PennyLane v0.10 Released

PennyLane Development
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Hi everyone! We’re very happy to announce the release of PennyLane version 0.10 :fireworks: :confetti_ball:

New and improved simulators :zap:

  • Use the new default.qubit.tf device, a quantum simulator written in TensorFlow, allowing simulated circuits to be trained faster via backpropagation

  • Experiment with an upgraded default.tensor plugin that now features two different tensor network representations to be used: "exact" and "mps"

default_qubit_tf_benchmark

New machine learning functionality and integrations :building_construction:

  • Convert a PennyLane QNode into a TorchLayer, allowing for creation of quantum and hybrid models using the torch.nn

  • Trade off extra computation for enhanced memory efficiency with the new “reversible” differentiation method which can be used in simulators

New templates using broadcasting :alembic:

  • Use the new UCCSD, SingleExcitationUnitary, and DoubleExcitationUnitary templates to create the Unitary Coupled-Cluster Singles and Doubles (UCCSD) ansatz and perform VQE-based quantum chemistry simulations using PennyLane-QChem

  • Specify the Squared error loss function for circuits with trainable parameters using the SquaredErrorLoss class from the new pennylane.qnn.cost module

Major upgrades to variable handling

Mark if a variable is differentiable or not by using the new pennylane.numpy.tensor class and specifying the requires_grad attribute

New plugin to complement the growing library of devices

In addition to @qiskit, @rigetti Forest, @GoogleAI Cirq, etc., you can now access Alpine Quantum Technologies’ online ion-trap platform using PennyLane.

The full release notes are available at Release notes — PennyLane 0.27.0 documentation

As always, this release would not have been possible without all the help from our contributors:

@Alain_Delgado_Gran, @antalszava , Jack Ceroni , @Maria_Schuld, Nicola Vitucci, @theodor, @josh, @nathan , @Tom_Bromley .

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