def find_spectrum(fname, adj=None):
''' '''
cells, spacing, zones, J, feedback = parse_qca_file(fname, one_zone=True)
h, J = qca_to_coef(cells, spacing, J, adj=adj)
h, J = normalized_coefs(h, J)
# get clocking schedule
s, eps, gamma = clock_schedule(scheme='linear')
spectrum = Spectrum()
spectrum.solve(h, J, eps, gamma, show=True, exact=False)
def find_spectrum(fname, adj=None):
''' '''
cells, spacing, zones, J, feedback = parse_qca_file(fname, one_zone=True)
h, J = qca_to_coef(cells, spacing, J, adj=adj)
h, J = normalized_coefs(h,J)
# get clocking schedule
s, eps, gamma = clock_schedule(scheme='linear')
spectrum = Spectrum()
spectrum.solve(h, J, eps, gamma, show=True, exact=False)
def main(fname, nsteps=100):
''' '''
print('\n\nRunning problem: {0}...'.format(os.path.splitext(fname)[0]))
base = os.path.splitext(fname)[0]
coef_file = base + '.txt'
json_file = base + '.json'
rt = int(os.path.basename(os.path.dirname(fname)))
_h, _J = load_coef_file(coef_file)
qbits, energies, spins, occ = load_json_file(json_file)
N = len(qbits)
h = [_h[k] for k in qbits]
qbit_map = {qb: i for i, qb in enumerate(qbits)}
J = np.zeros([N, N], dtype=float)
for i, x in _J.items():
i = qbit_map[i]
for j, v in x.items():
j = qbit_map[j]
J[i, j] = J[j, i] = v
s = np.linspace(0, 1, nsteps)
gammas, eps = query_schedule(sched_file, s)
# eps = np.linspace(0,1,nsteps)
# gammas = np.linspace(1,0,nsteps)
spec = Spectrum()
if N < EXACT_THRESH:
spec.solve(h, J, eps, gammas, show=SHOW, exact=True)
else:
spec.solve(h, J, eps, gammas, show=SHOW)
if True:
params = spec.ground_check(occ, show=SHOW)
else:
spec.build_causal(show=True)
params = spec.pull_causal(occ, energies)
append_data(params, rt, base)
def find_true_spectrum(h, J, gmin=0, gmax=1., emin=0., emax=1., nsteps=100):
''' '''
gmin = 1e-5
gammas = np.linspace(gmax, gmin, nsteps) # tranverse field scales
eps = np.linspace(emin, emax, nsteps) # ising problem scales
s = np.linspace(0, 1, nsteps) # annealing schedule parameter
if len(h) > EXACT_THRESH:
return
true_spectrum = Spectrum()
true_spectrum.solve(h, J, eps, gammas, exact=True, show=False)
true_spec = true_spectrum.spectrum
true_spec -= true_spec[:, 0].reshape([-1, 1])
plt.plot(s, true_spec, 'x')
plt.xlabel('Annealing parameter (s)', fontsize=FS)
plt.ylabel('Energy', fontsize=FS)
plt.show()
def find_true_spectrum(h, J, gmin=0, gmax=1., emin=0., emax=1., nsteps=100):
''' '''
gmin = 1e-5
gammas = np.linspace(gmax, gmin, nsteps) # tranverse field scales
eps = np.linspace(emin, emax, nsteps) # ising problem scales
s = np.linspace(0, 1, nsteps) # annealing schedule parameter
if len(h) > EXACT_THRESH:
return
true_spectrum = Spectrum()
true_spectrum.solve(h, J, eps, gammas, exact=True, show=False)
true_spec = true_spectrum.spectrum
true_spec -= true_spec[:,0].reshape([-1,1])
plt.plot(s, true_spec, 'x')
plt.xlabel('Annealing parameter (s)', fontsize=FS)
plt.ylabel('Energy', fontsize=FS)
plt.show()
def main(fname, nsteps=100):
''' '''
base = os.path.splitext(fname)[0]
coef_file = base + '.txt'
json_file = base + '.json'
rt = int(os.path.basename(os.path.dirname(fname)))
_h, _J = load_coef_file(coef_file)
qbits, energies, spins, occ = load_json_file(json_file)
N = len(qbits)
h = [_h[k] for k in qbits]
qbit_map = {qb: i for i, qb in enumerate(qbits)}
J = np.zeros([N, N], dtype=float)
for i, x in _J.items():
i = qbit_map[i]
for j, v in x.items():
j = qbit_map[j]
J[i, j] = J[j, i] = v
s = np.linspace(0, 1, nsteps)
gammas, eps = query_schedule(sched_file, s)
spec = Spectrum()
spec.solve(h, J, eps, gammas, show=True, exact=True)
spec.build_causal(show=False)
spec.compute_eqs()
pr = spec.predict_probs(rt)
Es = [eps[-1] * e for e in energies]
e_probs = spec.format_probs(occ, Es)
for e in pr:
print('{0:.4f} :: {1:.4f}:{2:.4f}'.format(e, e_probs[e], pr[e]))
def main(fname, nsteps=100):
''' '''
print('\n\nRunning problem: {0}...'.format(os.path.splitext(fname)[0]))
base = os.path.splitext(fname)[0]
coef_file = base+'.txt'
json_file = base+'.json'
rt = int(os.path.basename(os.path.dirname(fname)))
_h, _J = load_coef_file(coef_file)
qbits, energies, spins, occ = load_json_file(json_file)
N = len(qbits)
h = [_h[k] for k in qbits]
qbit_map = {qb:i for i,qb in enumerate(qbits)}
J = np.zeros([N,N], dtype=float)
for i, x in _J.items():
i = qbit_map[i]
for j, v in x.items():
j = qbit_map[j]
J[i,j] = J[j,i] = v
s = np.linspace(0,1,nsteps)
gammas, eps = query_schedule(sched_file, s)
# eps = np.linspace(0,1,nsteps)
# gammas = np.linspace(1,0,nsteps)
spec = Spectrum()
if N < EXACT_THRESH:
spec.solve(h, J, eps, gammas, show=SHOW, exact=True)
else:
spec.solve(h, J, eps, gammas, show=SHOW)
if True:
params = spec.ground_check(occ, show=SHOW)
else:
spec.build_causal(show=True)
params = spec.pull_causal(occ, energies)
append_data(params, rt, base)
def main(fname, nsteps=100):
''' '''
base = os.path.splitext(fname)[0]
coef_file = base+'.txt'
json_file = base+'.json'
rt = int(os.path.basename(os.path.dirname(fname)))
_h, _J = load_coef_file(coef_file)
qbits, energies, spins, occ = load_json_file(json_file)
N = len(qbits)
h = [_h[k] for k in qbits]
qbit_map = {qb:i for i,qb in enumerate(qbits)}
J = np.zeros([N,N], dtype=float)
for i, x in _J.items():
i = qbit_map[i]
for j, v in x.items():
j = qbit_map[j]
J[i,j] = J[j,i] = v
s = np.linspace(0,1,nsteps)
gammas, eps = query_schedule(sched_file, s)
spec = Spectrum()
spec.solve(h, J, eps, gammas, show=True, exact=True)
spec.build_causal(show=False)
spec.compute_eqs()
pr = spec.predict_probs(rt)
Es = [eps[-1]*e for e in energies]
e_probs = spec.format_probs(occ, Es)
for e in pr:
print('{0:.4f} :: {1:.4f}:{2:.4f}'.format(e, e_probs[e], pr[e]))
def estimate_spectrum(h, J, gmin=0., gmax=1., emin=0., emax=1., nsteps=10):
''' '''
gmin = 1e-5
emin = 1e-5
gammas = np.linspace(gmax, gmin, nsteps) # tranverse field scales
eps = np.linspace(emin, emax, nsteps) # ising problem scales
s = np.linspace(0, 1, nsteps) # annealing schedule parameter
gammas, eps = query_schedule(sched_file, s)
# show recursive tree
if False:
tree = compute_rp_tree(J)
show_tree(J, tree)
# check if solvable using exact methods
EXACT_check = EXACT and len(h) < EXACT_THRESH
approx_spectrum = Spectrum()
rp_spectrum = Spectrum()
true_spectrum = Spectrum()
# find approximate spectrum
print('\nFinding approximate spectrum...')
approx_spectrum.solve(h, J, eps, gammas, gset=0.5, rp_steps=10, show=False)
approx_spec = approx_spectrum.spectrum
if FULL_RP:
# find more accurate approximate spectrum
print('\nFinding more accurate spectrum...')
rp_spectrum.solve(h, J, eps, gammas, gset=2.0, rp_steps=20, show=False)
rp_spec = rp_spectrum.spectrum
if EXACT_check:
# find exact solution
print('\nFinding true spectrum...')
true_spectrum.solve(h, J, eps, gammas, exact=True, show=False)
true_spec = true_spectrum.spectrum
if ZERO:
approx_spec -= approx_spec[:, 0].reshape([-1, 1])
if FULL_RP:
rp_spec -= rp_spec[:, 0].reshape([-1, 1])
if EXACT_check:
true_spec -= true_spec[:, 0].reshape([-1, 1])
N = approx_spec.shape[1]
if FULL_RP:
N = min(N, rp_spec.shape[1])
if EXACT_check:
N = min(N, true_spec.shape[1])
N -= 5
# plotting
plt.figure('Spectrum')
plt.plot(s, approx_spec[:, :N], 'x', markersize=5)
if FULL_RP:
plt.plot(s, rp_spec[:, :N], 's')
if EXACT_check:
plt.plot(s, true_spec[:, :N], '-')
plt.xlabel('Annealing Parameter (s)', fontsize=FS)
plt.ylabel('$E$-$E_0$ (GHz)', fontsize=20)
plt.ylim([0, 15])
if SAVE:
plt.savefig(os.path.join(IMG_DIR, 'test_spectrum.eps'),
bbox_inches='tight')
plt.show()
xl, xr = int(.3 * nsteps), int(.47 * nsteps)
plt.figure('Zoom')
plt.plot(s[xl:xr], approx_spec[xl:xr, :N], 'x', markersize=5)
if FULL_RP:
plt.plot(s[xl:xr], rp_spec[xl:xr, :N], 's')
if EXACT_check:
plt.plot(s[xl:xr], true_spec[xl:xr, :N], '-')
plt.xlabel('Annealing Parameters (s)', fontsize=FS)
plt.ylabel('$E$-$E_0$ (GHz)', fontsize=20)
plt.xlim([s[xl], s[xr]])
plt.ylim([1, 4.5])
if SAVE:
plt.savefig(os.path.join(IMG_DIR, 'test_zoom.eps'),
bbox_inches='tight')
plt.show()
if False:
# compare_spectra(approx_spectrum, rp_spectrum)
if EXACT_check:
compare_spectra(rp_spectrum, true_spectrum)
elif FULL_RP:
compare_spectra(approx_spectrum, true_spectrum)
return
locate_crossings(s, spec)
def estimate_spectrum(h, J, gmin=0., gmax=1., emin=0., emax=1., nsteps=10):
''' '''
gmin = 1e-5
emin = 1e-5
gammas = np.linspace(gmax, gmin, nsteps) # tranverse field scales
eps = np.linspace(emin, emax, nsteps) # ising problem scales
s = np.linspace(0, 1, nsteps) # annealing schedule parameter
gammas, eps = query_schedule(sched_file, s)
# show recursive tree
if False:
tree = compute_rp_tree(J)
show_tree(J, tree)
# check if solvable using exact methods
EXACT_check = EXACT and len(h) < EXACT_THRESH
approx_spectrum = Spectrum()
rp_spectrum = Spectrum()
true_spectrum = Spectrum()
# find approximate spectrum
print('\nFinding approximate spectrum...')
approx_spectrum.solve(h, J, eps, gammas, gset=0.5, rp_steps=10, show=False)
approx_spec = approx_spectrum.spectrum
if FULL_RP:
# find more accurate approximate spectrum
print('\nFinding more accurate spectrum...')
rp_spectrum.solve(h, J, eps, gammas, gset=2.0, rp_steps=20, show=False)
rp_spec = rp_spectrum.spectrum
if EXACT_check:
# find exact solution
print('\nFinding true spectrum...')
true_spectrum.solve(h, J, eps, gammas, exact=True, show=False)
true_spec = true_spectrum.spectrum
if ZERO:
approx_spec -= approx_spec[:,0].reshape([-1,1])
if FULL_RP:
rp_spec -= rp_spec[:,0].reshape([-1,1])
if EXACT_check:
true_spec -= true_spec[:,0].reshape([-1,1])
N = approx_spec.shape[1]
if FULL_RP:
N = min(N, rp_spec.shape[1])
if EXACT_check:
N = min(N, true_spec.shape[1])
N -= 5
# plotting
plt.figure('Spectrum')
plt.plot(s, approx_spec[:,:N], 'x', markersize=5)
if FULL_RP:
plt.plot(s, rp_spec[:,:N], 's')
if EXACT_check:
plt.plot(s, true_spec[:,:N], '-')
plt.xlabel('Annealing Parameter (s)', fontsize=FS)
plt.ylabel('$E$-$E_0$ (GHz)', fontsize=20)
plt.ylim([0,15])
if SAVE:
plt.savefig(os.path.join(IMG_DIR, 'test_spectrum.eps'), bbox_inches='tight')
plt.show()
xl, xr = int(.3*nsteps), int(.47*nsteps)
plt.figure('Zoom')
plt.plot(s[xl:xr], approx_spec[xl:xr,:N], 'x', markersize=5)
if FULL_RP:
plt.plot(s[xl:xr], rp_spec[xl:xr,:N], 's')
if EXACT_check:
plt.plot(s[xl:xr], true_spec[xl:xr,:N], '-')
plt.xlabel('Annealing Parameters (s)', fontsize=FS)
plt.ylabel('$E$-$E_0$ (GHz)', fontsize=20)
plt.xlim([s[xl],s[xr]])
plt.ylim([1, 4.5])
if SAVE:
plt.savefig(os.path.join(IMG_DIR, 'test_zoom.eps'), bbox_inches='tight')
plt.show()
if False:
# compare_spectra(approx_spectrum, rp_spectrum)
if EXACT_check:
compare_spectra(rp_spectrum, true_spectrum)
elif FULL_RP:
compare_spectra(approx_spectrum, true_spectrum)
return
locate_crossings(s, spec)
