jarvis.io.qiskit.inputs

Module to solve Hermitian Matrix and predict badstructures.

Module Contents

Classes

HermitianSolver

Solve a Hermitian matrix using quantum algorithms.

Functions

decompose_Hamiltonian(H)

Decompose Hermitian matrix into Pauli basis.

get_bandstruct(w=[], atoms={}, ef=0, line_density=1, ylabel='eV', font=22, var_form=None, filename='bands.png', savefig=True, neigs=None, max_nk=None, tol=None, factor=1, verbose=False)

Compare bandstructures using quantum algos.

get_dos(w=[], grid=[2, 1, 1], proj=None, efermi=0.0, xrange=None, nenergy=100, sig=0.02, use_dask=True, filename='dos.png', savefig=True)

Get density of states.

jarvis.io.qiskit.inputs.decompose_Hamiltonian(H)[source]

Decompose Hermitian matrix into Pauli basis.

class jarvis.io.qiskit.inputs.HermitianSolver(mat=[], verbose=False)[source]

Bases: object

Solve a Hermitian matrix using quantum algorithms.

n_qubits(self)[source]

Get number of qubits required.

check_hermitian(self)[source]

Check if a matrix is Hermitian.

run_vqe(self, backend=Aer.get_backend('statevector_simulator'), var_form=None, optimizer=None, reps=None, mode='min_val')[source]

Run variational quantum eigensolver.

run_numpy(self)[source]

Obtain eigenvalues and vecs using Numpy solvers.

run_vqd(self, backend=Aer.get_backend('statevector_simulator'), var_form=None, optimizer=None, reps=2)[source]

Run variational quantum deflation.

jarvis.io.qiskit.inputs.get_bandstruct(w=[], atoms={}, ef=0, line_density=1, ylabel='eV', font=22, var_form=None, filename='bands.png', savefig=True, neigs=None, max_nk=None, tol=None, factor=1, verbose=False)[source]

Compare bandstructures using quantum algos.

jarvis.io.qiskit.inputs.get_dos(w=[], grid=[2, 1, 1], proj=None, efermi=0.0, xrange=None, nenergy=100, sig=0.02, use_dask=True, filename='dos.png', savefig=True)[source]

Get density of states.