aequira labs /
Quantum experiments and computational models
for interactive education.
Quantum measurement
Recorded outcomes determine the particle distribution. The field’s geometry and motion are illustrative.
Quantum measurement
Method
We compare recorded quantum-processor outcomes with an ideal interaction model. The interface isolates outcome classes and compares recordings obtained at different interaction settings.
Observations
The shared-state recording contains both retained and transferred outcomes. Residual outcomes indicate departures from the ideal prediction.
Educational objective
Use experimental control and repeated measurement to teach quantum probability. Future exhibits would let learners predict the effects of an interaction, compare their predictions with processor results, and investigate departures from an ideal model.
Research context
Particle assignments use saved IBM Quantum results from Living Worlds. Experiment selection changes the recording; rotation changes only the view. These experiments do not evaluate quantum advantage.
Scientific instruments as learning environments.
Quantum experiments in public spaces
Our objective is to connect quantum processors to immersive exhibits in museums and learning spaces. Visitors would choose experimental conditions, make predictions, and examine measured outcomes through a shared visual environment. Living Worlds is the proposed platform for this work. Recorded IBM Quantum experiments provide the current basis for its measurement studies.
From computation to sensing
The longer-term programme includes quantum sensing of magnetic signals from living systems. Proposed exhibits would connect those measurements to models of biological activity, allowing learners to examine how a signal supports an inference. The neural study develops the interaction model; biological sensing remains a separate research objective.
Learning through experimental reasoning
The educational aim is to make advanced science open to direct investigation. Learners would revise an explanation when a measurement disagrees with a prediction, distinguish uncertainty from error, and compare competing models. Planned evaluation would test whether these experiences improve reasoning about unfamiliar experiments. Educational outcomes have not yet been evaluated.