In a recent breakthrough that could reshape the conversation around quantum computing’s impact on digital assets, a team of cryptography researchers has published a paper—shared with CoinDesk—that dramatically reduces the projected timeline for a quantum attack on the world’s two largest cryptocurrencies, Bitcoin and Ethereum. According to the study, the researchers were able to cut the estimated quantum computing threat by roughly half, a development that carries profound implications for the security of blockchain networks, investors, and regulators alike.

The core of the research focuses on a specific mathematical operation that lies at the heart of Shor’s algorithm, the quantum procedure widely regarded as the most potent tool for breaking the public‑key cryptography that underpins Bitcoin, Ethereum, and countless other digital systems. Shor’s algorithm relies on efficiently solving the discrete logarithm problem and integer factorisation—tasks that classical computers find infeasible for large key sizes but that a sufficiently powerful quantum computer could theoretically accomplish in polynomial time. Historically, estimates of when a quantum computer might become capable of executing Shor’s algorithm at a scale large enough to threaten Bitcoin’s elliptic‑curve signatures have varied wildly, ranging from a decade to several decades.

Those timelines have been based on assumptions about the number of qubits required, error‑correction overhead, gate fidelity, and the speed at which quantum gates can be performed. The new paper introduces a critical variable into that equation: the efficiency of a core sub‑routine used within Shor’s algorithm, known as modular exponentiation. In March of this year, Google announced a milestone achievement—an experimental quantum processor that performed a specific modular exponentiation calculation faster than the best known classical approach. That result was widely interpreted as a signal that the quantum threat horizon was inching closer.

However, the researchers behind the latest study have now demonstrated, using a combination of human ingenuity and sophisticated AI agents, that the same calculation can be executed even more efficiently than Google’s reported performance. The team employed a hybrid approach. Human mathematicians first identified potential optimisations in the algorithm’s structure, focusing on reducing the depth of quantum circuits and minimising the number of ancillary qubits needed for error correction.

Concurrently, AI agents—trained on vast corpora of quantum‑computing literature and equipped with reinforcement‑learning capabilities—explored a massive search space of circuit configurations, proposing novel gate sequences that further trimmed computational overhead. When the human‑derived insights were merged with the AI‑generated circuit designs, the resulting implementation outperformed Google’s March benchmark by a significant margin. The researchers measured a reduction of roughly 50 percent in the number of quantum operations required to complete the modular exponentiation step. Since this step is a bottleneck in Shor’s algorithm, the overall resource estimate for breaking Bitcoin’s secp256k1 elliptic‑curve signatures shrinks accordingly.

What does a 50 percent reduction actually mean for the quantum‑security timeline? In practical terms, it suggests that a quantum computer would need roughly half the number of logical qubits—and consequently half the error‑correction overhead—to achieve the same cryptanalytic capability.

If earlier models projected that a 4,000‑logical‑qubit machine might threaten Bitcoin by 2035, the new findings push that date forward to around 2040, assuming linear progress. Conversely, if the field experiences exponential improvements, the halved requirement could accelerate the threat, bringing it nearer to the early 2030s.

Beyond the raw numbers, the study highlights an emerging paradigm in cryptographic research: the symbiotic relationship between human expertise and artificial intelligence. By leveraging AI to navigate the combinatorial explosion of possible quantum circuit designs, researchers can uncover efficiencies that would be virtually impossible to discover through manual analysis alone.

This collaborative model may become a standard tool in the ongoing race to both assess and mitigate quantum risks. For the cryptocurrency community, the findings are a mixed bag. On one hand, the reduction in threat severity provides a temporary reprieve, buying developers and exchanges more time to transition to quantum‑resistant cryptographic schemes. On the other hand, the very fact that the quantum attack timeline is being actively refined—and that progress can be accelerated by AI—underscores the urgency of adopting post‑quantum cryptography (PQC) standards.

Industry leaders have already begun exploring alternatives. The National Institute of Standards and Technology (NIST) is in the final stages of standardising several PQC algorithms, such as CRYSTALS‑Kyber for key encapsulation and CRYSTALS‑Dilithium for digital signatures.

Integrating these algorithms into blockchain protocols will require substantial changes to consensus mechanisms, wallet software, and network‑wide validation processes. Nonetheless, the growing consensus is that proactive migration is preferable to a reactive scramble after a quantum breakthrough.

Regulators are also paying close attention. Financial authorities in the European Union and the United States have issued advisories urging crypto‑service providers to conduct quantum‑risk assessments and to develop contingency plans.

The new research adds weight to those recommendations, illustrating that the quantum threat is not a static, distant possibility but a dynamic challenge that evolves with each scientific advance. In summary, the paper shared with CoinDesk marks a pivotal moment in the ongoing dialogue about quantum computing and cryptocurrency security.

By demonstrating that both humans and AI can jointly surpass previous benchmarks on a crucial component of Shor’s algorithm, the researchers have effectively halved the estimated quantum attack timeline for Bitcoin and Ethereum. While this development offers a short‑term buffer, it also accelerates the push toward quantum‑resistant technologies. Stakeholders across the blockchain ecosystem—developers, investors, exchanges, and regulators—must interpret these findings as a call to action, ensuring that the infrastructure supporting digital assets remains robust in the face of an increasingly capable quantum future.