Two milestones in quantum computing research — major gains in error-correction efficiency and the first experimental verification of a core scaling assumption — have materially changed the odds that a viable quantum computer is produced sometime in the next decade or so, according to Shinobi, writing in the latest print edition of Bitcoin Magazine, The Quantum Issue. Neither ubiquity nor easy access is guaranteed, the author cautions, but the possibility of viable machines emerging in the near future should not be dismissed.

The debate over whether a quantum computer presents a realistic threat to the Bitcoin network has been running for over a decade. The question is rooted in how Bitcoin secures itself: spending funds requires an elliptic-curve digital signature, the kind of mathematical problem Shor's algorithm could in principle break on a sufficiently capable quantum machine, while the SHA-256 hashing that underpins mining is considered far more resistant, since Grover's algorithm offers only a quadratic speedup. It was already a serious topic of conversation more than 13 years ago, when Shinobi first discovered Bitcoin. Since then, there has been considerable progress in both theory and real-world engineering. That does not inherently mean the technology will reach ubiquity, or even relative ease of access for those with large amounts of capital. But it is very possible that a number of viable machines will be produced in the near future.

Error Correction Improvements

The first major improvement has come in error correction. To account for the inherent noise involved in working at this kind of tiny scale, producing a logical qubit that is useful for computation in practice requires the use of multiple redundant physical qubits.

The prior state of the art was surface codes: a way of bundling multiple physical qubits together in a grid and using some of them as check qubits that periodically "check on" their neighbors to ensure no internal errors in the superposition have occurred, without collapsing the superposition. Each grid's empty spaces need to be filled with check qubits.

This check qubit requirement creates extra overhead that can get close to 1,000 physical qubits per logical qubit in total, and it scales poorly because check qubits can only check on the qubits immediately next to them. Every grouping of qubits therefore needs checkers positioned at equidistant spacing.

Quantum low-density parity-check (qLDPC) codes remove this bottleneck, allowing check qubits to check other qubits at large distances across the device — either through traces interwoven to communicate across chip sections, or by physically moving atoms, as with the neutral atom design. This has allowed a 10x reduction in the number of physical qubits necessary to produce a reliable logical qubit.

For Bitcoin's threat model, that overhead ratio is the crux. Any machine capable of running Shor's algorithm against elliptic-curve signatures would have to be assembled from reliable logical qubits, so how many physical qubits each one consumes largely determines the scale of hardware involved. That is not a gain to be sneezed at. It may not amount to a fully functional machine making progress toward greater efficiency, but it represents material efficiency gains in the engineering processes that underlie the production of a fully functional quantum computer.

Progress in Proving Fundamentals

The second milestone concerns a more fundamental question: whether the assertion that adding more physical qubits leads to a reduction in overall noise in the system, rather than an increase, actually holds. At this point that remains largely theory — and it is worth keeping in mind that, to this day, there has never been a fully functional quantum computer that has end-to-end performed a computation a classical computer is incapable of.

Google ran an experiment using its Sycamore (and later Willow) chips to experimentally verify the effect of adding more physical qubits. Both are superconducting processors, the same broad hardware family whose physical layout constraints the qLDPC work addresses, competing alongside neutral atom designs that arrange long-distance error checks by physically moving atoms. To be very clear, this was not a demonstration of performing computations, but simply a demonstration of storing information in memory without it decaying.

Using logical qubits composed of a bundle of 17 physical qubits, a bundle of 49, and a bundle of 101, Google demonstrated that the logical error rate — the frequency of data corruption — decreased as the physical qubit count went up. The test passed a critical threshold: the logical qubit created out of the independent physical qubits maintained coherence longer than any individual physical qubit it was composed of.

Again, this is not a jump to a fully functional quantum computer performing computations that classical machines are incapable of, but it is material progress in proving one of the fundamental assumptions underlying quantum computers.

AI

These are not the only areas where better solutions are being found in this problem space. Artificial intelligence has become a major component in these systems. It is being used in the actual process of reading and decoding information from a quantum computer, a big bottleneck for actually making use of such machines at scale.

AI is also being used in the development of new quantum algorithms optimized for these types of machines. Given the recent spate of AI helping to solve — or even disprove the existing conjectures of — major problems in the field of mathematics, it not a far-fetched leap to consider the possibility of major breakthroughs brought about by AI.

AI is being put to the same use in designing the physical quantum circuits built using different architectures. This is a genuinely complex problem: finding the optimal way to lay out quantum gates in a physical space to minimize noise at the quantum level, without creating so much empty space that latency, inefficiency, and other problems are introduced.

This is a factor that could well hypercharge progress toward solving the necessary fundamental problems.

Outlook Ahead

Ultimately, in the author's opinion, this comes down to one question: does the assumption that adding more physical qubits reduces noise actually hold when it comes to computation and the active manipulation of quantum information?

If that assumption holds — and is not experimentally disproven sometime in the near future — then there is a very realistic case that a viable quantum computer will be produced in the next ten years. There is a massive amount of resources being thrown at this problem, and there has been significant, if not overwhelming, progress in solving pieces of it. And if there fundamentally is a way to do something, human beings usually figure it out.

That uncertainty is not unique to Bitcoin. The wider security industry has already begun acting on the long-horizon risk: in 2024, the U.S. National Institute of Standards and Technology finalized its first post-quantum cryptography standards, approved replacements for the public-key algorithms a large-scale quantum machine could threaten. For readers tracking this space, the near-term markers are straightforward — whether logical qubit bundles keep scaling with falling error rates, and whether experiments progress from storing quantum information to actively computing with it, precisely the threshold the author identifies as the open question.

The message is not that it is time to panic — but the possibility should not be discounted.

This piece is featured in the latest print edition of Bitcoin Magazine, The Quantum Issue, and is shared as an early look at the ideas explored throughout the full issue. It first appeared on Bitcoin Magazine and is written by Shinobi.