Quantum VenTuring
Technical case studies
Interactive visuals that explain what’s real and what’s hard across sensing, quantum networking, and computing.
Sensing SNR explorer
How measurement time and bandwidth trade off in a quantum sensor. Model: SNR ≈ (Signal/NoiseDensity) × √(T / BW). Adjust assumptions to see regimes.
Interpretation: higher SNR means clearer detection. You can raise SNR by increasing signal, reducing noise density, widening integration time, or narrowing bandwidth — but bandwidth reduces temporal resolution.
Use it to ask better vendor questions
- What is the true noise density (nT/√Hz) in-field?
- How does SNR degrade vs. bandwidth and temperature?
- What calibration and drift terms dominate at long T?
Quantum networking: QKD link budget
Secret key rate vs distance using a simple DV‑QKD model. Channel loss ηch = 10^{−αL/10}. Click prob p = μ ηdet ηch + p_dark. QBER from dark counts. Rate ≈ sifted × secret fraction.
Interpretation: stronger detectors and lower loss push out the distance where key rate collapses; dark counts raise QBER and kill the secret fraction.
Use it to ask better vendor questions
- What’s the total budget for splice/connector losses?
- What QBER do you measure at target distance?
- How stable are detectors and timing jitter at f?
Quantum computing: QEC viability map
Surface‑code style back‑of‑envelope. Logical error per cycle: pL ≈ A (p / p_th)^{(d+1)/2} with p_th ≈ 1%, A ≈ 0.1. Overhead ≈ N_phys ≈ 2 d² per logical qubit. See feasibility vs physical error and code distance.
Interpretation: moving left/down (lower physical error, higher code distance) reduces logical error but increases overhead. The sweet spot is where pL beats the target at acceptable overhead.
Use it to ask better vendor questions
- What calibrated 2Q error and cycle time do you achieve?
- What code distance can you sustain with routing constraints?
- What is the physical‑to‑logical overhead in your roadmap?