from abc import abstractmethod
from qibo.config import raise_error
[docs]class AbstractHamiltonian:
"""Qibo abstraction for Hamiltonian objects."""
def __init__(self):
self._nqubits = None
@property
def nqubits(self):
return self._nqubits
@nqubits.setter
def nqubits(self, n):
if not isinstance(n, int):
raise_error(RuntimeError, f"nqubits must be an integer but is {type(n)}.")
if n < 1:
raise_error(ValueError, f"nqubits must be a positive integer but is {n}")
self._nqubits = n
[docs] @abstractmethod
def eigenvalues(self, k=6): # pragma: no cover
"""Computes the eigenvalues for the Hamiltonian.
Args:
k (int): Number of eigenvalues to calculate if the Hamiltonian
was created using a sparse matrix. This argument is ignored
if the Hamiltonian was created using a dense matrix.
See :meth:`qibo.backends.abstract.AbstractBackend.eigvalsh` for
more details.
"""
raise_error(NotImplementedError)
[docs] @abstractmethod
def eigenvectors(self, k=6): # pragma: no cover
"""Computes a tensor with the eigenvectors for the Hamiltonian.
Args:
k (int): Number of eigenvalues to calculate if the Hamiltonian
was created using a sparse matrix. This argument is ignored
if the Hamiltonian was created using a dense matrix.
See :meth:`qibo.backends.abstract.AbstractBackend.eigh` for
more details.
"""
raise_error(NotImplementedError)
[docs] def ground_state(self):
"""Computes the ground state of the Hamiltonian.
Uses :meth:`qibo.hamiltonians.AbstractHamiltonian.eigenvectors`
and returns eigenvector corresponding to the lowest energy.
"""
return self.eigenvectors()[:, 0]
[docs] @abstractmethod
def exp(self, a): # pragma: no cover
"""Computes a tensor corresponding to exp(-1j * a * H).
Args:
a (complex): Complex number to multiply Hamiltonian before
exponentiation.
"""
raise_error(NotImplementedError)
[docs] @abstractmethod
def expectation(self, state, normalize=False): # pragma: no cover
"""Computes the real expectation value for a given state.
Args:
state (array): the expectation state.
normalize (bool): If ``True`` the expectation value is divided
with the state's norm squared.
Returns:
Real number corresponding to the expectation value.
"""
raise_error(NotImplementedError)
[docs] @abstractmethod
def expectation_from_samples(self, freq, qubit_map=None): # pragma: no cover
"""Computes the real expectation value of a diagonal observable given the frequencies when measuring in the computational basis.
Args:
freq (collections.Counter): the keys are the observed values in binary form
and the values the corresponding frequencies, that is the number
of times each measured value/bitstring appears.
qubit_map (tuple): Mapping between frequencies and qubits. If None, [1,...,len(key)]
Returns:
Real number corresponding to the expectation value.
"""
raise_error(NotImplementedError)
@abstractmethod
def __add__(self, o): # pragma: no cover
"""Add operator."""
raise_error(NotImplementedError)
def __radd__(self, o):
"""Right operator addition."""
return self.__add__(o)
@abstractmethod
def __sub__(self, o): # pragma: no cover
"""Subtraction operator."""
raise_error(NotImplementedError)
@abstractmethod
def __rsub__(self, o): # pragma: no cover
"""Right subtraction operator."""
raise_error(NotImplementedError)
@abstractmethod
def __mul__(self, o): # pragma: no cover
"""Multiplication to scalar operator."""
raise_error(NotImplementedError)
def __rmul__(self, o):
"""Right scalar multiplication."""
return self.__mul__(o)
@abstractmethod
def __matmul__(self, o): # pragma: no cover
"""Matrix multiplication with other Hamiltonians or state vectors."""
raise_error(NotImplementedError)