Quantum Horizon: An evaluation of quantum computing as a threat to Bitcoin and Ethereum

Battle Count: 51

The paper is primarily about blockchain security and quantum threat assessment rather than trading strategies or market microstructure. However, it has indirect relevance: (1) a CRQC event or credible timeline compression could be a significant tail-risk event for crypto portfolios, relevant to risk management; (2) the 'harvest now, decrypt later' threat creates a long-horizon risk factor for crypto asset allocation; (3) governance delays in migration could create asymmetric risk events; (4) the quantum-readiness ranking could inform relative positioning across crypto assets. It does not address price prediction, alpha generation, or trading strategy development.

The models are described as 'small, reproducible calculations' and the code is publicly available. The Monte-Carlo forecast integrates four signals with tunable parameters (hardware doubling time, resource decline rate, fault-tolerance lag, survey weight). The mining-competitiveness model calibrates against a 2017 benchmark. The mempool-race and migration-race models are parameter sweeps. While conceptually accessible, understanding the quantum resource estimation (logical/physical qubit ratios, T-gate counts, surface code overhead) and the cryptographic details (ECDSA, BLS, KZG commitments) requires domain expertise. The models themselves are not computationally intensive.

Every quantitative claim is backed by a small, reproducible calculation or model. Model code, figures, and full numerical results are available at a public GitHub repository. The authors explicitly state that models were built so independent ones could check each other, and disagreements between methods are reported rather than averaged away. All load-bearing numbers are tied to primary or cross-checked sources with numbered endnotes.