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Two New Papers Sharply Lower Quantum Resource Estimates for Breaking Bitcoin Keys

Justin Drake reports two papers improving Shor's algorithm, one from Google Quantum AI and one from startup Oratomic. Google estimates about 1,000 logical qubits could break secp256k1 signatures, while Oratomic estimates about 26,000 atomic qubits could do so, both pending expert vetting.

Original post · 6 min read
Today is a monumentous day for quantum computing and cryptography. Two breakthrough papers just landed (links in next tweet). Both papers improve Shor's algorithm, infamous for cracking RSA and elliptic curve cryptography. The two results compound, optimising separate layers of the quantum stack. The results are shocking. I expect a narrative shift and a further R&D boost toward post-quantum cryptography.

The first paper is by Google Quantum AI. They tackle the (logical) Shor algorithm, tailoring it to crack Bitcoin and Ethereum signatures. The algorithm runs on ~1K logical qubits for the 256-bit elliptic curve secp256k1. Due to the low circuit depth, a fast superconducting computer would recover private keys in minutes. I'm grateful to have joined as a late paper co-author, in large part for the chance to interact with experts and the alpha gleaned from internal discussions.

The second paper is by a stealthy startup called Oratomic, with ex-Google and prominent Caltech faculty. Their starting point is Google's improvements to the logical quantum circuit. They then apply improvements at the physical layer, with tricks specific to neutral atom quantum computers. The result estimates that 26,000 atomic qubits are sufficient to break 256-bit elliptic curve signatures. This would be roughly a 40x improvement in physical qubit count over previous state-of-the-art. On the flip side, a single Shor run would take ~10 days due to the relatively slow speed of neutral atoms.

Below are my key takeaways. As a disclaimer, I am not a quantum expert. Time is needed for the results to be properly vetted. Based on my interactions with the team, I have faith the Google Quantum AI results are conservative. The Oratomic paper is much harder for me to assess, especially because of the use of more exotic qLDPC codes. I will take it with a grain of salt until the dust settles.

→ q-day: My confidence in q-day by 2032 has shot up significantly. IMO there's at least a 10% chance that by 2032 a quantum computer recovers a secp256k1 ECDSA private key from an exposed public key. While a cryptographically-relevant quantum computer (CRQC) before 2030 still feels unlikely, now is undoubtedly the time to start preparing.
→ censorship: The Google paper uses a zero-knowledge (ZK) proof to demonstrate the algorithm's existence without leaking actual optimisations. From now on, assume state-of-the-art algorithms will be censored. There may be self-censorship for moral or commercial reasons, or because of government pressure. A blackout in academic publications would be a tell-tale sign.
→ cracking time: A superconducting quantum computer, the type Google is building, could crack keys in minutes. This is because the optimised quantum circuit is just 100M Toffoli gates, which is surprisingly shallow. (Toffoli gates are hard because they require production of so-called "magic states".) Toffoli gates would consume ~10 microseconds on a superconducting platform, totalling ~1,000 sec of Shor runtime.
→ latency optimisations: Two latency optimisations bring key cracking time to single-digit minutes. The first parallelises computation across quantum devices. The second involves feeding the pubkey to the quantum computer mid-flight, after a generic setup phase.
→ fast- and slow-clock: At first approximation there are two families of quantum computers. The fast-clock flavour, which includes superconducting and photonic architectures, runs at roughly 100 kHz. The slow-clock flavour, which includes trapped ion and neutral atom architectures, runs roughly 1,000x slower (~100 Hz, or ~1 week to crack a single key).
→ qubit count: The size-optimised variant of the algorithm runs on 1,200 logical qubits. On a superconducting computer with surface code error correction that's roughly 500K physical qubits, a 400:1 physical-to-logical ratio. The surface code is conservative, assuming only four-way nearest-neighbour grid connectivity. It was demonstrated last year by Google on a real quantum computer.
→ future gains: Low-hanging fruit is still being picked, with at least one of the Google optimisations resulting from a surprisingly simple observation. Interestingly, AI was not (yet!) tasked to find optimisations. This was also the first time authors such as Craig Gidney attacked elliptic curves (as opposed to RSA). Shor logical qubit count could plausibly go under 1K soonish.
→ error correction: The physical-to-logical ratio for superconducting computers could go under 100:1. For superconducting computers that would be mean ~100K physical qubits for a CRQC, two orders of magnitude away from state of the art. Neutral atoms quantum computers are amenable to error correcting codes other than the surface code. While much slower to run, they can bring down the physical to logical qubit ratio closer to 10:1.
→ Bitcoin PoW: Commercially-viable Bitcoin PoW via Grover's algorithm is not happening any time soon. We're talking decades, possibly centuries away. This observation should help focus the discussion on ECDSA and Schnorr. (Side note: as unofficial Bitcoin security researcher, I still believe Bitcoin PoW is cooked due to the dwindling security budget.)
→ team quality: The folks at Google Quantum AI are the real deal. Craig Gidney (@CraigGidney) is arguably the world's top quantum circuit optimisooor. Just last year he squeezed 10x out of Shor for RSA, bringing the physical qubit count down from 10M to 1M. Special thanks to the Google team for patiently answering all my newb questions with detailed, fact-based answers. I was expecting some hype, but found none.
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More in Science & Health

U.S. Government, Google and Meta Launch $1.8 Billion AI Biology Data Effort

U.S. Government, Google and Meta Launch $1.8 Billion AI Biology Data Effort▶

Molly O'Shea reports a $1.8 billion coalition including Biohub, the Department of Energy, NIH, Meta, Google DeepMind and Isomorphic Labs to build the largest open, AI-ready biological dataset to help models predict biology and treat disease.

Original post · 1 min read
BREAKING: The U.S. government, Google, Meta & Zuckerberg-backed @biohub are launching a $1.8B push to build the data layer for AI biology.

The goal is to create the largest open, AI-ready biological dataset ever assembled, giving models the data needed to better predict how biology works & ultimately help treat disease.

The coalition is massive:

› Biohub committed $500M
› DOE is investing $500M+ across measurement, modeling & compute
› NIH is bringing $500M+ of federally funded datasets & repositories
› Meta, Google DeepMind & @IsomorphicLabs are jointly investing another $300M

This is effectively an infrastructure buildout for AI biology.

Instead of just building bigger models, they’re attacking one of the biggest bottlenecks in biology AI today, generating standardized, high-quality biological data at massive scale & making it openly available to researchers.

$1.8B committed to turning biology into something AI can increasingly model, predict & engineer.
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FDA Clears First Human Age-Reversal Trial From Life Biosciences

FDA Clears First Human Age-Reversal Trial From Life Biosciences▶

Vaibhav Sisinty reports the FDA cleared the first human age-reversal trial, citing animal research on partial reprogramming and Life Biosciences' $80 million raise to pursue it, though the post's claims about mouse and monkey results are not independently verified.

Original post · 1 min read
This is insane 🔥

The FDA cleared the first human age-reversal trial

A Harvard professor proved you can reverse aging by 75% in 6 weeks in mice cured blindness, reversed Alzheimer's, MS, ALS, kidney and liver disease then confirmed it works in monkeys

Now humans are next. Life Biosciences raised $80M to make it happen

His words "The eye is just the beginning. We believe we can treat every tissue. A whole body reset"
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Evidence API Lets Users Search 100M Papers for Scientific Claims

Evidence API Lets Users Search 100M Papers for Scientific Claims▶

James Zou announces the Evidence API from Paperclip, which lets users search over 100 million papers to find evidence for and against a scientific claim in about a second. He cites coffee and diabetes risk as an example query.

Original post · 1 min read
What if,
for any scientific claim,
you can search 100M+ papers to find the strongest evidence for and against it
⚡️in 1 second?

That’s now possible with Evidence API! 🚀
paperclip.gxl.ai/evidence-api

For example, does drinking coffee reduce diabetes risk?
paperclip.gxl.aiPaperclipPaperclip — search, read, and analyze 11M+ biomedical papers, regulatory documents, clinical trials, and biological databases (UniProt, PDB, ChEMBL) from the co
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Phone-Free Trial Shows Gains in Attention and Depression Symptoms

Noah Smith shares a post by Brandon Luu, MD, reporting that two weeks without phone internet improved sustained attention by an effect size comparable to about ten years of aging and reduced depression symptoms more than the average antidepressant effect.

Original post · 1 min read
Putting down your phone is more effective than antidepressants
Brandon Luu, MD @BrandonLuuMD
After blocking phone internet for 2 weeks:

1) Sustained attention improved by an effect size comparable to ~10 years of aging

2) Depression symptoms improved more than the average antidepressant effect
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Randomized Trial Finds Plain Water Gargling Cuts Respiratory Infections

Randomized Trial Finds Plain Water Gargling Cuts Respiratory Infections

Brandon Luu, MD, highlights a randomized trial in which gargling with plain water three times daily reduced upper respiratory infections by about 36%. He describes it as one of the cheapest cold-prevention interventions.

Original post · 1 min read
One of the cheapest cold-prevention interventions I’ve seen:

Gargling with plain water 3x/day reduced upper respiratory infections by ~36% in a randomized trial.
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Anduril and Palmer Luckey Win Wildfire XPRIZE for Rapid Fire Detection

Palmer Luckey says Anduril won the Wildfire XPRIZE for detecting and suppressing a wildfire within 10 minutes of ignition. He argues that even small gains could save the U.S. roughly $500 billion a year lost to wildfires.

Original post · 1 min read
Thanks, Peter! This is the beginning of the end for destructive wildfires. In a country that loses ~$500B every year to wildfires, even tiny gains make for huge savings, to say nothing of the incalculable value of lives and homes.
Peter H. Diamandis, MD @PeterDiamandis
Congrats to @PalmerLuckey and Anduril for winning the Wildfire XPRIZE - for detecting and suppressing a wildfire within 10 minutes of ignition!
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