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Release 0.8.0
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@josh146 josh146 released this 07 Feb 21:54
· 3326 commits to master since this release
v0.8.0
bca4a0d

See the release notes on the PennyLane website for code examples and more details.

New features since last release

  • Added a quantum chemistry package, pennylane.qchem, which supports integration with OpenFermion, Psi4, PySCF, and OpenBabel. Functions and classes for creating and solving VQE
    problems have also been addded. (#453) (#467)

    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.
    • 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

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

  • 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)

  • 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. QNodes that return probabilities fully support autodifferentiation. (#432)

  • 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)

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

  • Added high level qml.sum, qml.dot, qml.apply functions that act on QNode collections. qml.sum and qml.dot take the sum of a QNode collection, and a dot product of tensors/arrays/QNode collections, respectively.(#466)

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)

Improvements

  • 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 expt.tensornet.tf devices to default.tensor and default.tensor.tf. (#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 includes a new execute method, which allows for more convenient plugin design. As a result, the way samples are generated on qubit based devices has been unified. (#461) (#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)

Documentation

  • 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)

Contributors

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.

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