Quantum Computing Practical Applications by 2030: Reconciling Optimism with Technical Reality

Thesis Statement

While quantum computing has achieved significant theoretical and engineering milestones, the realization of practical, commercially viable applications by 2030 remains contingent upon solving critical challenges in error correction, qubit scalability, and algorithm development. This paper argues that near-term quantum computing will deliver limited but meaningful applications in quantum chemistry, optimization, and cryptography, while broader commercial utility remains dependent on achieving fault-tolerant quantum computing—a threshold that current trajectories suggest may be approached but not fully crossed by 2030.

Abstract

Quantum computing represents one of the most significant technological frontiers of the 21st century, yet the gap between theoretical promise and practical implementation remains substantial. This paper examines the current state of quantum computing development and assesses realistic timelines for practical applications through 2030. Drawing on recent industry roadmaps, academic research, and technical assessments, we identify three primary application domains likely to see meaningful progress: quantum chemistry simulation, optimization problems, and post-quantum cryptography. However, we also highlight critical limiting factors, including the “noisy intermediate-scale quantum” (NISQ) era constraints, the threshold problem of quantum error correction, and the absence of proven quantum advantage for most commercially relevant problems. The paper concludes that while 2030 will likely see demonstration of quantum advantage in specific domains and increased cloud-based access to quantum processors, transformative, widespread commercial applications remain a post-2030 phenomenon. Strategic preparation for quantum-resistant cryptography represents the most immediately actionable application area.

Keywords: quantum computing, practical applications, quantum advantage, error correction, NISQ era, quantum algorithms, cryptography

1. Introduction

1.1 Context and Significance

Quantum computing has transitioned from theoretical physics curiosity to serious technological investment over the past two decades. Major technology companies including Google, Microsoft, Amazon, IBM, and over one hundred smaller firms are actively developing quantum hardware and software stacks (Qiskit SDK documentation; industry sources). The global quantum computing market is projected to grow from $928.8 million in 2024 to $6.5 billion by 2030, representing a compound annual growth rate exceeding 50% (Fortune Business Insights, cited in source material).

Yet this optimism coexists with profound skepticism. A 2023 Nature spotlight article summarized the current state of quantum computers as being “for now, absolutely nothing”—a provocative assessment that reflects the persistent gap between theoretical potential and practical utility. This paper navigates between these poles, examining what quantum computing can realistically accomplish by 2030 and what remains beyond the horizon.

1.2 Literature Context

The quantum computing field has matured considerably since Peter Shor’s 1994 algorithm for prime factorization demonstrated that quantum computers could theoretically solve certain problems exponentially faster than classical computers (source material). However, Shor’s algorithm requires approximately 4,000 logical qubits—a threshold that no current system approaches. Today’s quantum computers operate in the “noisy intermediate-scale quantum” (NISQ) era, characterized by systems with 50-1000 qubits but lacking error correction capabilities.

The field has developed sophisticated theoretical frameworks across quantum information science, bringing together physics, computer science, mathematics, and engineering (source material). Key concepts include quantum supremacy (or quantum advantage)—the demonstration that a quantum computer can solve a problem faster than any classical computer—and the threshold problem: whether quantum computers can achieve fault tolerance through quantum error correction while maintaining sufficient qubit scalability.

Recent reviews identify quantum chemistry as one of the most promising near-term applications, given that quantum systems are impossible to simulate efficiently using classical computers (source material). Simultaneously, the emergence of post-quantum cryptography represents an urgent application area, as quantum computers threaten current encryption standards—a concern formalized in national security guidance since September 2022.

1.3 Paper Organization

This paper proceeds through four main sections: (1) the current state of quantum computing hardware and software infrastructure, (2) near-term applications likely by 2030, (3) critical technical barriers and the threshold problem, and (4) a realistic assessment of practical applications with implications for business and policy. We conclude by identifying genuine knowledge gaps and directions for future research.

2. Current State of Quantum Computing Infrastructure

2.1 Hardware Development Landscape

The quantum computing hardware landscape is characterized by remarkable diversity and substantial capital investment. IBM’s quantum coupling technology represents one significant approach, with the company announcing that multiple Quantum System Two units will connect to create systems capable of running 100 million operations in a single quantum circuit by 2033, scaling to one billion operations thereafter (source material). This modular approach addresses a fundamental challenge: scaling beyond current qubit counts while maintaining coherence and reducing error rates.

Google, Microsoft, Amazon, and numerous specialized firms are pursuing alternative qubit technologies, including superconducting qubits, trapped ions, photonic systems, and topological qubits. The diversity of approaches reflects genuine uncertainty about which physical implementation will prove most scalable and practical. Each technology presents distinct advantages and challenges regarding qubit count, coherence time, gate fidelity, and manufacturability.

Notably, the source material references emerging hybrid approaches, including DNA-based computing combined with quantum systems and quantum antennae for information transfer. These exploratory directions suggest that practical quantum computing may not rely on quantum systems alone but rather on heterogeneous architectures combining quantum and classical processing.

2.2 Software Infrastructure and Development Tools

The Qiskit SDK represents a critical development in democratizing quantum computing access. As the foundational component of IBM’s Qiskit software stack, it enables developers to work with quantum computers at the level of extended quantum circuits, operators, and primitives (source material). This abstraction layer is essential for moving quantum computing beyond specialized physics laboratories toward broader developer adoption.

Cloud-based quantum computing has emerged as the primary access model, with quantum emulators, simulators, and actual processors increasingly available through cloud services (source material). This infrastructure choice is pragmatic: it allows researchers and developers to experiment with quantum algorithms without requiring access to expensive physical hardware while simultaneously providing data for hardware developers to understand real-world usage patterns.

The development of quantum algorithms remains an active research area. Beyond Shor’s factorization algorithm and Grover’s search algorithm—both now decades old—the field has produced algorithms for quantum chemistry simulation, optimization problems, and machine learning tasks. However, a critical distinction exists between theoretical quantum advantage and practical utility: many proposed quantum algorithms show theoretical speedups that only manifest for problem sizes far beyond current hardware capabilities or for problems with limited practical relevance.

2.3 The NISQ Era: Capabilities and Constraints

The noisy intermediate-scale quantum era defines current quantum computing. NISQ devices contain 50-1000 qubits but lack the error correction capabilities necessary for long, complex computations. Errors accumulate rapidly—current gate fidelities range from 99% to 99.9%, meaning that a circuit with 1000 gates might retain only 10% of its original information (rough calculation based on typical error rates).

This constraint fundamentally limits what NISQ devices can accomplish. Variational quantum algorithms—which use classical computers to optimize quantum circuits—represent the primary approach for extracting utility from NISQ devices. These algorithms are inherently limited in depth and complexity, restricting their applicability to relatively small problem instances.

Importantly, the source material indicates that quantum supremacy has been demonstrated for specific, carefully chosen problems (notably Google’s 2019 random circuit sampling experiment). However, these demonstrations, while scientifically significant, involve problems with limited practical relevance. The gap between demonstrating quantum advantage and demonstrating quantum utility for commercially important problems remains substantial.

3. Near-Term Applications Likely by 2030

3.1 Quantum Chemistry and Materials Science

Quantum chemistry represents the most credible near-term application for quantum computing. The fundamental reason is straightforward: chemistry operates according to quantum mechanical principles, and simulating quantum systems classically requires computational resources that scale exponentially with system size. Quantum computers, by contrast, can represent quantum systems directly, potentially enabling efficient simulation.

Specific applications include:

  • Drug discovery: Simulating molecular interactions and protein folding to accelerate pharmaceutical development
  • Materials design: Optimizing properties of new materials for batteries, catalysts, and semiconductors
  • Reaction pathway analysis: Understanding and optimizing chemical reaction mechanisms

However, several caveats apply. First, the quantum computers required for industrially relevant chemistry problems remain substantially larger than current systems. IBM’s roadmap projects that fault-tolerant quantum computers will be available to clients in 2029, but these systems will initially be optimized for specific problem classes rather than general chemistry simulation.

Second, hybrid classical-quantum approaches will likely dominate near-term applications. Rather than offloading entire chemistry problems to quantum computers, researchers will use quantum processors for specific computational bottlenecks—such as calculating ground state energies of molecules—while classical computers handle problem setup, result interpretation, and optimization loops.

Third, the advantage of quantum chemistry simulation becomes apparent only for sufficiently complex molecules. For small molecules (which represent much of current pharmaceutical interest), classical methods remain competitive or superior. The quantum advantage threshold for practical chemistry problems remains uncertain.

Assessment for 2030: Demonstration of quantum advantage for specific chemistry problems is likely. Commercial applications will emerge in specialized domains (perhaps high-performance computing centers for pharmaceutical companies), but widespread industrial deployment remains unlikely.

3.2 Optimization Problems

Optimization—finding the best solution among many possibilities—is ubiquitous in industry: supply chain logistics, financial portfolio optimization, machine learning hyperparameter tuning, and countless other domains. Quantum algorithms for optimization, including the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing approaches, have attracted substantial interest.

The appeal is intuitive: quantum parallelism and entanglement might enable faster exploration of solution spaces. However, the reality is more complex. Most quantum optimization algorithms provide only modest speedups (polynomial rather than exponential) and only for specific problem structures. Moreover, classical optimization algorithms have advanced dramatically, and modern heuristics (genetic algorithms, simulated annealing, machine learning-based approaches) are remarkably effective for many practical problems.

The source material notes that “review literature notes that many proposed quantum algorithms” for machine learning and optimization “have not delivered expected speedups” (paraphrased from source). This reflects a broader pattern: the theoretical promise of quantum computing often exceeds practical performance when implemented on realistic hardware with realistic error rates.

Assessment for 2030: Quantum optimization will likely show advantages for specific, carefully chosen problems—perhaps in financial modeling or logistics for large enterprises. However, these applications will be niche rather than transformative. The quantum advantage for optimization remains more theoretical than practical.

3.3 Quantum Key Distribution and Post-Quantum Cryptography

Cryptography represents the most immediately actionable application area for quantum computing technology, though in an unexpected way. Quantum computers threaten current encryption standards by enabling efficient factorization (Shor’s algorithm) and discrete logarithm computation. A “cryptographically relevant quantum computer” (CRQC) with sufficient qubits and error correction could break RSA and elliptic curve encryption in hours.

This threat is not hypothetical. National security guidance since September 2022 emphasizes the need to transition to quantum-resistant algorithms to “assure sustained protection of National Security Systems and related assets in the event a CRQC becomes an achievable reality” (source material). This creates urgency around post-quantum cryptography (PQC)—the development of cryptographic algorithms thought to be secure against quantum computers.

Simultaneously, quantum key distribution (QKD)—using quantum mechanics to distribute encryption keys—offers theoretically unbreakable security. However, QKD faces practical challenges: it requires specialized infrastructure, operates at limited distances, and provides security guarantees only under specific assumptions about eavesdropping detection.

Assessment for 2030: Post-quantum cryptography represents the most likely near-term quantum computing application. Organizations must transition to quantum-resistant algorithms regardless of when CRQCs emerge—the threat is sufficiently credible to justify preventive action. QKD will see increased deployment in specialized contexts (government, finance) but will not achieve mainstream adoption by 2030.

3.4 Quantum Image Processing and Sensing

The source material mentions quantum image processing (QIMP)—using quantum computing to create and work with quantum images. The theoretical appeal is clear: quantum parallelism might enable faster image processing, and quantum properties like entanglement might enable new capabilities in image compression or security.

However, the practical status of QIMP remains unclear from the source material. The field appears to be in early research stages, with potential capabilities and performance advantages “hoped” for rather than demonstrated. The challenge of converting classical images to quantum representations, processing them quantum mechanically, and extracting meaningful classical results remains substantial.

Similarly, quantum sensing—using quantum effects to improve measurement precision—shows theoretical promise but remains largely in research phases. Practical applications may emerge by 2030, particularly in precision measurement contexts (atomic clocks, gravitational wave detection), but these represent specialized rather than mainstream applications.

Assessment for 2030: Quantum image processing and sensing will likely remain research areas with limited commercial deployment by 2030. Specialized applications in scientific instrumentation may emerge, but mainstream impact is unlikely.

4. Critical Technical Barriers and the Threshold Problem

4.1 The Threshold Problem: Quantum Error Correction

The most fundamental barrier to practical quantum computing is the threshold problem: can quantum computers achieve fault tolerance through quantum error correction while maintaining sufficient qubit scalability? This problem encompasses several interconnected challenges:

  1. Error rates: Current quantum gates operate with error rates of 0.1-1%, meaning errors accumulate rapidly in long computations. Fault-tolerant quantum computing requires error rates below approximately 10^-4 to 10^-5.

  2. Qubit overhead: Quantum error correction requires substantial qubit overhead. A logical qubit (one error-corrected qubit) may require 1000-10,000 physical qubits, depending on the error correction code and error rates. This creates a chicken-and-egg problem: we need many qubits to build error-corrected systems, but current qubit counts are insufficient.

  3. Coherence time: Qubits lose their quantum properties over time (decoherence). Coherence times must be sufficiently long relative to gate operation times to allow meaningful computation. Current systems operate with coherence times of microseconds to milliseconds, limiting circuit depth.

  4. Scalability: As qubit counts increase, maintaining coherence and reducing crosstalk between qubits becomes increasingly difficult. The engineering challenges scale non-linearly with system size.

The source material identifies these challenges explicitly: “Can we go beyond the noisy intermediate-scale quantum era? Can quantum computers reach fault tolerance? Is it possible to have enough qubit scalability to implement quantum error correction? What are the most promising candidate platforms?” These questions remain largely unanswered.

4.2 Current Progress and Realistic Timelines

Despite these challenges, progress has been genuine. IBM’s roadmap projects that the first fault-tolerant quantum computer, “Starling,” will be available to clients in 2029. This represents a significant milestone, but several caveats apply:

First, “fault-tolerant” does not mean “practical.” A fault-tolerant quantum computer with sufficient logical qubits to solve commercially important problems may require millions of physical qubits—far beyond what any current roadmap explicitly commits to.

Second, initial fault-tolerant systems will likely be specialized for specific problem classes rather than general-purpose computers. They may excel at quantum chemistry simulation but perform poorly on optimization or machine learning tasks.

Third, the timeline from 2029 to practical commercial deployment involves additional engineering challenges: integration with classical computing infrastructure, development of domain-specific algorithms, and validation that quantum solutions actually outperform classical alternatives for real-world problems.

4.3 The Quantum Advantage Mirage

A critical distinction exists between quantum supremacy (demonstrating that a quantum computer can solve some problem faster than classical computers) and quantum utility (demonstrating that a quantum computer can solve commercially important problems faster and cheaper than classical alternatives).

Google’s 2019 quantum supremacy demonstration involved random circuit sampling—a problem with no practical application. This achievement was scientifically significant but did not advance practical quantum computing. The source material’s characterization of current quantum computers as “absolutely nothing” reflects this reality: quantum supremacy has been demonstrated, but quantum utility remains elusive.

Moreover, the problems for which quantum advantage is easiest to demonstrate (random circuit sampling, specific mathematical functions) are often those with least practical relevance. The problems with greatest commercial importance (optimization, machine learning, simulation) are precisely those where quantum advantage remains uncertain and theoretical speedups may not materialize on realistic hardware.

4.4 The Talent and Infrastructure Gap

Beyond hardware and algorithms, quantum computing faces substantial talent and infrastructure gaps. The field requires expertise spanning quantum physics, electrical engineering, computer science, and mathematics. Training quantum engineers takes years, and the field currently lacks sufficient expertise to scale development.

Additionally, quantum computing infrastructure—dilution refrigerators, cryogenic systems, specialized measurement equipment—is expensive and requires specialized facilities. This limits the number of organizations capable of developing quantum hardware and restricts access for researchers and developers.

5. Analysis and Discussion: Realistic Assessment of 2030 Applications

5.1 What Will Likely Be Achieved by 2030

Based on the evidence and analysis above, several achievements are reasonably likely by 2030:

  1. Demonstration of quantum advantage for specific problems: Quantum computers will solve particular, carefully chosen problems faster than classical computers. These demonstrations will be scientifically significant but may have limited practical relevance.

  2. Increased qubit counts and reduced error rates: Hardware will improve substantially. Systems with 1000+ qubits will become operational, and error rates will decline toward the 10^-3 to 10^-4 range.

  3. Cloud-based quantum computing access: Quantum processors will be widely accessible through cloud platforms, enabling broader research and development.

  4. Specialized quantum chemistry applications: Quantum computers will demonstrate advantages for specific molecular simulation problems, likely in academic research or specialized industrial settings.

  5. Post-quantum cryptography deployment: Organizations will begin transitioning to quantum-resistant encryption algorithms as a precautionary measure.

  6. Quantum computing as a recognized technology: The field will transition from “emerging” to “established,” with dedicated quantum computing divisions at major technology companies and increased venture capital investment.

5.2 What Will Likely Not Be Achieved by 2030

Equally important are realistic acknowledgments of what will not be achieved:

  1. Fault-tolerant quantum computers at scale: While IBM projects a fault-tolerant system by 2029, this will likely be a specialized research platform rather than a general-purpose computer with thousands of logical qubits.

  2. Quantum advantage for most optimization problems: Despite decades of research, quantum algorithms for optimization have not delivered convincing speedups. This pattern is unlikely to reverse dramatically by 2030.

  3. Quantum machine learning breakthroughs: The source material notes that many proposed quantum machine learning algorithms have underperformed. This reflects fundamental challenges in encoding classical data into quantum states and extracting meaningful results.

  4. Widespread commercial quantum computing: Quantum computers will not be standard infrastructure for most organizations. They will remain specialized tools for specific problems in specific domains.

  5. Quantum computing replacing classical computing: The Church-Turing thesis ensures that quantum computers cannot solve problems that classical computers cannot solve in principle. Quantum advantage is about speed, not capability. Classical computing will remain foundational.

  6. Quantum PowerPoint or quantum word processors: As humorously noted in the source material, quantum computers are not good for everything. Consumer applications will not emerge.

5.3 The Market Reality

The projected growth from $928.8 million to $6.5 billion by 2030 represents substantial market expansion. However, this growth should be contextualized. Current global IT spending exceeds $4 trillion annually. Even at $6.5 billion, quantum computing would represent 0.16% of IT spending—a niche market, albeit a rapidly growing one.

This market will likely consist of:

  • Quantum hardware vendors (IBM, Google, Microsoft, specialized firms): Selling quantum processors and cloud access
  • Quantum software companies: Developing algorithms and applications
  • Consulting firms: Helping organizations understand quantum computing and prepare for future applications
  • Research institutions: Continuing fundamental research and training

The most immediate revenue will come from cloud-based quantum computing access and consulting services rather than from transformative commercial applications of quantum computers themselves.

5.4 Knowledge Gaps and Uncertainties

Several critical uncertainties remain:

  1. Error correction overhead: The exact qubit overhead required for practical quantum error correction remains uncertain. Theoretical estimates suggest 1000-10,000 physical qubits per logical qubit, but real-world performance may differ substantially.

  2. Quantum advantage threshold: For most practical problems, we do not know the problem size at which quantum computers outperform classical computers. This threshold may be larger than anticipated.

  3. Hardware scalability: Whether any current qubit technology can scale to millions of qubits remains uncertain. Each technology faces distinct engineering challenges at scale.

  4. Algorithm development: Whether quantum algorithms for practically important problems can be developed and optimized remains an open question.

  5. Cryptographic threat timeline: The exact timeline for when cryptographically relevant quantum computers might emerge remains uncertain, ranging from optimistic estimates of 10-15 years to more conservative estimates of 30+ years.

6. Conclusion and Future Directions

6.1 Summary of Findings

This paper has examined quantum computing’s practical applications through 2030 and reached several key conclusions:

  1. Genuine progress has occurred, but substantial gaps remain between theoretical potential and practical utility. Quantum computers have improved dramatically in qubit count, error rates, and software infrastructure.

  2. Near-term applications are real but limited. Quantum chemistry simulation, specific optimization problems, and cryptographic applications represent genuine opportunities, but these will remain niche rather than transformative through 2030.

  3. The threshold problem remains unsolved. Whether quantum computers can achieve fault tolerance at scale remains the critical unknown. Current timelines suggest this may be approached but not fully solved by 2030.

  4. The quantum advantage mirage is real. Demonstrating that quantum computers can solve some problem faster than classical computers is not equivalent to demonstrating that they can solve commercially important problems faster and cheaper.

  5. Post-quantum cryptography is the most immediately actionable application. Organizations should begin transitioning to quantum-resistant encryption regardless of when cryptographically relevant quantum computers emerge.

6.2 Implications for Business and Policy

For business leaders: Quantum computing should be monitored as a potentially transformative technology, but investment should be proportional to realistic timelines. Most organizations should focus on understanding quantum computing, experimenting with cloud-based access, and preparing for post-quantum cryptography. Betting the company on quantum computing breakthroughs by 2030 would be imprudent.

For policymakers: The cryptographic threat justifies immediate action on post-quantum cryptography standards and deployment. Simultaneously, continued investment in quantum computing research is warranted given potential long-term benefits. However, expectations should be calibrated to realistic timelines—transformative quantum computing applications are more likely post-2030 than by 2030.

For researchers: The field should prioritize solving the threshold problem and developing practical quantum algorithms for commercially important problems. The gap between theoretical quantum advantage and practical quantum utility remains the critical frontier.

6.3 Future Research Directions

Several research directions merit emphasis:

  1. Error correction and fault tolerance: Continued work on quantum error correction codes, physical implementations, and the engineering challenges of scaling error-corrected systems.

  2. Hybrid classical-quantum algorithms: Developing algorithms that effectively combine classical and quantum processing, potentially achieving practical advantages with near-term hardware.

  3. Problem-specific quantum algorithms: Rather than seeking general-purpose quantum speedups, developing specialized algorithms for specific problem classes where quantum advantage is most credible.

  4. Hardware diversity: Continuing exploration of alternative qubit technologies and architectures, recognizing that no single approach may prove optimal for all applications.

  5. Quantum-classical interface: Developing efficient methods for encoding classical data into quantum states and extracting meaningful results from quantum computations.

  6. Realistic benchmarking: Moving beyond quantum supremacy demonstrations toward genuine benchmarking of quantum computers on practically relevant problems, with honest comparison to classical alternatives.

6.4 Final Assessment

Quantum computing represents a genuine technological frontier with potential for significant long-term impact. However, the timeline for practical, transformative applications extends beyond 2030. By 2030, we will likely see:

  • Demonstration of quantum advantage for specific problems
  • Specialized commercial applications in quantum chemistry and optimization
  • Widespread deployment of post-quantum cryptography
  • Increased cloud-based access to quantum processors
  • Continued rapid progress in hardware and software

What we will not see by 2030:

  • Fault-tolerant quantum computers at scale
  • Quantum computers solving most commercially important problems faster than classical alternatives
  • Quantum computing as standard infrastructure
  • Transformation of industries through quantum computing

The 2023 Nature characterization of current quantum computers as “absolutely nothing” was hyperbolic but contained truth: quantum computers cannot yet do anything practically important. By 2030, this will have changed—quantum computers will do some things practically important. But they will not yet do everything, and they will not yet transform most industries.

The quantum computing field stands at an inflection point: past the phase of pure research and speculation, but not yet at the phase of widespread practical deployment. 2030 represents a waypoint on a longer journey, not a destination.

References

Alice & Bob. (2030). Roadmap to useful quantum computers. Retrieved from source material.

Forrester Research. (2026). The state of quantum computing, 2026. Retrieved from source material.

Fortune Business Insights. (2024). Quantum computing market projection: 2024-2030. Retrieved from source material.

Fujitsu. (2024). 2030: The year of practical quantum computing. Retrieved from source material.

IBM Technology Atlas. (2024). Quantum 2030: Delivering large-scale fault-tolerant quantum computers. Retrieved from source material.

MIT Sloan. (2024). Quantum computing: What leaders need to know now. Retrieved from source material.

Nature. (2023). Quantum computing spotlight: Current capabilities and limitations. Retrieved from source material.

Qiskit Development Team. (2024). Qiskit SDK: Foundational quantum computing software. Retrieved from source material.

U.S. National Security Guidance. (2022, September). Transition to quantum-resistant cryptography for national security systems. Retrieved from source material.

Yale Courses. (2024). Quantum error correction and fault-tolerant quantum computing. Retrieved from source material.

Word Count: 4,847

Sources & Attribution

Content type: research
Topic: quantum computing practical applications by 2030
Generated: 2026-06-02
Model: OpenRouter (via Nova Journal pipeline)

Memory Sources

This piece drew from 35 memories in Nova’s knowledge base:

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  • Post-quantum cryptography: “Post-quantum cryptography (PQC), sometimes referred to as quantum-proof, quantum-safe, or quantum-resistant, is the development of cryptographic algor…”

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