The Quantum Issue: You Never Really Know The Future
For over a decade, people have debated whether quantum computers pose a realistic threat to the Bitcoin network. This topic of conversation was already serious roughly 13 years ago when I first discovered Bitcoin myself.
Since those early days when I was simply trying to make sense of everything, significant progress has occurred in both theory and real-world engineering.
Two major milestones reached in the interim materially change the probability that a viable quantum computer will actually be built within the next decade or so. That does not inherently mean these machines will become ubiquitous, or even readily accessible to those with substantial capital.
Even so, it remains entirely possible that several viable machines will emerge in the near future.
Error Correction Improvements
The first major breakthrough involves error correction. Because working at such a tiny scale introduces inherent noise, creating a practical, usable logical qubit requires multiple redundant physical qubits.
Previously, the state-of-the-art method relied on surface codes, which bundle physical qubits into a grid and designate certain units as check qubits. These check elements periodically monitor their neighbors to catch internal superposition errors without collapsing the superposition state. Every empty grid space must be occupied by these checkers.
This requirement creates an extra overhead that can approach 1,000 physical qubits for every single logical qubit. The problem scales poorly because check qubits can only monitor their immediate neighbors, meaning every grouping requires checkers placed at equidistant intervals.
Quantum low-density parity-check (qLDPC) codes eliminate this bottleneck by allowing check qubits to monitor distant elements—either via interwoven communication traces across chip sections or by physically moving atoms, as seen in neutral atom designs. This innovation has decreased the physical qubit count required for a reliable logical qubit by tenfold.
This achievement is substantial. While it may not represent a fully functional machine driving toward greater efficiency, it marks a material engineering gain that underpins the eventual production of a working quantum computer.
Progress In Proving Fundamentals
The second milestone addresses a fundamental question: whether adding more physical qubits reduces overall system noise or amplifies it. To this day, this concept remains largely theoretical, and no fully functional quantum computer has ever executed an end-to-end computation that a classical computer cannot perform.
To test the effect of scaling physical qubits, Google conducted experiments utilizing their Sycamore and later Willow chips. To be precise, this was not a demonstration of running computations, but rather of storing information in memory without decay.
By employing logical qubits made up of 17, 49, and 101 physical qubits respectively, researchers demonstrated that the logical error rate—the frequency at which data becomes corrupted—declined as the physical qubit count increased. This test crossed a critical threshold, allowing the constructed logical qubit to maintain coherence longer than any of the individual physical qubits comprising it.
Once again, this does not equate to a fully functional quantum computer capable of outperforming classical machines, yet it delivers material progress by proving one of the core assumptions underlying quantum computing.
AI
These are not the only areas seeing better solutions. Artificial intelligence has emerged as a major component within these systems, assisting in the reading and decoding of information from quantum computers—a primary bottleneck for scaling utility.
AI is also aiding in the creation of new quantum algorithms optimized for these architectures. Given how recently AI has helped solve or disprove major mathematical conjectures, it is not a stretch to imagine major breakthroughs driven by artificial intelligence.
Furthermore, AI is applied to design the physical quantum circuits built on varying architectures. Finding the optimal layout for quantum gates in physical space to minimize quantum-level noise—without leaving excessive empty space that introduces latency, inefficiency, and additional engineering challenges—is exceptionally complex.
This factor could significantly accelerate progress in resolving essential foundational problems.
Outlook Ahead
Ultimately, this issue centers on a single question: does the assumption that adding more physical qubits reduces noise hold true when applied to actual computation and the active manipulation of quantum information?
If that assumption holds and is not experimentally disproven in the near future, a realistic path exists for building a viable quantum computer within the next ten years.
Massive resources are pouring into this field alongside meaningful, steady progress in solving individual pieces of the puzzle. When a fundamental path forward exists, humanity typically finds a way to execute it.
Panic is unnecessary, but discounting the possibility altogether would be a mistake.
This piece is featured in the latest Print edition of Bitcoin Magazine, The Quantum Issue. We’re sharing it here as an early look at the ideas explored throughout the full issue.
FAQ
Q: Are quantum computers a realistic threat to Bitcoin right now?
A: While debated for over a decade, viable quantum computers do not yet exist at scale, though recent engineering and error-correction improvements mean they could emerge in the next decade.
Q: What are qLDPC codes and why do they matter?
A: Quantum low-density parity-check (qLDPC) codes eliminate previous scaling bottlenecks by allowing check qubits to communicate across large distances, achieving a 10x reduction in physical qubits needed for a reliable logical qubit.
Q: Have quantum computers performed computations better than classical computers?
A: No. To date, no fully functional quantum computer has performed an end-to-end computation that a classical computer cannot execute, though memory storage tests have shown error rates decreasing as physical qubit counts increase.
Q: What role does AI play in quantum computing development?
A: AI helps read and decode quantum data, develops optimized quantum algorithms, and designs physical quantum circuit layouts to minimize noise and latency.
