Computational supplement

def transport_audit(): K = 3 law = {0:[F(1)],**n:[F(1,2),F(1,2)] for n in range(1,K+1)} for ell in range(K): entries = initial(localized(K,ell),K) pi = [F(0)]*K+[F(1)] d,target = K-ell,ell+1 for t in range(4): A,dk,ek = dense(entries) check("transport_iso_ A.reshape(-1,2).conj()[email protected](-1,2),I) if t: a = sum(pi[:K-d+1]) b = F(math.comb(t-1,d-1),2**t) if t>=d else F(0) for retain in (False,True): for i,j,E in units(): expected = float(F(3,4)*a)*E+(1-float(F(3,4)*a))*dep(E) \ if retain else float(F(3,4)*b)*E+ \ float(F(3,4)*(a-b))*(E+Z@E@Z)/2+ \ (1-float(F(3,4)*a))*dep(E) check("transport_channel_ transport_decode(channel(A)[i,j],dk,target,retain,t), expected) for lab in sorted(labels(x) for x in dk): DATA["occupancy"].append( transport_ledger(lab,target,retain,2)) if t<3: entries = advance(entries,t+1,law,{},lambda j,r:.75) pi = popstep(pi,law) entries = initial(localized(3),3) for j in (1,2): entries = advance(entries,j,law,{},lambda j,r:1.) A,dk,ek = dense(entries) d1,d2 = (((3,0),),()),((),((3,0),)) check("temporal_coherence", np.array(channel(A)[0,0][dk.index(d1),dk.index(d2)]), np.array(.25+0j))