"""Reproduce every v6 numerical figure and the raw model-results tables. All quantities are dimensionless examples; no hardware data are included. Run from any directory: python src/run_experiments.py [--quick] """ from pathlib import Path import argparse import json import platform import sys import numpy as np import scipy import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from scipy.stats import chi2 from risk_math import (ou_continuous_bounds, survival_fv, mean_exit_fv, mean_exit_time, laplace_exit, laplace_exit_fv, simulate_nested_sampled_exit, poisson_clearance) from calibration import diffusion_upper from quantum_checks import (cat_holonomy, gram_projector_check, cutoff_check, displacement_identity_check) ROOT=Path(__file__).resolve().parents[1] FIG=ROOT/'figures' RES=ROOT/'results' NAVY='#152642' TEAL='#00787f' GOLD='#bd7632' plt.rcParams.update({'font.family':'DejaVu Sans','font.size':10, 'axes.spines.top':False,'axes.spines.right':False, 'axes.labelcolor':NAVY,'text.color':NAVY, 'figure.facecolor':'white','savefig.facecolor':'white'}) def save(name): plt.tight_layout() plt.savefig(FIG/(name+'.pdf'),bbox_inches='tight') plt.savefig(FIG/(name+'.png'),dpi=180,bbox_inches='tight') plt.close() def write(name,data): (RES/name).write_text(json.dumps(data,indent=2,allow_nan=False)+'\n') def wilson(k,n): z=1.959963984540054 p=k/n denom=1+z*z/n center=(p+z*z/(2*n))/denom half=z*np.sqrt(p*(1-p)/n+z*z/(4*n*n))/denom return [float(center-half),float(center+half)] def reliability(): ts=np.geomspace(.05,12,70) cases=[] fig,axes=plt.subplots(1,2,figsize=(10,4)) for ax,d in zip(axes,(.08,.04)): fv=survival_fv(1,ts,1,d,cells=800) rows=[ou_continuous_bounds(1,float(t),1,d) for t in ts] estimate=fv['exit_probability'] mask=estimate>1e-10 ax.loglog(ts,[r['v5_direct_upper'] for r in rows],color=GOLD, label='v5 direct upper') ax.loglog(ts,[r['poisson_upper'] for r in rows],color=TEAL, label='positive-Poisson upper') ax.loglog(ts,[max(r['upper'],1e-16) for r in rows],color=NAVY, linestyle='--',label='best analytic upper') ax.loglog(ts[mask],estimate[mask],color='#a14065', label='survival PDE estimate') ax.set(title=f'R=1, kappa=1, d={d} (beta={1/(2*d):g})', xlabel='Operating time',ylabel='Continuous exit probability') ax.set_ylim(1e-9,1.3) ax.grid(alpha=.15,which='both') if d==.08: ax.legend(fontsize=8) for T in (.03,.2,2.,10.): b=ou_continuous_bounds(1,T,1,d) convergence=[] for cells in (100,200,400,800): p=survival_fv(1,[T],1,d,cells)['exit_probability'][0] mt=mean_exit_fv(1,1,d,cells) lt=laplace_exit_fv(.2,1,1,d,cells) convergence.append({'cells':cells,'exit_estimate':float(p) if np.isfinite(p) else None, 'exit_resolved':bool(np.isfinite(p)), 'mean_relative_error':abs(mt/b['mean_exit_time']-1), 'laplace_relative_error':abs(lt/laplace_exit(.2,1,1,d)-1)}) cases.append({'radius':1,'kappa':1,'diffusion':d,'T':T, 'bounds':b,'grid_convergence':convergence}) save('01_continuous_reliability') write('reliability_comparison.json',{'interpretation':'analytic bounds evaluated in float64; survival grids are estimates, not certified intervals','cases':cases}) def monitoring(quick): out=simulate_nested_sampled_exit(1,2,1,.08,.001, sample_strides=(1,5,10,50,100), trials=3000 if quick else 30000,seed=20261013) out['wilson_95']={s:wilson(k,out['trials']) for s,k in out['counts'].items()} out['continuous_pde_estimate']=float(survival_fv(1,[2],1,.08,800)['exit_probability'][0]) write('nested_monitoring.json',out) keys=['1','10','100'] vals=[out['rates'][s] for s in keys] intervals=np.array([out['wilson_95'][s] for s in keys]) err=np.vstack([np.array(vals)-intervals[:,0],intervals[:,1]-np.array(vals)]) plt.figure(figsize=(7,4)) plt.bar(['0.001','0.01','0.1'],vals,color=[TEAL,NAVY,GOLD],width=.6) plt.errorbar(np.arange(3),vals,yerr=err,fmt='none',ecolor=NAVY,capsize=4) plt.axhline(out['continuous_pde_estimate'],color='#a14065',linestyle='--', label='Continuous PDE estimate (800 cells)') plt.ylabel('Detected first-exit fraction') plt.xlabel('Observation interval on the same nested paths') plt.title(f'Sampling misses crossings / {out["trials"]:,} OU trajectories') plt.legend(fontsize=9) save('02_sampling_bias') def clearance(): times=np.geomspace(.1,20,65) new=np.array([poisson_clearance(float(t),1,.04,.001) for t in times]) old=np.sqrt(.08*(2*times+np.log(1000))) plt.figure(figsize=(7,4)) plt.semilogx(times,old,color=GOLD,label='v5 direct sufficient margin') plt.semilogx(times,new,color=TEAL,label='positive-Poisson sufficient margin') plt.xlabel('Operating time (kappa=1)') plt.ylabel('Required radius (d=0.04)') plt.title('One-event analytic risk budget: 0.001') plt.legend() plt.grid(alpha=.15) save('03_clearance_design') write('clearance_design.json',{'kappa':1,'diffusion':.04,'budget':.001, 'events':1,'times':times.tolist(), 'poisson_radius':new.tolist(),'v5_direct_radius':old.tolist(), 'T10_poisson':poisson_clearance(10,1,.04,.001), 'T10_v5_direct':float(np.sqrt(.08*(20+np.log(1000))))}) def graph_design(): rng=np.random.default_rng(62291) n,G,k,q0=8,4.,1.,2. edges=[(i,j) for i in range(n) for j in range(i+1,n)] incidence=np.zeros((len(edges),n)) for index,(i,j) in enumerate(edges): incidence[index,i]=1;incidence[index,j]=-1 def worst(gains): L=incidence.T@(gains[:,None]*incidence) Sigma=q0/2*np.linalg.inv(k*np.eye(n)+L) return float(np.max(np.diag(incidence@Sigma@incidence.T))) optimum=q0/(k+2*G/(n-1)) uniform=worst(np.full(len(edges),G/len(edges))) random_values=[worst(G*rng.dirichlet(np.ones(len(edges)))) for _ in range(1000)] assert abs(uniform-optimum)<1e-12 assert min(random_values)>=optimum-1e-12 plt.figure(figsize=(7,4)) plt.hist(random_values,bins=32,color=NAVY,alpha=.8,label='1,000 random budget allocations') plt.axvline(optimum,color=TEAL,linewidth=2,label='Proved uniform-complete optimum') plt.xlabel('Worst stationary pair variance (one coordinate)') plt.ylabel('Allocation count') plt.title('Graph stabilization at fixed gain budget / n=8, G=4') plt.legend(fontsize=8) save('04_graph_budget') write('graph_budget.json',{'seed':62291,'nodes':n,'gain_budget':G,'kappa':k, 'noise_covariance_q0':q0,'exact_optimum':optimum, 'uniform_value':uniform,'random_values':random_values, 'scope':'stationary variance in declared linear drift; no global braid action or hardware optimum claimed'}) def quantum(): out={'status':'algebra checks; ideal adiabatic branch, no laboratory or gap certification', 'cat_holonomy':[cat_holonomy(A) for A in (.25,.5,1,2,4,6,9)], 'coherent_gram_projector':gram_projector_check(), 'finite_cutoff':cutoff_check(),'shifted_root':displacement_identity_check()} write('quantum_checks.json',out) aa=np.geomspace(.05,10,200) phase=np.pi-2*np.pi*aa/np.sinh(2*aa) fig,ax=plt.subplots(1,2,figsize=(10,4)) ax[0].semilogx(aa,phase/np.pi,color=TEAL) ax[0].set(xlabel='A = |alpha| squared',ylabel='Relative holonomy phase / pi', title='Same root-exchange braid, different gate') ax[1].plot(aa,aa*np.tanh(aa),color=TEAL,label='Even cat mean photons') ax[1].plot(aa,aa/np.tanh(aa),color=GOLD,label='Odd cat mean photons') ax[1].set(xlabel='A = |alpha| squared',ylabel='Parity-jump rate / kappa_1', title='Larger separation also changes loss cost') ax[1].legend(fontsize=8) save('05_quantum_phase_resource') rows=out['finite_cutoff'] plt.figure(figsize=(7,4)) plt.semilogy([r['fock_dimension'] for r in rows], [r['analytic_boundary_residual'] for r in rows], 'o-',color=TEAL,label='Analytic truncated-state boundary residual') plt.semilogy([r['fock_dimension'] for r in rows], [r['normalized_coherent_jump_residual'] for r in rows], 's--',color=GOLD,label='Direct float64 residual (roundoff floor)') plt.xlabel('Fock cutoff dimension') plt.ylabel('Norm of (a_N squared - alpha squared) on truncated coherent state') plt.title('Near-dark convergence does not create an exact finite-cutoff kernel') plt.legend(fontsize=8) save('06_cutoff_diagnostic') def calibration(quick): rng=np.random.default_rng(63109) reps=500 if quick else 5000 n,k,h,d=40,1.7,.13,.24 scale=-np.expm1(-2*k*h)/k sample=rng.normal(0,np.sqrt(d*scale),(reps,n)) ss=np.sum(sample*sample,axis=1) upper=ss/(scale*chi2.ppf(.05,n)) covered=int(np.sum(d<=upper)) write('calibration_coverage.json',{'seed':63109,'repetitions':reps, 'scalar_innovations_per_repetition':n, 'kappa':k,'dt':h,'true_diffusion':d, 'confidence':.95,'covered':covered, 'empirical_coverage':covered/reps, 'wilson_95':wilson(covered,reps), 'assumptions':'known kappa and exact uncensored Gaussian state observations'}) def main(): parser=argparse.ArgumentParser() parser.add_argument('--quick',action='store_true',help='fewer Monte Carlo/calibration trials; overwrites result files') args=parser.parse_args() FIG.mkdir(exist_ok=True);RES.mkdir(exist_ok=True) reliability(); print('Continuous risk and PDE cross-checks complete',flush=True) monitoring(args.quick); print('Nested observation experiment complete',flush=True) clearance(); graph_design(); quantum();calibration(args.quick) write('runtime.json',{'python':sys.version,'platform':platform.platform(), 'numpy':np.__version__,'scipy':scipy.__version__, 'matplotlib':matplotlib.__version__,'quick':args.quick}) print('All six figures and result tables generated',flush=True) if __name__=='__main__':main()