Spin operators methods:

tqix.pis.spin_operators.Sx(S, use_gpu=False, device='cuda')
Parameters:

  • S (int) – dimension of spin operator

  • use_gpu (bool, optional) – if use gpu, defaults to False

  • device (str, optional) – name of compute device, defaults to ‘cuda’

Returns:

spin operator

Return type:

ndarray, tensor, sparse

tqix.pis.spin_operators.Sy(S, use_gpu=False, device='cuda')
Parameters:

  • S (int) – dimension of spin operator

  • use_gpu (bool, optional) – if use gpu, defaults to False

  • device (str, optional) – name of compute device, defaults to ‘cuda’

Returns:

spin operator

Return type:

ndarray, tensor, sparse

tqix.pis.spin_operators.Sz(S, use_gpu=False, device='cuda')
Parameters:

  • S (int) – dimension of spin operator

  • use_gpu (bool, optional) – if use gpu, defaults to False

  • device (str, optional) – name of compute device, defaults to ‘cuda’

Returns:

spin operator

Return type:

ndarray, tensor, sparse

tqix.pis.spin_operators.S_minus(S, use_gpu=False, device='cuda')
Parameters:

  • S (int) – dimension of spin operator

  • use_gpu (bool, optional) – if use gpu, defaults to False

  • device (str, optional) – name of compute device, defaults to ‘cuda’

Returns:

spin operator

Return type:

ndarray, tensor, sparse

tqix.pis.spin_operators.S_plus(S, use_gpu=False, device='cuda')
Parameters:

  • S (int) – dimension of spin operator

  • use_gpu (bool, optional) – if use gpu, defaults to False

  • device (str, optional) – name of compute device, defaults to ‘cuda’

Returns:

spin operator

Return type:

ndarray, tensor, sparse

tqix.pis.spin_operators.S_2(S)
Parameters:

S (int) – dimension of spin operator

Returns:

opr: spin operator

Return type:

ndarray, tensor, sparse