# Updated Operators¶

In version 0.36 of PennyLane we changed some things behind the scenes on how operators and arithmetic operations between them are handled. This realizes a few objectives:

1. To make it as easy to work with PennyLane operators as it would be with pen and paper.

2. To improve the efficiency of operator arithmetic.

In many cases, these changes should not break code and the difference to previous versions may not be noticeable.

Note

If you are looking for a quick fix, jump to the Troubleshooting section!

After visiting the Troubleshooting section, if you are still stuck then you can:

• Post on the PennyLane discussion forum.

• If you suspect that your issue is due to a bug in PennyLane itself, please open a bug report on the PennyLane GitHub page.

## Summary of the update¶

The opt-in feature qml.operation.enable_new_opmath() is now the default. Ideally, your code should not break. If it still does, it likely only requires some minor changes. For that, see the Troubleshooting section. You can still opt-out and run legacy code via qml.operation.disable_new_opmath().

• The underlying system for performing arithmetic with operators has been changed. Arithmetic can be carried out using standard Python operations like +, * and @ or via arithmetic functions located in op_math.

• You can now easily access Pauli operators via I, X, Y, and Z.

>>> from pennylane import I, X, Y, Z
>>> X(0)
X(0)


The original long-form names Identity, PauliX, PauliY, and PauliZ remain available and are functionally equivalent to I, X, Y, and Z, but use of the short-form names is now recommended.

• Operators in PennyLane can have a backend Pauli representation, which can be used to perform faster operator arithmetic. Now, the Pauli representation will be automatically used for calculations when available. You can access the pauli_rep attribute of any operator whenever it is available.

>>> op = X(0) + Y(0)
>>> op.pauli_rep
1.0 * X(0)
+ 1.0 * Y(0)
>>> type(op.pauli_rep)
pennylane.pauli.pauli_arithmetic.PauliSentence


You can transform the PauliSentence back to a suitable Operator via the operation() or hamiltonian() method.

>>> op.pauli_rep.operation()
X(0) + Y(0)

• Extensive improvements have been made to the string representations of PennyLane operators, making them shorter and possible to copy-paste as valid PennyLane code.

>>> 0.5 * X(0)
0.5 * X(0)
>>> 0.5 * (X(0) + Y(1))
0.5 * (X(0) + Y(1))


Sums with many terms are broken up into multiple lines, but can still be copied back as valid code:

>>> 0.5 * (X(0) @ X(1)) + 0.7 * (X(1) @ X(2)) + 0.8 * (X(2) @ X(3))
(
0.5 * (X(0) @ X(1))
+ 0.7 * (X(1) @ X(2))
+ 0.8 * (X(2) @ X(3))
)


The changes between the old and new system mainly concern Python operators + - * / @, that now create the following Operator subclass instances.

Legacy

Updated Operators

tensor products X(0) @ X(1)

operation.Tensor

ops.Prod

sums X(0) + X(1)

ops.Hamiltonian

ops.Sum

scalar products 1.5 * X(1)

ops.Hamiltonian

ops.SProd

qml.dot(coeffs,ops)

ops.Sum

ops.Sum

qml.Hamiltonian(coeffs, ops)

ops.Hamiltonian

ops.LinearCombination

qml.ops.LinearCombination(coeffs, ops)

n/a

ops.LinearCombination

The three main new opmath classes SProd, Prod, and Sum have already been around for a while. E.g., dot() has always returned a Sum instance.

Usage

Besides the python operators, you can also use the constructors s_prod(), prod(), and sum(). For composite operators, we can access their constituents via the op.operands attribute.

>>> op = qml.sum(X(0), X(1), X(2))
>>> op.operands
(X(0), X(1), X(2))


In case all terms are composed of operators with a valid pauli_rep, then the composite operator also has a valid pauli_rep in terms of a PauliSentence instance. This is often handy for fast arithmetic processing.

>>> op.pauli_rep
1.0 * X(0)
+ 1.0 * X(1)
+ 1.0 * X(2)


Further, composite operators can be simplified using simplify() or the op.simplify() method.

>>> op = 0.5 * X(0) + 0.5 * Y(0) - 1.5 * X(0) - 0.5 * Y(0) # no simplification by default
>>> op.simplify()
-1.0 * X(0)
>>> qml.simplify(op)
-1.0 * X(0)


Note that the simplification never happens in-place, such that the original operator is left unaltered.

>>> op
(
0.5 * X(0)
+ 0.5 * Y(0)
+ -1 * 1.5 * X(0)
+ -1 * 0.5 * Y(0)
)


We are often interested in obtaining a list of coefficients and pure operators. We can do so by using the op.terms() method.

>>> op = 0.5 * (X(0) @ X(1) + Y(0) @ Y(1) + 2 * Z(0) @ Z(1)) - 1.5 * I() + 0.5 * I()
>>> op.terms()
([0.5, 0.5, 1.0, -1.0], [X(1) @ X(0), Y(1) @ Y(0), Z(1) @ Z(0), I()])


As seen by this example, this method already takes care of arithmetic simplifications.

qml.Hamiltonian

The legacy classes Tensor and Hamiltonian will soon be deprecated. LinearCombination offers the same API as Hamiltonian but works well with new opmath classes.

Depending on whether or not new opmath is active, qml.Hamiltonian will return either of the two classes.

>>> import pennylane as qml
>>> from pennylane import X
>>> qml.operation.active_new_opmath()
True
>>> H = qml.Hamiltonian([0.5, 0.5], [X(0), X(1)])
>>> type(H)
pennylane.ops.op_math.linear_combination.LinearCombination

>>> qml.operation.disable_new_opmath()
>>> qml.operation.active_new_opmath()
False
>>> H = qml.Hamiltonian([0.5, 0.5], [X(0), X(1)])
>>> type(H)
pennylane.ops.qubit.hamiltonian.Hamiltonian


Both classes offer the same API and functionality, so a user does not have to worry about those implementation details.

## Troubleshooting¶

You may experience issues with PennyLane’s updated operator arithmetic in version v0.36 and above if you have existing code that works with an earlier version of PennyLane. To help identify a fix, select the option below that describes your situation.

A quick-and-dirty fix for this issue is to deactivate new opmath at the beginning of the script via qml.operation.disable_new_opmath(). We recommend to do the following checks instead

• Check explicit use of the legacy Tensor class. If you find it in your script it can just be changed from Tensor(*terms) to qml.prod(*terms) with the same call signature.

• Check explicit use of the op.obs attribute, where op is some operator. This is how the terms of a tensor product are accessed in Tensor instances. Use op.operands instead.

# new opmath enabled (default)
op = X(0) @ X(1)
assert op.operands == (X(0), X(1))

with qml.operation.disable_new_opmath_cm():
# context manager that disables new opmath temporarilly
op = X(0) @ X(1)
assert op.obs == [X(0), X(1)]

• Check explicit use of qml.ops.Hamiltonian. In that case, simply change to qml.Hamiltonian.

>>> op = qml.ops.Hamiltonian([0.5, 0.5], [X(0) @ X(1), X(1) @ X(2)])
ValueError: Could not create circuits. Some or all observables are not valid.
>>> op = qml.Hamiltonian([0.5, 0.5], [X(0) @ X(1), X(1) @ X(2)])
>>> isinstance(op, qml.ops.LinearCombination)
True

• Check if you are explicitly enabling and disabling new opmath somewhere in your script. Mixing both systems is not supported.

If for some unexpected reason your script still breaks, please post on the PennyLane discussion forum or open a bug report on the PennyLane GitHub page.

One of the reasons that LinearCombination exists is that the old Hamiltonian class is not compatible with new opmath tensor products. We can try to instantiate an old qml.ops.Hamiltonian class with a X(0) @ X(1) tensor product, which returns a Prod instance with new opmath enabled.

>>> qml.operation.active_new_opmath() # confirm opmath is active (by default)
True
>>> qml.ops.Hamiltonian([0.5], [X(0) @ X(1)])
PennyLaneDeprecationWarning: Using 'qml.ops.Hamiltonian' with new operator arithmetic is deprecated. Instead, use 'qml.Hamiltonian', or use 'qml.operation.disable_new_opmath()' to continue to access the legacy functionality. See https://docs.pennylane.ai/en/stable/development/deprecations.html for more details.
ValueError: Could not create circuits. Some or all observables are not valid.


However, using qml.Hamiltonian works as expected.

>>> qml.Hamiltonian([0.5], [X(0) @ X(1)])
0.5 * (X(0) @ X(1))


The API of LinearCombination is identical to that of Hamiltonian. We can group observables or simplify upon initialization.

>>> H1 = qml.Hamiltonian([0.5, 0.5, 0.5], [X(0) @ X(1), X(0), Y(0)], grouping_type="qwc", simplify=True)
>>> H2 = qml.ops.LinearCombination([0.5, 0.5, 0.5], [X(0) @ X(1), X(0), Y(0)], grouping_type="qwc", simplify=True)
>>> H1 == H2
True


One small difference is that ham.simplify() no longer alters the instance in-place. In either case (legacy/new opmath), the following works.

>>> H1 = qml.Hamiltonian([0.5, 0.5], [X(0) @ X(1), X(0) @ X(1)])
>>> H1 = H1.simplify() # work for new and legacy opmath


If you want to contribute a new feature to PennyLane or update an existing one, you likely also need to update the tests.

Note

Please refrain from explicitly using qml.operation.disable_new_opmath() and qml.operation.enable_new_opmath() anywhere in tests as that globally changes the status of new opmath and thereby can affect other tests.

def test_some_legacy_opmath_behavior():
qml.operation.disable_new_opmath() # dont do this
# testing some legacy behavior things

def test_some_new_opmath_behavior():
assert qml.operation.active_new_opmath()
# will fail because the previous test globally disabled new opmath


Instead, please use the fixtures below, or the context managers qml.operation.disable_new_opmath_cm() and qml.operation.enable_new_opmath_cm().

>>> with qml.operation.disable_new_opmath_cm():
...     op = qml.Hamiltonian([0.5], [X(0) @ X(1)])
>>> assert isinstance(op, qml.ops.Hamiltonian)


Our continuous integration (CI) test suite is running all tests with the new opmath enabled by default. We also periodically run the CI test suite with new opmath disabled, as we support both the new and legacy systems for a limited time. In case a test needs to be adopted for either case, you can use the following fixtures.

• Use @pytest.mark.usefixtures("use_legacy_opmath") to test functionality that is explicitly only supported by legacy opmath (e.g., for backward compatibility).

@pytest.mark.usefixtures("use_legacy_opmath")
def test_qml_hamiltonian_legacy_opmath():
assert qml.Hamiltonian == qml.ops.Hamiltonian

def test_qml_hamiltonian()
assert qml.Hamiltonian == qml.ops.LinearCombination

• Use @pytest.mark.usefixtures("use_new_opmath") to test functionality that only works with new opmath. That is because for the intermittent period of supporting both systems, we periodically run the test suite with new opmath disabled.

@pytest.mark.usefixtures("use_new_opmath")
def test_qml_hamiltonian_new_opmath():
assert qml.Hamiltonian == qml.ops.LinearCombination

• Use @pytest.mark.usefixtures("use_legacy_and_new_opmath") if you want to test support for both systems in one single test. You can use qml.operation.active_new_opmath inside the test to account for minor differences between both systems.

@pytest.mark.usefixtures("use_legacy_and_new_opmath")
def test_qml_hamiltonian_new_opmath():
if qml.operation.active_new_opmath():
assert qml.Hamiltonian == qml.ops.LinearCombination

if not qml.operation.active_new_opmath():
assert qml.Hamiltonian == qml.ops.Hamiltonian


One sharp bit about testing is that pytest runs collection and test execution separately. That means that instances generated outside the test, e.g., for parametrization, have been created using the respective system. So you may need to also put that creation in the appropriate context manager.

# in some test file
with qml.operation.disable_new_opmath_cm():
legacy_ham_example = qml.Hamiltonian(coeffs, ops) # creates a Hamiltonian instance

@pytest.mark.usefixtures("use_legacy_opmath")
@pytest.mark.parametrize("ham", [legacy_ham_example])
def test_qml_hamiltonian_legacy_opmath(ham):
assert isinstance(ham, qml.Hamiltonian) # True
assert isinstance(ham, qml.ops.Hamiltonian) # True


Alternatively, you can convert them back to legacy Hamiltonian instances using qml.operation.convert_to_legacy_H().

ham_example = qml.Hamiltonian(coeffs, ops) # creates a LinearCombination instance

@pytest.mark.usefixtures("use_new_opmath")
@pytest.mark.parametrize("ham", [ham_example])
def test_qml_hamiltonian_new_opmath(ham):
assert isinstance(ham, qml.Hamiltonian) # True
assert not isinstance(ham, qml.ops.Hamiltonian) # True

@pytest.mark.usefixtures("use_legacy_opmath")
@pytest.mark.parametrize("ham", [ham_example])
def test_qml_hamiltonian_legacy_opmath(ham):
# Most likely you wanted to test things with a Hamiltonian instance
legacy_ham_example = convert_to_legacy_H(ham)
assert isinstance(legacy_ham_example, qml.ops.Hamiltonian) # True
assert isinstance(legacy_ham_example, qml.Hamiltonian) # True because we are in legacy opmath context
assert not isinstance(legacy_ham_example, qml.ops.LinearCombination) # True


For all that, keep in mind that qml.Hamiltonian points to Hamiltonian and LinearCombination depending on the status of qml.operation.active_new_opmath(). So if you want to test something specifically for the old Hamiltonian class, use qml.ops.Hamiltonian instead.

The type of the final operator is determined by the outermost operation. The resulting object is a nested structure (sums of s/prods or s/prods of sums).

>>> qml.operation.enable_new_opmath() # default soon
>>> op = 0.5 * (X(0) @ X(1)) + 0.5 * (Y(0) @ Y(1))
>>> type(op)
pennylane.ops.op_math.sum.Sum

>>> op.operands
(0.5 * (X(0) @ X(1)), 0.5 * (Y(0) @ Y(1)))

>>> type(op.operands[0]), type(op.operands[1])
(pennylane.ops.op_math.sprod.SProd, pennylane.ops.op_math.sprod.SProd)

>>> op.operands[0].scalar, op.operands[0].base, type(op.operands[0].base)
(0.5, X(0) @ X(1), pennylane.ops.op_math.prod.Prod)


We could construct an equivalent operator with a different nesting structure.

>>> op = (0.5 * X(0)) @ X(1) + (0.5 * Y(0)) @ Y(1)
>>> op.operands
((0.5 * X(0)) @ X(1), (0.5 * Y(0)) @ Y(1))

>>> type(op.operands[0]), type(op.operands[1])
(pennylane.ops.op_math.prod.Prod, pennylane.ops.op_math.prod.Prod)

>>> op.operands[0].operands
(0.5 * X(0), X(1))

>>> type(op.operands[0].operands[0]), type(op.operands[0].operands[1])
(pennylane.ops.op_math.sprod.SProd,
pennylane.ops.qubit.non_parametric_ops.PauliX)


There is yet another way to construct the same, equivalent, operator. We can bring all of them to the same format by using op.simplify(), which brings the operator down to the form $$\sum_i c_i \hat{O}_i$$, where $$c_i$$ is a scalar coefficient and $$\hat{O}_i$$ is a pure operator or tensor product of operators.

>>> op1 = 0.5 * (X(0) @ X(1)) + 0.5 * (Y(0) @ Y(1))
>>> op2 = (0.5 * X(0)) @ X(1) + (0.5 * Y(0)) @ Y(1)
>>> op3 = 0.5 * (X(0) @ X(1) + Y(0) @ Y(1))
>>> qml.equal(op1, op2), qml.equal(op2, op3), qml.equal(op3, op1)
(True, False, False)

>>> op1 = op1.simplify()
>>> op2 = op2.simplify()
>>> op3 = op3.simplify()
>>> qml.equal(op1, op2), qml.equal(op2, op3), qml.equal(op3, op1)
(True, True, True)

>>> op1, op2, op3
(0.5 * (X(1) @ X(0)) + 0.5 * (Y(1) @ Y(0)),
0.5 * (X(1) @ X(0)) + 0.5 * (Y(1) @ Y(0)),
0.5 * (X(1) @ X(0)) + 0.5 * (Y(1) @ Y(0)))


We can also obtain those scalar coefficients and tensor product operators via the op.terms() method.

>>> coeffs, ops = op1.terms()
>>> coeffs, ops
([0.5, 0.5], [X(1) @ X(0), Y(1) @ Y(0)])
`

Please carefully read through the options above. If you are still stuck, you can:

• Post on the PennyLane discussion forum. Please include a complete block of code demonstrating your issue so that we can quickly troubleshoot.

• If you suspect that your issue is due to a bug in PennyLane itself, please open a bug report on the PennyLane GitHub page.