tensorcircuit.interfaces.tensorflow¶

Interface wraps quantum function as a tensorflow function

tensorcircuit.interfaces.tensorflow.tensorflow_interface(fun: Callable[[...], Any], ydtype: Any, jit: bool = False, enable_dlpack: bool = False) → Callable[[...], Any][source]¶

Wrap a quantum function on different ML backend with a tensorflow interface.

Example:

K = tc.set_backend("jax")


def f(params):
    c = tc.Circuit(1)
    c.rx(0, theta=params[0])
    c.ry(0, theta=params[1])
    return K.real(c.expectation([tc.gates.z(), [0]]))


f = tc.interfaces.tf_interface(f, ydtype=tf.float32, jit=True)

tfb = tc.get_backend("tensorflow")
grads = tfb.jit(tfb.grad(f))(tfb.ones([2]))
Parameters:

  • fun (Callable[..., Any]) – The quantum function with tensor in and tensor out

  • ydtype (Any) – output tf dtype or in str

  • jit (bool, optional) – whether to jit fun, defaults to False

  • enable_dlpack (bool, optional) – whether transform tensor backend via dlpack, defaults to False

Returns:

The same quantum function but now with torch tensor in and torch tensor out while AD is also supported

Return type:

Callable[…, Any]

tensorcircuit.interfaces.tensorflow.tf_dtype(dtype: str) → Any[source]¶
tensorcircuit.interfaces.tensorflow.tf_interface(fun: Callable[[...], Any], ydtype: Any, jit: bool = False, enable_dlpack: bool = False) → Callable[[...], Any]¶

Wrap a quantum function on different ML backend with a tensorflow interface.

Example:

K = tc.set_backend("jax")


def f(params):
    c = tc.Circuit(1)
    c.rx(0, theta=params[0])
    c.ry(0, theta=params[1])
    return K.real(c.expectation([tc.gates.z(), [0]]))


f = tc.interfaces.tf_interface(f, ydtype=tf.float32, jit=True)

tfb = tc.get_backend("tensorflow")
grads = tfb.jit(tfb.grad(f))(tfb.ones([2]))
Parameters:

  • fun (Callable[..., Any]) – The quantum function with tensor in and tensor out

  • ydtype (Any) – output tf dtype or in str

  • jit (bool, optional) – whether to jit fun, defaults to False

  • enable_dlpack (bool, optional) – whether transform tensor backend via dlpack, defaults to False

Returns:

The same quantum function but now with torch tensor in and torch tensor out while AD is also supported

Return type:

Callable[…, Any]

tensorcircuit.interfaces.tensorflow.tf_wrapper(fun: Callable[[...], Any], enable_dlpack: bool = False) → Callable[[...], Any][source]¶