Q-Day or Not Q-Day

Forecasting the First Practical RSA Compromise: A Scenario-Based Monte Carlo Assessment

SITG-Consulting Strategic Forecasting Laboratory

Research Note & Methodology Brief

1. Introduction – Why the Question Is Different

Every few weeks, a new prediction emerges regarding the arrival of cryptographically relevant quantum computing. Some analysts predict an operational machine within five years; others argue it will take twenty years or longer.

Most of these forecasts share a fundamental flaw: they attempt to predict hardware delivery rather than operational capability.

Predicting when a quantum computer of a certain qubit threshold will sit in a commercial laboratory is an engineering prediction. Predicting when an adversary can practically break public-key encryption to intercept or decrypt sensitive data is an operational risk assessment.

If strategic leadership treats cryptographic risk purely as a hardware milestone, they miss the complex ecosystem of algorithms, software optimization, classified research, and artificial intelligence that determines when encryption actually fails in practice.

2. The Question We Asked

To build a meaningful forecast, we deliberately reframed the primary inquiry.

Our Question:When could a hostile actor first possess sufficient computational capability to operationally compromise RSA-2048 using Shor’s Algorithm, or an equivalent computational breakthrough, at scale?

We intentionally did not ask when a commercial quantum computer would be announced, nor when a vendor would demonstrate a million physical qubits.

Operational capability can occur long before public announcement, and it can be achieved through combinations of technologies that bypass traditional hardware scaling hurdles entirely.

3. Why Conventional Forecasting Doesn't Work

Standard technological forecasting relies heavily on linear extrapolation—assuming that past progress dictates future velocity (similar to Moore’s Law). This approach breaks down when applied to post-quantum timelines for three main reasons:

• Non-Linear Interactions: Quantum engineering, quantum error correction (QEC), and artificial intelligence do not advance in isolation. They form tightly coupled feedback loops where progress in one domain exponentially accelerates another.

• Step-Function Discontinuities: Algorithmic breakthroughs do not occur linearly. A single mathematical discovery (such as compressing the resource requirements of Shor's algorithm or discovering higher-efficiency error correction codes) can reduce the required qubit footprint by orders of magnitude overnight.

• Asymmetric & Classified Capability: Major nation-states operate air-gapped, sovereign programs. The operational threshold will almost certainly be reached in secret before it is published in peer-reviewed literature or announced in a corporate press release.

4. Why We Selected a Scenario-Based Monte Carlo Model

When facing deep structural uncertainty across interconnected domains, deterministic timelines are misleading. We selected a scenario-based stochastic Monte Carlo architecture.

Rather than outputting a single target year, Monte Carlo models evaluate thousands or millions of plausible futures. Each iteration samples across parameter distributions:

1. Scenario Layer: Sets overarching structural trajectories (e.g., the pace of AI-driven scientific discovery).

2. Stochastic Layer: Varies underlying technical, engineering, and manufacturing parameters within those trajectories.

This dual approach captures both the broad macroeconomic/technological paths and the granular engineering probabilities that dictate real-world outcomes.

5. Model Architecture

The model continuously tracks interaction across five primary domains, updating dependent parameters dynamically throughout each simulated timeline:

• Quantum Physics & Engineering: Physical qubit scaling across multiple modalities (neutral atoms, superconducting, trapped ions, silicon spin), gate fidelities, logical qubit encoding ratios, error suppression, and cryogenic or optical control limits.

• Artificial Intelligence: AI performance in pulse calibration, automated error syndrome decoding, high-rate QEC discovery, autonomous code compilation, and mathematical research.

• Mathematics & Cryptanalysis: Quantum circuit depth compression, sub-quadratic polynomial factoring approaches, hybrid classical-quantum lattice reductions, and alternative integer factorization algorithms.

• Systems & Semiconductor Engineering: Control electronics bandwidth, precision laser availability, advanced packaging, wafer yield rates, and cryogenic manufacturing capacity.

• Strategic Environment: Sovereign black-budget allocations, state-sponsored talent acquisition, export controls, and sovereign on-premises deployment capabilities.

6. Scenario Design

Because artificial intelligence acts as a primary meta-accelerator across software, hardware, and physics, we anchored the macro-scenarios around the timeline for advanced AI and autonomous scientific discovery:

Scenario	Macro Assumption	Description
Conservative AGI (2027+)	Incremental AI Progress	AI assists in routine coding and optimization, but core scientific and mathematical breakthroughs remain human-led.
Expected AGI (2028)	Autonomous Research	AI models achieve autonomous research capabilities in physics and computer science, solving complex QEC and pulse-calibration problems.
Accelerated AGI (2026)	Near-Term Breakthrough	AI rapidly accelerates scientific discovery, discovering novel high-rate codes and optimizing quantum compilers ahead of standard schedules.

7. Simulation Process

For each iteration in the simulation suite ($N = 1,000,000$):

1. Initialize Trajectory: A macro AI scenario is selected based on prior distributions.

2. Sample Variables: Engineering limits, code efficiencies, and sovereign funding levels are sampled using Latin Hypercube Sampling across specified parameter ranges.

3. Execute Annual Timestep:

   • AI capability level modifies research velocity and QEC decoding efficiency.

   • Algorithmic footprint reductions decrease the target physical qubit requirement.

   • Hardware roadmaps progress based on manufacturing and control constraints.

   • Cross-domain feedback loops calculate compounding gains (e.g., better AI $\rightarrow$ better pulse calibration $\rightarrow$ lower error rates $\rightarrow$ fewer physical qubits required per logical qubit).

4. Evaluate Threshold: The simulation checks whether any modeled actor achieves the requisite gate fidelity, logical qubit count, and circuit depth to execute Shor's Algorithm against RSA-2048 in under 24 hours.

5. Record Date: The calendar year of first convergence is logged into the global distribution.

8. Results

Across the full scenario suite, the simulation converged on a concentrated timeline for practical operational capability.

Cumulative Probability of Operational RSA Compromise

Calendar Year	Cumulative Probability
2026	~5%
2027	~30%
2028	~65%
2029	~90%
2030	~95%
2031+	~100%

Key Planning Metrics

Metric	Result
Earliest Plausible Year (P5)	2026
Most Likely Year (Mode)	2028
Median (P50)	2028
Conservative Planning Target (P90)	2029
95% Confidence Interval	2026 to 2029

Scenario Breakdown

Scenario	Median (P50) Year	Probability of Risk by 2028
Conservative AGI Scenario	2029	~20%
Expected AGI Scenario	2028	~70%
Accelerated AGI Scenario	2027	~90%

9. What Actually Drove the Results

Analyzing the sensitivity of the simulation reveals that physical hardware scaling was not the dominant variable.

• AI-Accelerated Scientific Discovery: 28% Contribution

• Algorithmic Resource Compression: 22% Contribution

• High-Rate QEC Efficiency: 18% Contribution

• Autonomous Engineering & Control: 14% Contribution

• Sovereign Investment & Capability: 10% Contribution

• Hardware Scale (Qubit Count Alone): 8% Contribution

• AI-Accelerated Discovery (28% Elasticity): AI systems automating real-time error syndrome decoding and pulse control dramatically lowered hardware fidelity requirements.

• Algorithmic Resource Compression (22% Elasticity): Reductions in the circuit depth and physical qubit footprint needed for integer factorization (e.g., transitioning from millions of physical qubits down to tens of thousands via dynamic qubit architectures).

• High-Rate QEC Codes (18% Elasticity): Modern codes requiring physical-to-logical qubit ratios of 5:1 or 2:1 (e.g., LDPC or specialized neutral atom codes) compared to legacy 1000:1 surface codes.

10. Limitations

To maintain analytical integrity, the boundaries of this model must be clearly understood:

• No Claim of Public Availability: The model estimates when a single hostile actor (e.g., a classified sovereign program) achieves capability, not when commercial cloud quantum computing is broadly accessible.

• Model Dependency on Theoretical Algorithmic Bounds: The forecast assumes that recent mathematical reductions in Shor’s footprint hold up under real-world physical noise environments.

• Black-Swan Exclusions: The model does not account for catastrophic global geopolitical collapses or severe unmodeled supply-chain disruptions that freeze semiconductor fabrication globally.

11. Implications for Post-Quantum Cryptography (PQC) Planning

The results indicate that traditional multi-decade transition plans present severe organizational risk.

• "Harvest Now, Decrypt Later" (HNDL) is an Immediate Threat: Adversaries capturing encrypted traffic today will likely possess the operational capability to decrypt RSA-2048 payloads well before the end of the decade.

• Migration Timelines Exceed Risk Windows: Typical enterprise cryptographic migrations take 5 to 7 years. Organizations starting PQC migration in 2026 may complete deployment after the P50 median risk window has passed.

• Focus on Cryptographic Agility: Rather than attempting a single lift-and-shift replacement, enterprise architectures must prioritize agility—the capability to rapidly swap algorithms and hybrid keys without re-engineering core application infrastructure.

12. SITG-Consulting Conclusion

Focusing on commercial qubit counts yields a false sense of security. The convergence of artificial intelligence, optimized error correction, dynamic physical architectures, and algorithmic compression has significantly shortened the timeline to operational cryptographic compromise.

Navigating post-quantum security requires moving away from static assumptions and monitoring the multi-domain accelerators that truly dictate risk. Organizations must align their migration strategies not with marketing roadmaps, but with the non-linear realities of convergent technological evolution.