Is There a Silver Lining Behind The Looming Dark Clouds of Quantum’s Crypto-busting Powers?

Insider Brief

• Google’s analysis of quantum algorithms capable of breaking current cryptography also implies that the same class of machines could enable meaningful near-term quantum applications.
• The logical qubit requirements for cryptographic attacks and for applications such as quantum simulation, optimization, and quantum-enhanced AI appear to fall within a similar range.
• While hardware capable of reaching this scale does not yet exist, the convergence of risk and capability suggests organizations should prepare both for post-quantum security and for emerging quantum computing opportunities.
• Image: Daniel Páscoa on Unsplash

The alarm over Google Quantum AI’s latest findings has centered on risk, but the same advance is sharpening the case that useful quantum applications may arrive sooner than expected.

A recent white paper written by a Google-led research team outlined how a sufficiently large quantum system — on the order of hundreds of thousands of physical qubits — could run Shor’s algorithm to break the elliptic curve cryptography used in Bitcoin and other blockchain networks in minutes. The reaction across the crypto industry has been immediate and largely negative, focusing on vulnerabilities and migration timelines.

But there is another way to look at this paper — and several other similar reports on research advances — which is gaining traction among some researchers and technologists. According to this more positive take, the papers also show that same resource requirements implied by the attack are not far from those needed to unlock practical quantum advantages in other domains.

For example, Ben Goertzel, AI expert and founder and CEO of SingularityNET, writes in a Substack post, that the same class of machines capable of executing large-scale cryptographic attacks would also be capable of running meaningful quantum-enhanced AI systems, for example. In other words, the threshold for “dangerous” quantum computing may also be the threshold for “useful” quantum computing.

Rather than a distant, binary event — either quantum computers break encryption or they don’t — the technology appears to be approaching a regime where multiple capabilities emerge at once.

A Shared Hardware Threshold

The Google analysis suggests that breaking widely used cryptographic systems would require roughly 1,000–1,500 logical qubits, built on top of error-corrected architectures comprising hundreds of thousands of physical qubits. That scale remains beyond today’s hardware, but, at least to some experts, this and other recent papers suggest the trajectory is clearer than in prior years.

Goertzel and other experts point out that the range of thousands of logical qubits has long been viewed as a threshold for more advanced quantum applications, including optimization, simulation and certain forms of machine learning. If so, the first generation of quantum computers powerful enough to pose a credible threat to encryption may also be the first generation capable of delivering nontrivial commercial value.

This convergence challenges a common narrative that treats quantum risk as an isolated cybersecurity problem. Instead, this silver lining approach indicates there’s broader inflection point approaching in computing.

Most current responses to the quantum threat focus on defense — namely, the adoption of post-quantum cryptography (PQC). Governments and standards bodies have already begun pushing for migration to new cryptographic schemes designed to resist quantum attacks.

The Google paper reveals that even partial success rates in quantum attacks could destabilize blockchain systems by undermining transaction finality and key security. But, companies and organizations taking a purely defensive posture in quantum may miss a larger opportunity.

If the same hardware advances that enable cryptographic attacks also enable new computational capabilities, then organizations that prepare only for risk mitigation may find themselves not recognizing emerging advantages and preparing to exploit them.

In his post, Goertzel frames this as a distinction between “quantum-resistant” and “quantum-oriented” systems. The former seek to survive quantum disruption; the latter are designed to use quantum computation as a core resource.

This distinction is still largely theoretical, but it aligns with broader trends in computing, where new architectures — from GPUs to specialized AI chips — have driven waves of application innovation once they reached sufficient scale.

Near-Term Use Cases Come Into Focus

According to experts, like Goertzel, several areas stand out as potential early beneficiaries of this convergence. Quantum simulation, particularly in chemistry and materials science, has long been cited as a leading application. Systems capable of supporting thousands of logical qubits could begin to model molecular interactions beyond the reach of classical computers, with implications for drug discovery and energy technologies.

Optimization problems — ranging from logistics to financial modeling — may also be ideal places to test the power of quantum computers. While quantum advantage in these areas remains contested, larger and more reliable systems could tip the balance for specific, high-value use cases.

Machine learning is a more speculative but — as Goertzel points out — increasingly researched frontier. Quantum-enhanced algorithms for probabilistic reasoning or pattern search could complement classical AI systems, particularly in domains involving large, complex state spaces.

The key point is not that these applications are guaranteed, but that they share the same underlying resource requirements as the cryptographic threat with progress toward one implying progress toward the other.

Engineering, Not Algorithms, Remains the Bottleneck

Despite the attention on algorithms, hardware remains one of the biggest bottlenecks, if not the biggest bottleneck.

The circuits described in Google’s analysis, while complex, are based on known techniques in quantum computing. Experts broadly agree that similar designs could be reproduced by other advanced research groups within a reasonable timeframe.

What cannot yet be reproduced is the hardware required to run them at scale.

Building a system with hundreds of thousands of physical qubits, low error rates, and fast control systems represents a massive engineering challenge. It will require advances in fabrication, error correction, and system integration, as well as significant capital investment.

This gap between algorithmic feasibility and hardware reality provides a window—though its duration is uncertain.

As research begins to clear some of the fog the surrounds the future of quantum computing — in both its constructive and disruptive potential — a dual-track transition is emerging.

On one track, industries must harden existing systems against quantum threats, adopting new cryptographic standards and updating infrastructure. This is already underway, though progress varies widely across sectors.

On the other track, organizations must begin exploring how quantum computing could be integrated into their operations—not as a distant possibility, but as a medium-term capability.

The timing of these tracks may overlap more than previously assumed.

The Google findings suggest that the arrival of cryptographically relevant quantum computers will not be an isolated event, but part of a broader technological shift. When that change occurs, it may bring both disruption and — for those prepared to break quantum’s inner silver lining — an equal measure of opportunity.