A number of Python modules exisit for modelling the quantum outputs of quantum optical systems. With only one or two optical components and simple quantum states, system outputs can be calculated by hand. However, when the complexity increases, the benefits of having a Python module to check results or just save time is obvious. With quantum comms, computers and sensors being investigated seriously, the complexity is already high.
The availability of symbolic algebra programs in Python and Octave certainly are valuable for checking algebra, so you could start from scratch yourself to build somethings. However, in the case of quantum optics there are more rules for how things like creation operators and annihilation operators, hamiltonians etc act on states, so building from scratch is far from trivial.
Given a number of Python modules exist for performing this symbolic algebra, would there be any kind of consensus as to which one might be the best and most versatile to use, with the greatest number of users?
many thanks,
Neil
When you need to compute the evolution of a quantum state, QuTIP - as mentioned before - is an obvious choice. But if you are only interested in photonic states, that is, when qubit are simulated by photons occupying different degrees of freedom, I can suggest Perceval.
It's a platform designed for photonic quantum computing which supports symbolic computation. The advantage is that in Perceval you do not specify the evolution matrix of your quantum state but the transfer matrix of the linear optical circuit.
In fact, when working with optical element, finding their exact hamiltonian might not be very easy. Furthermore, storing the evolution operator (computed via exponentiation) and performing matrix multiplication might not be the smartest approach.
If you have the transfer matrix of a circuit you can just compute expectations on photonic states via permanent calculation.
I hope the difference is clear and that this can help you.
many thanks for your help, i'll investigate both and see how i get on. Neil
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