Navigating the Quantum Computing Threat Landscape for Blockchains: A Comprehensive Survey
Bibliographic record
Abstract
Quantum computers pose a significant threat to blockchain technology's security, which heavily relies on public-key cryptography and hash functions. The cryptographic algorithms used in blockchains, based on large odd prime numbers and discrete logarithms, can be easily compromised by quantum computing algorithms like Shor's algorithm and its future qubit variations. This survey paper comprehensively examines the impact of quantum computers on blockchain security and explores potential mitigation strategies. We begin by surveying the existing literature on blockchains and quantum computing, providing insights into the current state of research. We then present an overview of blockchain, highlighting its key components and functionalities. We delve into the preliminaries and key definitions of quantum computing, establishing a foundation for understanding the implications on blockchain security. The application of blockchains in cybersecurity is explored, considering their strengths and vulnerabilities in light of evolving quantum computing capabilities. The survey focuses on the quantum security of blockchain's fundamental building blocks, including digital signatures, hash functions, consensus algorithms, and smart contracts. We analyze the vulnerabilities introduced by quantum computers and discuss potential countermeasures and enhancements to ensure the integrity and confidentiality of blockchain systems. Furthermore, we investigate the quantum attack surface of blockchains, identifying potential avenues for exploiting quantum computing to strengthen existing attacks. We emphasize the need for developing quantum-resistant defenses and explore solutions for mitigating the threat of quantum computers to blockchains, including the adoption of quantum and post-quantum blockchain architectures. By examining vulnerabilities and discussing mitigation strategies, we aim to guide researchers, practitioners, and policymakers in developing robust and secure blockchain systems capable of withstanding advancements in quantum computing technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".