tensorcircuit.interfaces.torch

Interface wraps quantum function as a torch function

tensorcircuit.interfaces.torch.pytorch_interface(fun: Callable[[...], Any], jit: bool = False, enable_dlpack: bool = False) → Callable[[...], Any]

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

Example:

import torch

tc.set_backend("tensorflow")


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


f_torch = tc.interfaces.torch_interface(f, jit=True)

a = torch.ones([2], requires_grad=True)
b = f_torch(a)
c = b ** 2
c.backward()

print(a.grad)

Parameters:

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

• 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.torch.torch_interface(fun: Callable[[...], Any], jit: bool = False, enable_dlpack: bool = False) → Callable[[...], Any]

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

Example:

import torch

tc.set_backend("tensorflow")


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


f_torch = tc.interfaces.torch_interface(f, jit=True)

a = torch.ones([2], requires_grad=True)
b = f_torch(a)
c = b ** 2
c.backward()

print(a.grad)

Parameters:

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

• 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.torch.torch_interface_kws(f: Callable[[...], Any], jit: bool = True, enable_dlpack: bool = False) → Callable[[...], Any]

similar to py:meth:tensorcircuit.interfaces.torch.torch_interface, but now the interface support static arguments for function f, which is not a tensor and can be used with keyword arguments

Example:

tc.set_backend("tensorflow")

def f(tensor, integer):
    r = 0.
    for i in range(integer):
        r += tensor
    return r

fnew = tc.interfaces.torch_interface_kws(f)

print(fnew(torch.ones([2]), integer=3))
print(fnew(torch.ones([2]), integer=4))

Parameters:

• f (Callable[..., Any]) – _description_

• jit (bool, optional) – _description_, defaults to True

• enable_dlpack (bool, optional) – _description_, defaults to False

Returns:

_description_

Return type:

Callable[…, Any]