AI is making quantum systems more tractable — and that progress could shorten the timeline to a so‑called Q‑Day, when quantum machines can break the encryption many blockchains and financial systems rely on. Recent research shows machine learning, deep neural networks and transformer models can approximate massive quantum states and act as practical surrogate models, turning previously intractable characterization tasks into actionable insights.
How AI is accelerating quantum research
Researchers are applying three linked AI paradigms — classical machine learning, deep learning and transformer-based models — to forecast physical properties, optimize algorithms and benchmark quantum hardware. These approaches avoid the exponential blow-up of traditional simulation and tomography by recognizing patterns in measurement data and predicting system behavior.
Industry examples demonstrate concrete gains: university teams and companies are using quantum-aware regressors and neural models to improve manufacturing models and device calibration, sometimes achieving significant accuracy improvements on small datasets. As labs adopt AI for calibration, verification and benchmarking, the practical pace of quantum development can speed up.
Why crypto and finance should care
Most public-key systems that underpin secure wallets, exchanges and banking — including RSA and elliptic-curve cryptography (ECC) — are theoretically vulnerable to large quantum computers. Security analysts warn that AI-enabled advances in characterizing and scaling quantum devices could reduce the time before machines are capable of running algorithms that crack keys such as RSA‑2048. A 2025 analysis from Post Quantum projects a capable machine could appear as early as 2030, and the UK’s National Cyber Security Centre advises organizations to begin migrating to quantum-safe systems by 2028.
Estimates vary: one industry survey found 61% of security professionals expect current encryption to be compromised within two years, while other models push the highest odds out a decade or more. For blockchains and custodians the practical risks include future decryption of recorded transactions and exposure of long-lived keys if migration to post-quantum algorithms is delayed.
Bottom line: AI is a force multiplier for quantum research. Organizations that manage keys, custody assets or handle sensitive financial data should assess cryptographic exposure, inventory long-lived secrets and begin planning migration to post-quantum cryptography. The technical promise of AI-backed quantum computing is significant — so is the near-term task of practical risk management.
Source: Decrypt. Read the original coverage for full details.