Quimb

**Enhancements:**

- tensor network fitting: add `method="tree"` for when ansatz is a tree - [`tensor_network_fit_tree`](quimb.tensor.fitting.tensor_network_fit_tree)
- tensor network fitting: fix `method="als"` for complex networks
- tensor network fitting: allow `method="als"` to use a iterative solver suited to much larger tensors, by default a custom conjugate gradient implementation.
- [`tensor_network_distance`](quimb.tensor.fitting.tensor_network_distance) and fitting: support hyper indices explicitly via `output_inds` kwarg
- add [`tn.make_overlap`](quimb.tensor.tensor_core.TensorNetwork.make_overlap) and [`tn.overlap`](quimb.tensor.tensor_core.TensorNetwork.overlap) for computing the overlap between two tensor networks, $\langle O |T \rangle$, with explicit handling of outer indices to address hyper networks. Add `output_inds` to [`tn.norm`](quimb.tensor.tensor_core.TensorNetwork.norm) and [`tn.make_norm`](quimb.tensor.tensor_core.TensorNetwork.make_norm) also, as well as the `squared` kwarg.
- replace all `numba` based paralellism (`prange` and parallel vectorize) with explicit thread pool based parallelism. Should be more reliable and no need to set `NUMBA_NUM_THREADS` anymore. Remove env var `QUIMB_NUMBA_PAR`.
- [`Circuit`](quimb.tensor.circuit.Circuit): add `dtype` and `convert_eager` options. `dtype` specifies what the computation should be performed in. `convert_eager` specifies whether to apply this (and any `to_backend` calls) as soon as gates are applied (the default for MPS circuit simulation) or just prior to contraction (the default for exact contraction simulation).
- [`tn.full_simplify`](quimb.tensor.tensor_core.TensorNetwork.full_simplify): add `check_zero` (by default set of `"auto"`) option which explicitly checks for zero tensor norms when equalizing norms to avoid `log10(norm)` resulting in -inf or nan. Since it creates a data dependency that breaks e.g. `jax` tracing, it is optional.
- [`schematic.Drawing`](quimb.schematic.Drawing): add `shorten` kwarg to [line drawing](quimb.schematic.Drawing.line) and [curve drawing](quimb.schematic.Drawing.curve) and examples to {ref}`schematic`.
- [`TensorNetwork`](quimb.tensor.tensor_core.TensorNetwork): add `.backend` and `.dtype_name` properties.


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**Breaking Changes**

- renamed `MatrixProductState.partial_trace` and `MatrixProductState.ptr` to [MatrixProductState.partial_trace_to_mpo](quimb.tensor.tensor_1d.MatrixProductState.partial_trace_to_mpo) to avoid confusion with other `partial_trace` methods that usually produce a dense matrix.

**Enhancements:**

- add [`Circuit.sample_gate_by_gate`](quimb.tensor.circuit.Circuit.sample_gate_by_gate) and related methods [`CircuitMPS.reordered_gates_dfs_clustered`](quimb.tensor.circuit.Circuit.reordered_gates_dfs_clustered) and [`CircuitMPS.get_qubit_distances`](quimb.tensor.circuit.CircuitMPS.get_qubit_distances) for sampling a circuit using the 'gate by gate' method introduced in https://arxiv.org/abs/2112.08499.
- add [`Circuit.draw`](quimb.tensor.circuit.Circuit.draw) for drawing a very simple circuit schematic.
- [`Circuit`](quimb.tensor.circuit.Circuit): by default turn on `simplify_equalize_norms` and use a `group_size=10` for sampling. This should result in faster and more stable sampling.
- [`Circuit`](quimb.tensor.circuit.Circuit): use `numpy.random.default_rng` for random number generation.
- add [`qtn.circ_a2a_rand`](quimb.tensor.circuit_gen.circ_a2a_rand) for generating random all-to-all circuits.
- expose [`qtn.edge_coloring`](quimb.tensor.tensor_arbgeom_tebd.edge_coloring) as top level function and allow layers to be returned grouped.
- add docstring for [`tn.contract_compressed`](quimb.tensor.tensor_core.TensorNetwork.contract_compressed) and by default pick up important settings from the supplied contraction path optimizer (`max_bond` and `compress_late`)
- add [`Tensor.rand_reduce`](quimb.tensor.tensor_core.Tensor.rand_reduce) for randomly removing a tensor index by contracting a random vector into it. One can also supply the value `"r"` to `isel` selectors to use this.
- add `fit-zipup` and `fit-projector` shorthand methods to the general 1d tensor network compression function
- add [`MatrixProductState.compute_local_expectation`](quimb.tensor.tensor_1d.MatrixProductState.compute_local_expectation) for computing many local expectations for a MPS at once, to match the interface for this method elsewhere. These can either be computed via canonicalization (`method="canonical"`), or via explicit left and right environment contraction (`method="envs"`)
- specialize [`CircuitMPS.local_expectation`](quimb.tensor.circuit.CircuitMPS.local_expectation) to make use of the MPS form.
- add [`PEPS.product_state`](quimb.tensor.tensor_2d.PEPS.product_state) for constructing a PEPS representing a product state.
- add [`PEPS.vacuum`](quimb.tensor.tensor_2d.PEPS.vacuum) for constructing a PEPS representing the vacuum state $|000\ldots0\rangle$.
- add [`PEPS.zeros`](quimb.tensor.tensor_2d.PEPS.zeros) for constructing a PEPS whose entries are all zero.
- [`tn.gauge_all_simple`](quimb.tensor.tensor_core.TensorNetwork.gauge_all_simple): improve scheduling and add `damping` and `touched_tids` options.
- [`qtn.SimpleUpdateGen`](quimb.tensor.tensor_arbgeom_tebd.SimpleUpdateGen): add gauge difference update checking and `tol` and `equilibrate` settings. Update `.plot()` method. Default to a small `cutoff`.
- add [`psi.sample_configuration_cluster`](quimb.tensor.tensor_arbgeom.TensorNetworkGenVector.sample_configuration_cluster) for sampling a tensor network using the simple update or cluster style environment approximation.
- add the new doc {ref}`ex-circuit-sampling`

---


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**Bug fixes:**

- fix for MPS sampling with fixed seed ({issue}`247` and {pull}`248`)
- fix for `mps_gate_with_mpo_lazy` ({issue}`246`).

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**Enhancements:**

- support for numpy v2.0 and scipy v1.14
- add MPS sampling: [`MatrixProductState.sample_configuration`](quimb.tensor.tensor_1d.MatrixProductState.sample_configuration) and [`MatrixProductState.sample`](quimb.tensor.tensor_1d.MatrixProductState.sample) (generating multiple samples) and use these for [`CircuitMPS.sample`](quimb.tensor.circuit.CircuitMPS.sample) and [`CircuitPermMPS.sample`](quimb.tensor.circuit.CircuitPermMPS.sample).
- add basic [`.plot()`](quimb.tensor.tensor_arbgeom_tebd.TEBDGen.plot) method for SimpleUpdate classes
- add [`edges_1d_chain`](quimb.tensor.geometry.edges_1d_chain) for generating 1D chain edges
- [operatorbuilder](quimb.experimental.operatorbuilder): better coefficient placement for long range MPO building

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**Enhancements:**

- [`TNOptimizer`](quimb.tensor.optimize.TNOptimizer) can now accept an arbitrary pytree (nested combination of dicts, lists, tuples, etc. with `TensorNetwork`, `Tensor` or raw `array_like` objects as the leaves) as the target object to optimize.
- [`TNOptimizer`](quimb.tensor.optimize.TNOptimizer) can now directly optimize [`Circuit`](quimb.tensor.circuit.Circuit) objects, returning a new optimized circuit with updated parameters.
- [`Circuit`](quimb.tensor.circuit.Circuit): add `.copy()`, `.get_params()` and `.set_params()` interface methods.
- Update generic TN optimizer docs.
- add [`tn.gen_inds_loops`](quimb.tensor.tensor_core.TensorNetwork.gen_inds_loops) for generating all loops of indices in a TN.
- add [`tn.gen_inds_connected`](quimb.tensor.tensor_core.TensorNetwork.gen_inds_connected) for generating all connected sets of indices in a TN.
- make SVD fallback error catching more generic ({pull}`238`)
- fix some windows + numba CI issues.
- [`approx_spectral_function`](quimb.linalg.approx_spectral.approx_spectral_function) add plotting and tracking
- add dispatching to various tensor primitives to allow overriding

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**Enhancements:**

- [`CircuitMPS`](quimb.tensor.circuit.CircuitMPS) now supports multi qubit gates, including arbitrary multi-controls (which are treated in a low-rank manner), and faster simulation via better orthogonality center tracking.
- add [`CircuitPermMPS`](quimb.tensor.circuit.CircuitPermMPS)
- add [`MatrixProductState.gate_nonlocal`](quimb.tensor.tensor_1d.MatrixProductState.gate_nonlocal) for applying a gate, supplied as a raw matrix, to a non-local and arbitrary number of sites. The kwarg `contract="nonlocal"` can be used to force this method, or the new option `"auto-mps"` will select this method if the gate is non-local ({issue}`230`)
- add [`MatrixProductState.gate_with_mpo`](quimb.tensor.tensor_1d.MatrixProductState.gate_with_mpo) for applying an MPO to an MPS, and immediately compressing back to MPS form using [`tensor_network_1d_compress`](quimb.tensor.tensor_1d_compress.tensor_network_1d_compress)
- add [`MatrixProductState.gate_with_submpo`](quimb.tensor.tensor_1d.MatrixProductState.gate_with_submpo) for applying an MPO acting only of a subset of sites to an MPS
- add [`MatrixProductOperator.from_dense`](quimb.tensor.tensor_1d.MatrixProductOperator.from_dense) for constructing MPOs from dense matrices, including an only subset of sites
- add [`MatrixProductOperator.fill_empty_sites`](quimb.tensor.tensor_1d.MatrixProductOperator.fill_empty_sites) for 'completing' an MPO which only has tensors on a subset of sites with (by default) identities
- [`MatrixProductState`](quimb.tensor.tensor_1d.MatrixProductState) and [`MatrixProductOperator`](quimb.tensor.tensor_1d.MatrixProductOperator), now support the ``sites`` kwarg in common constructors, enabling the TN to act on a subset of the full ``L`` sites.
- add [`TensorNetwork.drape_bond_between`](quimb.tensor.tensor_core.TensorNetwork.drape_bond_between) for 'draping' an existing bond between two tensors through a third
- add [`Tensor.new_ind_pair_with_identity`](quimb.tensor.tensor_core.Tensor.new_ind_pair_with_identity)
- TN2D, TN3D and arbitrary geom classical partition function builders ([`TN_classical_partition_function_from_edges`](quimb.tensor.tensor_builder.TN_classical_partition_function_from_edges)) now all support `outputs=` kwarg specifying non-marginalized variables
- add simple dense 1-norm belief propagation algorithm [`D1BP`](quimb.experimental.belief_propagation.d1bp.D1BP)
- add [`qtn.enforce_1d_like`](quimb.tensor.tensor_1d_compress.enforce_1d_like) for checking whether a tensor network is 1D-like, including automatically adding strings of identities between non-local bonds, expanding applicability of [`tensor_network_1d_compress`](quimb.tensor.tensor_1d_compress.tensor_network_1d_compress)
- add [`MatrixProductState.canonicalize`](quimb.tensor.tensor_1d.MatrixProductState.canonicalize) as (by default *non-inplace*) version of `canonize`, to follow the pattern of other tensor network methods. `canonize` is now an alias for `canonicalize_` [note trailing underscore].
- add [`MatrixProductState.left_canonicalize`](quimb.tensor.tensor_1d.MatrixProductState.left_canonicalize) as (by default *non-inplace*) version of `left_canonize`, to follow the pattern of other tensor network methods. `left_canonize` is now an alias for `left_canonicalize_` [note trailing underscore].
- add [`MatrixProductState.right_canonicalize`](quimb.tensor.tensor_1d.MatrixProductState.right_canonicalize) as (by default *non-inplace*) version of `right_canonize`, to follow the pattern of other tensor network methods. `right_canonize` is now an alias for `right_canonicalize_` [note trailing underscore].

**Bug fixes:**

- [`Circuit.apply_gate_raw`](quimb.tensor.circuit.Circuit.apply_gate_raw): fix kwarg bug ({pull}`226`)
- fix for retrieving `opt_einsum.PathInfo` for single scalar contraction ({issue}`231`)


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