s2 = exatn.Tensor("S2", exatn.TensorShape([2, 2]))
# Make a dictionary to feed to the tensor network
tensor_dict = {
z0.getName(): z0,
t0.getName(): t0,
t1.getName(): t1,
t2.getName(): t2,
h0.getName(): h0,
s0.getName(): s0,
s1.getName(): s1,
s2.getName(): s2
}
# Declare a tensor network:
network = exatn.TensorNetwork(
"{0,1} 3-site MPS closure", # network name
"Z0() = T0(a,b) * T1(b,c,d) * T2(d,e) * H0(a,c,f,g) * S0(f,h) * S1(h,g,i) * S2(i,e)", # network specification
tensor_dict)
network.printIt()
num_server = exatn.getNumServer()
# Create participating ExaTN tensors:
create_z0 = op_factory.createTensorOpShared(exatn.TensorOpCode.CREATE)
create_z0.setTensorOperand(z0)
num_server.submit(create_z0)
create_t0 = op_factory.createTensorOpShared(exatn.TensorOpCode.CREATE)
create_t0.setTensorOperand(t0)
num_server.submit(create_t0)
create_t1 = op_factory.createTensorOpShared(exatn.TensorOpCode.CREATE)
qzero = np.array([1.0, 0.0], dtype=complex)
hadamard = np.array([[1., 1.],[1., -1.]], dtype=complex)
cnot = np.reshape(np.array([[1, 0, 0, 0],[0, 1, 0, 0],[0, 0, 0, 1],[0, 0, 1, 0]], dtype=complex), (2,2,2,2))
exatn.createTensor('Q0', qzero)
exatn.createTensor('Q1', qzero)
exatn.createTensor('Q2', qzero)
exatn.createTensor('H', hadamard)
exatn.createTensor('CNOT', cnot)
exatn.registerTensorIsometry('H', [0], [1])
exatn.registerTensorIsometry('CNOT', [0,1], [2,3])
circuit = exatn.TensorNetwork('QuantumCircuit')
circuit.appendTensor(1, 'Q0')
circuit.appendTensor(2, 'Q1')
circuit.appendTensor(3, 'Q2')
circuit.appendTensorGate(4, 'H', [0])
circuit.appendTensorGate(5, 'H', [1])
circuit.appendTensorGate(6, 'H', [2])
circuit.appendTensorGate(7, 'CNOT', [1,2])
circuit.printIt()
inverse = exatn.TensorNetwork(circuit)
inverse.rename('InverseCircuit')
inverse.appendTensorGate(8, 'CNOT', [1,2], True)
import sys
from pathlib import Path
sys.path.insert(1, str(Path.home()) + '/.exatn')
import exatn
import numpy as np
# Demonstrate simple tensor network manipulation
exatn.createTensor('X', [2, 2], 0)
exatn.createTensor('Y', [2, 2], 0)
exatn.createTensor('Z', [2, 2], 0)
exatn.initTensorRnd('X')
exatn.initTensorRnd('Y')
exatn.initTensorRnd('Z')
tNet = exatn.TensorNetwork('test')
tNet.appendTensor(1, 'X')
tNet.appendTensor(2, 'Y')
tNet.appendTensor(3, 'Z')
# print tensor network
tNet.printIt()
tNetOriginal = exatn.TensorNetwork(tNet)
# Merge X and Y
pattern = tNet.mergeTensors(1, 2, 4)
print("After merge:")
tNet.printIt()
# Print the generic merge pattern
print(pattern)
# Create the merged tensor
pattern = pattern.replace("D", tNet.getTensor(4).getName())
import sys
from pathlib import Path
sys.path.insert(1, str(Path.home()) + '/.exatn')
import exatn, numpy as np
qzero = np.array([1.0, 0.0], dtype=complex)
unitary = np.reshape(np.array([1, -1j, -1j, 1], dtype=complex), (2, 2))
exatn.createTensor('Q0', qzero)
exatn.createTensor('U', unitary)
exatn.registerTensorIsometry('U', [0], [1])
circuit = exatn.TensorNetwork('QuantumCircuit')
circuit.appendTensor(1, 'Q0')
circuit.appendTensorGate(2, 'U', [0])
circuit.printIt()
conj_circuit = exatn.TensorNetwork(circuit)
conj_circuit.rename('ConjugatedCircuit')
conj_circuit.conjugate()
conj_circuit.printIt()
assert (exatn.evaluate(circuit))
assert (exatn.evaluate(conj_circuit))
print(exatn.getLocalTensor(circuit.getTensor(0).getName()))
print(exatn.getLocalTensor(conj_circuit.getTensor(0).getName()))
shape_rank = shape.getRank()
shape_dimext0 = shape.getDimExtent(0)
shape_dimext1 = shape.getDimExtent(1)
print("The example TensorShape rank is: " + str(shape_rank))
print("The example TensorShape DimExtent[0,1] are: [" + str(shape_dimext0) +
"," + str(shape_dimext1) + "]")
print("\n")
leg = exatn.TensorLeg(1, 4)
print("The tensor leg direction is: " + str(leg.getDirection()))
print("The tensor leg ID is: " + str(leg.getTensorId()))
print("\n\n")
#build an example tensor network
network = exatn.TensorNetwork("{0,1} 3-site MPS closure", exatn.Tensor("Z0"),
[])
network.appendTensor(
1, exatn.Tensor("T0", [2, 2]),
[exatn.TensorLeg(4, 0), exatn.TensorLeg(2, 0)])
network.appendTensor(
2, exatn.Tensor("T1", [2, 2, 2]),
[exatn.TensorLeg(1, 1),
exatn.TensorLeg(4, 1),
exatn.TensorLeg(3, 0)])
network.appendTensor(
3, exatn.Tensor("T2", [2, 2]),
[exatn.TensorLeg(2, 2), exatn.TensorLeg(7, 1)])
network.appendTensor(4, exatn.Tensor("H0", [2, 2, 2, 2]), [
exatn.TensorLeg(1, 0),
exatn.TensorLeg(2, 1),
h23 = exatn.getTensor('H23')
z0 = exatn.getTensor('Z0')
# Declare the Hamiltonian Operator
ham = exatn.TensorOperator('Hamiltonian')
ham.appendComponent(h01, [[0, 0], [1, 1]], [[0, 2], [1, 3]], 1.0)
ham.appendComponent(h12, [[1, 0], [2, 1]], [[1, 2], [2, 3]], 1.0)
ham.appendComponent(h23, [[2, 0], [3, 1]], [[2, 2], [3, 3]], 1.0)
# Declare the ket MPS tensor network:
# Q0----Q1----Q2----Q3
# | | | |
mps_ket = exatn.TensorNetwork(
'MPS', 'Z0(i0,i1,i2,i3)+=Q0(i0,j0)*Q1(j0,i1,j1)*Q2(j1,i2,j2)*Q3(j2,i3)', {
'Z0': z0,
'Q0': q0,
'Q1': q1,
'Q2': q2,
'Q3': q3
})
# Declare the ket tensor network expansion:
# Q0----Q1----Q2----Q3
# | | | |
ket = exatn.TensorExpansion()
ket.appendComponent(mps_ket, 1.0)
ket.rename('MPSket')
# Declare the bra tensor network expansion (conjugated ket):
# | | | |
# Q0----Q1----Q2----Q3
bra = exatn.TensorExpansion(ket)