Release 0.36.0

Release 0.36.0

New features since last release

Estimate errors in a quantum circuit 🧮

• This version of PennyLane lays the foundation for estimating the total error in a quantum circuit from the combination of individual gate errors. (#5154) (#5464) (#5465) (#5278) (#5384)

Two new user-facing classes enable calculating and propagating gate errors in PennyLane:

• qml.resource.SpectralNormError: the spectral norm error is defined as the distance, in spectral norm, between the true unitary we intend to apply and the approximate unitary that is actually applied.

• qml.resource.ErrorOperation: a base class that inherits from qml.operation.Operation and represents quantum operations which carry some form of algorithmic error.

  SpectralNormError can be used for back-of-the-envelope type calculations like obtaining the spectral norm error between two unitaries via get_error:

  import pennylane as qml 
  from pennylane.resource import ErrorOperation, SpectralNormError
  
  intended_op = qml.RY(0.40, 0) 
  actual_op = qml.RY(0.41, 0) # angle of rotation is slightly off

  >>> SpectralNormError.get_error(intended_op, actual_op) 
  0.004999994791668309 

  SpectralNormError is also a key tool to specify errors in larger quantum circuits:

• For operations representing a major building block of an algorithm, we can create a custom operation that inherits from ErrorOperation. This child class must override the error method and should return a SpectralNormError instance:

  class MyErrorOperation(ErrorOperation):
     
     def __init__(self, error_val, wires): 
         self.error_val = error_val 
         super().__init__(wires=wires)
  
     def error(self): 
         return SpectralNormError(self.error_val)

  In this toy example, MyErrorOperation introduces an arbitrary SpectralNormError when called in a QNode. It does not require a decomposition or matrix representation when used with null.qubit (suggested for use with resource and error estimation since circuit executions are not required to calculate resources or errors).

  dev = qml.device("null.qubit")
  
  @qml.qnode(dev) 
  def circuit(): 
     MyErrorOperation(0.1, wires=0) 
     MyErrorOperation(0.2, wires=1) 
     return qml.state() 

  The total spectral norm error of the circuit can be calculated using qml.specs:

  >>> qml.specs(circuit)()['errors'] 
  {'SpectralNormError': SpectralNormError(0.30000000000000004)} 

• PennyLane already includes a number of built-in building blocks for algorithms like QuantumPhaseEstimation and TrotterProduct. TrotterProduct now propagates errors based on the number of steps performed in the Trotter product. QuantumPhaseEstimation now propagates errors based on the error of its input unitary.

  dev = qml.device('null.qubit') 
  hamiltonian = qml.dot([1.0, 0.5, -0.25], [qml.X(0), qml.Y(0), qml.Z(0)])
  
  @qml.qnode(dev) 
  def circuit(): 
     qml.TrotterProduct(hamiltonian, time=0.1, order=2) 
     qml.QuantumPhaseEstimation(MyErrorOperation(0.01, wires=0), estimation_wires=[1, 2, 3]) 
     return qml.state()

  Again, the total spectral norm error of the circuit can be calculated using qml.specs:

  >>> qml.specs(circuit)()["errors"] 
  {'SpectralNormError': SpectralNormError(0.07616666666666666)} 

  Check out our error propagation demo to see how to use these new features in a real-world example!

Access an extended arsenal of quantum algorithms 🏹

• The Fast Approximate BLock-Encodings (FABLE) algorithm for embedding a matrix into a quantum circuit as outlined in arXiv:2205.00081 is now accessible via the qml.FABLE template. (#5107)

  The usage of qml.FABLE is similar to qml.BlockEncode but provides a more efficient circuit construction at the cost of a user-defined approximation level, tol. The number of wires that qml.FABLE operates on is 2*n + 1, where n defines the dimension of the $2^n \times 2^n$ matrix that we want to block-encode.

  import numpy as np
  
  A = np.array([[0.1, 0.2], [0.3, 0.4]]) 
  dev = qml.device('default.qubit', wires=3)
  
  @qml.qnode(dev) 
  def circuit(): 
     qml.FABLE(A, tol = 0.001, wires=range(3)) 
     return qml.state() 

  >>> mat = qml.matrix(circuit)() 
  >>> 2 * mat[0:2, 0:2] 
  array([[0.1+0.j, 0.2+0.j], [0.3+0.j, 0.4+0.j]]) 

• A high-level interface for amplitude amplification and its variants is now available via the new qml.AmplitudeAmplification template. (#5160)

  Based on arXiv:quant-ph/0005055, given a state $\vert \Psi \rangle = \alpha \vert \phi \rangle + \beta \vert \phi^{\perp} \rangle$, qml.AmplitudeAmplification amplifies the amplitude of $\vert \phi \rangle$.

  Here's an example with a target state $\vert \phi \rangle = \vert 2 \rangle = \vert 010 \rangle$, an input state $\vert \Psi \rangle = H^{\otimes 3} \vert 000 \rangle$, as well as an oracle that flips the sign of $\vert \phi \rangle$ and does nothing to $\vert \phi^{\perp} \rangle$, which can be achieved in this case through qml.FlipSign.

  @qml.prod 
  def generator(wires): 
     for wire in wires: 
         qml.Hadamard(wires=wire)
  
  U = generator(wires=range(3)) 
  O = qml.FlipSign(2, wires=range(3)) 

  Here, U is a quantum operation that is created by decorating a quantum function with @qml.prod. This could alternatively be done by creating a user-defined custom operation with a decomposition. Amplitude amplification can then be set up within a circuit:

  dev = qml.device("default.qubit")
  
  @qml.qnode(dev) 
  def circuit(): 
     generator(wires=range(3)) # prepares |Psi> = U|0> 
     qml.AmplitudeAmplification(U, O, iters=10)
     return qml.probs(wires=range(3)) 

  >>> print(np.round(circuit(), 3)) 
  [0.01 0.01 0.931 0.01 0.01 0.01 0.01 0.01 ] 

  As expected, we amplify the $\vert 2 \rangle$ state.

• Reflecting about a given quantum state is now available via qml.Reflection. This operation is very useful in the amplitude amplification algorithm and offers a generalization of qml.FlipSign, which operates on basis states. (#5159)

  qml.Reflection works by providing an operation, $U$, that prepares the desired state, $\vert \psi \rangle$, that we want to reflect about. In other words, $U$ is such that $U \vert 0 \rangle = \vert \psi \rangle$. In PennyLane, $U$ must be an Operator. For example, if we want to reflect about $\vert \psi \rangle = \vert + \rangle$, then $U = H$:

  U = qml.Hadamard(wires=0)
  dev = qml.device('default.qubit') 
  
  @qml.qnode(dev) 
  def circuit(): 
     qml.Reflection(U) 
     return qml.state() 

  >>> circuit() 
  tensor([0.-6.123234e-17j, 1.+6.123234e-17j], requires_grad=True) 

• Performing qubitization is now easily accessible with the new qml.Qubitization operator. (#5500)

  qml.Qubitization encodes a Hamiltonian into a suitable unitary operator. When applied in conjunction with quantum phase estimation (QPE), it allows for computing the eigenvalue of an eigenvector of the given Hamiltonian.

  H = qml.dot([0.1, 0.3, -0.3], [qml.Z(0), qml.Z(1), qml.Z(0) @ qml.Z(2)]) 
  @qml.qnode(qml.device("default.qubit")) 
  def circuit(): 
     # initialize the eigenvector 
     qml.PauliX(2) 
     # apply QPE 
     measurements = qml.iterative_qpe(
         qml.Qubitization(H, control = [3,4]), ancilla = 5, iters = 3
     ) 
     return qml.probs(op = measurements) 

Make use of more methods to map from molecules 🗺️

• A new function called qml.bravyi_kitaev has been added to perform the Bravyi-Kitaev mapping of fermionic Hamiltonians to qubit Hamiltonians. (#5390)

  This function presents an alternative mapping to qml.jordan_wigner or qml.parity_transform which can help us measure expectation values more efficiently on hardware. Simply provide a fermionic Hamiltonian (created from from_string, FermiA, FermiC, FermiSentence, or FermiWord) and the number of qubits / spin orbitals in the system, n:

  >>> fermi_ham = qml.fermi.from_string('0+ 1+ 1- 0-') 
  >>> qubit_ham = qml.bravyi_kitaev(fermi_ham, n=6, tol=0.0) 
  >>> print(qubit_ham) 
  0.25 * I(0) + -0.25 * Z(0) + -0.25 * (Z(0) @ Z(1)) + 0.25 * Z(1) 

• The qml.qchem.hf_state function has been upgraded to be compatible with qml.parity_transform and the new Bravyi-Kitaev mapping (qml.bravyi_kitaev). (#5472) (#5472)

  >>> state_bk = qml.qchem.hf_state(2, 6, basis="bravyi_kitaev") 
  >>> print(state_bk) 
  [1 0 0 0 0 0] 
  >>> state_parity = qml.qchem.hf_state(2, 6, basis="parity") 
  >>> print(state_parity) 
  [1 0 0 0 0 0] 

Calculate dynamical Lie algebras 👾

The dynamical Lie algebra (DLA) of a set of operators captures the range of unitary evolutions that the operators can generate. In v0.36 of PennyLane, we have added support for calculating important DLA concepts including:

• A new qml.lie_closure function to compute the Lie closure of a list of operators, providing one way to obtain the DLA. (#5161) (#5169) (#5627)

  For a list of operators ops = [op1, op2, op3, ..], one computes all nested commutators between ops until no new operators are generated from commutation. All these operators together form the DLA, see e.g. section IIB of arXiv:2308.01432.

  Take for example the following operators:

  from pennylane import X, Y, Z 
  ops = [X(0) @ X(1), Z(0), Z(1)] 

  A first round of commutators between all elements yields the new operators Y(0) @ X(1) and X(0) @ Y(1) (omitting scalar prefactors).

  >>> qml.commutator(X(0) @ X(1), Z(0)) 
  -2j * (Y(0) @ X(1)) 
  >>> qml.commutator(X(0) @ X(1), Z(1)) 
  -2j * (X(0) @ Y(1)) 

  A next round of commutators between all elements further yields the new operator Y(0) @ Y(1).

  >>> qml.commutator(X(0) @ Y(1), Z(0)) 
  -2j * (Y(0) @ Y(1)) 

  After that, no new operators emerge from taking nested commutators and we have the resulting DLA. This can now be done in short via qml.lie_closure as follows.

  >>> ops = [X(0) @ X(1), Z(0), Z(1)] 
  >>> dla = qml.lie_closure(ops) 
  >>> dla 
  [X(0) @ X(1), Z(0), Z(1), -1.0 * (Y(0) @ X(1)), -1.0 * (X(0) @ Y(1)), -1.0 * (Y(0) @ Y(1))] 

• Computing the structure constants (the adjoint representation) of a dynamical Lie algebra. (5406)

  For example, we can compute the adjoint representation of the transverse field Ising model DLA.

  >>> dla = [X(0) @ X(1), Z(0), Z(1), Y(0) @ X(1), X(0) @ Y(1), Y(0) @ Y(1)] 
  >>> structure_const = qml.structure_constants(dla) 
  >>> structure_const.shape 
  (6, 6, 6) 

  Visit the documentation of qml.structure_constants to understand how structure constants are a useful way to represent a DLA.

• Computing the center of a dynamical Lie algebra. (#5477)

  Given a DLA g, we can now compute its centre. The center is the collection of operators that commute with all other operators in the DLA.

  >>> g = [X(0), X(1) @ X(0), Y(1), Z(1) @ X(0)] 
  >>> qml.center(g) 
  [X(0)] 

  To help explain these concepts, check out the dynamical Lie algebras demo.

Improvements 🛠

Simulate mixed-state qutrit systems

• Mixed qutrit states can now be simulated with the default.qutrit.mixed device. (#5495) (#5451) (#5186) (#5082) (#5213)

  Thanks to contributors from the University of British Columbia, a mixed-state qutrit device is now available for simulation, providing a noise-capable equivalent to default.qutrit.

  dev = qml.device("default.qutrit.mixed")
  
  def circuit(): 
     qml.TRY(0.1, wires=0)
  
  @qml.qnode(dev) 
  def shots_circuit(): 
     circuit() 
     return qml.sample(), qml.expval(qml.GellMann(wires=0, index=1))
  
  @qml.qnode(dev) 
  def density_matrix_circuit(): 
     circuit() 
     return qml.state() 

  >>> shots_circuit(shots=5) 
  (array([0, 0, 0, 0, 0]), 0.19999999999999996) 
  >>> density_matrix_circuit() 
  tensor([[0.99750208+0.j, 0.04991671+0.j, 0. +0.j], [0.04991671+0.j, 0.00249792+0.j, 0. +0.j], [0. +0.j, 0. +0.j, 0. +0.j]], requires_grad=True) 

  However, there's one crucial ingredient that we still need to add: support for qutrit noise operations. Keep your eyes peeled for this to arrive in the coming releases!

Work easily and efficiently with operators

• This release completes the main phase of PennyLane's switchover to an updated approach for handling arithmetic operations between operators. The new approach is now enabled by default and is intended to realize 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, this update should not break code. If issues do arise, check out the updated operator troubleshooting page and don't hesitate to reach out to us on the PennyLane discussion forum. As a last resort the old behaviour can be enabled by calling qml.operation.disable_new_opmath(), but this is not recommended because support will not continue in future PennyLane versions (v0.36 and higher). (#5269)

• A new class called qml.ops.LinearCombination has been introduced. In essence, this class is an updated equivalent of the now-deprecated qml.ops.Hamiltonian but for usage with the new operator arithmetic. (#5216)

• qml.ops.Sum now supports storing grouping information. Grouping type and method can be specified during construction using the grouping_type and method keyword arguments of qml.dot, qml.sum, or qml.ops.Sum. The grouping indices are stored in Sum.grouping_indices. (#5179)

  a = qml.X(0) 
  b = qml.prod(qml.X(0), qml.X(1)) 
  c = qml.Z(0) 
  obs = [a, b, c] 
  coeffs = [1.0, 2.0, 3.0]
  
  op = qml.dot(coeffs, obs, grouping_type="qwc") 

  >>> op.grouping_indices 
  ((2,), (0, 1)) 

  Additionally, grouping_type and method can be set or changed after construction using Sum.compute_grouping():

  a = qml.X(0) 
  b = qml.prod(qml.X(0), qml.X(1)) 
  c = qml.Z(0) 
  obs = [a, b, c] 
  coeffs = [1.0, 2.0, 3.0]
  
  op = qml.dot(coeffs, obs) 

  >>> op.grouping_indices is None 
  True 
  >>> op.compute_grouping(grouping_type="qwc") 
  >>> op.grouping_indices 
  ((2,), (0, 1)) 

  Note that the grouping indices refer to the lists returned by Sum.terms(), not Sum.operands.

• A new function called qml.operation.convert_to_legacy_H that converts Sum, SProd, and Prod to Hamiltonian instances has been added. This function is intended for developers and will be removed in a future release without a deprecation cycle. (#5309)

• The qml.is_commuting function now accepts Sum, SProd, and Prod instances. (#5351)

• Operators can now be left-multiplied by NumPy arrays (i.e., arr * op). (#5361)

• op.generator(), where op is an Operator instance, now returns operators consistent with the global setting for qml.operator.active_new_opmath() wherever possible. Sum, SProd and Prod instances will be returned even after disabling the new operator arithmetic in cases where they offer additional functionality not available using legacy operators. (#5253) (#5410) (#5411) (#5421)

• Prod instances temporarily have a new obs property, which helps smoothen the transition of the new operator arithmetic system. In particular, this is aimed at preventing breaking code that uses Tensor.obs. The property has been immediately deprecated. Moving forward, we recommend using op.operands. (#5539)

• qml.ApproxTimeEvolution is now compatible with any operator that has a defined pauli_rep. (#5362)

• Hamiltonian.pauli_rep is now defined if the Hamiltonian is a linear combination of Pauli operators. (#5377)

• Prod instances created with qutrit operators now have a defined eigvals() method. (#5400)

• qml.transforms.hamiltonian_expand and qml.transforms.sum_expand can now handle multi-term observables with a constant offset (i.e., terms like qml.I()). (#5414) (#5543)

• qml.qchem.taper_operation is now compatible with the new operator arithmetic. (#5326)

• The warning for an observable that might not be hermitian in QNode executions has been removed. This enables jit-compilation. (#5506)

• qml.transforms.split_non_commuting will now work with single-term operator arithmetic. (#5314)

• LinearCombination and Sum now accept _grouping_indices on initialization. This addition is relevant to developers only. (#5524)

• Calculating the dense, differentiable matrix for PauliSentence and operators with Pauli sentences is now faster. (#5578)

Community contributions 🥳

• ExpectationMP, VarianceMP, CountsMP, and SampleMP now have a process_counts method (similar to process_samples). This allows for calculating measurements given a counts dictionary. (#5256) (#5395)

• Type-hinting has been added in the Operator class for better interpretability. (#5490)

• An alternate strategy for sampling with multiple different shots values has been implemented via the shots.bins() method, which samples all shots at once and then processes each separately. (#5476)

Mid-circuit measurements and dynamic circuits

• A new module called qml.capture that will contain PennyLane's own capturing mechanism for hybrid quantum-classical programs has been added. (#5509)

• The dynamic_one_shot transform has been introduced, enabling dynamic circuit execution on circuits with finite shots and devices that natively support mid-circuit measurements. (#5266)

• The QubitDevice class and children classes support the dynamic_one_shot transform provided that they support mid-circuit measurement operations natively. (#5317)

• default.qubit can now be provided a random seed for sampling mid-circuit measurements with finite shots. This (1) ensures that random behaviour is more consistent with dynamic_one_shot and defer_measurements and (2) makes our continuous-integration (CI) have less failures due to stochasticity. (#5337)

Performance and broadcasting

• Gradient transforms may now be applied to batched/broadcasted QNodes as long as the broadcasting is in non-trainable parameters. (#5452)

• The performance of computing the matrix of qml.QFT has been improved. (#5351)

• qml.transforms.broadcast_expand now supports shot vectors when returning qml.sample(). (#5473)

• LightningVJPs is now compatible with Lightning devices using the new device API. (#5469)

Device capabilities

• Obtaining classical shadows using the default.clifford device is now compatible with stim v1.13.0. (#5409)

• default.mixed has improved support for sampling-based measurements with non-NumPy interfaces. (#5514) (#5530)

• default.mixed now supports arbitrary state-based measurements with qml.Snapshot. (#5552)

• null.qubit has been upgraded to the new device API and has support for all measurements and various modes of differentiation. (#5211)

Other improvements

• Entanglement entropy can now be calculated with qml.math.vn_entanglement_entropy, which computes the von Neumann entanglement entropy from a density matrix. A corresponding QNode transform, qml.qinfo.vn_entanglement_entropy, has also been added. (#5306)

• qml.draw and qml.draw_mpl will now attempt to sort the wires if no wire order is provided by the user or the device. (#5576)

• A clear error message is added in KerasLayer when using the newest version of TensorFlow with Keras 3 (which is not currently compatible with KerasLayer), linking to instructions to enable Keras 2. (#5488)

• qml.ops.Conditional now stores the data, num_params, and ndim_param attributes of the operator it wraps. (#5473)

• The molecular_hamiltonian function calls PySCF directly when method='pyscf' is selected. (#5118)

• cache_execute has been replaced with an alternate implementation based on @transform. (#5318)

• QNodes now defer diff_method validation to the device under the new device API. (#5176)

• The device test suite has been extended to cover gradient methods, templates and arithmetic observables. (#5273) (#5518)

• A typo and string formatting mistake have been fixed in the error message for ClassicalShadow._convert_to_pauli_words when the input is not a valid pauli_rep. (#5572)

• Circuits running on lightning.qubit and that return qml.state() now preserve the dtype when specified. (#5547)

Breaking changes 💔

• qml.matrix() called on the following will now raise an error if wire_order is not specified: * tapes with more than one wire * quantum functions * Operator classes where num_wires does not equal to 1 * QNodes if the device does not have wires specified. * PauliWords and PauliSentences with more than one wire. (#5328) (#5359)

• single_tape_transform, batch_transform, qfunc_transform, op_transform, gradient_transform and hessian_transform have been removed. Instead, switch to using the new qml.transform function. Please refer to the transform docs <https://docs.pennylane.ai/en/stable/code/qml_transforms.html#custom-transforms>_ to see how this can be done. (#5339)

• Attempting to multiply PauliWord and PauliSentence with * will raise an error. Instead, use @ to conform with the PennyLane convention. (#5341)

• DefaultQubit now uses a pre-emptive key-splitting strategy to avoid reusing JAX PRNG keys throughout a single execute call. (#5515)

• qml.pauli.pauli_mult and qml.pauli.pauli_mult_with_phase have been removed. Instead, use qml.simplify(qml.prod(pauli_1, pauli_2)) to get the reduced operator. (#5324)

>>> op = qml.simplify(qml.prod(qml.PauliX(0), qml.PauliZ(0))) 
>>> op -1j*(PauliY(wires=[0])) 
>>> [phase], [base] = op.terms() 
>>> phase, base 
(-1j, PauliY(wires=[0])) 

• The dynamic_one_shot transform now uses sampling (SampleMP) to get back the values of the mid-circuit measurements. (#5486)

• Operator dunder methods now combine like-operator arithmetic classes via lazy=False. This reduces the chances of getting a RecursionError and makes nested operators easier to work with. (#5478)

• The private functions _pauli_mult, _binary_matrix and _get_pauli_map from the pauli module have been removed. The same functionality can be achieved using newer features in the pauli module. (#5323)

• MeasurementProcess.name and MeasurementProcess.data have been removed. Use MeasurementProcess.obs.name and MeasurementProcess.obs.data instead. (#5321)

• Operator.validate_subspace(subspace) has been removed. Instead, use qml.ops.qutrit.validate_subspace(subspace). (#5311)

• The contents of qml.interfaces has been moved inside qml.workflow. The old import path no longer exists. (#5329)

• Since default.mixed does not support snapshots with measurements, attempting to do so will result in a DeviceError instead of getting the density matrix. (#5416)

• LinearCombination._obs_data has been removed. You can still use LinearCombination.compare to check mathematical equivalence between a LinearCombination and another operator. (#5504)

Deprecations 👋

• Accessing qml.ops.Hamiltonian is deprecated because it points to the old version of the class that may not be compatible with the new approach to operator arithmetic. Instead, using qml.Hamiltonian is recommended because it dispatches to the LinearCombination class when the new approach to operator arithmetic is enabled. This will allow you to continue to use qml.Hamiltonian with existing code without needing to make any changes. (#5393)

• qml.load has been deprecated. Instead, please use the functions outlined in the Importing workflows quickstart guide. (#5312)

• Specifying control_values with a bit string in qml.MultiControlledX has been deprecated. Instead, use a list of booleans or 1s and 0s. (#5352)

• qml.from_qasm_file has been deprecated. Instead, please open the file and then load its content using qml.from_qasm. (#5331)

>>> with open("test.qasm", "r") as f: 
...    circuit = qml.from_qasm(f.read()) 

Documentation 📝

• A new page explaining the shapes and nesting of return types has been added. (#5418)

• Redundant documentation for the evolve function has been removed. (#5347)

• The final example in the compile docstring has been updated to use transforms correctly. (#5348)

• A link to the demos for using qml.SpecialUnitary and qml.QNGOptimizer has been added to their respective docstrings. (#5376)

• A code example in the qml.measure docstring has been added that showcases returning mid-circuit measurement statistics from QNodes. (#5441)

• The computational basis convention used for qml.measure — 0 and 1 rather than ±1 — has been clarified in its docstring. (#5474)

• A new Release news section has been added to the table of contents, containing release notes, deprecations, and other pages focusing on recent changes. (#5548)

• A summary of all changes has been added in the "Updated Operators" page in the new "Release news" section in the docs. (#5483) (#5636)

Bug fixes 🐛

• Patches the QNode so that parameter-shift will be considered best with lightning if qml.metric_tensor is in the transform program. (#5624)

• Stopped printing the ID of qcut.MeasureNode and qcut.PrepareNode in tape drawing. (#5613)

• Improves the error message for setting shots on the new device interface, or trying to access a property that no longer exists. (#5616)

• Fixed a bug where qml.draw and qml.draw_mpl incorrectly raised errors for circuits collecting statistics on mid-circuit measurements while using qml.defer_measurements. (#5610)

• Using shot vectors with param_shift(... broadcast=True) caused a bug. This combination is no longer supported and will be added again in the next release. Fixed a bug with custom gradient recipes that only consist of unshifted terms. (#5612) (#5623)

• qml.counts now returns the same keys with dynamic_one_shot and defer_measurements. (#5587)

• null.qubit now automatically supports any operation without a decomposition. (#5582)

• Fixed a bug where the shape and type of derivatives obtained by applying a gradient transform to a QNode differed based on whether the QNode uses classical coprocessing. (#4945)

• ApproxTimeEvolution, CommutingEvolution, QDrift, and TrotterProduct now de-queue their input observable. (#5524)

• (In)equality of qml.HilbertSchmidt instances is now reported correctly by qml.equal. (#5538)

• qml.ParticleConservingU1 and qml.ParticleConservingU2 no longer raise an error when the initial state is not specified but default to the all-zeros state. (#5535)

• qml.counts no longer returns negative samples when measuring 8 or more wires. (#5544) (#5556)

• The dynamic_one_shot transform now works with broadcasting. (#5473)

• Diagonalizing gates are now applied when measuring qml.probs on non-computational basis states on a Lightning device. (#5529)

• two_qubit_decomposition no longer diverges at a special case of a unitary matrix. (#5448)

• The qml.QNSPSAOptimizer now correctly handles optimization for legacy devices that do not follow the new device API. (#5497)

• Operators applied to all wires are now drawn correctly in a circuit with mid-circuit measurements. (#5501)

• Fixed a bug where certain unary mid-circuit measurement expressions would raise an uncaught error. (#5480)

• Probabilities now sum to 1 when using the torch interface with default_dtype set to torch.float32. (#5462)

• Tensorflow can now handle devices with float32 results but float64 input parameters. (#5446)

• Fixed a bug where the argnum keyword argument of qml.gradients.stoch_pulse_grad references the wrong parameters in a tape, creating an inconsistency with other differentiation methods and preventing some use cases. (#5458)

• Bounded value failures due to numerical noise with calls to np.random.binomial is now avoided. (#5447)

• Using @ with legacy Hamiltonian instances now properly de-queues the previously existing operations. (#5455)

• The QNSPSAOptimizer now properly handles differentiable parameters, resulting in being able to use it for more than one optimization step. (#5439)

• The QNode interface now resets if an error occurs during execution. (#5449)

• Failing tests due to changes with Lightning's adjoint diff pipeline have been fixed. (#5450)

• Failures occurring when making autoray-dispatched calls to Torch with paired CPU data have been fixed. (#5438)

• jax.jit now works with qml.sample with a multi-wire observable. (#5422)

• qml.qinfo.quantum_fisher now works with non-default.qubit devices. (#5423)

• We no longer perform unwanted dtype promotion in the pauli_rep of SProd instances when using Tensorflow. (#5246)

• Fixed TestQubitIntegration.test_counts in tests/interfaces/test_jax_qnode.py to always produce counts for all outcomes. (#5336)

• Fixed PauliSentence.to_mat(wire_order) to support identities with wires. (#5407)

• CompositeOp.map_wires now correctly maps the overlapping_ops property. (#5430)

• DefaultQubit.supports_derivatives has been updated to correctly handle circuits containing mid-circuit measurements and adjoint differentiation. (#5434)

• SampleMP, ExpectationMP, CountsMP, and VarianceMP constructed with eigvals can now properly process samples. (#5463)

• Fixed a bug in hamiltonian_expand that produces incorrect output dimensions when shot vectors are combined with parameter broadcasting. (#5494)

• default.qubit now allows measuring Identity on no wires and observables containing Identity on no wires. (#5570)

• Fixed a bug where TorchLayer does not work with shot vectors. (#5492)

• Fixed a bug where the output shape of a QNode returning a list containing a single measurement is incorrect when combined with shot vectors. (#5492)

• Fixed a bug in qml.math.kron that makes Torch incompatible with NumPy. (#5540)

• Fixed a bug in _group_measurements that fails to group measurements with commuting observables when they are operands of Prod. (#5525)

• qml.equal can now be used with sums and products that contain operators on no wires like I and GlobalPhase. (#5562)

• CompositeOp.has_diagonalizing_gates now does a more complete check of the base operators to ensure consistency between op.has_diagonalzing_gates and op.diagonalizing_gates() (#5603)

• Updated the method kwarg of qml.TrotterProduct().error() to be more clear that we are computing upper-bounds. (#5637)

Contributors ✍️

This release contains contributions from (in alphabetical order):

Tarun Kumar Allamsetty, Guillermo Alonso, Mikhail Andrenkov, Utkarsh Azad, Gabriel Bottrill, Thomas Bromley, Astral Cai, Diksha Dhawan, Isaac De Vlugt, Amintor Dusko, Pietropaolo Frisoni, Lillian M. A. Frederiksen, Diego Guala, Austin Huang, Soran Jahangiri, Korbinian Kottmann, Christina Lee, Vincent Michaud-Rioux, Mudit Pandey, Kenya Sakka, Jay Soni, Matthew Silverman, David Wierichs.