Methodology

What we measure

The Quantum Stability Monitor tracks longitudinal stability of quantum computing platforms — not a cross-platform performance ranking. We run the same simple circuits on each platform every week and ask: how consistent are the results over time?

Each platform is analyzed only against its own prior runs. Drift, volatility, and predictability are the signals we care about.

The circuit family

We use a family of 24 circuits: 6 circuit depths (1–6 CNOT gates) × 4 input states (|00⟩, |01⟩, |10⟩, |11⟩).

Each circuit is an alternating CNOT sequence:

CNOT(0→1), CNOT(1→0), CNOT(0→1), ...

The reference (correct) output for each circuit is determined by classical simulation. These are deliberately simple — the goal is to act as a litmus test, not to stress the hardware.

Each weekly run

• 10 circuits sampled from the 24, stratified across depths and input states
• 100 shots per circuit
• Results compared against the reference output to compute success probability

Success probability

For each circuit execution:

success_probability = (shots matching reference output) / (total shots)

A perfect QPU would score 1.0 on every circuit. Real hardware scores lower due to gate errors, decoherence, readout errors, and crosstalk.

What the charts show

• Success over time: each weekly run produces one data point — the mean success probability across the 10 sampled circuits, with ±1σ band
• Consistency over time: within-run standard deviation per week — an upward trend indicates growing variability
• Distribution by circuit depth: box plots (median, IQR, outliers) show how fidelity degrades with circuit complexity
• Mean success by circuit depth: average success rate at each depth level
• Mean success by input state: ideally results should not depend on the input — deviations suggest state-preparation or readout asymmetry
• Temporal drift: per-circuit completion time vs. success probability — reveals whether hardware quality degrades within a run

Data

All raw results are stored as CSV files committed to the GitHub repository. One row per circuit execution, including the full shot histogram (counts_json), timestamps, and SDK version.

This project is maintained by Insight Softmax.