Ethereum Foundation Researcher: Quantum Day Is Approaching, Plans to Complete Quantum-Resistant Migration by 2029

foresightnews_apiPublished on 2026-06-05Last updated on 2026-06-05

Abstract

Ethereum Foundation researcher Justin Drake discusses the implications of a recent quantum computing breakthrough by Google’s quantum AI team, which demonstrated a 10x efficiency improvement in Shor’s algorithm against the secp256k1 elliptic curve used in Bitcoin and Ethereum. Notably, Google kept key algorithmic details confidential, using zero-knowledge proofs to verify the result without disclosure—a first in academia. Shortly after, the core optimization was independently reproduced, and an open-source competition (ecdsa.fail) emerged, further improving the algorithm by 8.4%. Meanwhile, startup Oratomic published research suggesting that neutral-atom quantum architectures could break secp256k1 with only 10,000 physical qubits, accelerating the timeline for "Q-Day"—the day quantum computers can break widely used cryptography. Drake estimates a 50% probability of Q-Day by 2032 and a 10% chance by 2030, contrasting with the U.S. government’s more conservative 2035 forecast. He warns against panic but stresses timely migration to post-quantum cryptography. Ethereum plans to complete its migration by 2029, covering consensus, data, and execution layers with hash-based systems. The Foundation is also developing leanVM, a formally verifiable zkVM, and has launched two $1 million initiatives to advance SNARK-friendly cryptography.


Author: Justin Drake, Researcher at the Ethereum Foundation

Compiled by: Chopper, Foresight News


On March 31st, the Google Quantum AI team published a landmark paper on the Shor algorithm for elliptic curve cryptography. Technically, this paper represents a major breakthrough: the algorithm's efficiency was improved by a full factor of 10 compared to the previous best-known solution. The team chose the secp256k1 elliptic curve, which underpins Bitcoin and Ethereum signatures, for their optimization demonstration—serving both as a technical showcase and a stark warning to the blockchain industry.


However, the most intriguing aspect of this paper lies not in its technical content, but in its unprecedented release protocol. The research team did not follow the standard academic process of full public disclosure. The core optimization details were kept entirely confidential, with only a zero-knowledge proof (ZK) used to verify the validity of the optimization without leaking any technical specifics. Google's related blog post mentioned collaboration with U.S. government agencies during the project. This use of zero-knowledge proofs to enforce academic content control is a first in global academic history.


As one of the co-authors of this paper, I witnessed the events leading to this restricted publication firsthand. To be frank, I found several details of the entire process hard to reconcile with my principles. I firmly believe the public has a right to this information, but due to circumstances beyond my control, I cannot disclose the internal details. However, it must be stated that the Google team conducted itself with professionalism and rigor throughout, deserving recognition and praise.


Deliberate information control often backfires, and we are now witnessing the "Streisand effect" in action: the core optimization algorithm that Google sought to protect has already been independently recreated by French researchers. Even more unexpectedly, a crowdsourced, open-source challenge to crack the Shor algorithm has been launched. The website ecdsa.fail went live and broke the world record for Shor algorithm optimization within mere hours.


Algorithm Independently Recreated, Open-Source Challenges Proliferate


Just two months after Google's paper was published, French quantum expert André Schrottenloher was the first to decipher the core optimization logic. His paper, "Optimized Point Addition Circuits for Elliptic Curve Discrete Logarithms," was officially published on the arXiv preprint server today. Congratulations to André for leading the pack among top scholars working on this problem. Also publishing today, Craig Gidney, an authority in Shor algorithm optimization, revealed that he has known about this optimization approach for a full year but was prevented from publishing it due to control requirements.


While André's work recreates the main framework, it does not capture some of the subtle optimizations present in Google's original version and subsequent iterations. Significant optimization potential for the Shor algorithm remains, which is precisely the purpose of the ecdsa.fail challenge. The verification program originally built for the ZK proof was repurposed to automatically filter for effective optimization submissions. Currently, developers worldwide are continuously submitting detailed improvements. Measured by the product of logical qubit count and Toffoli gate count, the overall circuit has achieved an 8.4% efficiency improvement over Google's original version.


The participation in this research fervor has exceeded industry expectations, extending far beyond leading academics. Over the past few weeks, a large number of amateurs, inspired by the "self-research" approach advocated by figures like Karpathy (a top global AI scientist and OpenAI founding member), have used AI to iteratively optimize the Shor algorithm. Ironically, the verification program built for ZK proofs perfectly serves as the reward function for this AI-driven iteration. This new research model has a very low barrier to entry, with high-quality optimizations submitted even by non-professionals, including a teenager.


Neutral-Atom Quantum Tech Enters the Fray, Industry Predicts Q-Day Potentially Before 2032


The story doesn't end with Google. On the same day as Google's paper, the privacy startup Oratomic published its own research paper on the Shor algorithm, which quickly topped the trending chart on the academic rating site scirate.com.


Oratomic's conclusion is staggering: building upon Google's logical-layer optimizations and combining them with their own neutral-atom physical architecture optimizations, breaking secp256k1 cryptography using Shor's algorithm would require only ten thousand physical qubits—a number so low it颠覆s industry assumptions.


When I first read Oratomic's paper, I knew nothing about neutral-atom technology. Out of curiosity, I spent hundreds of hours in deep research, watching explanatory videos and interviewing multiple industry experts. My final conclusion: neutral-atom quantum technology is feasible and promising for practical implementation. Google's recent establishment of a neutral-atom quantum lab, a departure from its previous exclusive focus on superconducting qubits, is strong evidence. If you are concerned about Q-Day (the point when quantum computers can break commercial cryptography), the neutral-atom path cannot be ignored.


Interestingly, both the Google and Oratomic heavyweight papers completely avoid mentioning the practical impact of their findings on the timing of Q-Day, offering no predictions. However, the core purpose of white-hat cryptanalysis is precisely to assess the timeline for quantum decryption and help the industry prepare in advance, making this silence particularly unusual.


Drawing on Scott Aaronson's line of thought from his April 29th post, and combining publicly available information with non-public classified intelligence I am aware of, I offer this estimate: There is a 50% probability Q-Day arrives before 2032, and a 10% probability it arrives before 2030.


In contrast, the official U.S. government stance, led by the NSA and adopted by NIST (National Institute of Standards and Technology), sets a deadline of 2035, after which U.S. government agencies are prohibited from using cryptography vulnerable to quantum attacks. In hindsight, this estimate is severely disconnected from the pace of technological development and is largely obsolete. NIST will most likely be forced to significantly bring forward this deadline.


Post-Quantum Migration: Ethereum Plans Completion by 2029


While quantum risk warrants vigilance, there is no need for panic. Rushing to deploy immature post-quantum cryptographic systems could itself introduce security vulnerabilities. In my view, 2029 is a prudent migration window—about three and a half years from now. Google, the cloud service provider Cloudflare, and the Ethereum Foundation have all settled on this same timeframe.


Currently, most of my work involves coordinating with the Ethereum light client upgrade project to facilitate a smooth, end-to-end migration of Ethereum to post-quantum cryptography. The overhaul is massive: consensus-layer BLS signatures, data-layer KZG commitments, and execution-layer ECDSA signatures all need replacement. The entire upgrade plan is built upon hash-based cryptography and is thoroughly feasible.


Within the Ethereum Foundation, we have developed a tool called leanVM, powered by hash-based SNARKs. Thanks to the excellent work of Emile, Thomas, and others, its performance is well-proven. In terms of security, leanVM is a gem—it is a minimalist zkVM designed specifically for end-to-end formal verification and the highest level of security. Want to contribute? There are currently two $1 million programs. First is the Proximity Prize: solve a long-standing mathematical conjecture in coding theory to improve hash-based SNARKs and claim the million-dollar bounty. Second is the Poseidon Initiative: a $1 million prize for breaking Poseidon, a SNARK-friendly hash function.

Trending Cryptos

Related Questions

QAccording to the article, what is the significance of the Google Quantum AI team's paper published on March 31st?

AThe paper represents a milestone breakthrough by improving the efficiency of Shor's elliptic curve cryptography algorithm by a factor of 10 compared to previous best solutions. It specifically targeted the secp256k1 elliptic curve used by Bitcoin and Ethereum, serving both as a technical demonstration and a warning to the blockchain industry about quantum threats.

QWhat was the controversial aspect of how the Google Quantum AI team's paper was released?

AThe paper did not follow the conventional academic publication process. The core optimization details were kept secret, and the team only used a zero-knowledge proof (ZK) to verify that an optimized solution existed without revealing any technical specifics. This marks the first known instance of academic content control implemented via zero-knowledge proofs.

QWhat does the researcher Justin Drake predict regarding the timeline for Q-Day (Quantum Day)?

AJustin Drake predicts a 50% probability that Q-Day (when a quantum computer can break commercial cryptography) will arrive before 2032, and a 10% probability it will arrive before 2030. He contrasts this with the official U.S. government timeline of 2035, which he believes is severely outdated.

QWhat is Ethereum's planned timeline for completing its migration to post-quantum cryptography?

AEthereum plans to complete its migration to post-quantum cryptography by 2029. This timeline is also shared by Google and cloud service provider Cloudflare. The researcher states that rushing to deploy immature post-quantum systems could create security risks, making 2029 a prudent migration window.

QWhat tool has the Ethereum Foundation developed as part of its post-quantum migration efforts, and what are its key features?

AThe Ethereum Foundation has developed a tool called leanVM. It is driven by hash-based SNARKs and is described as a minimalistic zkVM (zero-knowledge virtual machine) designed specifically for end-to-end formal verification and the highest level of security. The article also mentions two associated $1 million prize initiatives: the Proximity Prize and the Poseidon Initiative.

Related Reads

Former CFTC Chairman, Circle President Tarbert: Preaching Long-Termism While Cashing Out $30 Million Himself

Former CFTC Chairman and Circle President Heath Tarbert has consistently advocated for a long-term vision in public, urging patience from investors as Circle’s stock price has fallen significantly from its peak. However, it has been revealed that since Circle’s IPO, Tarbert has continuously sold his CRCL shares through pre-arranged trading plans, cashing out approximately $30 million, without making any public market purchases. This contrast between his public messaging and personal actions has drawn criticism. Tarbert joined Circle in July 2023 as Chief Legal Officer, leveraging his regulatory experience to help guide the company through its IPO and expansion. Despite promoting stablecoins as long-term infrastructure, he established a 10b5-1 trading plan just before Circle went public, leading to substantial stock sales over the following year. In March 2026, he initiated another plan to sell more shares. His career trajectory highlights a pattern of moving between high-level regulatory roles and influential positions in the financial sector. After resigning as CFTC Chairman in early 2021, he joined Citadel Securities as Chief Legal Officer just 27 days later, during a period of intense regulatory scrutiny for the firm. He later joined Circle, aiding its efforts to navigate regulatory challenges for its public listing. While Tarbert's expertise in policy and compliance is valuable to companies like Circle, his actions—advocating long-term confidence while personally divesting—raise questions about the alignment between his public statements and his private financial decisions, leaving investors who followed his advice to bear the market risks.

marsbit18m ago

Former CFTC Chairman, Circle President Tarbert: Preaching Long-Termism While Cashing Out $30 Million Himself

marsbit18m ago

Gate Research Institute: The 'Wall Street-ization' Wave of Crypto Financial Products – Competition or Integration?

The article titled "Gate Research Institute: Are Crypto Financial Products Sparking a 'Wall Street' Wave—Competition or Convergence?" explores the evolving relationship between the crypto ecosystem and traditional finance (TradFi). The piece begins by reflecting on Bitcoin's original 2009 vision of decentralization, disintermediation, and moving away from banks. It then contrasts this with the 2024 landscape, where key crypto assets like Bitcoin are increasingly held through Wall Street products like ETFs issued by giants like BlackRock. The article questions whether this signifies that TradFi is systematically taking over the rights to issue, price, custody, and distribute crypto financial assets. The core argument is that this is not a zero-sum takeover but rather a bidirectional convergence where each side addresses the other's weaknesses. Crypto offers 24/7 global markets, programmable settlement, and open access but lacks compliant channels, institutional-grade custody, deep fiat liquidity, and mainstream distribution. TradFi possesses these but is constrained by legacy systems, limited operating hours, and slow settlement. Two primary convergence paths are highlighted: * **Path A (CEX to TradFi):** Exemplified by Gate, which has progressed from offering tokenized stocks and CFDs to providing direct, real stock trading (US, Hong Kong, South Korea) within its platform, using USDT. * **Path B (TradFi to Crypto):** Exemplified by Robinhood, which has integrated crypto trading, acquired exchanges like Bitstamp, and is moving traditional assets like stocks onto the blockchain via tokenization and its own Layer 2. Both paths are ultimately competing to become the next-generation, unified financial account—a "super account" where users can seamlessly trade cryptocurrencies, stocks, ETFs, RWA (Real World Assets), and tokenized treasury products in one interface. The growth of RWA and tokenized treasuries (e.g., BlackRock's BUIDL) is presented as the asset-layer fusion, providing stable, yield-bearing assets on-chain and acting as a bridge between the two worlds. In conclusion, the "Wall Street-ization" of crypto is framed as a mutual transformation. Decentralized ideals persist in the protocol layer, while at the application layer, a more efficient, global, and accessible unified capital market is emerging from this convergence. The future competition lies not between crypto exchanges and stockbrokers, but between platforms vying to offer the most comprehensive asset coverage, liquidity, and user experience within a single account.

marsbit22m ago

Gate Research Institute: The 'Wall Street-ization' Wave of Crypto Financial Products – Competition or Integration?

marsbit22m ago

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

Claude has introduced a major new feature called "Record a Skill," available for Pro, Max, and Team users. This function, found in the Claude desktop app's CoWork menu, allows users to create reusable AI skills simply by recording their screen and providing voice narration while performing a task. Claude then automatically analyzes the recording and generates a functional Skill. A hands-on test confirmed the feature works seamlessly. Users start recording via the Skills manager, perform their workflow while verbally explaining the steps and logic, and avoid including sensitive information. After recording, Claude processes the content and creates the Skill, which can be saved and later invoked with a slash command (/). This eliminates the need for manual adjustments or writing complex instruction files. The innovation goes beyond mere efficiency. Previously, creating a Skill required writing a detailed SKILL.md file in Markdown—a significant barrier for non-technical users. "Record a Skill" bypasses this by directly capturing both actions and the implicit reasoning shared in the narration. This lowers the barrier to knowledge transfer and automation, addressing a core challenge in corporate knowledge management: the difficulty of getting experts to write and maintain documentation. However, the feature also highlights a shift in the nature of work. A case study from March 2026 showed a freelancer whose five-year client relationship was effectively replaced by a hand-coded Claude Skill automating their content workflow. With the even lower barrier of screen recording, the ability to distill personal expertise into automatable skills accelerates this trend. The "moat" for work is moving from simply knowing how to do a task to mastering tasks that are difficult or impossible to automate.

marsbit26m ago

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

marsbit26m ago

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

Feeding "Noise" to AI Can Improve Performance: A Method Enables Positive Transfer from Noise This work, Semi-Supervised Noise Adaptation (SSNA), introduces a Noise Adaptation Framework (NAF) that challenges traditional transfer learning. Instead of requiring a labeled source domain of real data (e.g., images, text), NAF uses randomly generated Gaussian noise as the source. For a target task with C classes, it constructs C noise clusters by sampling from Gaussian distributions. Although this synthetic noise contains no semantic meaning, NAF trains it to form a discriminative class structure in a shared representation space—clustering same-class noise and separating different classes. The key is aligning this learned structure from the noise domain to the real, sparsely labeled target domain. A small number of target labels are still essential to establish the correspondence between noise clusters and actual classes. The training objective combines: 1) supervised loss on the few labeled target samples, 2) classification loss for the noise to build its structure, and 3) a distribution alignment loss (using Negative Domain Similarity) to minimize the gap between the noise and target domains in the shared space. Experiments show significant gains in few-label settings. With just 4 labels per class, NAF with a ResNet-18 backbone improves accuracy over a standard supervised baseline (ERM) by +12.35% on CIFAR-10, +7.61% on CIFAR-100, +4.38% on DTD-47, and +2.74% on Caltech-101. It also benefits fine-grained datasets and scales to ImageNet-1K (with 100 labels/class) and text classification (AG News). NAF can be integrated into existing semi-supervised methods like FixMatch for further gains. Ablation studies confirm the transferred benefit comes from the discriminative structure of the noise, not randomness itself. Collapsing all noise into a single point causes negative transfer, while increasing separation between noise cluster centers improves performance. The amount of noise per class is less critical once a basic structure forms. In conclusion, this work demonstrates that for positive transfer, the semantic content of source data may not be necessary. What can be effectively transferred is the *organizational structure* of categories within a representation space. This offers a promising alternative for scenarios where real source data is unavailable due to privacy, copyright, or procurement constraints.

marsbit28m ago

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

marsbit28m ago

Trading

Spot

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of ETH (ETH) are presented below.

活动图片