Expected probability before sampling.
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Hardware-constrained learning for quantum computing and artificial intelligence
Simulation studio
Set the state angle, choose a measurement basis, and run repeated shots to compare expected probabilities against sampled outcomes.
This lab helps learners distinguish pre-measurement amplitudes from post-measurement outcomes and build intuition for Born-rule sampling.
Subject context
Explore state geometry, superposition, interference, tunneling, entanglement, and measurement collapse through visual-first interactive labs.
Live lab
These academy-style labs are designed as compact, browser-playable teaching surfaces: enough interaction to make the core idea legible, without pretending to be a full research workbench.
Interactive academy lab
Repeated measurement turns a probability distribution into concrete counts; the more shots you take, the closer the histogram leans toward the Born-rule expectation.
Controls
Outputs
Expected probability before sampling.
Observed count of |0> outcomes.
Observed count of |1> outcomes.
Before measurement the state carries amplitudes. After measurement you only keep sampled outcomes, so repetition is what reveals the original distribution.
What this teaches
This lab helps learners distinguish pre-measurement amplitudes from post-measurement outcomes and build intuition for Born-rule sampling.
Set the state angle, choose a measurement basis, and run repeated shots to compare expected probabilities against sampled outcomes.
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