PennyLaneAI/pennylane v0.35.0

Release 0.35.0

on GitHub

New features since last release

Qiskit 1.0 integration 🔌

• This version of PennyLane makes it easier to import circuits from Qiskit. (#5218) (#5168)

  The qml.from_qiskit function converts a Qiskit QuantumCircuit into a PennyLane quantum function. Although qml.from_qiskit already exists in PennyLane, we have made a number of improvements to make importing from Qiskit easier. And yes — qml.from_qiskit functionality is compatible with both Qiskit 1.0 and earlier versions! Here's a comprehensive list of the improvements:

  • You can now append PennyLane measurements onto the quantum function returned by qml.from_qiskit. Consider this simple Qiskit circuit:

    import pennylane as qml 
    from qiskit import QuantumCircuit
    
    qc = QuantumCircuit(2) 
    qc.rx(0.785, 0) 
    qc.ry(1.57, 1) 

    We can convert it into a PennyLane QNode in just a few lines, with PennyLane measurements easily included:

    >>> dev = qml.device("default.qubit") 
    >>> measurements = qml.expval(qml.Z(0) @ qml.Z(1)) 
    >>> qfunc = qml.from_qiskit(qc, measurements=measurements) 
    >>> qnode = qml.QNode(qfunc, dev) 
    >>> qnode() 
    tensor(0.00056331, requires_grad=True) 

  • Quantum circuits that already contain Qiskit-side measurements can be faithfully converted with qml.from_qiskit. Consider this example Qiskit circuit:

    qc = QuantumCircuit(3, 2) # Teleportation
    
    qc.rx(0.9, 0) # Prepare input state on qubit 0
    
    qc.h(1) # Prepare Bell state on qubits 1 and 2 qc.cx(1, 2)
    
    qc.cx(0, 1) # Perform teleportation 
    qc.h(0) 
    qc.measure(0, 0) 
    qc.measure(1, 1)
    
    with qc.if_test((1, 1)): # Perform first conditional 
        qc.x(2) 

    This circuit can be converted into PennyLane with the Qiskit measurements still accessible. For example, we can use those results as inputs to a mid-circuit measurement in PennyLane:

    @qml.qnode(dev) 
    def teleport(): 
        m0, m1 = qml.from_qiskit(qc)() 
        qml.cond(m0, qml.Z)(2) 
        return qml.density_matrix(2) 

    >>> teleport() 
    tensor([[0.81080498+0.j , 0. +0.39166345j], 
    [0. -0.39166345j, 0.18919502+0.j ]], requires_grad=True) 

  • It is now more intuitive to handle and differentiate parametrized Qiskit circuits. Consider the following circuit:

    from qiskit.circuit import Parameter 
    from pennylane import numpy as np
    
    angle0 = Parameter("x") angle1 = Parameter("y")
    
    qc = QuantumCircuit(2, 2) 
    qc.rx(angle0, 0) 
    qc.ry(angle1, 1) 
    qc.cx(1, 0) 

    We can convert this circuit into a QNode with two arguments, corresponding to x and y:

    measurements = qml.expval(qml.PauliZ(0)) 
    qfunc = qml.from_qiskit(qc, measurements) 
    qnode = qml.QNode(qfunc, dev) 

    The QNode can be evaluated and differentiated:

    >>> x, y = np.array([0.4, 0.5], requires_grad=True) 
    >>> qnode(x, y) 
    tensor(0.80830707, requires_grad=True) 
    
    >>> qml.grad(qnode)(x, y) 
    (tensor(-0.34174675, requires_grad=True), tensor(-0.44158016, requires_grad=True)) 

    This shows how easy it is to make a Qiskit circuit differentiable with PennyLane.

  • In addition to circuits, it is also possible to convert operators from Qiskit to PennyLane with a new function called qml.from_qiskit_op. (#5251)

    A Qiskit SparsePauliOp can be converted to a PennyLane operator using qml.from_qiskit_op:

    >>> from qiskit.quantum_info import SparsePauliOp 
    >>> qiskit_op = SparsePauliOp(["II", "XY"]) 
    >>> qiskit_op SparsePauliOp(['II', 'XY'], coeffs=[1.+0.j, 1.+0.j]) 
    >>> pl_op = qml.from_qiskit_op(qiskit_op) 
    >>> pl_op I(0) + X(1) @ Y(0) 

    Combined with qml.from_qiskit, it becomes easy to quickly calculate quantities like expectation values by converting the whole workflow to PennyLane:

    qc = QuantumCircuit(2) # Create circuit 
    qc.rx(0.785, 0) 
    qc.ry(1.57, 1)
    
    measurements = qml.expval(pl_op) # Create QNode 
    qfunc = qml.from_qiskit(qc, measurements) 
    qnode = qml.QNode(qfunc, dev) 

    >>> qnode() # Evaluate! 
    tensor(0.29317504, requires_grad=True) 

Native mid-circuit measurements on Default Qubit 💡

• Mid-circuit measurements can now be more scalable and efficient in finite-shots mode with default.qubit by simulating them in a similar way to what happens on quantum hardware. (#5088) (#5120)

  Previously, mid-circuit measurements (MCMs) would be automatically replaced with an additional qubit using the @qml.defer_measurements transform. The circuit below would have required thousands of qubits to simulate.

  Now, MCMs are performed in a similar way to quantum hardware with finite shots on default.qubit. For each shot and each time an MCM is encountered, the device evaluates the probability of projecting onto |0> or |1> and makes a random choice to collapse the circuit state. This approach works well when there are a lot of MCMs and the number of shots is not too high.

  import pennylane as qml
  
  dev = qml.device("default.qubit", shots=10)
  
  @qml.qnode(dev) 
  def f(): 
      for i in range(1967): 
          qml.Hadamard(0) 
          qml.measure(0) 
      return qml.sample(qml.PauliX(0)) 

  >>> f() 
  tensor([-1, -1, -1, 1, 1, -1, 1, -1, 1, -1], requires_grad=True) 

Work easily and efficiently with operators 🔧

• Over the past few releases, PennyLane's approach to operator arithmetic has been in the process of being overhauled. We have 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.

  The updated operator arithmetic functionality is still being finalized, but can be activated using qml.operation.enable_new_opmath(). In the next release, the new behaviour will become the default, so we recommend enabling now to become familiar with the new system!

  The following updates have been made in this version of PennyLane:

  • You can now easily access Pauli operators via I, X, Y, and Z: (#5116)

    >>> from pennylane import I, X, Y, Z 
    >>> X(0) X(0) ```
    
    The original long-form names `Identity`, `PauliX`, `PauliY`, and `PauliZ` remain available, but use of the short-form names is now recommended.
    

  • A new qml.commutator function is now available that allows you to compute commutators between PennyLane operators. (#5051) (#5052) (#5098)

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

  • 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. (#4989) (#5001) (#5003) (#5017) (#5027)

    The Pauli representation can be optionally accessed via op.pauli_rep:

    >>> qml.operation.enable_new_opmath() 
    >>> op = X(0) + Y(0) 
    >>> op.pauli_rep 
    1.0 * X(0) + 1.0 * 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. (#5116) (#5138)

    >>> 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)) 
    ) 
    

  • Linear combinations of operators and operator multiplication via Sum and Prod, respectively, have been updated to reach feature parity with Hamiltonian and Tensor, respectively. This should minimize the effort to port over any existing code. (#5070) (#5132) (#5133)

    Updates include support for grouping via the pauli module:

    >>> obs = [X(0) @ Y(1), Z(0), Y(0) @ Z(1), Y(1)] 
    >>> qml.pauli.group_observables(obs) 
    [[Y(0) @ Z(1)], [X(0) @ Y(1), Y(1)], [Z(0)]] 

New Clifford device 🦾

• A new default.clifford device enables efficient simulation of large-scale Clifford circuits defined in PennyLane through the use of stim as a backend. (#4936) (#4954) (#5144)

  Given a circuit with only Clifford gates, one can use this device to obtain the usual range of PennyLane measurements as well as the state represented in the Tableau form of Aaronson & Gottesman (2004):

  import pennylane as qml
  
  dev = qml.device("default.clifford", tableau=True) 
  @qml.qnode(dev) 
  def circuit(): 
      qml.CNOT(wires=[0, 1]) 
      qml.PauliX(wires=[1]) 
      qml.ISWAP(wires=[0, 1]) 
      qml.Hadamard(wires=[0]) 
      return qml.state() 

  >>> circuit() 
  array([[0, 1, 1, 0, 0], 
        [1, 0, 1, 1, 1], 
        [0, 0, 0, 1, 0], 
        [1, 0, 0, 1, 1]]) 

  The default.clifford device also supports the PauliError, DepolarizingChannel, BitFlip and PhaseFlip noise channels when operating in finite-shot mode.

Improvements 🛠

Faster gradients with VJPs and other performance improvements

• Vector-Jacobian products (VJPs) can result in faster computations when the output of your quantum Node has a low dimension. They can be enabled by setting device_vjp=True when loading a QNode. In the next release of PennyLane, VJPs are planned to be used by default, when available.

  In this release, we have unlocked:

  • Adjoint device VJPs can be used with jax.jacobian, meaning that device_vjp=True is always faster when using JAX with default.qubit. (#4963)

  • PennyLane can now use lightning-provided VJPs. (#4914)

  • VJPs can be used with TensorFlow, though support has not yet been added for tf.Function and Tensorflow Autograph. (#4676)

• Measuring qml.probs is now faster due to an optimization in converting samples to counts. (#5145)

• The performance of circuit-cutting workloads with large numbers of generated tapes has been improved. (#5005)

• Queueing (AnnotatedQueue) has been removed from qml.cut_circuit and qml.cut_circuit_mc to improve performance for large workflows. (#5108)

Community contributions 🥳

• A new function called qml.fermi.parity_transform has been added for parity mapping of a fermionic Hamiltonian. (#4928)

  It is now possible to transform a fermionic Hamiltonian to a qubit Hamiltonian with parity mapping.

  import pennylane as qml 
  fermi_ham = qml.fermi.FermiWord({(0, 0) : '+', (1, 1) : '-'})
  
  qubit_ham = qml.fermi.parity_transform(fermi_ham, n=6) 

>>> print(qubit_ham) 
-0.25j * Y(0) + (-0.25+0j) * (X(0) @ Z(1)) + (0.25+0j) * X(0) + 0.25j * (Y(0) @ Z(1)) 

• The transform split_non_commuting now accepts measurements of type probs, sample, and counts, which accept both wires and observables. (#4972)

• The efficiency of matrix calculations when an operator is symmetric over a given set of wires has been improved. (#3601)

• The pennylane/math/quantum.py module now has support for computing the minimum entropy of a density matrix. (#3959)

  >>> x = [1, 0, 0, 1] / np.sqrt(2) 
  >>> x = qml.math.dm_from_state_vector(x) 
  >>> qml.math.min_entropy(x, indices=[0]) 
  0.6931471805599455 

• A function called apply_operation that applies operations to device-compatible states has been added to the new qutrit_mixed module found in qml.devices. (#5032)

• A function called measure has been added to the new qutrit_mixed module found in qml.devices that measures device-compatible states for a collection of measurement processes. (#5049)

• A partial_trace function has been added to qml.math for taking the partial trace of matrices. (#5152)

Other operator arithmetic improvements

• The following capabilities have been added for Pauli arithmetic: (#4989) (#5001) (#5003) (#5017) (#5027) (#5018)

  • You can now multiply PauliWord and PauliSentence instances by scalars (e.g., 0.5 * PauliWord({0: "X"}) or 0.5 * PauliSentence({PauliWord({0: "X"}): 1.})).

  • You can now intuitively add and subtract PauliWord and PauliSentence instances and scalars together (scalars are treated implicitly as multiples of the identity, I). For example, ps1 + pw1 + 1. for some Pauli word pw1 = PauliWord({0: "X", 1: "Y"}) and Pauli sentence ps1 = PauliSentence({pw1: 3.}).

  • You can now element-wise multiply PauliWord, PauliSentence, and operators together with qml.dot (e.g., qml.dot([0.5, -1.5, 2], [pw1, ps1, id_word]) with id_word = PauliWord({})).

  • qml.matrix now accepts PauliWord and PauliSentence instances (e.g., qml.matrix(PauliWord({0: "X"}))).

  • It is now possible to compute commutators with Pauli operators natively with the new commutator method.

    >>> op1 = PauliWord({0: "X", 1: "X"}) 
    >>> op2 = PauliWord({0: "Y"}) + PauliWord({1: "Y"}) 
    >>> op1.commutator(op2) 2j * Z(0) @ X(1) + 2j * X(0) @ Z(1) 

• Composite operations (e.g., those made with qml.prod and qml.sum) and scalar-product operations convert Hamiltonian and Tensor operands to Sum and Prod types, respectively. This helps avoid the mixing of incompatible operator types. (#5031) (#5063)

• qml.Identity() can be initialized without wires. Measuring it is currently not possible, though. (#5106)

• qml.dot now returns a Sum class even when all the coefficients match. (#5143)

• qml.pauli.group_observables now supports grouping Prod and SProd operators. (#5070)

• The performance of converting a PauliSentence to a Sum has been improved. (#5141) (#5150)

• Akin to qml.Hamiltonian features, the coefficients and operators that make up composite operators formed via Sum or Prod can now be accessed with the terms() method. (#5132) (#5133) (#5164)

  >>> qml.operation.enable_new_opmath() 
  >>> op = X(0) @ (0.5 * X(1) + X(2)) 
  >>> op.terms() 
  ([0.5, 1.0], 
   [X(1) @ X(0), 
    X(2) @ X(0)]) 

• String representations of ParametrizedHamiltonian have been updated to match the style of other PL operators. (#5215)

Other improvements

• The pl-device-test suite is now compatible with the qml.devices.Device interface. (#5229)

• The QSVT operation now determines its data from the block encoding and projector operator data. (#5226) (#5248)

• The BlockEncode operator is now JIT-compatible with JAX. (#5110)

• The qml.qsvt function uses qml.GlobalPhase instead of qml.exp to define a global phase. (#5105)

• The tests/ops/functions/conftest.py test has been updated to ensure that all operator types are tested for validity. (#4978)

• A new pennylane.workflow module has been added. This module now contains qnode.py, execution.py, set_shots.py, jacobian_products.py, and the submodule interfaces. (#5023)

• A more informative error is now raised when calling adjoint_jacobian with trainable state-prep operations. (#5026)

• qml.workflow.get_transform_program and qml.workflow.construct_batch have been added to inspect the transform program and batch of tapes at different stages. (#5084)

• All custom controlled operations such as CRX, CZ, CNOT, ControlledPhaseShift now inherit from ControlledOp, giving them additional properties such as control_wire and control_values. Calling qml.ctrl on RX, RY, RZ, Rot, and PhaseShift with a single control wire will return gates of types CRX, CRY, etc. as opposed to a general Controlled operator. (#5069) (#5199)

• The CI will now fail if coverage data fails to upload to codecov. Previously, it would silently pass and the codecov check itself would never execute. (#5101)

• qml.ctrl called on operators with custom controlled versions will now return instances of the custom class, and it will flatten nested controlled operators to a single multi-controlled operation. For PauliX, CNOT, Toffoli, and MultiControlledX, calling qml.ctrl will always resolve to the best option in CNOT, Toffoli, or MultiControlledX depending on the number of control wires and control values. (#5125)

• Unwanted warning filters have been removed from tests and no PennyLaneDeprecationWarnings are being raised unexpectedly. (#5122)

• New error tracking and propagation functionality has been added (#5115) (#5121)

• The method map_batch_transform has been replaced with the method _batch_transform implemented in TransformDispatcher. (#5212)

• TransformDispatcher can now dispatch onto a batch of tapes, making it easier to compose transforms when working in the tape paradigm. (#5163)

• qml.ctrl is now a simple wrapper that either calls PennyLane's built in create_controlled_op or uses the Catalyst implementation. (#5247)

• Controlled composite operations can now be decomposed using ZYZ rotations. (#5242)

• New functions called qml.devices.modifiers.simulator_tracking and qml.devices.modifiers.single_tape_support have been added to add basic default behavior onto a device class. (#5200)

Breaking changes 💔

• Passing additional arguments to a transform that decorates a QNode must now be done through the use of functools.partial. (#5046)

• qml.ExpvalCost has been removed. Users should use qml.expval() moving forward. (#5097)

• Caching of executions is now turned off by default when max_diff == 1, as the classical overhead cost outweighs the probability that duplicate circuits exists. (#5243)

• The entry point convention registering compilers with PennyLane has changed. (#5140)

  To allow for packages to register multiple compilers with PennyLane, the entry_points convention under the designated group name pennylane.compilers has been modified.

  Previously, compilers would register qjit (JIT decorator), ops (compiler-specific operations), and context (for tracing and program capture).

  Now, compilers must register compiler_name.qjit, compiler_name.ops, and compiler_name.context, where compiler_name is replaced by the name of the provided compiler.

  For more information, please see the documentation on adding compilers.

• PennyLane source code is now compatible with the latest version of black. (#5112) (#5119)

• gradient_analysis_and_validation has been renamed to find_and_validate_gradient_methods. Instead of returning a list, it now returns a dictionary of gradient methods for each parameter index, and no longer mutates the tape. (#5035)

• Multiplying two PauliWord instances no longer returns a tuple (new_word, coeff) but instead PauliSentence({new_word: coeff}). The old behavior is still available with the private method PauliWord._matmul(other) for faster processing. (#5045)

• Observable.return_type has been removed. Instead, you should inspect the type of the surrounding measurement process. (#5044)

• ClassicalShadow.entropy() no longer needs an atol keyword as a better method to estimate entropies from approximate density matrix reconstructions (with potentially negative eigenvalues). (#5048)

• Controlled operators with a custom controlled version decompose like how their controlled counterpart decomposes as opposed to decomposing into their controlled version. (#5069) (#5125)

  For example:

  >>> qml.ctrl(qml.RX(0.123, wires=1), control=0).decomposition() 
  [ 
    RZ(1.5707963267948966, wires=[1]), 
    RY(0.0615, wires=[1]), 
    CNOT(wires=[0, 1]), 
    RY(-0.0615, wires=[1]), 
    CNOT(wires=[0, 1]), 
    RZ(-1.5707963267948966, wires=[1]) 
  ] 

• QuantumScript.is_sampled and QuantumScript.all_sampled have been removed. Users should now validate these properties manually. (#5072)

• qml.transforms.one_qubit_decomposition and qml.transforms.two_qubit_decomposition have been removed. Instead, you should use qml.ops.one_qubit_decomposition and qml.ops.two_qubit_decomposition. (#5091)

Deprecations 👋

• Calling qml.matrix without providing a wire_order on objects where the wire order could be ambiguous now raises a warning. In the future, the wire_order argument will be required in these cases. (#5039)

• Operator.validate_subspace(subspace) has been relocated to the qml.ops.qutrit.parametric_ops module and will be removed from the Operator class in an upcoming release. (#5067)

• Matrix and tensor products between PauliWord and PauliSentence instances are done using the @ operator, * will be used only for scalar multiplication. Note also the breaking change that the product of two PauliWord instances now returns a PauliSentence instead of a tuple (new_word, coeff). (#4989) (#5054)

• MeasurementProcess.name and MeasurementProcess.data are now deprecated, as they contain dummy values that are no longer needed. (#5047) (#5071) (#5076) (#5122)

• qml.pauli.pauli_mult and qml.pauli.pauli_mult_with_phase are now deprecated. Instead, you should use qml.simplify(qml.prod(pauli_1, pauli_2)) to get the reduced operator. (#5057)

• The private functions _pauli_mult, _binary_matrix and _get_pauli_map from the pauli module have been deprecated, as they are no longer used anywhere and the same functionality can be achieved using newer features in the pauli module. (#5057)

• Sum.ops, Sum.coeffs, Prod.ops and Prod.coeffs will be deprecated in the future. (#5164)

Documentation 📝

• The module documentation for pennylane.tape now explains the difference between QuantumTape and QuantumScript. (#5065)

• A typo in a code example in the qml.transforms API has been fixed. (#5014)

• Documentation for qml.data has been updated and now mentions a way to access the same dataset simultaneously from multiple environments. (#5029)

• A clarification for the definition of argnum added to gradient methods has been made. (#5035)

• A typo in the code example for qml.qchem.dipole_of has been fixed. (#5036)

• A development guide on deprecations and removals has been added. (#5083)

• A note about the eigenspectrum of second-quantized Hamiltonians has been added to qml.eigvals. (#5095)

• A warning about two mathematically equivalent Hamiltonians undergoing different time evolutions has been added to qml.TrotterProduct and qml.ApproxTimeEvolution. (#5137)

• A reference to the paper that provides the image of the qml.QAOAEmbedding template has been added. (#5130)

• The docstring of qml.sample has been updated to advise the use of single-shot expectations instead when differentiating a circuit. (#5237)

• A quick start page has been added called "Importing Circuits". This explains how to import quantum circuits and operations defined outside of PennyLane. (#5281)

Bug fixes 🐛

• QubitChannel can now be used with jitting. (#5288)

• Fixed a bug in the matplotlib drawer where the colour of Barrier did not match the requested style. (#5276)

• qml.draw and qml.draw_mpl now apply all applied transforms before drawing. (#5277)

• ctrl_decomp_zyz is now differentiable. (#5198)

• qml.ops.Pow.matrix() is now differentiable with TensorFlow with integer exponents. (#5178)

• The qml.MottonenStatePreparation template has been updated to include a global phase operation. (#5166)

• Fixed a queuing bug when using qml.prod with a quantum function that queues a single operator. (#5170)

• The qml.TrotterProduct template has been updated to accept scalar products of operators as an input Hamiltonian. (#5073)

• Fixed a bug where caching together with JIT compilation and broadcasted tapes yielded wrong results Operator.hash now depends on the memory location, id, of a JAX tracer instead of its string representation. (#3917)

• qml.transforms.undo_swaps can now work with operators with hyperparameters or nesting. (#5081)

• qml.transforms.split_non_commuting will now pass the original shots along. (#5081)

• If argnum is provided to a gradient transform, only the parameters specified in argnum will have their gradient methods validated. (#5035)

• StatePrep operations expanded onto more wires are now compatible with backprop. (#5028)

• qml.equal works well with qml.Sum operators when wire labels are a mix of integers and strings. (#5037)

• The return value of Controlled.generator now contains a projector that projects onto the correct subspace based on the control value specified. (#5068)

• CosineWindow no longer raises an unexpected error when used on a subset of wires at the beginning of a circuit. (#5080)

• tf.function now works with TensorSpec(shape=None) by skipping batch size computation. (#5089)

• PauliSentence.wires no longer imposes a false order. (#5041)

• qml.qchem.import_state now applies the chemist-to-physicist sign convention when initializing a PennyLane state vector from classically pre-computed wavefunctions. That is, it interleaves spin-up/spin-down operators for the same spatial orbital index, as standard in PennyLane (instead of commuting all spin-up operators to the left, as is standard in quantum chemistry). (#5114)

• Multi-wire controlled CNOT and PhaseShift are now be decomposed correctly. (#5125) (#5148)

• draw_mpl no longer raises an error when drawing a circuit containing an adjoint of a controlled operation. (#5149)

• default.mixed no longer throws ValueError when applying a state vector that is not of type complex128 when used with tensorflow. (#5155)

• ctrl_decomp_zyz no longer raises a TypeError if the rotation parameters are of type torch.Tensor (#5183)

• Comparing Prod and Sum objects now works regardless of nested structure with qml.equal if the operators have a valid pauli_rep property. (#5177)

• Controlled GlobalPhase with non-zero control wires no longer throws an error. (#5194)

• A QNode transformed with mitigate_with_zne now accepts batch parameters. (#5195)

• The matrix of an empty PauliSentence instance is now correct (all-zeros). Further, matrices of empty PauliWord and PauliSentence instances can now be turned into matrices. (#5188)

• PauliSentence instances can handle matrix multiplication with PauliWord instances. (#5208)

• CompositeOp.eigendecomposition is now JIT-compatible. (#5207)

• QubitDensityMatrix now works with JAX-JIT on the default.mixed device. (#5203) (#5236)

• When a QNode specifies diff_method="adjoint", default.qubit no longer tries to decompose non-trainable operations with non-scalar parameters such as QubitUnitary. (#5233)

• The overwriting of the class names of I, X, Y, and Z no longer happens in the initialization after causing problems with datasets. This now happens globally. (#5252)

• The adjoint_metric_tensor transform now works with jax. (#5271)

Contributors ✍️

This release contains contributions from (in alphabetical order):

Abhishek Abhishek, Mikhail Andrenkov, Utkarsh Azad, Trenten Babcock, Gabriel Bottrill, Thomas Bromley, Astral Cai, Skylar Chan, Isaac De Vlugt, Diksha Dhawan, Lillian Frederiksen, Pietropaolo Frisoni, Eugenio Gigante, Diego Guala, David Ittah, Soran Jahangiri, Jacky Jiang, Korbinian Kottmann, Christina Lee, Xiaoran Li, Vincent Michaud-Rioux, Romain Moyard, Pablo Antonio Moreno Casares, Erick Ochoa Lopez, Lee J. O'Riordan, Mudit Pandey, Alex Preciado, Matthew Silverman, Jay Soni.