Quantum Propagation is an algorithm that replaces the Born rule for measurements with an infinite non-deterministic series (random paths) that converges to same probabilities. The cover of random paths is a research topic in Non-Deterministic Path Semantics.
For instructions, see comments in the source.
Here I post screenshots with formulas of interesting quantum functions.
The quantum function is f
and the measurement is g
.
g := and
is a shorthand forg := \(a, b) = a && b
.g := eq
is a shorthand forg := \(a, b) = a == b
.
Each sample is a random path.
- 20 000 samples
f := [(0, 1), (1, 0), (-1, 0), (0, 1)]
g := and
- 20 000 samples
f := [(0, 1), (2, 0), (-2, 0), (0, 1)]
g := and
- 20 000 samples
f := [(0.53975284, 0.8347775), (-0.6443964, 0.28524667), (0.778826, 0.6626748), (0.84524584, 0.6032202)]
g := eq
- 20 000 samples
f := [(-0.69797146, -0.024551803), (-0.2906545, 0.67269856), (-0.99042594, -0.5189728), (-0.6754978, -0.5213077)]
g := and
- 20 000 samples
f := [(-0.5742652, -0.66125774), (0.5340726, -0.1768929), (-0.94883776, -0.21300188), (0.98182935, -0.90915376)]
g := eq
- 20 000 samples
f := [(-0.18238503, -0.70073056), (0.7934853, -0.77827376), (0.46978903, 0.9165352), (-0.07867549, -0.4303975)]
g := and
- 20 000 samples
f := [(0.8935131, 0.9591373), (-0.05023841, 0.4859118), (-0.89832544, -0.49549758), (-0.5120574, -0.7996857)]
g := eq
- 20 000 samples
f := [(0.87223727, -0.36542666), (0.52803975, 0.38853496), (0.65145344, -0.9732157), (0.123947814, -0.54745203)]
g := eq
- 20 000 samples
f := [(0.15241292, -0.085435204), (-0.8511916, 0.57789814), (-0.27446622, -0.437507), (-0.10607258, 0.14709546)]
g := eq
- 20 000 samples
f := [(-0.20913196, -0.8422289), (-0.995504, -0.16501884), (0.5780767, -0.83560455), (-0.42357144, -0.5135646)]
g := eq
- 20 000 samples
f := [(0.101158865, -0.30092537), (-0.03949814, -0.29077327), (-0.8659539, 0.23232798), (0.34448284, 0.96728605)]
g := eq
- 20 000 samples
f := [(-0.59925824, 0.08073656), (0.26387855, -0.9507098), (0.64888, 0.96241504), (0.9280084, -0.0011931247)]
g := and
- 20 000 samples
f := [(-0.91212034, -0.9685161), (-0.9454618, 0.95944816), (0.96339244, 0.24654308), (0.15042756, 0.11154972)]
g := and
- 20 000 samples
f := [(0.10193367, 0.03380558), (-0.9020964, 0.41431585), (-0.28806764, 0.15325238), (-0.3483436, -0.12292808)]
g := eq
- 20 000 samples
f := [(0.9315611, -0.82049036), (-0.37166578, -0.6874265), (-0.6722896, -0.7504237), (0.5782123, -0.24667512)]
g := eq