def test_gs_search_cpx(self):
L = 4
J = -1.0
hxs = [-1.3, -0.3]
exEs = [-6.10160339, -4.09296160]
for hx, exE in zip(hxs, exEs):
# Set up variational wave function
rbm = nets.CpxRBM.partial(numHidden=6, bias=False)
_, params = rbm.init_by_shape(random.PRNGKey(1), [(1, L)])
rbmModel = nn.Model(rbm, params)
psi = NQS(rbmModel)
# Set up hamiltonian for ground state search
hamiltonianGS = op.Operator()
for l in range(L):
hamiltonianGS.add(
op.scal_opstr(J, (op.Sz(l), op.Sz((l + 1) % L))))
hamiltonianGS.add(op.scal_opstr(hx, (op.Sx(l), )))
# Set up exact sampler
exactSampler = sampler.ExactSampler(L)
delta = 2
tdvpEquation = jVMC.tdvp.TDVP(exactSampler,
snrTol=1,
svdTol=1e-8,
rhsPrefactor=1.,
diagonalShift=delta,
makeReal='real')
# Perform ground state search to get initial state
ground_state_search(psi,
hamiltonianGS,
tdvpEquation,
exactSampler,
numSteps=100,
stepSize=2e-2)
obs = measure({"energy": hamiltonianGS}, psi, exactSampler)
self.assertTrue(
jnp.max(jnp.abs((obs['energy']['mean'] - exE) / exE)) < 1e-3)
def test_nonzeros(self):
L=4
lDim=2
key = random.PRNGKey(3)
s = random.randint(key, (24,L), 0, 2, dtype=np.int32).reshape(get_shape((-1, L)))
h=op.Operator()
h.add(op.scal_opstr(2., (op.Sp(0),)))
h.add(op.scal_opstr(2., (op.Sp(1),)))
h.add(op.scal_opstr(2., (op.Sp(2),)))
sp,matEl=h.get_s_primes(s)
logPsi=jnp.ones(s.shape[:-1])
logPsiSP=jnp.ones(sp.shape[:-1])
tmp = h.get_O_loc(logPsi,logPsiSP)
self.assertTrue( jnp.sum(jnp.abs( tmp - 2. * jnp.sum(-(s[...,:3]-1), axis=-1) )) < 1e-7 )
import jVMC
import jVMC.operator as op
import numpy as np
import jax
import jax.numpy as jnp
import time
L = 128
J = -1.0
hx = -0.3
numStates = 1000
timingReps = 10
hamiltonian = op.Operator()
for l in range(L):
hamiltonian.add(op.scal_opstr(J, (op.Sz(l), op.Sz((l + 1) % L))))
hamiltonian.add(op.scal_opstr(hx, (op.Sx(l), )))
states = jnp.array(jax.random.bernoulli(jax.random.PRNGKey(0),
shape=(jax.device_count(), numStates,
L)),
dtype=np.int32)
print("* Compute off-diagonal configurations")
t0 = time.perf_counter()
sp, me = hamiltonian.get_s_primes(states)
sp.block_until_ready()
t1 = time.perf_counter()
def test_time_evolution(self):
L = 4
J = -1.0
hx = -0.3
weights = jnp.array([
0.23898957, 0.12614753, 0.19479055, 0.17325271, 0.14619853,
0.21392751, 0.19648707, 0.17103704, -0.15457255, 0.10954413,
0.13228065, -0.14935214, -0.09963073, 0.17610707, 0.13386381,
-0.14836467
])
# Set up variational wave function
rbm = nets.CpxRBM.partial(numHidden=2, bias=False)
_, params = rbm.init_by_shape(random.PRNGKey(0), [(1, L)])
rbmModel = nn.Model(rbm, params)
psi = NQS(rbmModel)
psi.set_parameters(weights)
# Set up hamiltonian for time evolution
hamiltonian = op.Operator()
for l in range(L):
hamiltonian.add(op.scal_opstr(J, (op.Sz(l), op.Sz((l + 1) % L))))
hamiltonian.add(op.scal_opstr(hx, (op.Sx(l), )))
# Set up ZZ observable
ZZ = op.Operator()
for l in range(L):
ZZ.add((op.Sz(l), op.Sz((l + 1) % L)))
# Set up exact sampler
exactSampler = sampler.ExactSampler(L)
# Set up adaptive time stepper
stepper = jVMCstepper.AdaptiveHeun(timeStep=1e-3, tol=1e-5)
tdvpEquation = jVMC.tdvp.TDVP(exactSampler,
snrTol=1,
svdTol=1e-8,
rhsPrefactor=1.j,
diagonalShift=0.,
makeReal='imag')
t = 0
obs = []
times = []
times.append(t)
newMeas = measure({'E': hamiltonian, 'ZZ': ZZ}, psi, exactSampler)
obs.append([newMeas['E']['mean'], newMeas['ZZ']['mean']])
while t < 0.5:
dp, dt = stepper.step(0,
tdvpEquation,
psi.get_parameters(),
hamiltonian=hamiltonian,
psi=psi,
numSamples=0)
psi.set_parameters(dp)
t += dt
times.append(t)
newMeas = measure({'E': hamiltonian, 'ZZ': ZZ}, psi, exactSampler)
obs.append([newMeas['E']['mean'], newMeas['ZZ']['mean']])
obs = np.array(jnp.asarray(obs))
# Check energy conservation
obs[:, 0] = np.abs((obs[:, 0] - obs[0, 0]) / obs[0, 0])
self.assertTrue(np.max(obs[:, 0]) < 1e-3)
# Check observable dynamics
zz = interp1d(np.array(times), obs[:, 1, 0])
refTimes = np.arange(0, 0.5, 0.05)
netZZ = zz(refTimes)
refZZ = np.array([
0.882762129306284, 0.8936168721790617, 0.9257753299594491,
0.9779836185039352, 1.0482156449061142, 1.1337654450614298,
1.231369697427413, 1.337354107391303, 1.447796176316155,
1.558696104640795, 1.666147269524912, 1.7664978782554912,
1.8564960156892512, 1.9334113379450693, 1.9951280521882777,
2.0402054805651546, 2.067904337137255, 2.078178742959828,
2.071635856483114, 2.049466698269522, 2.049466698269522
])
self.assertTrue(np.max(np.abs(netZZ - refZZ[:len(netZZ)])) < 1e-3)
flush=True)
L = inp["system"]["L"]
# Initialize output manager
outp = OutputManager(wdir + inp["general"]["data_output"],
append=inp["general"]["append_data"])
# Set up variational wave function
psi = init_net(inp["network"], [(L, )])
outp.print("** Network properties")
outp.print(" Number of parameters: %d" % (len(psi.get_parameters())))
# Set up hamiltonian for ground state search
hamiltonianGS = op.Operator()
hz0 = 0.0
if "hz0" in inp["system"].keys():
hz0 = inp["system"]["hz0"]
for l in range(L):
hamiltonianGS.add(
op.scal_opstr(inp["system"]["J0"], (op.Sz(l), op.Sz((l + 1) % L))))
hamiltonianGS.add(op.scal_opstr(inp["system"]["hx0"], (op.Sx(l), )))
if np.abs(hz0) > 1e-10:
hamiltonianGS.add(op.scal_opstr(hz0, (op.Sz(l), )))
# Set up hamiltonian
hamiltonian = op.Operator()
lbda = 0.0
if "lambda" in inp["system"].keys():
lbda = inp["system"]["lambda"]
print("Creation of the directory %s failed" % wdir)
else:
print("Successfully created the directory %s " % wdir)
global_defs.set_pmap_devices(jax.devices()[mpi.rank % jax.device_count()])
print(" -> Rank %d working with device %s" % (mpi.rank, global_defs.devices()),
flush=True)
# L = inp["system"]["L"]
L = 2
# Initialize output manager
# outp = OutputManager(wdir+inp["general"]["data_output"], append=inp["general"]["append_data"])
# Set up hamiltonian for ground state search
hamiltonianGS = op.Operator()
hamiltonianGS.add((op.PzDiag(0), op.PzDiag(1)))
sampler = jVMC.sampler.ExactSampler(L, lDim=4)
print(sampler.get_basis().shape)
print(sampler.get_basis())
print()
configurations, matrix_element = hamiltonianGS.get_s_primes(
sampler.get_basis())
print(configurations)
print()
print(matrix_element)