[00:02:43] Charles Edwards: At the most simplistic level, traditional transistor chips work in ones and zeros. You can have one state or the other. But things get weird at the quantum level, and Albert Einstein always struggled with this—it doesn’t make logical sense anymore. At the atomic level, things can have multiple states at the same time; it’s probability-based.

[00:03:10] Charles Edwards: A quantum computer tries to replicate quantum states and physics, potentially being in multiple states at once. That becomes interesting in programming and encryption—you can model multiple values simultaneously and zero in on an optimization very quickly. For example, if you’re trying to guess a password, a quantum computer can simulate all possibilities almost at once and solve problems extremely fast.

[00:03:41] Charles Edwards: Google had a big headline about a year ago with their Willow chip, where they solved a mathematical equation that would take any classical supercomputer more time than the age of the universe—trillions of years. That’s exciting because it means new innovations are possible. Industries like AstraZeneca have used it for drug discovery, defense has experimented with it for communications and landmine detection, and HSBC has tested it for bond optimization. So when people say, “Oh, quantum has no use today,” that’s false. It’s already in early-stage use.

[00:04:57] Charles Edwards: It’s still very early, like if you were using ChatGPT five or six years ago—even Sam Altman said it was terrible back then. Quantum computing today is at that early stage, already proving use cases and moving along the hockey-stick trajectory of tech adoption.

[00:05:10] Preston Pysh: That’s a great summary. The top uses I’ve seen include material and chemistry simulations—things like batteries, semiconductors, and pharmaceuticals; optimization problems in logistics; and crypto security. And then, of course, in machine learning and AI acceleration, it could be massive.

[00:07:10] Preston Pysh: There are three key components: superposition, entanglement, and interference. Qubits exist in multiple states (superposition), they become entangled, and interference between entangled qubits enables fast solutions. It’s like solving a system of equations—rather than step by step, you solve all simultaneously.

[00:08:54] Charles Edwards: That’s a good analogy. Another key issue is error rates. There’s a big difference between physical qubits and logical qubits. Physical qubits have high error rates; logical qubits are error-corrected and stable. When we talk about qubits in this conversation, we’re referring to logical ones.

[00:09:31] Charles Edwards: Many claim you need millions of qubits to break encryption, but that’s based on physical qubits. With logical qubits, it’s just a few thousand. And the error rates are dropping fast—adding more qubits actually helps reduce noise.

[00:10:04] Preston Pysh: That’s an important point. A lot of labs publish numbers in physical qubits, not logical ones. When you entangle qubits, you get noise—heat, vibration, etc.—and that makes scaling difficult. Predicting timelines is tough because it depends on solving the noise problem. Charles, you’re bullish on shorter timelines; others are more conservative. The noise issue seems key. Would you agree?

[00:12:08] Charles Edwards: Yes and no. The number of qubits is increasing faster than Moore’s Law—doubling roughly every 18 months. The U.S. Department of Defense recently said there’s tangible risk of Q-Day within three years. Multiple sources—Jameson Lopp, McKinsey & Company, and researchers at Microsoft, IonQ, and Meta—converge on 2–9 years for when encryption could be broken. Bitcoin’s Elliptic Curve Cryptography (ECC) is weaker than RSA, so it’s at risk sooner.

[00:14:19] Charles Edwards: They estimate only about 2,300 logical qubits would be needed to break Bitcoin’s encryption, and several firms project reaching that within 4–5 years. That’s why I think we have until roughly 2027–2029 to upgrade Bitcoin’s security.

[00:15:21] Charles Edwards: Two big Bitcoin discussions are needed: (1) deciding on quantum-proof encryption and wallet upgrades; (2) deciding what to do about Satoshi’s coins and lost coins in legacy P2PK (Pay-to-Public-Key) addresses that will inevitably be taken by quantum machines if not migrated.

[00:15:41] Preston Pysh: I want to play devil’s advocate. Many experts providing aggressive timelines have incentives to get funding or hype their field. Others are skeptical. IBM, for example, projects about 200 logical qubits by 2029, based on over 100,000 physical qubits. That’s still far from the 2,300 logical needed to break ECC.

[00:19:02] Charles Edwards: True, but firms like IonQ project 8,000 logical qubits by 2029, PsiQuantum aims for 1 million physical qubits by 2028, and Rigetti Computing expects 100 logical qubits by 2026. Others like OQC (Oxford Quantum Circuits) target 5,000 by 2031. It only takes one company to get there first—and China is spending double what the U.S. spends. Their latest machine was reportedly a million times faster than Google’s. Over $55 billion is being invested globally.

[00:22:21] Preston Pysh: Could AI help mitigate the noise factor by optimizing calibration?

[00:22:48] Charles Edwards: Yes. Rigetti announced AI-based improvements that sped up calibration dramatically. AI will speed up quantum progress, and quantum will later accelerate AI—they’re symbiotic.

[00:24:36] Preston Pysh: When I researched entanglement, I learned that qubits can’t just be grouped like CPUs—they must all be entangled, not stitched together modularly. So you can’t just link multiple 1,000-qubit systems. Is that correct?

[00:24:39] Charles Edwards: Essentially, yes. It’s not modular like classical computing. However, scaling is improving—error rates drop as qubits increase. Like AI five years ago, it looks limited now but is improving exponentially.

[00:26:06] Preston Pysh: Let’s talk about Bitcoin. Early Bitcoin addresses were P2PK, or Pay-to-Public-Key. Roughly 25% of all Bitcoin—mostly Satoshi Nakamoto’s coins—are in this format and vulnerable. From 2010–2017, Bitcoin moved to P2PKH (Pay-to-Public-Key-Hash), which hides the public key until spent and is more secure for now.

[00:28:04] Preston Pysh: The proposed solution is BIP360, authored by BeastHunter. It’s a soft fork introducing post-quantum signatures. Migration could take years, though, because post-quantum signatures are 1–20 kilobytes versus the 70 bytes today.

[00:33:32] Preston Pysh: If everyone moved funds (over $100 in value) to quantum-safe wallets, migration could take 10–30 months. That doesn’t include consensus delays, testing, or disagreements—meaning we must start now.

[00:37:07] Charles Edwards: Exactly. Consensus takes years. If Q-Day hits before we’re ready, trust in Bitcoin collapses. It’s better to prepare early than be one minute too late.

[00:39:25] Preston Pysh: The Majorana-based qubit breakthrough from Microsoft in 2024 caught my attention. They aim for a million-qubit processor by 2027 but haven’t revealed how many logical qubits that means. IBM plans 200 logical qubits by 2029.

[00:41:47] Charles Edwards: Most people underestimate the pace because the data is scattered. But if you read investor reports, you see exponential growth. Quantum progress is real.

[00:47:12] Preston Pysh: Jensen Huang from NVIDIA said in January that quantum was 15–30 years away. By June, he said we’re at an inflection point and began investing billions. That’s a dramatic pivot—similar to early skepticism about Bitcoin or AI.

[00:49:57] Charles Edwards: Exactly. I’m still long Bitcoin, but I also maintain a quantum hedge—a diversified basket of quantum equities. We’re building a Quantum Index Fund to track pure-play companies. I want Bitcoin to thrive, but we need to act fast—within the next year—to secure it.

[00:51:00] Preston Pysh: Great discussion, Charles. Thanks for joining and sharing your insights.

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