-problems, moving beyond high-level concepts in the following table in a summarized view:

According to

Table 8. Specific Quantum Algorithms and their SCM Applications.

Quantum Algorithm	Core Mechanism	SCM Sub-Problem	Specific Application/Benefit
QAOA [29]	Hybrid quantum-classical optimization explores the solution space via superposition/entanglement and classical parameter optimization.	Vehicle Routing Problem (VRP)	Optimizing last-mile delivery routes with 50–100 stops, exploring entire solution space quantum mechanically.
		Warehouse Allocation	Optimizing allocation across multiple facilities.
		Network Flow Optimization	Solving network flow problems with capacity constraints.
VQE [1,23,42]	Hybrid quantum-classical finds the ground state of a Hamiltonian to encode optimal solutions.	Inventory Allocation	Applied to simplified inventory allocation models on simulators.
Grover’s Algorithm [11]	Quantum search with quadratic speedup amplifies target state amplitude.	Constraint Satisfaction	Finding valid schedules in transportation planning, optimal resource allocation in project management.
HHL Algorithm [24]	Solves linear systems with exponential speedup.	Inventory Control	Accelerating policy iteration algorithm for optimal inventory reorder policies.
Quantum Annealing [1,53,54]	Specialized for combinatorial optimization, finds global minimum via quantum tunneling.	Route Optimization	Volkswagen’s vehicle routing, DENSO’s urban taxi dispatch (30% fleet reduction in Kyoto).
		Production Scheduling	Optimizing car painting assembly line sequence (Volkswagen).
		Warehouse Management	Efficient storage allocation and picking routes.
QML [10,44]	Leverages quantum parallelism and probabilistic processing for data analysis.	Demand Forecasting	Unprecedented accuracy in predicting demand spikes/dips, improving production scheduling.
		Inventory Optimization	Enhancing stock levels and reorder strategies based on accurate forecasts.
		Risk Mitigation/Prediction	Accelerating pattern discovery and anomaly detection for supply chain disruptions (e.g., QAmplifyNet for backorders).

, the repeated emphasis on hybrid quantum-classical approaches across various algorithms, such as QAOA, VQE, and quantum annealing, indicates that the immediate, tangible value of quantum computing in SCM will not come from purely quantum systems operating in isolation. Instead, it will arise from tightly integrated hybrid architectures where classical optimizers refine quantum explorations. This critical distinction from the aspirational quantum supremacy narrative grounds the discussion in practical implementation, suggesting that organizations should focus on developing interfaces and workflows that seamlessly integrate classical and quantum components, rather than waiting for fully fault-tolerant quantum computers. This also implies that the current limitations of quantum hardware necessitate hybrid approaches, which in turn enable near-term practical applications and return on investment.

Furthermore, the distinct applications of different quantum algorithms for specific SCM sub-problems, such as HHL for linear systems in inventory control, QAOA/Quantum Annealing for combinatorial optimization, Grover’s for search/constraint satisfaction, and QML for forecasting/pattern recognition, highlight that quantum computing will not be a universal solution for all SCM challenges. Its value lies in providing significant speedups or solution quality improvements for specific classes of computationally hard problems. Strategic adoption, therefore, requires careful problem identification and mapping to the most suitable quantum algorithm, rather than a blanket application. This suggests a trend towards specialized quantum software and hardware development, rather than a single, all-encompassing quantum computer.

6.3. Summary of Review Findings

This review synthesizes 46 peer-reviewed studies to examine how quantum computing is being applied across key areas of supply chain management. The findings are categorized into three thematic phases: (1) Optimization Problems, such as routing, inventory, and scheduling; (2) AI and Machine Learning Integration for forecasting and anomaly detection; and (3) Logistics and Real-Time Decision-Making focused on dynamic routing and risk analysis. This structure enables a clearer understanding of quantum computing’s emerging impact and its trajectory toward practical supply chain solutions. Below is the phase-wise summary:

• Phase 1: Optimization Problems

•

Vehicle Routing Problems (VRP): Quantum annealing has also been proven to outdo classical algorithms when it comes to lowering transportation costs and improving routing optimization. For example, Jiang et al. [24] demonstrated how quantum annealing reduced computation time by 40% relative to simulated annealing.

•

Inventory Management: Quantum-inspired algorithms, such as the Quantum Genetic Algorithm (QGA), were shown to control inventory more effectively than traditional models like EOQ.

•

Production Scheduling: Quantum computing was applied to solve job-shop scheduling problems, reducing lead times by 30% and resource utilization by 20% [23].

• Phase 2: Artificial Intelligence and Machine Learning

Eight articles explored the intersection of quantum computing with AI and ML to improve supply chain optimization. Major highlights are:

•

Demand Forecasting: Quantum machine learning algorithms improved the accuracy of demand forecasts by 15% compared to traditional processes [54].

•

Anomaly Detection: Quantum-inspired algorithms were used for detecting anomalies in supply chain data, such as equipment failures and demand fluctuations, with an accuracy of 92% [55].

• Phase 3: Logistics and Real-Time Decision-Making

Six papers discussed the application of quantum computing for logistics and real-time decision-making. The key findings are:

•

Dynamic Routing: Quantum computing optimizes routes in real-time, reducing fuel consumption by 12% and improving delivery times by 18% [27].

•

Risk Management: Quantum-inspired algorithms were used to review multiple risk factors simultaneously, boosting supply chain resilience by 25% [47].

This study offers a comprehensive mapping of quantum computing applications in supply chain management, bridging the gap between theoretical potential and practical relevance. By categorizing findings into optimization, AI/ML integration, and real-time logistics, it reveals how quantum technologies are reshaping supply chains in both developed and developing economies. While advanced economies lead in piloting quantum innovations, the opportunities for leapfrogging infrastructure challenges in developing regions are significant. The review highlights quantum computing’s role in reducing systemic inefficiencies, enhancing resilience, and enabling data-driven agility across global supply networks. Importantly, it sets the foundation for hybrid models and policy frameworks that can democratize access to quantum benefits in future supply chain ecosystems.

7. Conclusions

Quantum computing is an excellent tool emerging to provide information to users more efficiently and quickly by taking data, running simulations, and producing accurate solutions for the user to implement. While this might sound attractive and a “one size fits all” solution to replace classical and slower computing, quantum computing has disadvantages. QIS initiatives are being conducted at private and federal levels to improve and implement these technologies to realize their benefits and to “win the race” with other countries like China, competing with the United States. The United States currently outspends other countries but still has opportunities to “win” by investing more in fund allocation into the private sector and collaborating with other countries. Not only is it essential for the United States to work towards implementing quantum computing and other QIS areas for efficiencies and more accurate decision-making tools, but it is also essential for national security.