User guide#
Quantum Mind is a library for quantum robotics with a module for each of its three threads, each next to the classical model it has to beat, plus a Bloch-sphere viewer.
flowchart TB
core["quantum_mind.core<br/>Model · Param · fit · compare · recovery"]
fam["quantum_mind.families<br/>4 quantum-like families"]
bel["quantum_mind.belief<br/>COBRA belief filter"]
cmp["quantum_mind.computing<br/>robot QUBOs · QAOA adapter"]
sen["quantum_mind.sensing<br/>sensor models · Kalman fusion"]
app["quantum_mind.applications<br/>robot decision layer"]
circ["quantum_mind.circuits<br/>circuits + run()"]
sim["quantum_mind.quantum<br/>state-vector simulator"]
viz["quantum_mind.viz<br/>Bloch-sphere viewer"]
hw[("Aer · IBM Quantum · Amazon Braket")]
core --> fam --> app
core --> bel --> app
cmp --> app
sen --> app
fam --> circ --> hw
sim --> viz
circ --> viz
fam --> viz
What separates quantum-like models from quantum computing, and how to test each.
Order effects, quantum-like Bayesian networks, trust and belief dynamics, and bistable perception.
Robot problems as QUBOs, classical baselines and a Qiskit QAOA adapter.
Quantum limits, accelerometer models and a multi-rate Kalman filter.
A density-matrix belief whose measurement basis is rotated by the previous sensor frame.
Questioning designs, trust, intent resolution, a human-model ensemble and a ROS 2 node.
Trajectories, animations, interactive HTML, live updates and tomography.
The exact state-vector simulator and the circuits of the quantum-like families.
Status and scope#
Area |
What exists |
What does not (yet) |
|---|---|---|
Quantum-like models |
4 families with classical baselines, fitting, BIC/AIC, recovery studies, per-person fitting and comparison, partial pooling; published aggregate data |
full hierarchical Bayesian inference (only MAP partial pooling) |
Quantum computing for robots |
three robot problems as QUBOs, classical baselines, a Qiskit QAOA adapter, solver comparison, benchmark |
runs on quantum hardware; problems too large to enumerate |
Quantum sensing for robots |
quantum limits, MEMS and atom-interferometer models, multi-rate Kalman filter, sensor-suite study |
measured sensor data; hardware |
COBRA belief filter |
context-rotated density-matrix filter, online mode, Bayes and incoherent limits, benchmark with paired tests |
real-person data; reliable training (the context mechanism is found on some restarts only) |
Robotics |
simulated populations for three question domains, questioning designs, trust protocol, intent resolution, |
a run of the ROS 2 node inside a ROS 2 installation; data from human-robot studies |
Simulator and circuits |
exact state-vector simulator with adjoint gradients; Qiskit export; circuits for the families; IBM submission through the Qiskit Runtime Sampler |
noise-aware training; runs on hardware |
Viewer |
trajectories, animations, interactive HTML, live widget, tomography, concurrence timeline, Q-sphere |
Limitations#
Fitting aggregate counts assumes a homogeneous population. Individual differences can hide or mimic quantum-like structure (example 14).
Several quantum-like models have been challenged by further tests; the family pages list these.
COBRA is a classical simulation of the quantum-probability formalism. No quantum advantage is claimed, and its advantage over classical filters is shown only on synthetic data built to contain a context effect.
Hardware noise distorts circuits by a few percent of total variation for small circuits, and more for deep ones. No hardware results are included.