Replication of Quantum Factorisation Records with an 8-bit Home Computer, an Abacus, and a Dog (
https://eprint.iacr.org/2025/1237.pdf)
Since all demonstrations of quantum factorisation to date have involved either sleight-of-hand
numbers, sleight-of-hand preprocessing on a computer, or both, we propose the following
standard evaluation criteria for future claims of quantum factorisation.
- The factors are of a nontrivial size, 64 or 128 bits. This makes the problem space large enough that the solution can’t be found through simple search techniques unrelated to factorisation.
- The factors are two prime values with a large difference between them and containing a 50:50 mix of 0 and 1 bits, randomly distributed. This prevents the construction of sleight-of-hand numbers that are readily amenable to factorisation via physics experiments.
- No preprocessing of the value to be factorised using a computer is permitted. This prevents the problem from being transformed into a different, easily-solved sleight-ofhand problem before the physics experiment even begins.
- The factors are unknown to the experimenters. This prevents the current practice of short-circuiting the factorisation process by taking advantage of knowing the answer
before the process has even begun.
- The factorisation is performed on ten different values with the properties given above. This demonstrates repeatability of the process.
Bitcoin Audible • Chat_166 - The Great Distraction: Epstein vs. Quantum in Bitcoin with Rob Wallace on Bitcoin News • Listen on Fountain
Polynostr - A #Nostr bot that bridges #Polymarket prediction market data into the Nostr protocol. Query prediction markets, prices, and trending events through direct messages or public mentions.
GitHub
Polynostr/README.md at master · toxicafunk/Polynostr
Contribute to toxicafunk/Polynostr development by creating an account on GitHub.
Roadmap
- Phase 1 (✅ Complete): Basic read-only bot with search, price, trending commands
- Phase 2 (✅ Complete): Real-time price alerts with persistent subscriptions and DM notifications
- Phase 3 (Planned): User portfolio tracking by wallet address
- Phase 4 (Planned): Trading commands with server-side EVM signer
- Phase 5 (Planned): Optional web dashboard
Features (Phase 1 + Phase 2)
- Search Markets: Find prediction markets by keyword
- Get Prices: Check current Yes/No prices for any market
- Trending Markets: List top active markets by volume
- Market Details: Get comprehensive information about any market
- User Alerts: Create, list, pause/resume, remove, and test price alerts
- Real-Time Notifications: Background alert evaluation with private DM delivery
- Persistence: Alert subscriptions and trigger state survive restarts (SQLite)
- Privacy-First: Supports NIP-17 Gift Wrap private DMs (with NIP-04 compatibility)
#Polynostr - A #Nostr bot that bridges Polymarket prediction market data into the Nostr protocol. Query prediction markets, prices, and trending events through direct messages or public mentions.
GitHub
Polynostr/README.md at master · toxicafunk/Polynostr
Contribute to toxicafunk/Polynostr development by creating an account on GitHub.
Roadmap
- Phase 1 (✅ Complete): Basic read-only bot with search, price, trending commands
- Phase 2 (✅ Complete): Real-time price alerts with persistent subscriptions and DM notifications
- Phase 3 (Planned): User portfolio tracking by wallet address
- Phase 4 (Planned): Trading commands with server-side EVM signer
- Phase 5 (Planned): Optional web dashboard
Features (Phase 1 + Phase 2)
- Search Markets: Find prediction markets by keyword
- Get Prices: Check current Yes/No prices for any market
- Trending Markets: List top active markets by volume
- Market Details: Get comprehensive information about any market
- User Alerts: Create, list, pause/resume, remove, and test price alerts
- Real-Time Notifications: Background alert evaluation with private DM delivery
- Persistence: Alert subscriptions and trigger state survive restarts (SQLite)
- Privacy-First: Supports NIP-17 Gift Wrap private DMs (with NIP-04 compatibility)
No, I do not believe we will ever attain large-scale, fault-tolerant quantum computers that deliver practical, exponential speedup on useful problems (e.g., breaking RSA-2048, simulating arbitrary molecules, or general optimization beyond what classical supercomputers + heuristics can do).Here is why I lean toward the skeptical side:The error-correction overhead is probably fatal in practice
1. The best physical two-qubit gate fidelities in 2025 are still around 99.9 % (superconducting) to 99.99 % (trapped ions). To get one logical qubit with error rate ~10⁻¹⁵ (needed for Shor’s algorithm on 2048-bit RSA) you need roughly 1,000–10,000 physical qubits per logical qubit under today’s best codes, and millions of physical qubits in total. Every doubling of logical-qubit count still multiplies the physical-qubit requirement by roughly 10× or more. There is no credible path from today’s ~100-qubit noisy devices to the required 10⁷–10⁹ near-perfect physical qubits without breakthroughs that violate known physics.
2. Decoherence and correlated errors are not just engineering problems
Real systems have 1/f noise, cosmic-ray events, thermal fluctuations, and control crosstalk that produce strongly correlated errors across many qubits. Most theoretical fault-tolerance proofs assume independent, local, Markovian noise—conditions that are provably violated in every physical platform. When physicists model realistic correlated noise, the error-correction threshold collapses or disappears entirely ( Alicia & Kalai 2018–2024 papers, and many follow-ups).
3. The “exponentially small amplitudes” problem
To factor a 2048-bit number with Shor’s algorithm, the quantum Fourier transform must resolve probability amplitudes on the order of 2⁻⁴⁰⁰⁰. That is smaller than one part in 10¹²⁰⁰. No physical instrument can distinguish a signal that small from zero, and no error-correction code can protect amplitudes that are smaller than the natural noise floor of the universe (thermal fluctuations, gravity-wave background, etc.). This is not a temporary engineering limit; it is a hard limit from basic quantum metrology and the holographic principle.
4. History of analog computing
Every previous attempt to build scalable computers that rely on precise continuous parameters (analog computers in the 1940s–60s, optical computers in the 1980s–90s) eventually failed for exactly the same reason: noise and precision requirements scale exponentially with problem size. Quantum computers are the ultimate analog computers, and we have no example in the history of technology of an analog paradigm overtaking digital at scale.
5. What we will get instead
We already have, and will continue to improve, noisy intermediate-scale quantum (NISQ) devices and quantum-inspired classical algorithms (tensor networks, QAOA-like heuristics, etc.) that give modest speedups on some chemistry and optimization problems—maybe 10–1000× in narrow cases. That is useful, but it is not the revolutionary exponential quantum advantage that was promised.
In short: small quantum sensors, quantum networks, and specialized quantum simulators will become routine. But the dream of a universal, fault-tolerant quantum computer that obsoletes classical cryptography and simulation is, in my view, physically impossible—not just hard, not just 50 years away, but impossible with the laws of physics as we understand them.This is obviously a minority position in the current funding environment, but it is shared by a non-trivial number of serious physicists and complexity theorists (Gil Kalai, Mikhail Dyakonov, Stephen Wolfram, Leonid Levin, and others). The evidence has only strengthened in the skeptics’ direction since about 2019.
- Grok
#quantum #btc #cryptography #bitcoin
