Fair Math

[Fair Math @FairMath]

1/8 Today, we’re introducing a completely new FHERMA.

What started as a challenge and benchmarking platform for FHE is evolving into a much bigger infrastructure layer: a common system for discovering, evaluating and using the state-of-the-art building blocks/components behind modern privacy technologies and the applications built on top of them.

[Fair Math @FairMath]

Poulpy CKKS Bootstrapping board on FHERMA, with benchmark results for the full bootstrapping kernel and for each stage separately: SlotsToCoeffs, ModUp, CoeffsToSlots, and EvalMod.

All runs use the same keys and inputs, making it easy to inspect the full bootstrap and each kernel under the same setup.
Based on Poulpy 0.8.3, CPU only.

FHERMA Boards are probably one of the most interesting features on the platform. Anyone can combine results from different kernels into a board like this and share it as a single view.

https://t.co/xIeQG9jK44

[Fair Math @FairMath]

Today's best result is 260 µs — 20% faster than yesterday's best.

https://t.co/CByPbWf1ua

[Fair Math @FairMath]

Current fastest submission in one of the NVIDIA × FHERMA challenges:
Negacyclic polynomial multiplication, degree 32,768, with 868-bit coefficients is under 0.5 ms on GPU.

https://t.co/4ZHmmIxntC

[Fair Math @FairMath]

Current fastest submission in one of the NVIDIA × FHERMA challenges:
Negacyclic polynomial multiplication, degree 32,768, with 868-bit coefficients is under 0.5 ms on GPU.

https://t.co/4ZHmmIxntC

[Fair Math @FairMath]

NVIDIA × Fair Math Challenges are LIVE on FHERMA!

We're excited to collaborate with @NVIDIAHPCDev to bring GPU acceleration layer support to the FHE ecosystem using NVIDIA's cuPQC.

To kickstart this initiative, we're launching two new developer challenges with GPU compute:

- Polynomial Multiplication: Build high-performance polynomial multiplication on cuPQC's bigint backend.  (Great entry point for GPU/CUDA devs!)

- Key Switching in CKKS: Optimize one of FHE's biggest bottlenecks using GPU-native arithmetic.

Whether you're an FHE researcher or a CUDA/GPU dev, you can help build open-source infrastructure that projects worldwide will use.

Join the challenges: https://t.co/9KeQ9qBkeN

#FHE #Cryptography #CUDA #GPU #cuPQC #NVIDIA #OpenSource

[Fair Math @FairMath]

Join us next week for the OpenFHE webinar on Efficient Large-Integer Arithmetic for FHE.

We’ll present recent results from the collaboration between NVIDIA, Fair Math and Duality Technologies, including cuPQC, FHERMA, and our recent joint work on large-integer arithmetic.

Register here: https://t.co/Iw4G3rmGiw

[Fair Math @FairMath]

8/8 FHERMA is live.
Explore the platform:
https://t.co/AD3D41KCuv
Read the full vision:
https://t.co/weG8uescPl
Start building:
https://t.co/RWSM0XVcqE

[Fair Math @FairMath]

1/8 Today, we’re introducing a completely new FHERMA.

What started as a challenge and benchmarking platform for FHE is evolving into a much bigger infrastructure layer: a common system for discovering, evaluating and using the state-of-the-art building blocks/components behind modern privacy technologies and the applications built on top of them.

[Fair Math @FairMath]

7/8 The new FHERMA is infrastructure for making this fragmented and constantly changing computational landscape usable.
A common way to:
• discover what exists
• understand where different approaches win
• compare implementations across workloads and hardware
• bring better implementations into real systems as the state of the art evolves

[Fair Math @FairMath]

Happy to share our collaboration with @nvidia and @DualityTech research teams: "Efficient Large-Integer Arithmetic for FHE".

Efficient large-integer arithmetic is a key building block of modern fully homomorphic encryption (FHE). In this paper, we discuss the algorithms and implementation techniques behind high-performance arithmetic for vectorized FHE schemes, examine the trade-offs between different approaches, and highlight promising directions for future improvements.

It was a pleasure collaborating with researchers from NVIDIA and Duality Technologies on this work.

[Fair Math @FairMath]

Dear researchers,
If you reference the results of the FHERMA Challenges in your work, please use one of the following papers as a citation:
https://t.co/N3UJWYy4Bj
https://t.co/Zm9f0746Ik

[Justin Drake @drakefjustin]

Today a crazy quantum story just got wilder.

On March 31, the Google Quantum AI team published a landmark result on Shor's algorithm for elliptic curve cryptography. Technically, the paper was a bombshell: a dramatic 10x improvement over the state-of-the-art. As a stunt and wakeup call to the blockchain space, those optimisations were illustrated on secp256k1, the elliptic curve underlying Bitcoin and Ethereum signatures.

But perhaps the most striking part of the paper was sociological, not technical. Instead of following standard academic process, the optimisations were kept secret, hidden behind a zero-knowledge (ZK) proof. Google's accompanying blog post mentions they "engaged with the U.S. government". The ZK proof demonstrates the existence of algorithmic improvements without leaking details. Academic censorship with ZK, a historic first!

As a co-author of the Google paper I witnessed some of the context surrounding this censorship. To be honest, multiple aspects of that context don't sit well with me. As much as I believe the general public ought to know more, I am limited in my ability to whistleblow. Though let me be clear about one thing: the Google team's professionalism has been absolutely exemplary, and they deserve nothing but praise.

Censorship has a way of backfiring. The Streisand effect, where an attempt to bury something only draws more attention to it, is exactly what's unfolding today. First, Google's key optimisation has been rediscovered by the French. And in a thrilling turn of events, a collaborative Shor-at-home challenge just launched. The initiative, available at ecdsa[.]fail, breached a new Shor world record in a matter of hours.

Let's start with the rediscovery. Just two months after Google's paper, French quantum expert André Schrottenloher cracks the main secret optimisation. His paper, titled "Optimized Point Addition Circuits for Elliptic Curve Discrete Logarithms", landed on the arXiv today. Big congrats to André, who beat several other nerdsnipped experts to it. In a blog post also published today, Craig Gidney, the world expert on Shor optimisations, revealed that he'd been sitting on this very optimisation for a whole year under censorship pressure.

Interestingly, André missed a handful of minor optimisations, both from Google's original publication and from improvements found since. It's plausible there's still plenty of juice left to squeeze out of Shor, and this is exactly what the ecdsa[.]fail challenge is about. The verifier program developed for the ZK proof does double duty, automatically filtering for valid submissions. Dozens of compounding small and micro improvements are rolling in. As of the time of writing there's an 8.4% improvement to Google's circuit, as measured by the product of logical qubit count and Toffoli gate count. Nice!

The nerdsnipping ran deeper than anyone expected. Over the last few weeks it became clear it extended well beyond André and other quantum experts. Behind the scenes, a small army of amateurs quietly got to work. Inspired by Karpathy-style autoresearch, they turned AI on Shor. Ironically, the verifier program for the ZK proof makes an ideal reward function for AIs. The barrier to entry for this modern style of research is refreshingly low, with several non-experts, even a teenager, finding nice optimisations. Get in touch if you'd like to join a Telegram group with fellow autoresearchers :)

Part 2: neutral atoms and qday

The story doesn't end with Google. On the same day Google went public, a stealthy startup called Oratomic published its own Shor paper in a coordinated release. It made a splash, ultimately becoming the most upvoted paper on scirate[.]com, a website ranking arXiv papers.

Oratomic's claim was wild. By building on Google's logical optimisations and applying custom physical optimisations for neutral atoms, they claimed just 10K physical qubits were sufficient to run Shor's algorithm on secp256k1. That number is mind-bogglingly low.

Knowing essentially nothing about neutral atoms when Oratomic's paper landed, I was intrigued and decided to learn more about the tech. I fell straight down the rabbit hole and spent a couple hundred hours on the topic. I got a little obsessed and watched every YouTube video I could find and spoke to a bunch of experts.

My conclusion? The tech is real, very real. Even Google recently decided to start a neutral atom lab, a notable pivot from their sole focus on superconducting qubits. If you care about qday, i.e. the day a quantum computer will break the first piece of cryptography in production, neutral atoms demand your attention. I shared some of my learnings on Shor and neutral atoms in a 30min talk at the ZKProof cryptography conference. You can find it on YouTube by searching "zkproof neutral atom".

Here's an interesting observation about this duo of breakthrough papers: neither Google nor Oratomic say a word about what their results mean for qday. No timelines. Zero. Nada. That is especially baffling given that the whole point of whitehat quantum cryptanalysis is to inform qday estimations and help the general public make good decisions.

So let me attempt to partially fill the silence, similarly to what Scott Aaronson did in his April 29 post. Given everything I know, including scary non-public information, I now put the odds of qday by 2032 at 50%. 10% by 2030.

Anecdotally, the US government has its own date: 2035. Originating at the NSA and later adopted by NIST, it's when branches of the US government will be disallowed from using quantum-vulnerable cryptography. In plain language: with hindsight, that date is a joke and should be discounted entirely. I don't see how NIST avoids being forced to pull it forward by years.

Part 3: post-quantum cryptography

There are good reasons to sound the alarm today, but please do not panic. Rushing carelessly towards immature post-quantum cryptography is a recipe for disaster. IMO a good target date for migration is 2029, roughly 3.5 years out. 2029 happens to be the date selected by Google, Cloudflare, and the Ethereum Foundation.

These days most of my time goes to safely migrating Ethereum towards post-quantum cryptography as part of the broader lean Ethereum effort. There's a lot to do. We need to rip out and replace BLS signatures at the consensus layer, KZG commitments at the data layer, and ECDSA signatures at the execution layer.

The plan to get there is compelling, and is based on hash-based cryptography. Within the Ethereum Foundation we've developed a Swiss army knife called leanVM (github[.]com/leanEthereum/leanVM) powered by the magic of hash-based SNARKs. Thanks to truly exceptional work by Emile, Thomas, and others, its performance is derisked. Regarding security, leanVM is a jewel, a minimal zkVM crafted for end-to-end formal verification and maximum security.

Want to help? There are two $1M initiatives. First, the Proximity Prize (proximityprize[.]org). Solve a long-standing mathematical conjecture in coding theory, improve hash-based SNARKs, and go home a millionaire. Second, the Poseidon Initiative (poseidon-initiative[.]info), offers $1M for breaking Poseidon, the SNARK-friendly hash function.

[Fair Math @FairMath]

@grok, explain why this matters.

[Fair Math @FairMath]

An AI agent without the ability to privately read internet is like an employee thinking out loud in front of competitors.

[Fair Math @FairMath]

Does your agent need an invisibility cloak?
Request it: https://t.co/MNE54TpHMI

[Fair Math @FairMath]

4/ We’re currently onboarding a small number of teams building autonomous agents and agent-based systems. If you want to try an invisibility cloak for your agent or autonomous system, just send request at https://t.co/hclQr9elQA.

[Fair Math @FairMath]

1/ We’re opening private testing for Manaslu: a privacy-preserving data retrieval framework for AI agents.

AI agents today continuously read from public data sources: markets, blockchains, APIs, search systems, datasets, knowledge bases.
Every retrieval request leaks sensitive information about the agent:
- intent
- strategy
- objectives
- behavioral patterns

That’s the problem Manaslu is designed to solve.

[Fair Math @FairMath]

3/ End-to-end encrypted with post quantum cryptography.
- No trusted intermediaries.
- No threshold networks.
- No weak trust assumptions