The Efficiency Revolution
Traditional methods for verifying quantum states, such as Bell state preparation, often require 80 to 300 evaluations. SoftQuantus QCOS has demonstrated the ability to achieve high-fidelity results in just 17 evaluations, representing an 80% reduction in operational overhead.
The Cost Challenge
Cloud-based quantum computing operates on a pay-per-use model where every circuit execution costs money. For enterprises exploring quantum solutions, the mathematics is straightforward:
Traditional Approach
200 evaluations × $0.50/eval
= $100 per optimization
QCOS Approach
17 evaluations × $0.50/eval
= $8.50 per optimization
At scale, this difference becomes transformative. An organization running 1,000 optimization cycles per month would see annual costs drop from $1.2M to $102K — enabling previously uneconomical quantum experiments.
Key Findings
Cost Reduction
Lowering the number of evaluations directly reduces the "bill shock" associated with cloud-based quantum execution, making quantum computing accessible for more use cases.
Green Quantum Initiatives
Reducing compute time aligns with European "Green Deal" mandates for sustainable and energy-efficient digital infrastructure.
Increased Throughput
Higher efficiency allows organizations to run more experiments in the same hardware window, accelerating the time-to-market for quantum-ready algorithms.
Predictive Optimization
QCOS uses noise-aware mapping to ensure that even with fewer samples, the probability of a successful result is maximized.
European Market Context
The European quantum computing market is projected to grow significantly through 2032, with a particular emphasis on sustainable computing practices. QCOS's sample efficiency directly supports two key European priorities:
Green Deal Alignment
Less compute time means lower energy consumption per quantum operation, supporting Europe's sustainability mandates.
Competitive Advantage
European enterprises can achieve quantum results at a fraction of the cost, accelerating time-to-value.
How QCOS Achieves This
The QCOS Autopilot uses several proprietary techniques to minimize hardware evaluations:
- •Noise-aware parameter initialization — Start closer to optimal solutions
- •Adaptive shot allocation — Use more measurements only when needed
- •Gradient-free optimization — Avoid costly gradient estimation loops
- •Early stopping criteria — Detect convergence before overshooting
Practical Takeaways
For CFOs
View sample efficiency as a direct lever for ROI, not just a technical benchmark. The 80% reduction in evaluations translates directly to 80% cost savings per experiment.
For Developers
Leverage QCOS to run complex circuits on NISQ hardware that would otherwise be too noisy or expensive. Efficiency unlocks new algorithmic possibilities.
References
- 1.Fortune Business Insights. (2025). Europe Quantum Computing Market Size & Analysis, 2032.
- 2.StartUs Insights. (2026). Quantum Computing Outlook 2026.
- 3.European Commission. (2025). Quantum Europe Strategy — Green Deal Alignment.
- 4.SoftQuantus. (2025). QCOS Autopilot Bell State Benchmark Report.