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Revert "Adding string representation for some operators" (#6501)
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Reverts #6493
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@PietropaoloFrisoni
PietropaoloFrisoni authored Nov 1, 2024
1 parent b851ce3 commit 3ac1957
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2 changes: 1 addition & 1 deletion doc/development/adding_operators.rst
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Expand Up @@ -79,7 +79,7 @@ The basic components of operators are the following:

>>> op = qml.PauliX(0)
>>> op.diagonalizing_gates()
[H(0)]
[Hadamard(wires=[0])]
>>> op.eigvals()
[ 1 -1]

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2 changes: 1 addition & 1 deletion doc/introduction/inspecting_circuits.rst
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Expand Up @@ -331,7 +331,7 @@ Using the above example, we get:
<class 'networkx.classes.multidigraph.MultiDiGraph'>
>>> for k, v in g2.adjacency():
... print(k, v)
H(0) {expval(Z(0)): {0: {'wire': 0}}}
Hadamard(wires=[0]) {expval(Z(0)): {0: {'wire': 0}}}
CNOT(wires=[1, 2]) {CNOT(wires=[2, 3]): {0: {'wire': 2}}, CNOT(wires=[3, 1]): {0: {'wire': 1}}}
CNOT(wires=[2, 3]) {CNOT(wires=[3, 1]): {0: {'wire': 3}}}
CNOT(wires=[3, 1]) {}
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2 changes: 1 addition & 1 deletion doc/introduction/operations.rst
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Expand Up @@ -68,7 +68,7 @@ These operator functions act on operators to produce new operators.
>>> op = qml.sum(qml.Hadamard(0), op)
>>> op = qml.s_prod(1.2, op)
>>> op
1.2 * (H(0) + X(0) @ Z(1))
1.2 * (Hadamard(wires=[0]) + X(0) @ Z(1))

Operator to Other functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^
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3 changes: 0 additions & 3 deletions doc/releases/changelog-0.39.0.md
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Expand Up @@ -192,9 +192,6 @@

<h4>Other Improvements</h4>

* Added string representation for the `qml.S`, `qml.T`, and `qml.SX` operators.
[(#6493)](https://github.com/PennyLaneAI/pennylane/pull/6493)

* Added `qml.H` as an alias for the Hadamard operator.
[(#6450)](https://github.com/PennyLaneAI/pennylane/pull/6450)

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2 changes: 1 addition & 1 deletion pennylane/ops/op_math/controlled.py
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Expand Up @@ -103,7 +103,7 @@ def circuit(x):
and individual :class:`~.operation.Operator`'s.
>>> qml.ctrl(qml.Hadamard(0), (1,2))
Controlled(H(0), control_wires=[1, 2])
Controlled(Hadamard(wires=[0]), control_wires=[1, 2])
Controlled operations work with all other forms of operator math and simplification:
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38 changes: 19 additions & 19 deletions pennylane/ops/op_math/controlled_ops.py
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Expand Up @@ -384,7 +384,7 @@ def compute_decomposition(wires): # pylint: disable=arguments-differ
**Example:**
>>> print(qml.CY.compute_decomposition([0, 1]))
[CRY(3.141592653589793, wires=[0, 1])), S(0)]
[CRY(3.141592653589793, wires=[0, 1])), S(wires=[0])]
"""
return [qml.CRY(np.pi, wires=wires), qml.S(wires=wires[0])]
Expand Down Expand Up @@ -709,20 +709,20 @@ def compute_decomposition(wires): # pylint: disable=arguments-differ
>>> qml.CCZ.compute_decomposition((0,1,2))
[CNOT(wires=[1, 2]),
Adjoint(T(2)),
Adjoint(T(wires=[2])),
CNOT(wires=[0, 2]),
T(2),
T(wires=[2]),
CNOT(wires=[1, 2]),
Adjoint(T(2)),
Adjoint(T(wires=[2])),
CNOT(wires=[0, 2]),
T(2),
T(1),
T(wires=[2]),
T(wires=[1]),
CNOT(wires=[0, 1]),
H(2),
T(0),
Adjoint(T(1)),
Hadamard(wires=[2]),
T(wires=[0]),
Adjoint(T(wires=[1])),
CNOT(wires=[0, 1]),
H(2)]
Hadamard(wires=[2])]
"""
return [
Expand Down Expand Up @@ -962,20 +962,20 @@ def compute_decomposition(wires): # pylint: disable=arguments-differ
**Example:**
>>> qml.Toffoli.compute_decomposition((0,1,2))
[H(2),
[Hadamard(wires=[2]),
CNOT(wires=[1, 2]),
Adjoint(T(2)),
Adjoint(T(wires=[2])),
CNOT(wires=[0, 2]),
T(2),
T(wires=[2]),
CNOT(wires=[1, 2]),
Adjoint(T(2)),
Adjoint(T(wires=[2])),
CNOT(wires=[0, 2]),
T(2),
T(1),
T(wires=[2]),
T(wires=[1]),
CNOT(wires=[0, 1]),
H(2),
T(0),
Adjoint(T(1)),
Hadamard(wires=[2]),
T(wires=[0]),
Adjoint(T(wires=[1])),
CNOT(wires=[0, 1])]
"""
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4 changes: 2 additions & 2 deletions pennylane/ops/op_math/linear_combination.py
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Expand Up @@ -58,7 +58,7 @@ class LinearCombination(Sum):
>>> obs = [qml.X(0) @ qml.Z(1), qml.Z(0) @ qml.Hadamard(2)]
>>> H = qml.ops.LinearCombination(coeffs, obs)
>>> print(H)
0.2 * (X(0) @ Z(1)) + -0.543 * (Z(0) @ H(2))
0.2 * (X(0) @ Z(1)) + -0.543 * (Z(0) @ Hadamard(wires=[2]))
The coefficients can be a trainable tensor, for example:
Expand All @@ -67,7 +67,7 @@ class LinearCombination(Sum):
>>> obs = [qml.X(0) @ qml.Z(1), qml.Z(0) @ qml.Hadamard(2)]
>>> H = qml.ops.LinearCombination(coeffs, obs)
>>> print(H)
0.2 * (X(0) @ Z(1)) + -0.543 * (Z(0) @ H(2))
0.2 * (X(0) @ Z(1)) + -0.543 * (Z(0) @ Hadamard(wires=[2]))
A ``LinearCombination`` can store information on which commuting observables should be measured together in
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4 changes: 2 additions & 2 deletions pennylane/ops/op_math/pow.py
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Expand Up @@ -79,7 +79,7 @@ def pow(base, z=1, lazy=True, id=None):
>>> qml.pow(qml.X(0), 0.5)
X(0)**0.5
>>> qml.pow(qml.X(0), 0.5, lazy=False)
SX(0)
SX(wires=[0])
>>> qml.pow(qml.X(0), 0.1, lazy=False)
X(0)**0.1
>>> qml.pow(qml.X(0), 2, lazy=False)
Expand Down Expand Up @@ -118,7 +118,7 @@ class Pow(ScalarSymbolicOp):
>>> sqrt_x = Pow(qml.X(0), 0.5)
>>> sqrt_x.decomposition()
[SX(0)]
[SX(wires=[0])]
>>> qml.matrix(sqrt_x)
array([[0.5+0.5j, 0.5-0.5j],
[0.5-0.5j, 0.5+0.5j]])
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4 changes: 2 additions & 2 deletions pennylane/ops/qubit/hamiltonian.py
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Expand Up @@ -102,15 +102,15 @@ class Hamiltonian(Observable):
>>> obs = [qml.X(0) @ qml.Z(1), qml.Z(0) @ qml.Hadamard(2)]
>>> H = qml.Hamiltonian(coeffs, obs)
>>> print(H)
0.2 * (X(0) @ Z(1)) + -0.543 * (Z(0) @ H(2))
0.2 * (X(0) @ Z(1)) + -0.543 * (Z(0) @ Hadamard(wires=[2]))
The coefficients can be a trainable tensor, for example:
>>> coeffs = tf.Variable([0.2, -0.543], dtype=tf.double)
>>> obs = [qml.X(0) @ qml.Z(1), qml.Z(0) @ qml.Hadamard(2)]
>>> H = qml.Hamiltonian(coeffs, obs)
>>> print(H)
0.2 * (X(0) @ Z(1)) + -0.543 * (Z(0) @ H(2))
0.2 * (X(0) @ Z(1)) + -0.543 * (Z(0) @ Hadamard(wires=[2]))
A ``qml.Hamiltonian`` stores information on which commuting observables should be measured
together in a circuit:
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47 changes: 13 additions & 34 deletions pennylane/ops/qubit/non_parametric_ops.py
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Expand Up @@ -338,7 +338,7 @@ def compute_diagonalizing_gates(wires: WiresLike) -> list[qml.operation.Operator
**Example**
>>> print(qml.X.compute_diagonalizing_gates(wires=[0]))
[H(0)]
[Hadamard(wires=[0])]
"""
return [Hadamard(wires=wires)]

Expand Down Expand Up @@ -529,7 +529,7 @@ def compute_diagonalizing_gates(wires: WiresLike) -> list[qml.operation.Operator
**Example**
>>> print(qml.Y.compute_diagonalizing_gates(wires=[0]))
[Z(0), S(0), H(0)]
[Z(0), S(wires=[0]), Hadamard(wires=[0])]
"""
return [
Z(wires=wires),
Expand Down Expand Up @@ -815,13 +815,6 @@ class S(Operation):

batch_size = None

def __repr__(self) -> str:
"""String representation."""
wire = self.wires[0]
if isinstance(wire, str):
return f"S('{wire}')"
return f"S({wire})"

@staticmethod
@lru_cache()
def compute_matrix() -> np.ndarray: # pylint: disable=arguments-differ
Expand Down Expand Up @@ -934,13 +927,6 @@ class T(Operation):

batch_size = None

def __repr__(self) -> str:
"""String representation."""
wire = self.wires[0]
if isinstance(wire, str):
return f"T('{wire}')"
return f"T({wire})"

@staticmethod
@lru_cache()
def compute_matrix() -> np.ndarray: # pylint: disable=arguments-differ
Expand Down Expand Up @@ -1051,13 +1037,6 @@ class SX(Operation):

basis = "X"

def __repr__(self) -> str:
"""String representation."""
wire = self.wires[0]
if isinstance(wire, str):
return f"SX('{wire}')"
return f"SX({wire})"

@staticmethod
@lru_cache()
def compute_matrix() -> np.ndarray: # pylint: disable=arguments-differ
Expand Down Expand Up @@ -1343,7 +1322,7 @@ def compute_decomposition(wires: WiresLike) -> list[qml.operation.Operator]:
[Z(0),
CNOT(wires=[0, 1]),
SX(1),
SX(wires=[1]),
RX(1.5707963267948966, wires=[0]),
RY(1.5707963267948966, wires=[0]),
RX(1.5707963267948966, wires=[0])]
Expand Down Expand Up @@ -1459,12 +1438,12 @@ def compute_decomposition(wires: WiresLike) -> list[qml.operation.Operator]:
**Example:**
>>> print(qml.ISWAP.compute_decomposition((0,1)))
[S(0),
S(1),
H(0),
[S(wires=[0]),
S(wires=[1]),
Hadamard(wires=[0]),
CNOT(wires=[0, 1]),
CNOT(wires=[1, 0]),
H(1)]
Hadamard(wires=[1])]
"""
return [
Expand Down Expand Up @@ -1584,18 +1563,18 @@ def compute_decomposition(wires: WiresLike) -> list[qml.operation.Operator]:
**Example:**
>>> print(qml.SISWAP.compute_decomposition((0,1)))
[SX(0),
[SX(wires=[0]),
RZ(1.5707963267948966, wires=[0]),
CNOT(wires=[0, 1]),
SX(0),
SX(wires=[0]),
RZ(5.497787143782138, wires=[0]),
SX(0),
SX(wires=[0]),
RZ(1.5707963267948966, wires=[0]),
SX(1),
SX(wires=[1]),
RZ(5.497787143782138, wires=[1]),
CNOT(wires=[0, 1]),
SX(0),
SX(1)]
SX(wires=[0]),
SX(wires=[1])]
"""
return [
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6 changes: 3 additions & 3 deletions pennylane/ops/qubit/parametric_ops_multi_qubit.py
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Expand Up @@ -482,10 +482,10 @@ def compute_decomposition(
**Example:**
>>> qml.PauliRot.compute_decomposition(1.2, "XY", wires=(0,1))
[H(0),
[Hadamard(wires=[0]),
RX(1.5707963267948966, wires=[1]),
MultiRZ(1.2, wires=[0, 1]),
H(0),
Hadamard(wires=[0]),
RX(-1.5707963267948966, wires=[1])]
"""
Expand Down Expand Up @@ -1286,7 +1286,7 @@ def compute_decomposition(phi: TensorLike, wires: WiresLike) -> list["qml.operat
**Example:**
>>> qml.IsingXY.compute_decomposition(1.23, wires=(0,1))
[H(0), CY(wires=[0, 1]), RY(0.615, wires=[0]), RX(-0.615, wires=[1]), CY(wires=[0, 1]), H(0)]
[Hadamard(wires=[0]), CY(wires=[0, 1]), RY(0.615, wires=[0]), RX(-0.615, wires=[1]), CY(wires=[0, 1]), Hadamard(wires=[0])]
"""
return [
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2 changes: 1 addition & 1 deletion pennylane/ops/qubit/parametric_ops_single_qubit.py
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Expand Up @@ -700,7 +700,7 @@ def simplify(self) -> "Rot":
>>> qml.Rot(np.pi / 2, 0.1, -np.pi / 2, wires=0).simplify()
RX(0.1, wires=[0])
>>> qml.Rot(np.pi, np.pi/2, 0, 0).simplify()
H(0)
Hadamard(wires=[0])
"""
p0, p1, p2 = [p % (4 * np.pi) for p in self.data]
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44 changes: 22 additions & 22 deletions pennylane/ops/qubit/qchem_ops.py
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Expand Up @@ -224,22 +224,22 @@ def compute_decomposition(phi: TensorLike, wires: WiresLike) -> list["qml.operat
**Example:**
>>> qml.SingleExcitation.compute_decomposition(1.23, wires=(0,1))
[Adjoint(T(0)),
H(0),
S(0),
Adjoint(T(1)),
Adjoint(S(1)),
H(1),
[Adjoint(T(wires=[0])),
Hadamard(wires=[0]),
S(wires=[0]),
Adjoint(T(wires=[1])),
Adjoint(S(wires=[1])),
Hadamard(wires=[1]),
CNOT(wires=[1, 0]),
RZ(-0.615, wires=[0]),
RY(0.615, wires=[1]),
CNOT(wires=[1, 0]),
Adjoint(S(0)),
H(0),
T(0),
H(1),
S(1),
T(1)]
Adjoint(S(wires=[0])),
Hadamard(wires=[0]),
T(wires=[0]),
Hadamard(wires=[1]),
S(wires=[1]),
T(wires=[1])]
"""
# This decomposition was found by plugging the matrix representation
Expand Down Expand Up @@ -680,14 +680,14 @@ def compute_decomposition(phi: TensorLike, wires: WiresLike) -> list["qml.operat
>>> qml.DoubleExcitation.compute_decomposition(1.23, wires=(0,1,2,3))
[CNOT(wires=[2, 3]),
CNOT(wires=[0, 2]),
H(3),
H(0),
Hadamard(wires=[3]),
Hadamard(wires=[0]),
CNOT(wires=[2, 3]),
CNOT(wires=[0, 1]),
RY(0.15375, wires=[1]),
RY(-0.15375, wires=[0]),
CNOT(wires=[0, 3]),
H(3),
Hadamard(wires=[3]),
CNOT(wires=[3, 1]),
RY(0.15375, wires=[1]),
RY(-0.15375, wires=[0]),
Expand All @@ -696,14 +696,14 @@ def compute_decomposition(phi: TensorLike, wires: WiresLike) -> list["qml.operat
RY(-0.15375, wires=[1]),
RY(0.15375, wires=[0]),
CNOT(wires=[3, 1]),
H(3),
Hadamard(wires=[3]),
CNOT(wires=[0, 3]),
RY(-0.15375, wires=[1]),
RY(0.15375, wires=[0]),
CNOT(wires=[0, 1]),
CNOT(wires=[2, 0]),
H(0),
H(3),
Hadamard(wires=[0]),
Hadamard(wires=[3]),
CNOT(wires=[0, 2]),
CNOT(wires=[2, 3])]
Expand Down Expand Up @@ -1268,11 +1268,11 @@ def compute_decomposition(phi: TensorLike, wires: WiresLike) -> list["qml.operat
**Example:**
>>> qml.FermionicSWAP.compute_decomposition(0.2, wires=(0, 1))
[H(0),
H(1),
[Hadamard(wires=[0]),
Hadamard(wires=[1]),
MultiRZ(0.1, wires=[0, 1]),
H(0),
H(1),
Hadamard(wires=[0]),
Hadamard(wires=[1]),
RX(1.5707963267948966, wires=[0]),
RX(1.5707963267948966, wires=[1]),
MultiRZ(0.1, wires=[0, 1]),
Expand Down
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