tensorcircuit.experimental

Experimental features

tensorcircuit.experimental.adaptive_vmap(f: Callable[[...], Any], vectorized_argnums: Union[int, Sequence[int]] = 0, static_argnums: Optional[Union[int, Sequence[int]]] = None, chunk_size: Optional[int] = None) → Callable[[...], Any]

tensorcircuit.experimental.dynamics_matrix(f: Callable[[...], Any], *, kernel: str = 'dynamics', postprocess: Optional[str] = None, mode: str = 'fwd') → Callable[[...], Any]

tensorcircuit.experimental.dynamics_rhs(f: Callable[[...], Any], h: Any) → Callable[[...], Any]

tensorcircuit.experimental.evol_global(c: Any, h_fun: Callable[[...], Any], t: float, *args: Any, **solver_kws: Any) → Any

ode evolution of time dependent Hamiltonian on circuit of all qubits [only jax backend support for now]

Parameters

• c (Circuit) – _description_

• h_fun (Callable[..., Tensor]) – h_fun should return a SPARSE Hamiltonian matrix with input arguments time and *args

• t (float) – _description_

Returns

_description_

Return type

Circuit

tensorcircuit.experimental.evol_local(c: Any, index: Sequence[int], h_fun: Callable[[...], Any], t: float, *args: Any, **solver_kws: Any) → Any

ode evolution of time dependent Hamiltonian on circuit of given indices [only jax backend support for now]

Parameters

• c (Circuit) – _description_

• index (Sequence[int]) – _description_

• h_fun (Callable[..., Tensor]) – h_fun should return a dense Hamiltonian matrix with input arguments time and *args

• t (float) – evolution time

Returns

_description_

Return type

Circuit

tensorcircuit.experimental.finite_difference_differentiator(f: Callable[[...], Any], argnums: Tuple[int, ...] = (0,), shifts: Tuple[float, float] = (0.001, 0.002)) → Callable[[...], Any]

tensorcircuit.experimental.hamiltonian_evol(tlist: Any, h: Any, psi0: Any, callback: Optional[Callable[[...], Any]] = None) → Any

Fast implementation of static full Hamiltonian evolution (default as imaginary time)

Parameters

• tlist (Tensor) – _description_

• h (Tensor) – _description_

• psi0 (Tensor) – _description_

• callback (Optional[Callable[..., Any]], optional) – _description_, defaults to None

Returns

Tensor

Return type

result dynamics on tlist

tensorcircuit.experimental.parameter_shift_grad(f: Callable[[...], Any], argnums: Union[int, Sequence[int]] = 0, jit: bool = False, shifts: Tuple[float, float] = (1.5707963267948966, 2)) → Callable[[...], Any]

similar to grad function but using parameter shift internally instead of AD, vmap is utilized for evaluation, so the speed is still ok

Parameters

• f (Callable[..., Tensor]) – quantum function with weights in and expectation out

• argnums (Union[int, Sequence[int]], optional) – label which args should be differentiated, defaults to 0

• jit (bool, optional) – whether jit the original function f at the beginning, defaults to False

• shifts (Tuple[float, float]) – two floats for the delta shift on the numerator and dominator, defaults to (pi/2, 2) for parameter shift

Returns

the grad function

Return type

Callable[…, Tensor]

tensorcircuit.experimental.parameter_shift_grad_v2(f: Callable[[...], Any], argnums: Union[int, Sequence[int]] = 0, jit: bool = False, random_argnums: Optional[Sequence[int]] = None, shifts: Tuple[float, float] = (1.5707963267948966, 2)) → Callable[[...], Any]

similar to grad function but using parameter shift internally instead of AD, vmap is utilized for evaluation, v2 also supports random generator for finite measurememt shot, only jax backend is supported, since no vmap randomness is available in tensorflow

Parameters

• f (Callable[..., Tensor]) – quantum function with weights in and expectation out

• argnums (Union[int, Sequence[int]], optional) – label which args should be differentiated, defaults to 0

• jit (bool, optional) – whether jit the original function f at the beginning, defaults to False

• shifts (Tuple[float, float]) – two floats for the delta shift on the numerator and dominator, defaults to (pi/2, 2) for parameter shift

Returns

the grad function

Return type

Callable[…, Tensor]

tensorcircuit.experimental.qng(f: Callable[[...], Any], kernel: str = 'qng', postprocess: Optional[str] = 'qng', mode: str = 'fwd') → Callable[[...], Any]

tensorcircuit.experimental.qng2(f: Callable[[...], Any], kernel: str = 'qng', postprocess: Optional[str] = 'qng', mode: str = 'rev') → Callable[[...], Any]