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mrossinekmrossinek
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fix: remove jupyter-execute blocks
The Jupyter Sphinx integration is currently unable to properly suppress warnings resulting in prints to stderr [1]. This causes the docs to fail building properly. Qiskit Terra already removed the usage of `jupyter-execute` statements a while ago [2], so we are following suit in this regard, too. [1]: jupyter/jupyter-sphinx#178 [2]: Qiskit/qiskit#9346
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‎qiskit_nature/second_q/hamiltonians/lattices/hyper_cubic_lattice.py

+2-2
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@@ -1,6 +1,6 @@
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# This code is part of Qiskit.
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#
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# (C) Copyright IBM 2021, 2022.
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# (C) Copyright IBM 2021, 2023.
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#
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# This code is licensed under the Apache License, Version 2.0. You may
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# obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -30,7 +30,7 @@ class HyperCubicLattice(Lattice):
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tuples of `size`, `edge_parameters`, and `boundary_conditions`.
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For example,
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.. jupyter-execute::
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.. code-block:: python
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from qiskit_nature.second_q.hamiltonians.lattices import (
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BoundaryCondition,

‎qiskit_nature/second_q/operators/fermionic_op.py

+8-8
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@@ -46,7 +46,7 @@ class FermionicOp(SparseLabelOp):
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A ``FermionicOp`` is initialized with a dictionary, mapping terms to their respective
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coefficients:
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.. jupyter-execute::
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.. code-block:: python
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from qiskit_nature.second_q.operators import FermionicOp
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@@ -62,7 +62,7 @@ class FermionicOp(SparseLabelOp):
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If you have very restricted memory resources available, or would like to avoid the additional
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copy, the dictionary will be stored by reference if you disable ``copy`` like so:
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.. jupyter-execute::
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.. code-block:: python
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some_big_data = {
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"+_0 -_0": 1.0,
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Addition
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.. jupyter-execute::
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.. code-block:: python
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FermionicOp({"+_1": 1}, num_spin_orbitals=2) + FermionicOp({"+_0": 1}, num_spin_orbitals=2)
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Sum
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.. jupyter-execute::
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.. code-block:: python
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sum(FermionicOp({label: 1}, num_spin_orbitals=3) for label in ["+_0", "-_1", "+_2 -_2"])
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Scalar multiplication
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.. jupyter-execute::
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.. code-block:: python
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0.5 * FermionicOp({"+_1": 1}, num_spin_orbitals=2)
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Operator multiplication
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.. jupyter-execute::
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.. code-block:: python
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op1 = FermionicOp({"+_0 -_1": 1}, num_spin_orbitals=2)
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op2 = FermionicOp({"-_0 +_0 +_1": 1}, num_spin_orbitals=2)
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print(op1 @ op2)
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Tensor multiplication
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.. jupyter-execute::
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.. code-block:: python
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op = FermionicOp({"+_0 -_1": 1}, num_spin_orbitals=2)
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print(op ^ op)
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Adjoint
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.. jupyter-execute::
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.. code-block:: python
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FermionicOp({"+_0 -_1": 1j}, num_spin_orbitals=2).adjoint()
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‎qiskit_nature/second_q/operators/polynomial_tensor.py

+7-7
Original file line numberDiff line numberDiff line change
@@ -79,7 +79,7 @@ class PolynomialTensor(LinearMixin, GroupMixin, TolerancesMixin, Mapping):
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made about the *contents* of the keys. However, the length of each key determines the dimension
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of the matrix which it maps, too. For example:
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.. code-block:: python
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import numpy as np
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@@ -96,7 +96,7 @@ class PolynomialTensor(LinearMixin, GroupMixin, TolerancesMixin, Mapping):
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axis of the matrix, when an operator gets built from the tensor. This means, that the previous
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example would expand for example like so:
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.. code-block:: python
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from qiskit_nature.second_q.operators import FermionicOp, PolynomialTensor
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@@ -113,28 +113,28 @@ class PolynomialTensor(LinearMixin, GroupMixin, TolerancesMixin, Mapping):
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Addition
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matrix = np.array([[0, 1], [2, 3]], dtype=float)
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0.5 * PolynomialTensor({"+-": matrix}) + PolynomialTensor({"+-": matrix})
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Operator multiplication
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.. code-block:: python
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tensor = PolynomialTensor({"+-": matrix})
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print(tensor @ tensor)
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Tensor multiplication
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.. code-block:: python
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print(tensor ^ tensor)
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You can also implement more advanced arithmetic via the :meth:`apply` and :meth:`einsum`
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methods.
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.. code-block:: python
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print(PolynomialTensor.apply(np.transpose, tensor))
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print(PolynomialTensor.apply(np.conjugate, 1j * tensor))
@@ -148,7 +148,7 @@ class PolynomialTensor(LinearMixin, GroupMixin, TolerancesMixin, Mapping):
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it needs to support more than 2-dimensional arrays, we rely on the
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`sparse <https://sparse.pydata.org/en/stable/index.html>`_ library.
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import sparse as sp
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‎qiskit_nature/second_q/operators/spin_op.py

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@@ -61,7 +61,7 @@ class SpinOp(SparseLabelOp):
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A ``SpinOp`` is initialized with a dictionary, mapping terms to their respective
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coefficients. For example:
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from qiskit_nature.second_q.operators import SpinOp
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@@ -72,7 +72,7 @@ class SpinOp(SparseLabelOp):
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are :math:`S^x, S^y, S^z` for spin 3/2 system.
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The two qutrit Heisenberg model with transverse magnetic field is
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SpinOp({
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"X_0 X_1": -1,
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An example using labels with powers would be:
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from qiskit_nature.second_q.operators import SpinOp
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@@ -99,7 +99,7 @@ class SpinOp(SparseLabelOp):
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If you have very restricted memory resources available, or would like to avoid the additional
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copy, the dictionary will be stored by reference if you disable ``copy`` like so:
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.. jupyter-execute::
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some_big_data = {
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"X_0 Y_0": 1.0,
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Addition
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SpinOp({"X_1": 1}, num_spins=2) + SpinOp({"X_0": 1}, num_spins=2)
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Sum
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sum(SpinOp({label: 1}, num_spins=3) for label in ["X_0", "Z_1", "X_2 Z_2"])
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Scalar multiplication
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0.5 * SpinOp({"X_1": 1}, num_spins=2)
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Operator multiplication
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op1 = SpinOp({"X_0 Z_1": 1}, num_spins=2)
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op2 = SpinOp({"Z_0 X_0 X_1": 1}, num_spins=2)
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print(op1 @ op2)
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Tensor multiplication
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op = SpinOp({"X_0 Z_1": 1}, num_spins=2)
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print(op ^ op)
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Adjoint
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SpinOp({"X_0 Z_1": 1j}, num_spins=2).adjoint()
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‎qiskit_nature/second_q/operators/vibrational_op.py

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@@ -49,7 +49,7 @@ class VibrationalOp(SparseLabelOp):
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A ``VibrationalOp`` is initialized with a dictionary, mapping terms to their respective
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coefficients:
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from qiskit_nature.second_q.operators import VibrationalOp
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@@ -67,7 +67,7 @@ class VibrationalOp(SparseLabelOp):
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If you have very restricted memory resources available, or would like to avoid the additional
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copy, the dictionary will be stored by reference if you disable ``copy`` like so:
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.. jupyter-execute::
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.. code-block:: python
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some_big_data = {
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"+_0_0 -_0_0": 1.0,
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If :code:`num_modals` is not provided then the maximum :code:`modal_index` per
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mode will determine the :code:`num_modals` for that mode.
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from qiskit_nature.second_q.operators import VibrationalOp
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Addition
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VibrationalOp({"+_1_0": 1}, num_modals=[2, 2]) + VibrationalOp({"+_0_0": 1}, num_modals=[2, 2])
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Sum
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sum(VibrationalOp({label: 1}, num_modals=[1, 1, 1]) for label in ["+_0_0", "-_1_0", "+_2_0 -_2_0"])
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Scalar multiplication
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0.5 * VibrationalOp({"+_1_0": 1}, num_modals=[1, 1])
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Operator multiplication
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op1 = VibrationalOp({"+_0_0 -_1_0": 1}, num_modals=[1, 1])
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op2 = VibrationalOp({"-_0_0 +_0_0 +_1_0": 1}, num_modals=[1, 1])
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print(op1 @ op2)
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Tensor multiplication
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op = VibrationalOp({"+_0_0 -_1_0": 1}, num_modals=[1, 1])
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print(op ^ op)
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Adjoint
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VibrationalOp({"+_0_0 -_1_0": 1j}, num_modals=[1, 1]).adjoint()
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