1.3.0

Enhancements

• Added time dependent evolutions to :class:~quimb.evo.Evolution when integrating a pure state - see :ref:time-dependent-evolution - as well as supporting LinearOperator defined hamiltonians (:pull:40).
• Allow the :class:~quimb.evo.Evolution callback compute= to optionally access the Hamiltonian (:pull:49).
• Added :meth:quimb.tensor.tensor_core.Tensor.randomize and :meth:quimb.tensor.tensor_core.TensorNetwork.randomize to randomize tensor and tensor network entries.
• Automatically squeeze tensor networks when rank-simplifying.
• Add :meth:~quimb.tensor.tensor_1d.TensorNetwork1DFlat.compress_site for compressing around single sites of MPS etc.
• Add :func:~quimb.tensor.tensor_gen.MPS_ghz_state and :func:~quimb.tensor.tensor_gen.MPS_w_state for building bond dimension 2 open boundary MPS reprentations of those states.
• Various changes in conjunction with autoray <https://github.com/jcmgray/autoray>_ to improve the agnostic-ness of tensor network operations with respect to the backend array type.
• Add :func:~quimb.tensor.tensor_core.new_bond on top of :meth:quimb.tensor.tensor_core.Tensor.new_ind and :meth:quimb.tensor.tensor_core.Tensor.expand_ind for more graph orientated construction of tensor networks, see :ref:tn-creation-graph-style.
• Add the :func:~quimb.gen.operators.fsim gate.
• Make the parallel number generation functions use new numpy 1.17+ functionality rather than randomgen (which can still be used as the underlying bit generator) (:pull:50)
• TN: rename contraction_complexity to :meth:~quimb.tensor.tensor_core.TensorNetwork.contraction_width.
• TN: update :meth:quimb.tensor.tensor_core.TensorNetwork.rank_simplify, to handle hyper-edges.
• TN: add :meth:quimb.tensor.tensor_core.TensorNetwork.diagonal_reduce, to automatically collapse all diagonal tensor axes in a tensor network, introducing hyper edges.
• TN: add :meth:quimb.tensor.tensor_core.TensorNetwork.antidiag_gauge, to automatically flip all anti-diagonal tensor axes in a tensor network allowing subsequent diagonal reduction.
• TN: add :meth:quimb.tensor.tensor_core.TensorNetwork.column_reduce, to automatically identify tensor axes with a single non-zero column, allowing the corresponding index to be cut.
• TN: add :meth:quimb.tensor.tensor_core.TensorNetwork.full_simplify, to iteratively perform all the above simplifications in a specfied order until nothing is left to be done.
• TN: add num_tensors and num_indices attributes, show num_indices in __repr__.
• TN: various improvements to the pytorch optimizer (:pull:34)
• TN: add some built-in 1D quantum circuit ansatzes:
  :func:~quimb.tensor.circuit_gen.circ_ansatz_1D_zigzag,
  :func:~quimb.tensor.circuit_gen.circ_ansatz_1D_brickwork, and
  :func:~quimb.tensor.circuit_gen.circ_ansatz_1D_rand.
• TN: add parametrized tensors :class:~quimb.tensor.tensor_core.PTensor and so trainable, TN based quantum circuits -- see :ref:example-tn-training-circuits.

Bug fixes:

• Fix consistency of :func:~quimb.calc.fidelity by making the unsquared version the default for the case when either state is pure, and always return a real number.
• Fix a bug in the 2D system example for when j != 1.0
• Add environment variable QUIMB_NUMBA_PAR to set whether numba should use automatic parallelization - mainly to fix travis segfaults.
• Make cache import and initilization of petsc4py and slepc4py more robust.