Tensorcircuit

Latest version: v0.12.0

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0.1.1

Added

- Add `quoperator` method to get `QuOperator` representation of the circuit unitary

- Add `coo_sparse_matrix_from_numpy` method on backend, where the scipy coo matrix is converted to sparse tensor in corresponding backend

- Add sparse tensor to scipy coo matrix implementation in `numpy` method

Changed

- `tc.quantum.PauliStringSum2COO`, `tc.quantum.PauliStringSum2Dense`, and `tc.quantum.heisenberg_hamiltonian` now return the tensor in current backend format if `numpy` option sets to False. (Breaking change: previously, the return are fixed in TensorFlow format)

0.1.0

Added

- `DMCircuit` also supports array instead of gate as the operator

Fixed

- fix translation issue to qiskit when the input parameter is in numpy form

- type conversion in measure API when high precision is set

- fix bug in to_qiskit with new version qiskit

0.0.220509

Added

- Add `eigvalsh` method on backend

Changed

- `post_select` method return the measurement result int tensor now, consistent with `cond_measure`

- `Circuit.measure` now point to `measure_jit`

0.0.220413

Added

- Add `expectation_ps` method for `DMCircuit`

- Add `measure` and `sample` for `DMCircuit`

Fixed

- With `Circuit.vis_tex`, for the Circuit has customized input state, the default visualization is psi instead of all zeros now

- `general_kraus` is synced with `apply_general_kraus` for `DMCircuit`

- Fix dtype incompatible issue in kraus methods between status and prob

0.0.220402

Added

- add `utils.append` to build function pipeline

- add `mean` method on backends

- add trigonometric methods on backends

- add `conditional_gate` to support quantum ops based on previous measurment results

- add `expectation_ps` as shortcut to get Pauli string expectation

- add `append` and `prepend` to compose circuits

- add `matrix` method to get the circuit unitary matrix

Changed

- change the return information of `unitary_kraus` and `general_kraus` methods

- add alias for any gate as unitary

0.0.220328

Added

- add QuOperator convert tools which can convert MPO in the form of TensorNetwork and Quimb into MPO in the form of QuOperator

Changed

- quantum Hamiltonian generation now support the direct return of numpy form matrix

Fixed

- unitary_kraus and general_kraus API now supports the mix input of array and Node as kraus list

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