StarkWare says Bitcoin quantum-safe “last resort” cut 79%

StarkWare says optimization efforts connected to its Quantum-Safe Bitcoin (QSB) work have driven down the estimated computational cost of constructing a quantum-resistant Bitcoin transaction to under $67—down from about $320 for the first QSB transaction demonstrated on mainnet in August. The latest figure, published in a Sept. 23 update and reflected on the Yukon QSB dashboard, suggests the benchmark cost has continued to edge lower to roughly $66.

While the QSB approach is designed to improve resilience against future quantum threats without requiring changes to Bitcoin’s consensus rules, StarkWare cautions that the new numbers are based on benchmark tests. The development nonetheless matters for anyone evaluating practical “emergency” defenses for large holders who may be most exposed if public keys are later compromised by quantum-capable machines.

Key takeaways

• StarkWare reports QSB transaction construction costs fell to about $66–$67 after a week of optimization.


• The cost decline follows the Quantum-Safe Bitcoin Optimization Challenge, which generated improvements across the benchmarked transaction-building steps.


• The earliest mainnet QSB demonstration required ~3,100 GPU-hours and cost around $320 in compute, excluding Bitcoin network fees.


• QSB is positioned as an “emergency” measure; StarkWare says broader, long-term protection likely still requires a consensus-layer soft fork.


• StarkWare emphasizes the latest optimizations are verified via benchmarks, not renewed full mainnet demonstrations.

From a $320 demo to a sub-$67 benchmark

StarkWare’s update ties the cost reduction to participant work in the Quantum-Safe Bitcoin Optimization Challenge, a collaborative effort involving Yukon Research and Eigen Labs. The initiative focused on reducing the GPU computation needed to prepare a QSB transaction.

In August, StarkWare said a QSB transaction was mined and confirmed on Bitcoin’s mainnet. According to that earlier account, the engineering effort behind the demonstration involved Tomer Giladi and submission via MARA’s Slipstream service. Preparing the transaction required approximately 3,100 GPU-hours across roughly 100 GPUs, translating to about $320 in compute cost, excluding network fees.

The new update describes how the challenge achieved a sharper efficiency gap. StarkWare reports that the competition produced 62 accepted improvements across two computational tasks required to build a QSB transaction. Based on benchmark testing, these changes reduced the estimated computing cost by about 79%—bringing it down to the current $66–$67 range shown on the Yukon dashboard.

Why QSB is framed as an “emergency” defense

The broader motivation for QSB is the risk that a sufficiently powerful quantum computer could break the elliptic-curve digital signatures Bitcoin relies on. If that future capability arrives, attackers could potentially steal coins whose public keys are exposed, making timing and practicality important for holders who need protection under constrained conditions.

StarkWare’s QSB design, first published by researcher Avihu Levy in April, aims to add hash-based protection against quantum attacks without changing Bitcoin’s consensus rules. That non-consensus approach is part of what makes QSB attractive as an add-on defense—particularly if consensus changes remain difficult or slow.

However, Levy previously characterized QSB as a “last resort measure,” citing cost, complexity, and limited applicability. StarkWare’s latest messaging continues that framing: even with a nearly 80% reduction, a few hundred dollars per transaction still signals that QSB remains closer to an emergency tool than a fully routine mechanism for all holders.

The challenge’s impact—and what remains unproven

The Optimization Challenge opened on Sept. 16, inviting developers, researchers, and AI agents to refine the software needed to generate QSB transactions more efficiently. StarkWare says the resulting improvements were accepted across two computational tasks that together determine the overall benchmark cost.

The key point for investors and builders is the distinction between benchmark performance and real-world transaction preparation at full scale. StarkWare’s update presents the new $66–$67 number as an estimated computational cost derived from performance tests. That means the update demonstrates algorithmic and engineering efficiency, but it does not, by itself, confirm that the most recent optimizations have been used to create and confirm additional QSB mainnet transactions.

Still, the direction of travel is meaningful: getting from roughly $320 to under $67 transforms the practicality of the technique for larger holders who may prioritize the ability to execute a protective transaction when conditions warrant it. In its Sept. 23 update, StarkWare argued that a construction costing a few hundred dollars is “a demo,” while costs closer to $67 are more plausible for emergency use by holders with significant balances—without requiring network rule changes.

Soft forks remain the long-term focus

QSB exists within a broader debate on how Bitcoin should prepare for quantum threats. StarkWare says it continues to favor a soft fork—a change to Bitcoin’s consensus rules—as a better “long-term answer” for wider, more reliable quantum protection across the ecosystem.

This matters because emergency add-ons, while potentially useful, are inherently narrower in scope: they do not automatically provide the systemic coverage that consensus-level changes can offer. The latest cost reductions therefore read less like a final end-state and more like progress toward making an interim strategy less prohibitive while the community works through governance, implementation, and security trade-offs for longer-range solutions.

For now, readers should watch the Yukon QSB dashboard for whether the benchmark estimates keep moving and whether the community follows up with additional mainnet demonstrations using the newly optimized construction pathways. The biggest open question remains whether benchmark gains translate into repeated, end-to-end operational readiness when real constraints—beyond compute estimates—come into play.

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