PyTorch2.0's jit is still not good enough to support jit in tc

[refraction-ray]

Issue Description

Simply replacing torch.jit.script or torch.jit.trace with backend.jit still fail for tc functions

Example scripts:

@torch.jit.script
def f(param):
    c = tc.Circuit(6)
    for i in range(5):
        for j in range(5):
            c.rzz(i, i+1, theta=param[i, j])
    return c.expectation_ps(z=[1])

f(torch.ones([5, 5]))

or

@partial(torch.jit.trace, example_inputs=torch.ones([5, 5]))
def f(param):
    c = tc.Circuit(6)
    for i in range(5):
        for j in range(5):
            c.rzz(i, i+1, theta=param[i, j])
    return c.expectation_ps(z=[1])

f(torch.ones([5, 5]))

actually the latter somehow works, but very fragile, for example, if the jit transformation is nested with grad or vmap operation, torch mostly fails

Proposed Solution

1. Wait for further development of torch or 2. use tf/jax backend with torch interface instead or 3. actually maybe slightly fix in the exsisting tc codebase may work but currently have no time to try 4. or try torch.compile later.

Additional References

[refraction-ray]

On the other hand, torch.vmap seems to work fine at least at syntax level, detailed performance is not benchmarked

[refraction-ray]

pytorch/pytorch#98724

[refraction-ray]

torch2.3 is okay for functional transformation nesting, but this version doesn't include support for macOS x86...

https://dev-discuss.pytorch.org/t/pytorch-macos-x86-builds-deprecation-starting-january-2024/1690

pytorch/pytorch#114602