# What are the libararies for Machine Learning on NISQ Chip? And What are the roadmaps?

Nowadays quantum learning is hiring. And we can see mainly two different area. One of them is variational algorithms part. And the other one is classical learning for quantum systems like NISQ. (Some of scientists call both areas as quantum learning wheras some scientists call the second one as a classical learning for quantum system) Regarding variational part, there are many libraries like tensorflow quantum, qiskit ... But for the second part, I would like to learn what is good. For instance we can simulate photonic chips with Simphony or gds helpers. But my aim is not to simulate photonic chips. My aim is to train integrated photonic chips for noises. So which libraries are good for that. For instance is cirq enough for that? or should I use another libraries? Or should I start to simulate my chip and after that I should use some additional libraries like tensorflow quantum or qiskit or cirq?

You will have very limited support simulating photonic circuits in Cirq (and therefore Tensorflow Quantum). Those libraries mainly deal with evolving systems of qubits where a state $$|\psi\rangle$$ is expressed over a discrete basis like $$|\psi\rangle = \sum_i c_i |i\rangle$$, as opposed to photonic systems containing continous variables (such as a wavefunction $$\psi(x)$$ expressed in the basis of a continous operator $$\hat{x}$$).