Release notes

This page contains the release notes for PennyLane.

Release 0.12.0 (development release)

New features since last release

  • The quantum state of a QNode can now be returned using the state() return function. (#818)

    Consider the following QNode:

    import pennylane as qml
    from pennylane.beta.tapes import qnode
    from pennylane.beta.queuing import state
    dev = qml.device("default.qubit", wires=3)
    def qfunc(x, y):
        qml.RZ(x, wires=0)
        qml.CNOT(wires=[0, 1])
        qml.RY(y, wires=1)
        qml.CNOT(wires=[0, 2])
        return state()

    Calling the QNode will return its state

    >>> qfunc(0.56, 0.1)
    array([0.95985437-0.27601028j, 0.        +0.j        ,
         0.04803275-0.01381203j, 0.        +0.j        ,
         0.        +0.j        , 0.        +0.j        ,
         0.        +0.j        , 0.        +0.j        ])

    Differentiating the state is not yet fully supported, but is currently available when using the classical backpropagation differentiation method (diff_method="backprop") with a compatible device.

  • Summation of two Wires objects is now supported and will return a Wires object containing the set of all wires defined by the terms in the summation. (#812)

  • Quantum noisy channels: quantum channels provide a general formalism for discussing state evolution, including the evolution of pure states into mixed states due to noise and decoherence. It allows the user to simulate noise, benchmark algorithms running on real hardware, and to test error-correction techniques. Moreover, differentiable quantum channels could be a unique feature for PennyLane not present in other libraries.

    (#760) (#766) (#778)

  • The controlled-Y operation is now available via qml.CY. For devices that do not natively support the controlled-Y operation, it will be decomposed into qml.RY, qml.CNOT, and qml.S operations. (#806)


  • QNode caching has been introduced, allowing the QNode to keep track of the results of previous device executions and reuse those results in subsequent calls. (#817)

    Caching is available by passing a caching argument to the QNode:

    from pennylane.beta.tapes import qnode
    from pennylane.beta.queuing import expval
    dev = qml.device("default.qubit", wires=2)
    @qnode(dev, caching=10)  # cache up to 10 evaluations
    def qfunc(x):
        qml.RX(x, wires=0)
        qml.RX(0.3, wires=1)
        qml.CNOT(wires=[0, 1])
        return expval(qml.PauliZ(1))
    qfunc(0.1)  # first evaluation executes on the device
    qfunc(0.1)  # second evaluation accesses the cached result
  • Sped up the application of certain gates in default.qubit by using array/tensor manipulation tricks. The following gates are affected: PauliX, PauliY, PauliZ, Hadamard, SWAP, S, T, CNOT, CZ. (#772)

  • Adds arithmetic operations (addition, tensor product, subtraction, and scalar multiplication) between Hamiltonian, Tensor, and Observable objects, and inline arithmetic operations between Hamiltonians and other observables. (#765)

    Hamiltonians can now easily be defined as sums of observables:

    >>> H = 3 * qml.PauliZ(0) - (qml.PauliX(0) @ qml.PauliX(1)) + qml.Hamiltonian([4], [qml.PauliZ(0)])
    >>> print(H)
    (7.0) [Z0] + (-1.0) [X0 X1]
  • Adds compare() method to Observable and Hamiltonian classes, which allows for comparison between observable quantities. (#765)

    >>> H = qml.Hamiltonian([1], [qml.PauliZ(0)])
    >>> obs = qml.PauliZ(0) @ qml.Identity(1)
    >>> print(
    >>> H = qml.Hamiltonian([2], [qml.PauliZ(0)])
    >>> obs = qml.PauliZ(1) @ qml.Identity(0)
    >>> print(
  • Adds simplify() method to the Hamiltonian class. (#765)

    >>> H = qml.Hamiltonian([1, 2], [qml.PauliZ(0), qml.PauliZ(0) @ qml.Identity(1)])
    >>> H.simplify()
    >>> print(H)
    (3.0) [Z0]
  • Added a new bit-flip mixer to the qml.qaoa module. (#774)

Breaking changes

Bug fixes

  • Changed to use lists for storing variable values inside BaseQNode allowing complex matrices to be passed to QubitUnitary. (#773)



This release contains contributions from (in alphabetical order):

Aroosa Ijaz, Juan Miguel Arrazola, Thomas Bromley, Jack Ceroni, Josh Izaac, Antal Száva

Release 0.11.0 (current release)

New features since last release

New and improved simulators

* Added a new device, `default.qubit.autograd`, a pure-state qubit simulator written using Autograd. This device supports classical backpropagation (`diff_method="backprop"`); this can be faster than the parameter-shift rule for computing quantum gradients when the number of parameters to be optimized is large. [(#721)]( ```pycon >>> dev = qml.device("default.qubit.autograd", wires=1) >>> @qml.qnode(dev, diff_method="backprop") ... def circuit(x): ... qml.RX(x[1], wires=0) ... qml.Rot(x[0], x[1], x[2], wires=0) ... return qml.expval(qml.PauliZ(0)) >>> weights = np.array([0.2, 0.5, 0.1]) >>> grad_fn = qml.grad(circuit) >>> print(grad_fn(weights)) array([-2.25267173e-01, -1.00864546e+00, 6.93889390e-18]) ``` See the [device documentation]( for more details. * A new experimental C++ state-vector simulator device is now available, `lightning.qubit`. It uses the C++ Eigen library to perform fast linear algebra calculations for simulating quantum state-vector evolution. `lightning.qubit` is currently in beta; it can be installed via `pip`: ```console $ pip install pennylane-lightning ``` Once installed, it can be used as a PennyLane device: ```pycon >>> dev = qml.device("lightning.qubit", wires=2) ``` For more details, please see the [lightning qubit documentation](

New algorithms and templates

  • Added built-in QAOA functionality via the new qml.qaoa module. (#712) (#718) (#741) (#720)

    This includes the following features:

    • New qml.qaoa.x_mixer and qml.qaoa.xy_mixer functions for defining Pauli-X and XY mixer Hamiltonians.

    • MaxCut: The qml.qaoa.maxcut function allows easy construction of the cost Hamiltonian and recommended mixer Hamiltonian for solving the MaxCut problem for a supplied graph.

    • Layers: qml.qaoa.cost_layer and qml.qaoa.mixer_layer take cost and mixer Hamiltonians, respectively, and apply the corresponding QAOA cost and mixer layers to the quantum circuit

    For example, using PennyLane to construct and solve a MaxCut problem with QAOA:

    wires = range(3)
    graph = Graph([(0, 1), (1, 2), (2, 0)])
    cost_h, mixer_h = qaoa.maxcut(graph)
    def qaoa_layer(gamma, alpha):
        qaoa.cost_layer(gamma, cost_h)
        qaoa.mixer_layer(alpha, mixer_h)
    def antatz(params, **kwargs):
        for w in wires:
        # repeat the QAOA layer two times
        qml.layer(qaoa_layer, 2, params[0], params[1])
    dev = qml.device('default.qubit', wires=len(wires))
    cost_function = qml.VQECost(ansatz, cost_h, dev)
  • Added an ApproxTimeEvolution template to the PennyLane templates module, which can be used to implement Trotterized time-evolution under a Hamiltonian. (#710)

  • Added a qml.layer template-constructing function, which takes a unitary, and repeatedly applies it on a set of wires to a given depth. (#723)

    def subroutine():
        qml.CNOT(wires=[0, 1])
    dev = qml.device('default.qubit', wires=3)
    def circuit():
        qml.layer(subroutine, 3)
        return [qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))]

    This creates the following circuit:

    >>> circuit()
    >>> print(circuit.draw())
    0: ──H──╭C──X──H──╭C──X──H──╭C──X──┤ ⟨Z⟩
    1: ─────╰X────────╰X────────╰X─────┤ ⟨Z⟩
  • Added the qml.utils.decompose_hamiltonian function. This function can be used to decompose a Hamiltonian into a linear combination of Pauli operators. (#671)

    >>> A = np.array(
    ... [[-2, -2+1j, -2, -2],
    ... [-2-1j,  0,  0, -1],
    ... [-2,  0, -2, -1],
    ... [-2, -1, -1,  0]])
    >>> coeffs, obs_list = decompose_hamiltonian(A)

New device features

  • It is now possible to specify custom wire labels, such as ['anc1', 'anc2', 0, 1, 3], where the labels can be strings or numbers. (#666)

    Custom wire labels are defined by passing a list to the wires argument when creating the device:

    >>> dev = qml.device("default.qubit", wires=['anc1', 'anc2', 0, 1, 3])

    Quantum operations should then be invoked with these custom wire labels:

    >>> @qml.qnode(dev)
    >>> def circuit():
    ...    qml.Hadamard(wires='anc2')
    ...    qml.CNOT(wires=['anc1', 3])
    ...    ...

    The existing behaviour, in which the number of wires is specified on device initialization, continues to work as usual. This gives a default behaviour where wires are labelled by consecutive integers.

    >>> dev = qml.device("default.qubit", wires=5)
  • An integrated device test suite has been added, which can be used to run basic integration tests on core or external devices. (#695) (#724) (#733)

    The test can be invoked against a particular device by calling the pl-device-test command line program:

    $ pl-device-test --device=default.qubit --shots=1234 --analytic=False

    If the tests are run on external devices, the device and its dependencies must be installed locally. For more details, please see the plugin test documentation.


  • The functions implementing the quantum circuits building the Unitary Coupled-Cluster (UCCSD) VQE ansatz have been improved, with a more consistent naming convention and improved docstrings. (#748)

    The changes include:

    • The terms 1particle-1hole (ph) and 2particle-2hole (pphh) excitations were replaced with the names single and double excitations, respectively.

    • The non-differentiable arguments in the UCCSD template were renamed accordingly: phs_wires, pphhd_wires

    • The term virtual, previously used to refer the unoccupied orbitals, was discarded.

    • The Usage Details sections were updated and improved.

  • Added support for TensorFlow 2.3 and PyTorch 1.6. (#725)

  • Returning probabilities is now supported from photonic QNodes. As with qubit QNodes, photonic QNodes returning probabilities are end-to-end differentiable. (#699)

    >>> dev = qml.device("strawberryfields.fock", wires=2, cutoff_dim=5)
    >>> @qml.qnode(dev)
    ... def circuit(a):
    ...     qml.Displacement(a, 0, wires=0)
    ...     return qml.probs(wires=0)
    >>> print(circuit(0.5))
    [7.78800783e-01 1.94700196e-01 2.43375245e-02 2.02812704e-03 1.26757940e-04]

Breaking changes

  • The pennylane.plugins and pennylane.beta.plugins folders have been renamed to pennylane.devices and pennylane.beta.devices, to reflect their content better. (#726)

Bug fixes

  • The PennyLane interface conversion functions can now convert QNodes with pre-existing interfaces. (#707)


  • The interfaces section of the documentation has been renamed to ‘Interfaces and training’, and updated with the latest variable handling details. (#753)


This release contains contributions from (in alphabetical order):

Juan Miguel Arazzola, Thomas Bromley, Jack Ceroni, Alain Delgado Gran, Shadab Hussain, Theodor Isacsson, Josh Izaac, Nathan Killoran, Maria Schuld, Antal Száva, Nicola Vitucci.

Release 0.10.0

New features since last release

New and improved simulators

  • Added a new device,, a pure-state qubit simulator written using TensorFlow. As a result, it supports classical backpropagation as a means to compute the Jacobian. This can be faster than the parameter-shift rule for computing quantum gradients when the number of parameters to be optimized is large. is designed to be used with end-to-end classical backpropagation (diff_method="backprop") with the TensorFlow interface. This is the default method of differentiation when creating a QNode with this device.

    Using this method, the created QNode is a ‘white-box’ that is tightly integrated with your TensorFlow computation, including AutoGraph support:

    >>> dev = qml.device("", wires=1)
    >>> @tf.function
    ... @qml.qnode(dev, interface="tf", diff_method="backprop")
    ... def circuit(x):
    ...     qml.RX(x[1], wires=0)
    ...     qml.Rot(x[0], x[1], x[2], wires=0)
    ...     return qml.expval(qml.PauliZ(0))
    >>> weights = tf.Variable([0.2, 0.5, 0.1])
    >>> with tf.GradientTape() as tape:
    ...     res = circuit(weights)
    >>> print(tape.gradient(res, weights))
    tf.Tensor([-2.2526717e-01 -1.0086454e+00  1.3877788e-17], shape=(3,), dtype=float32)

    See the documentation for more details.

  • The default.tensor plugin has been significantly upgraded. It now allows two different tensor network representations to be used: "exact" and "mps". The former uses a exact factorized representation of quantum states, while the latter uses a matrix product state representation. (#572) (#599)

New machine learning functionality and integrations

  • PennyLane QNodes can now be converted into Torch layers, allowing for creation of quantum and hybrid models using the torch.nn API. (#588)

    A PennyLane QNode can be converted into a torch.nn layer using the qml.qnn.TorchLayer class:

    >>> @qml.qnode(dev)
    ... def qnode(inputs, weights_0, weight_1):
    ...    # define the circuit
    ...    # ...
    >>> weight_shapes = {"weights_0": 3, "weight_1": 1}
    >>> qlayer = qml.qnn.TorchLayer(qnode, weight_shapes)

    A hybrid model can then be easily constructed:

    >>> model = torch.nn.Sequential(qlayer, torch.nn.Linear(2, 2))
  • Added a new “reversible” differentiation method which can be used in simulators, but not hardware.

    The reversible approach is similar to backpropagation, but trades off extra computation for enhanced memory efficiency. Where backpropagation caches the state tensors at each step during a simulated evolution, the reversible method only caches the final pre-measurement state.

    Compared to the parameter-shift method, the reversible method can be faster or slower, depending on the density and location of parametrized gates in a circuit (circuits with higher density of parametrized gates near the end of the circuit will see a benefit). (#670)

    >>> dev = qml.device("default.qubit", wires=2)
    ... @qml.qnode(dev, diff_method="reversible")
    ... def circuit(x):
    ...     qml.RX(x, wires=0)
    ...     qml.RX(x, wires=0)
    ...     qml.CNOT(wires=[0,1])
    ...     return qml.expval(qml.PauliZ(0))
    >>> qml.grad(circuit)(0.5)

New templates and cost functions

  • Added the new templates UCCSD, SingleExcitationUnitary, andDoubleExcitationUnitary, which together implement the Unitary Coupled-Cluster Singles and Doubles (UCCSD) ansatz to perform VQE-based quantum chemistry simulations using PennyLane-QChem. (#622) (#638) (#654) (#659) (#622)

  • Added module pennylane.qnn.cost with class SquaredErrorLoss. The module contains classes to calculate losses and cost functions on circuits with trainable parameters. (#642)


  • Improves the wire management by making the Operator.wires attribute a wires object. (#666)

  • A significant improvement with respect to how QNodes and interfaces mark quantum function arguments as differentiable when using Autograd, designed to improve performance and make QNodes more intuitive. (#648) (#650)

    In particular, the following changes have been made:

    • A new ndarray subclass pennylane.numpy.tensor, which extends NumPy arrays with the keyword argument and attribute requires_grad. Tensors which have requires_grad=False are treated as non-differentiable by the Autograd interface.

    • A new subpackage pennylane.numpy, which wraps autograd.numpy such that NumPy functions accept the requires_grad keyword argument, and allows Autograd to differentiate pennylane.numpy.tensor objects.

    • The argnum argument to qml.grad is now optional; if not provided, arguments explicitly marked as requires_grad=False are excluded for the list of differentiable arguments. The ability to pass argnum has been retained for backwards compatibility, and if present the old behaviour persists.

  • The QNode Torch interface now inspects QNode positional arguments. If any argument does not have the attribute requires_grad=True, it is automatically excluded from quantum gradient computations. (#652) (#660)

  • The QNode TF interface now inspects QNode positional arguments. If any argument is not being watched by a tf.GradientTape(), it is automatically excluded from quantum gradient computations. (#655) (#660)

  • QNodes have two new public methods: QNode.set_trainable_args() and QNode.get_trainable_args(). These are designed to be called by interfaces, to specify to the QNode which of its input arguments are differentiable. Arguments which are non-differentiable will not be converted to PennyLane Variable objects within the QNode. (#660)

  • Added decomposition method to PauliX, PauliY, PauliZ, S, T, Hadamard, and PhaseShift gates, which decomposes each of these gates into rotation gates. (#668)

  • The CircuitGraph class now supports serializing contained circuit operations and measurement basis rotations to an OpenQASM2.0 script via the new CircuitGraph.to_openqasm() method. (#623)

Breaking changes

  • Removes support for Python 3.5. (#639)


  • Various small typos were fixed.


This release contains contributions from (in alphabetical order):

Thomas Bromley, Jack Ceroni, Alain Delgado Gran, Theodor Isacsson, Josh Izaac, Nathan Killoran, Maria Schuld, Antal Száva, Nicola Vitucci.

Release 0.9.0

New features since last release

New machine learning integrations

  • PennyLane QNodes can now be converted into Keras layers, allowing for creation of quantum and hybrid models using the Keras API. (#529)

    A PennyLane QNode can be converted into a Keras layer using the KerasLayer class:

    from pennylane.qnn import KerasLayer
    def circuit(inputs, weights_0, weight_1):
       # define the circuit
       # ...
    weight_shapes = {"weights_0": 3, "weight_1": 1}
    qlayer = qml.qnn.KerasLayer(circuit, weight_shapes, output_dim=2)

    A hybrid model can then be easily constructed:

    model = tf.keras.models.Sequential([qlayer, tf.keras.layers.Dense(2)])
  • Added a new type of QNode, qml.qnodes.PassthruQNode. For simulators which are coded in an external library which supports automatic differentiation, PennyLane will treat a PassthruQNode as a “white box”, and rely on the external library to directly provide gradients via backpropagation. This can be more efficient than the using parameter-shift rule for a large number of parameters. (#488)

    Currently this behaviour is supported by PennyLane’s device backend, compatible with the 'tf' interface using TensorFlow 2:

    dev = qml.device('', wires=2)
    @qml.qnode(dev, diff_method="backprop")
    def circuit(params):
        qml.RX(params[0], wires=0)
        qml.RX(params[1], wires=1)
        qml.CNOT(wires=[0, 1])
        return qml.expval(qml.PauliZ(0))
    qnode = PassthruQNode(circuit, dev)
    params = tf.Variable([0.3, 0.1])
    with tf.GradientTape() as tape:
        res = qnode(params)
    grad = tape.gradient(res, params)

New optimizers

  • Added the qml.RotosolveOptimizer, a gradient-free optimizer that minimizes the quantum function by updating each parameter, one-by-one, via a closed-form expression while keeping other parameters fixed. (#636) (#539)

  • Added the qml.RotoselectOptimizer, which uses Rotosolve to minimizes a quantum function with respect to both the rotation operations applied and the rotation parameters. (#636) (#539)

    For example, given a quantum function f that accepts parameters x and a list of corresponding rotation operations generators, the Rotoselect optimizer will, at each step, update both the parameter values and the list of rotation gates to minimize the loss:

    >>> opt = qml.optimize.RotoselectOptimizer()
    >>> x = [0.3, 0.7]
    >>> generators = [qml.RX, qml.RY]
    >>> for _ in range(100):
    ...     x, generators = opt.step(f, x, generators)

New operations

  • Added the PauliRot gate, which performs an arbitrary Pauli rotation on multiple qubits, and the MultiRZ gate, which performs a rotation generated by a tensor product of Pauli Z operators. (#559)

    dev = qml.device('default.qubit', wires=4)
    def circuit(angle):
        qml.PauliRot(angle, "IXYZ", wires=[0, 1, 2, 3])
        return [qml.expval(qml.PauliZ(wire)) for wire in [0, 1, 2, 3]]
    >>> circuit(0.4)
    [1.         0.92106099 0.92106099 1.        ]
    >>> print(circuit.draw())
     0: ──╭RI(0.4)──┤ ⟨Z⟩
     1: ──├RX(0.4)──┤ ⟨Z⟩
     2: ──├RY(0.4)──┤ ⟨Z⟩
     3: ──╰RZ(0.4)──┤ ⟨Z⟩

    If the PauliRot gate is not supported on the target device, it will be decomposed into Hadamard, RX and MultiRZ gates. Note that identity gates in the Pauli word result in untouched wires:

    >>> print(circuit.draw())
     0: ───────────────────────────────────┤ ⟨Z⟩
     1: ──H──────────╭RZ(0.4)──H───────────┤ ⟨Z⟩
     2: ──RX(1.571)──├RZ(0.4)──RX(-1.571)──┤ ⟨Z⟩
     3: ─────────────╰RZ(0.4)──────────────┤ ⟨Z⟩

    If the MultiRZ gate is not supported, it will be decomposed into CNOT and RZ gates:

    >>> print(circuit.draw())
     0: ──────────────────────────────────────────────────┤ ⟨Z⟩
     1: ──H──────────────╭X──RZ(0.4)──╭X──────H───────────┤ ⟨Z⟩
     2: ──RX(1.571)──╭X──╰C───────────╰C──╭X──RX(-1.571)──┤ ⟨Z⟩
     3: ─────────────╰C───────────────────╰C──────────────┤ ⟨Z⟩
  • PennyLane now provides DiagonalQubitUnitary for diagonal gates, that are e.g., encountered in IQP circuits. These kinds of gates can be evaluated much faster on a simulator device. (#567)

    The gate can be used, for example, to efficiently simulate oracles:

    dev = qml.device('default.qubit', wires=3)
    # Function as a bitstring
    f = np.array([1, 0, 0, 1, 1, 0, 1, 0])
    def circuit(weights1, weights2):
        qml.templates.StronglyEntanglingLayers(weights1, wires=[0, 1, 2])
        # Implements the function as a phase-kickback oracle
        qml.DiagonalQubitUnitary((-1)**f, wires=[0, 1, 2])
        qml.templates.StronglyEntanglingLayers(weights2, wires=[0, 1, 2])
        return [qml.expval(qml.PauliZ(w)) for w in range(3)]
  • Added the TensorN CVObservable that can represent the tensor product of the NumberOperator on photonic backends. (#608)

New templates

  • Added the ArbitraryUnitary and ArbitraryStatePreparation templates, which use PauliRot gates to perform an arbitrary unitary and prepare an arbitrary basis state with the minimal number of parameters. (#590)

    dev = qml.device('default.qubit', wires=3)
    def circuit(weights1, weights2):
          qml.templates.ArbitraryStatePreparation(weights1, wires=[0, 1, 2])
          qml.templates.ArbitraryUnitary(weights2, wires=[0, 1, 2])
          return qml.probs(wires=[0, 1, 2])
  • Added the IQPEmbedding template, which encodes inputs into the diagonal gates of an IQP circuit. (#605)

  • Added the SimplifiedTwoDesign template, which implements the circuit design of Cerezo et al. (2020). (#556)

  • Added the BasicEntanglerLayers template, which is a simple layer architecture of rotations and CNOT nearest-neighbour entanglers. (#555)

  • PennyLane now offers a broadcasting function to easily construct templates: qml.broadcast() takes single quantum operations or other templates and applies them to wires in a specific pattern. (#515) (#522) (#526) (#603)

    For example, we can use broadcast to repeat a custom template across multiple wires:

    from pennylane.templates import template
    def mytemplate(pars, wires):
        qml.RY(pars, wires=wires)
    dev = qml.device('default.qubit', wires=3)
    def circuit(pars):
        qml.broadcast(mytemplate, pattern="single", wires=[0,1,2], parameters=pars)
        return qml.expval(qml.PauliZ(0))
    >>> circuit([1, 1, 0.1])
    >>> print(circuit.draw())
     0: ──H──RY(1.0)──┤ ⟨Z⟩
     1: ──H──RY(1.0)──┤
     2: ──H──RY(0.1)──┤

    For other available patterns, see the broadcast function documentation.

Breaking changes

  • The QAOAEmbedding now uses the new MultiRZ gate as a ZZ entangler, which changes the convention. While previously, the ZZ gate in the embedding was implemented as

    CNOT(wires=[wires[0], wires[1]])
    RZ(2 * parameter, wires=wires[0])
    CNOT(wires=[wires[0], wires[1]])

    the MultiRZ corresponds to

    CNOT(wires=[wires[1], wires[0]])
    RZ(parameter, wires=wires[0])
    CNOT(wires=[wires[1], wires[0]])

    which differs in the factor of 2, and fixes a bug in the wires that the CNOT was applied to. (#609)

  • Probability methods are handled by QubitDevice and device method requirements are modified to simplify plugin development. (#573)

  • The internal variables All and Any to mark an Operation as acting on all or any wires have been renamed to AllWires and AnyWires. (#614)


  • A new Wires class was introduced for the internal bookkeeping of wire indices. (#615)

  • Improvements to the speed/performance of the default.qubit device. (#567) (#559)

  • Added the "backprop" and "device" differentiation methods to the qnode decorator. (#552)

    • "backprop": Use classical backpropagation. Default on simulator devices that are classically end-to-end differentiable. The returned QNode can only be used with the same machine learning framework (e.g., simulator with the tensorflow interface).

    • "device": Queries the device directly for the gradient.

    Using the "backprop" differentiation method with the device, the created QNode is a ‘white-box’, and is tightly integrated with the overall TensorFlow computation:

    >>> dev = qml.device("", wires=1)
    >>> @qml.qnode(dev, interface="tf", diff_method="backprop")
    >>> def circuit(x):
    ...     qml.RX(x[1], wires=0)
    ...     qml.Rot(x[0], x[1], x[2], wires=0)
    ...     return qml.expval(qml.PauliZ(0))
    >>> vars = tf.Variable([0.2, 0.5, 0.1])
    >>> with tf.GradientTape() as tape:
    ...     res = circuit(vars)
    >>> tape.gradient(res, vars)
    <tf.Tensor: shape=(3,), dtype=float32, numpy=array([-2.2526717e-01, -1.0086454e+00,  1.3877788e-17], dtype=float32)>
  • The circuit drawer now displays inverted operations, as well as wires where probabilities are returned from the device: (#540)

    >>> @qml.qnode(dev)
    ... def circuit(theta):
    ...     qml.RX(theta, wires=0)
    ...     qml.CNOT(wires=[0, 1])
    ...     qml.S(wires=1).inv()
    ...     return qml.probs(wires=[0, 1])
    >>> circuit(0.2)
    array([0.99003329, 0.        , 0.        , 0.00996671])
    >>> print(circuit.draw())
    0: ──RX(0.2)──╭C───────╭┤ Probs
    1: ───────────╰X──S⁻¹──╰┤ Probs
  • You can now evaluate the metric tensor of a VQE Hamiltonian via the new VQECost.metric_tensor method. This allows VQECost objects to be directly optimized by the quantum natural gradient optimizer (qml.QNGOptimizer). (#618)

  • The input check functions in pennylane.templates.utils are now public and visible in the API documentation. (#566)

  • Added keyword arguments for step size and order to the qnode decorator, as well as the QNode and JacobianQNode classes. This enables the user to set the step size and order when using finite difference methods. These options are also exposed when creating QNode collections. (#530) (#585) (#587)

  • The decomposition for the CRY gate now uses the simpler form RY @ CNOT @ RY @ CNOT (#547)

  • The underlying queuing system was refactored, removing the qml._current_context property that held the currently active QNode or OperationRecorder. Now, all objects that expose a queue for operations inherit from QueuingContext and register their queue globally. (#548)

  • The PennyLane repository has a new benchmarking tool which supports the comparison of different git revisions. (#568) (#560) (#516)


  • Updated the development section by creating a landing page with links to sub-pages containing specific guides. (#596)

  • Extended the developer’s guide by a section explaining how to add new templates. (#564)

Bug fixes

  • tf.GradientTape().jacobian() can now be evaluated on QNodes using the TensorFlow interface. (#626)

  • RandomLayers() is now compatible with the qiskit devices. (#597)

  • DefaultQubit.probability() now returns the correct probability when called with device.analytic=False. (#563)

  • Fixed a bug in the StronglyEntanglingLayers template, allowing it to work correctly when applied to a single wire. (544)

  • Fixed a bug when inverting operations with decompositions; operations marked as inverted are now correctly inverted when the fallback decomposition is called. (#543)

  • The QNode.print_applied() method now correctly displays wires where qml.prob() is being returned. #542


This release contains contributions from (in alphabetical order):

Ville Bergholm, Lana Bozanic, Thomas Bromley, Theodor Isacsson, Josh Izaac, Nathan Killoran, Maggie Li, Johannes Jakob Meyer, Maria Schuld, Sukin Sim, Antal Száva.

Release 0.8.1


  • Beginning of support for Python 3.8, with the test suite now being run in a Python 3.8 environment. (#501)


  • Present templates as a gallery of thumbnails showing the basic circuit architecture. (#499)

Bug fixes

  • Fixed a bug where multiplying a QNode parameter by 0 caused a divide by zero error when calculating the parameter shift formula. (#512)

  • Fixed a bug where the shape of differentiable QNode arguments was being cached on the first construction, leading to indexing errors if the QNode was re-evaluated if the argument changed shape. (#505)


This release contains contributions from (in alphabetical order):

Ville Bergholm, Josh Izaac, Johannes Jakob Meyer, Maria Schuld, Antal Száva.

Release 0.8.0

New features since last release

  • Added a quantum chemistry package, pennylane.qchem, which supports integration with OpenFermion, Psi4, PySCF, and OpenBabel. (#453)

    Features include:

    • Generate the qubit Hamiltonians directly starting with the atomic structure of the molecule.

    • Calculate the mean-field (Hartree-Fock) electronic structure of molecules.

    • Allow to define an active space based on the number of active electrons and active orbitals.

    • Perform the fermionic-to-qubit transformation of the electronic Hamiltonian by using different functions implemented in OpenFermion.

    • Convert OpenFermion’s QubitOperator to a Pennylane Hamiltonian class.

    • Perform a Variational Quantum Eigensolver (VQE) computation with this Hamiltonian in PennyLane.

    Check out the quantum chemistry quickstart, as well the quantum chemistry and VQE tutorials.

  • PennyLane now has some functions and classes for creating and solving VQE problems. (#467)

    • qml.Hamiltonian: a lightweight class for representing qubit Hamiltonians

    • qml.VQECost: a class for quickly constructing a differentiable cost function given a circuit ansatz, Hamiltonian, and one or more devices

      >>> H = qml.vqe.Hamiltonian(coeffs, obs)
      >>> cost = qml.VQECost(ansatz, hamiltonian, dev, interface="torch")
      >>> params = torch.rand([4, 3])
      >>> cost(params)
      tensor(0.0245, dtype=torch.float64)
  • Added a circuit drawing feature that provides a text-based representation of a QNode instance. It can be invoked via qnode.draw(). The user can specify to display variable names instead of variable values and choose either an ASCII or Unicode charset. (#446)

    Consider the following circuit as an example:

    def qfunc(a, w):
        qml.CRX(a, wires=[0, 1])
        qml.Rot(w[0], w[1], w[2], wires=[1])
        qml.CRX(-a, wires=[0, 1])
        return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))

    We can draw the circuit after it has been executed:

    >>> result = qfunc(2.3, [1.2, 3.2, 0.7])
    >>> print(qfunc.draw())
     0: ──H──╭C────────────────────────────╭C─────────╭┤ ⟨Z ⊗ Z⟩
     1: ─────╰RX(2.3)──Rot(1.2, 3.2, 0.7)──╰RX(-2.3)──╰┤ ⟨Z ⊗ Z⟩
    >>> print(qfunc.draw(charset="ascii"))
     0: --H--+C----------------------------+C---------+| <Z @ Z>
     1: -----+RX(2.3)--Rot(1.2, 3.2, 0.7)--+RX(-2.3)--+| <Z @ Z>
    >>> print(qfunc.draw(show_variable_names=True))
     0: ──H──╭C─────────────────────────────╭C─────────╭┤ ⟨Z ⊗ Z⟩
     1: ─────╰RX(a)──Rot(w[0], w[1], w[2])──╰RX(-1*a)──╰┤ ⟨Z ⊗ Z⟩
  • Added QAOAEmbedding and its parameter initialization as a new trainable template. (#442)

  • Added the qml.probs() measurement function, allowing QNodes to differentiate variational circuit probabilities on simulators and hardware. (#432)

    def circuit(x):
        qml.RY(x, wires=0)
        qml.RX(x, wires=1)
        qml.CNOT(wires=[0, 1])
        return qml.probs(wires=[0])

    Executing this circuit gives the marginal probability of wire 1:

    >>> circuit(0.2)
    [0.40066533 0.59933467]

    QNodes that return probabilities fully support autodifferentiation.

  • Added the convenience load functions qml.from_pyquil, qml.from_quil and qml.from_quil_file that convert pyQuil objects and Quil code to PennyLane templates. This feature requires version 0.8 or above of the PennyLane-Forest plugin. (#459)

  • Added a qml.inv method that inverts templates and sequences of Operations. Added a @qml.template decorator that makes templates return the queued Operations. (#462)

    For example, using this function to invert a template inside a QNode:

    def ansatz(weights, wires):
        for idx, wire in enumerate(wires):
            qml.RX(weights[idx], wires=[wire])
        for idx in range(len(wires) - 1):
            qml.CNOT(wires=[wires[idx], wires[idx + 1]])
    dev = qml.device('default.qubit', wires=2)
    def circuit(weights):
        qml.inv(ansatz(weights, wires=[0, 1]))
        return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))
  • Added the QNodeCollection container class, that allows independent QNodes to be stored and evaluated simultaneously. Experimental support for asynchronous evaluation of contained QNodes is provided with the parallel=True keyword argument. (#466)

  • Added a high level function, that maps a quantum circuit template over a list of observables or devices, returning a QNodeCollection. (#466)

    For example:

    >>> def my_template(params, wires, **kwargs):
    >>>    qml.RX(params[0], wires=wires[0])
    >>>    qml.RX(params[1], wires=wires[1])
    >>>    qml.CNOT(wires=wires)
    >>> obs_list = [qml.PauliX(0) @ qml.PauliZ(1), qml.PauliZ(0) @ qml.PauliX(1)]
    >>> dev = qml.device("default.qubit", wires=2)
    >>> qnodes =, obs_list, dev, measure="expval")
    >>> qnodes([0.54, 0.12])
    array([-0.06154835  0.99280864])
  • Added high level qml.sum,, qml.apply functions that act on QNode collections. (#466)

    qml.apply allows vectorized functions to act over the entire QNode collection:

    >>> qnodes =, obs_list, dev, measure="expval")
    >>> cost = qml.apply(np.sin, qnodes)
    >>> cost([0.54, 0.12])
    array([-0.0615095  0.83756375])

    qml.sum and take the sum of a QNode collection, and a dot product of tensors/arrays/QNode collections, respectively.

Breaking changes

  • Deprecated the old-style QNode such that only the new-style QNode and its syntax can be used, moved all related files from the pennylane/beta folder to pennylane. (#440)


  • Added the Tensor.prune() method and the Tensor.non_identity_obs property for extracting non-identity instances from the observables making up a Tensor instance. (#498)

  • Renamed the expt.tensornet and devices to default.tensor and (#495)

  • Added a serialization method to the CircuitGraph class that is used to create a unique hash for each quantum circuit graph. (#470)

  • Added the Observable.eigvals method to return the eigenvalues of observables. (#449)

  • Added the Observable.diagonalizing_gates method to return the gates that diagonalize an observable in the computational basis. (#454)

  • Added the Operator.matrix method to return the matrix representation of an operator in the computational basis. (#454)

  • Added a QubitDevice class which implements common functionalities of plugin devices such that plugin devices can rely on these implementations. The new QubitDevice also includes a new execute method, which allows for more convenient plugin design. In addition, QubitDevice also unifies the way samples are generated on qubit-based devices. (#452) (#473)

  • Improved documentation of AmplitudeEmbedding and BasisEmbedding templates. (#441) (#439)

  • Codeblocks in the documentation now have a ‘copy’ button for easily copying examples. (#437)


  • Update the developers plugin guide to use QubitDevice. (#483)

Bug fixes

  • Fixed a bug in CVQNode._pd_analytic, where non-descendant observables were not Heisenberg-transformed before evaluating the partial derivatives when using the order-2 parameter-shift method, resulting in an erroneous Jacobian for some circuits. (#433)


This release contains contributions from (in alphabetical order):

Juan Miguel Arrazola, Ville Bergholm, Alain Delgado Gran, Olivia Di Matteo, Theodor Isacsson, Josh Izaac, Soran Jahangiri, Nathan Killoran, Johannes Jakob Meyer, Zeyue Niu, Maria Schuld, Antal Száva.

Release 0.7.0

New features since last release

  • Custom padding constant in AmplitudeEmbedding is supported (see ‘Breaking changes’.) (#419)

  • StronglyEntanglingLayer and RandomLayer now work with a single wire. (#409) (#413)

  • Added support for applying the inverse of an Operation within a circuit. (#377)

  • Added an OperationRecorder() context manager, that allows templates and quantum functions to be executed while recording events. The recorder can be used with and without QNodes as a debugging utility. (#388)

  • Operations can now specify a decomposition that is used when the desired operation is not supported on the target device. (#396)

  • The ability to load circuits from external frameworks as templates has been added via the new qml.load() function. This feature requires plugin support — this initial release provides support for Qiskit circuits and QASM files when pennylane-qiskit is installed, via the functions qml.from_qiskit and qml.from_qasm. (#418)

  • An experimental tensor network device has been added (#416) (#395) (#394) (#380)

  • An experimental tensor network device which uses TensorFlow for backpropagation has been added (#427)

  • Custom padding constant in AmplitudeEmbedding is supported (see ‘Breaking changes’.) (#419)

Breaking changes

  • The pad parameter in AmplitudeEmbedding() is now either None (no automatic padding), or a number that is used as the padding constant. (#419)

  • Initialization functions now return a single array of weights per function. Utilities for multi-weight templates Interferometer() and CVNeuralNetLayers() are provided. (#412)

  • The single layer templates RandomLayer(), CVNeuralNetLayer() and StronglyEntanglingLayer() have been turned into private functions _random_layer(), _cv_neural_net_layer() and _strongly_entangling_layer(). Recommended use is now via the corresponding Layers() templates. (#413)


  • Added extensive input checks in templates. (#419)

  • Templates integration tests are rewritten - now cover keyword/positional argument passing, interfaces and combinations of templates. (#409) (#419)

  • State vector preparation operations in the default.qubit plugin can now be applied to subsets of wires, and are restricted to being the first operation in a circuit. (#346)

  • The QNode class is split into a hierarchy of simpler classes. (#354) (#398) (#415) (#417) (#425)

  • Added the gates U1, U2 and U3 parametrizing arbitrary unitaries on 1, 2 and 3 qubits and the Toffoli gate to the set of qubit operations. (#396)

  • Changes have been made to accomodate the movement of the main function in pytest._internal to pytest._internal.main in pip 19.3. (#404)

  • Added the templates BasisStatePreparation and MottonenStatePreparation that use gates to prepare a basis state and an arbitrary state respectively. (#336)

  • Added decompositions for BasisState and QubitStateVector based on state preparation templates. (#414)

  • Replaces the pseudo-inverse in the quantum natural gradient optimizer (which can be numerically unstable) with np.linalg.solve. (#428)


This release contains contributions from (in alphabetical order):

Ville Bergholm, Josh Izaac, Nathan Killoran, Angus Lowe, Johannes Jakob Meyer, Oluwatobi Ogunbayo, Maria Schuld, Antal Száva.

Release 0.6.1

New features since last release

  • Added a print_applied method to QNodes, allowing the operation and observable queue to be printed as last constructed. (#378)


  • A new Operator base class is introduced, which is inherited by both the Observable class and the Operation class. (#355)

  • Removed deprecated @abstractproperty decorators in (#374)

  • The CircuitGraph class is updated to deal with Operation instances directly. (#344)

  • Comprehensive gradient tests have been added for the interfaces. (#381)


Bug fixes

  • Replaces the existing np.linalg.norm normalization with hand-coded normalization, allowing AmplitudeEmbedding to be used with differentiable parameters. AmplitudeEmbedding tests have been added and improved. (#376)


This release contains contributions from (in alphabetical order):

Ville Bergholm, Josh Izaac, Nathan Killoran, Maria Schuld, Antal Száva

Release 0.6.0

New features since last release

  • The devices default.qubit and default.gaussian have a new initialization parameter analytic that indicates if expectation values and variances should be calculated analytically and not be estimated from data. (#317)

  • Added C-SWAP gate to the set of qubit operations (#330)

  • The TensorFlow interface has been renamed from "tfe" to "tf", and now supports TensorFlow 2.0. (#337)

  • Added the S and T gates to the set of qubit operations. (#343)

  • Tensor observables are now supported within the expval, var, and sample functions, by using the @ operator. (#267)

Breaking changes

  • The argument n specifying the number of samples in the method Device.sample was removed. Instead, the method will always return Device.shots many samples. (#317)


  • The number of shots / random samples used to estimate expectation values and variances, Device.shots, can now be changed after device creation. (#317)

  • Unified import shortcuts to be under qml in and (#329)

  • The quantum natural gradient now uses scipy.linalg.pinvh which is more efficient for symmetric matrices than the previously used scipy.linalg.pinv. (#331)

  • The deprecated qml.expval.Observable syntax has been removed. (#267)

  • Remainder of the unittest-style tests were ported to pytest. (#310)

  • The do_queue argument for operations now only takes effect within QNodes. Outside of QNodes, operations can now be instantiated without needing to specify do_queue. (#359)


  • The docs are rewritten and restructured to contain a code introduction section as well as an API section. (#314)

  • Added Ising model example to the tutorials (#319)

  • Added tutorial for QAOA on MaxCut problem (#328)

  • Added QGAN flow chart figure to its tutorial (#333)

  • Added missing figures for gallery thumbnails of state-preparation and QGAN tutorials (#326)

  • Fixed typos in the state preparation tutorial (#321)

  • Fixed bug in VQE tutorial 3D plots (#327)

Bug fixes

  • Fixed typo in measurement type error message in (#341)


This release contains contributions from (in alphabetical order):

Shahnawaz Ahmed, Ville Bergholm, Aroosa Ijaz, Josh Izaac, Nathan Killoran, Angus Lowe, Johannes Jakob Meyer, Maria Schuld, Antal Száva, Roeland Wiersema.

Release 0.5.0

New features since last release

  • Adds a new optimizer, qml.QNGOptimizer, which optimizes QNodes using quantum natural gradient descent. See for more details. (#295) (#311)

  • Adds a new QNode method, QNode.metric_tensor(), which returns the block-diagonal approximation to the Fubini-Study metric tensor evaluated on the attached device. (#295)

  • Sampling support: QNodes can now return a specified number of samples from a given observable via the top-level pennylane.sample() function. To support this on plugin devices, there is a new Device.sample method.

    Calculating gradients of QNodes that involve sampling is not possible. (#256)

  • default.qubit has been updated to provide support for sampling. (#256)

  • Added controlled rotation gates to PennyLane operations and default.qubit plugin. (#251)

Breaking changes

  • The method Device.supported was removed, and replaced with the methods Device.supports_observable and Device.supports_operation. Both methods can be called with string arguments (dev.supports_observable('PauliX')) and class arguments (dev.supports_observable(qml.PauliX)). (#276)

  • The following CV observables were renamed to comply with the new Operation/Observable scheme: MeanPhoton to NumberOperator, Homodyne to QuadOperator and NumberState to FockStateProjector. (#254)


  • The AmplitudeEmbedding function now provides options to normalize and pad features to ensure a valid state vector is prepared. (#275)

  • Operations can now optionally specify generators, either as existing PennyLane operations, or by providing a NumPy array. (#295) (#313)

  • Adds a Device.parameters property, so that devices can view a dictionary mapping free parameters to operation parameters. This will allow plugin devices to take advantage of parametric compilation. (#283)

  • Introduces two enumerations: Any and All, representing any number of wires and all wires in the system respectively. They can be imported from pennylane.operation, and can be used when defining the Operation.num_wires class attribute of operations. (#277)

    As part of this change:

    • All is equivalent to the integer 0, for backwards compatibility with the existing test suite

    • Any is equivalent to the integer -1 to allow numeric comparison operators to continue working

    • An additional validation is now added to the Operation class, which will alert the user that an operation with num_wires = All is being incorrectly.

  • The one-qubit rotations in pennylane.plugins.default_qubit no longer depend on Scipy’s expm. Instead they are calculated with Euler’s formula. (#292)

  • Creates an ObservableReturnTypes enumeration class containing Sample, Variance and Expectation. These new values can be assigned to the return_type attribute of an Observable. (#290)

  • Changed the signature of the RandomLayer and RandomLayers templates to have a fixed seed by default. (#258)

  • has been cleaned up, removing the non-working shebang, and removing unused imports. (#262)


  • A documentation refactor to simplify the tutorials and include Sphinx-Gallery. (#291)

    • Examples and tutorials previously split across the examples/ and doc/tutorials/ directories, in a mixture of ReST and Jupyter notebooks, have been rewritten as Python scripts with ReST comments in a single location, the examples/ folder.

    • Sphinx-Gallery is used to automatically build and run the tutorials. Rendered output is displayed in the Sphinx documentation.

    • Links are provided at the top of every tutorial page for downloading the tutorial as an executable python script, downloading the tutorial as a Jupyter notebook, or viewing the notebook on GitHub.

    • The tutorials table of contents have been moved to a single quick start page.

  • Fixed a typo in QubitStateVector. (#296)

  • Fixed a typo in the default_gaussian.gaussian_state function. (#293)

  • Fixed a typo in the gradient recipe within the RX, RY, RZ operation docstrings. (#248)

  • Fixed a broken link in the tutorial documentation, as a result of the qml.expval.Observable deprecation. (#246)

Bug fixes

  • Fixed a bug where a PolyXP observable would fail if applied to subsets of wires on default.gaussian. (#277)


This release contains contributions from (in alphabetical order):

Simon Cross, Aroosa Ijaz, Josh Izaac, Nathan Killoran, Johannes Jakob Meyer, Rohit Midha, Nicolás Quesada, Maria Schuld, Antal Száva, Roeland Wiersema.

Release 0.4.0

New features since last release

  • pennylane.expval() is now a top-level function, and is no longer a package of classes. For now, the existing pennylane.expval.Observable interface continues to work, but will raise a deprecation warning. (#232)

  • Variance support: QNodes can now return the variance of observables, via the top-level pennylane.var() function. To support this on plugin devices, there is a new Device.var method.

    The following observables support analytic gradients of variances:

    • All qubit observables (requiring 3 circuit evaluations for involutory observables such as Identity, X, Y, Z; and 5 circuit evals for non-involutary observables, currently only qml.Hermitian)

    • First-order CV observables (requiring 5 circuit evaluations)

    Second-order CV observables support numerical variance gradients.

  • pennylane.about() function added, providing details on current PennyLane version, installed plugins, Python, platform, and NumPy versions (#186)

  • Removed the logic that allowed wires to be passed as a positional argument in quantum operations. This allows us to raise more useful error messages for the user if incorrect syntax is used. (#188)

  • Adds support for multi-qubit expectation values of the pennylane.Hermitian() observable (#192)

  • Adds support for multi-qubit expectation values in default.qubit. (#202)

  • Organize templates into submodules (#195). This included the following improvements:

    • Distinguish embedding templates from layer templates.

    • New random initialization functions supporting the templates available in the new submodule pennylane.init.

    • Added a random circuit template (RandomLayers()), in which rotations and 2-qubit gates are randomly distributed over the wires

    • Add various embedding strategies

Breaking changes

  • The Device methods expectations, pre_expval, and post_expval have been renamed to observables, pre_measure, and post_measure respectively. (#232)


  • default.qubit plugin now uses np.tensordot when applying quantum operations and evaluating expectations, resulting in significant speedup (#239), (#241)

  • PennyLane now allows division of quantum operation parameters by a constant (#179)

  • Portions of the test suite are in the process of being ported to pytest. Note: this is still a work in progress.

    Ported tests include:







    • test_templates*.py



    • (partial)

Bug fixes

  • Fixed a bug in Device.supported, which would incorrectly mark an operation as supported if it shared a name with an observable (#203)

  • Fixed a bug in Operation.wires, by explicitly casting the type of each wire to an integer (#206)

  • Removed code in PennyLane which configured the logger, as this would clash with users’ configurations (#208)

  • Fixed a bug in default.qubit, in which QubitStateVector operations were accidentally being cast to np.float instead of np.complex. (#211)


This release contains contributions from:

Shahnawaz Ahmed, riveSunder, Aroosa Ijaz, Josh Izaac, Nathan Killoran, Maria Schuld.

Release 0.3.1

Bug fixes

  • Fixed a bug where the interfaces submodule was not correctly being packaged via

Release 0.3.0

New features since last release

  • PennyLane now includes a new interfaces submodule, which enables QNode integration with additional machine learning libraries.

  • Adds support for an experimental PyTorch interface for QNodes

  • Adds support for an experimental TensorFlow eager execution interface for QNodes

  • Adds a PyTorch+GPU+QPU tutorial to the documentation

  • Documentation now includes links and tutorials including the new PennyLane-Forest plugin.


  • Printing a QNode object, via print(qnode) or in an interactive terminal, now displays more useful information regarding the QNode, including the device it runs on, the number of wires, it’s interface, and the quantum function it uses:

    >>> print(qnode)
    <QNode: device='default.qubit', func=circuit, wires=2, interface=PyTorch>


This release contains contributions from:

Josh Izaac and Nathan Killoran.

Release 0.2.0

New features since last release

  • Added the Identity expectation value for both CV and qubit models (#135)

  • Added the submodule, containing some commonly used QML models to be used as ansatz in QNodes (#133)

  • Added the qml.Interferometer CV operation (#152)

  • Wires are now supported as free QNode parameters (#151)

  • Added ability to update stepsizes of the optimizers (#159)


  • Removed use of hardcoded values in the optimizers, made them parameters (see #131 and #132)

  • Created the new PlaceholderExpectation, to be used when both CV and qubit expval modules contain expectations with the same name

  • Provide a way for plugins to view the operation queue before applying operations. This allows for on-the-fly modifications of the queue, allowing hardware-based plugins to support the full range of qubit expectation values. (#143)

  • QNode return values now support any form of sequence, such as lists, sets, etc. (#144)

  • CV analytic gradient calculation is now more robust, allowing for operations which may not themselves be differentiated, but have a well defined _heisenberg_rep method, and so may succeed operations that are analytically differentiable (#152)

Bug fixes

  • Fixed a bug where the variational classifier example was not batching when learning parity (see #128 and #129)

  • Fixed an inconsistency where some initial state operations were documented as accepting complex parameters - all operations now accept real values (#146)


This release contains contributions from:

Christian Gogolin, Josh Izaac, Nathan Killoran, and Maria Schuld.

Release 0.1.0

Initial public release.


This release contains contributions from:

Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, and Nathan Killoran.