file.write('{} '.format(str(gate) + '(' + str(angle) + ')') +
str(j) + '\n')
else:
file.write('{} '.format(str(gate)) + str(j) + ' \n')
for e in evens:
file.write('CNOT {0} {1}\n'.format(*e))
for e in odds:
file.write('CNOT {0} {1}\n'.format(*e))
file.write("}")
file.close()
return open(file_name).read()
@profile
def execute(nq, rounds):
src = write_qasm(rounds, nq)
qpu = xacc.getAccelerator('tnqvm')
xacc.setOption('tnqvm-verbose', '')
f = xacc.compileKernel(qpu, src)
buf = qpu.createBuffer('q', nq)
qpu.execute(buf, f)
if __name__ == '__main__':
opts = parse_args(sys.argv[1:])
xacc.Initialize(['--compiler', 'quil'])
execute(opts.n, opts.r)
import pyxacc as xacc
src = """__qpu__ variable(AcceleratorBuffer ab, double ta, double tp, double tq, double h, double J) {
anneal ta tp tq;
0 0 h;
1 1 h;
0 1 J;
}"""
xacc.Initialize(['--compiler', 'dwave-qmi'])
qpu = xacc.getAccelerator('dwave')
qubits = qpu.createBuffer('qbits')
p = xacc.Program(qpu, src)
p.build()
variable = p.getKernel('variable')
for t in [20., 30.]:
variable.execute(qubits, [t, 0., 0., 2., 3.])
xacc.Finalize()
import pyxaccvqe as vqe
import pyxacc as xacc
import numpy as np
from stochopy import Evolutionary
from mpi4py import MPI
# Initialize MPI and XACC
mpi_comm = MPI.COMM_WORLD
mpi_rank = mpi_comm.Get_rank()
xacc.Initialize()
xacc.setOption('n-electrons', '2')
op = vqe.compile("""&FCI NORB= 2,NELEC= 2,MS2= 0,
ORBSYM=1,5,
ISYM=0,
/ i a j b
0.6744931033260081E+00 1 1 1 1
0.6634720448605567E+00 2 2 1 1
0.6973979494693358E+00 2 2 2 2
0.1812875358123322E+00 2 1 2 1
-0.1252477303982147E+01 1 1 0 0
-0.4759344611440753E+00 2 2 0 0
0.7137758743754461E+00 0 0 0 0
""")
# Define PSO objective function
def f(params):
# note 'no-mpi' at the compute energy, hamiltonian term
# level, but we are using mpi at the higher PSO swarm level
return vqe.execute(
def main(argv=None):
# Store results to this CSV file
file = open('projectq_out.csv', 'w')
file.write('theta, Z0_pq, Z0_tnqvm\n') #, Z1, Z0Z1\n')
# Initialize XACC
xacc.Initialize()
# Indicate that we want to use the ProjectQ QASM
# Compiler/Transpiler
xacc.setOption('compiler', 'projectq-qasm')
# Get reference to the TNQVM Accelerator
tnqvm = xacc.getAccelerator('tnqvm')
# Allocate and AcceleratorBuffer
xacc_qbits = tnqvm.createBuffer('qreg', 2)
# Loop over H2 state prep variational parameters
for theta in np.linspace(-np.pi, np.pi, 100):
# Create a ProjectQ Engine with our CommandPrinter
output = StringIO.StringIO()
eng = MainEngine(Simulator(), [XaccCommandPrinter(output)])
# Allocate some ProjectQ qubits
qreg = eng.allocate_qureg(2)
# Run Init State Circuit and Generate the Z0 QubitOperator
op = Z0Term(qreg, theta)
eng.flush()
# Get Expectation Value of Z0 operator
e_pq = eng.backend.get_expectation_value(op, qreg)
Measure | qreg[0]
# Get the ProjectQ QASM
qasm = output.getvalue()
# Generate an XACC Kernel
xaccKernel = getXACCKernel(qasm, tnqvm)
# Execute, no params since theta has
# already been input to Z0Term function
xaccKernel.execute(xacc_qbits, [])
# Get the expectation value
e_tnqvm = xacc_qbits.getExpectationValueZ()
# Reset the qubits for the next iteration
xacc_qbits.resetBuffer()
# Store the results to a CSV file
file.write(str(theta) + ', ' + str(e_pq) + ', ' + str(e_tnqvm) + '\n')
file.flush()
file.close()
# Finalize the framework
xacc.Finalize()