According to Charles Edwards (@caprioleio), up to 20–30% of BTC held in legacy P2PK outputs could be taken by a future quantum computer within 2–8 years, and he proposes either allowing theft-related dumping or enforcing a migration window that burns unmigrated coins (source: Charles Edwards on X, Oct 15, 2025). According to Bitcoin Wiki, P2PK outputs reveal public keys on-chain, leaving any unspent P2PK UTXOs inherently exposed if Shor’s algorithm breaks secp256k1 ECDSA (source: Bitcoin Wiki, Pay-to-Pubkey). According to NIST’s Post-Quantum Cryptography program, no cryptographically relevant quantum computer exists today, though ECDSA is not quantum-safe and migration to standardized PQC schemes like CRYSTALS-Dilithium will be required once timelines warrant (source: NIST PQC status reports, 2022–2024). According to Roetteler et al. (Microsoft Research), breaking a single secp256k1 key demands very large fault-tolerant quantum resources beyond current hardware, making the specific 2–8 year horizon uncertain for traders to price (source: Roetteler et al., 2017, Quantum Resource Estimates for ECC).

According to @caprioleio, Bitcoin must be upgraded to be quantum-proof by 2026, with a warning of severe consequences if no upgrade occurs. Source: https://twitter.com/caprioleio/status/1972473521730462153 The post sets a concrete 2026 timeline for quantum risk management around BTC’s signature schemes, signaling a near-term governance and security focus for market participants. Source: https://twitter.com/caprioleio/status/1972473521730462153 Bitcoin’s current signatures use ECDSA and Schnorr (BIP340) over secp256k1, both based on the discrete logarithm problem that Shor’s algorithm would break on a sufficiently large fault-tolerant quantum computer, underscoring why post-quantum migration is being standardized globally. Source: https://developer.bitcoin.org/devglossary.html#term-ecdsa https://github.com/bitcoin/bips/blob/master/bip-0340.mediawiki https://csrc.nist.gov/projects/post-quantum-cryptography

According to Google DeepMind, AlphaTensor-Quantum has outperformed existing methods in key arithmetic benchmarks, particularly for complex circuits used in Shor's algorithm and quantum chemistry simulations, automatically identifying optimal human-designed solutions. This advancement could impact trading strategies in quantum computing sectors by enhancing algorithmic efficiency and performance metrics.