Designing quantum experiments with a genetic algorithm
This work addresses the challenge of creating exotic non-Gaussian states for quantum-enhanced phase measurements, which is incremental as it applies a known optimization method to a specific domain problem.
The authors tackled the problem of designing quantum optics experiments for engineering quantum states with specific properties, using a genetic algorithm, and achieved up to a 100-fold improvement over classical states and a 20-fold improvement over optimal Gaussian states in noise-free quantum metrology, with the algorithm also finding states that outperform alternatives under realistic noise and limited data conditions.
We introduce a genetic algorithm that designs quantum optics experiments for engineering quantum states with specific properties. Our algorithm is powerful and flexible, and can easily be modified to find methods of engineering states for a range of applications. Here we focus on quantum metrology. First, we consider the noise-free case, and use the algorithm to find quantum states with a large quantum Fisher information (QFI). We find methods, which only involve experimental elements that are available with current or near-future technology, for engineering quantum states with up to a 100-fold improvement over the best classical state, and a 20-fold improvement over the optimal Gaussian state. Such states are a superposition of the vacuum with a large number of photons (around $80$), and can hence be seen as Schrödinger-cat-like states. We then apply the two most dominant noise sources in our setting -- photon loss and imperfect heralding -- and use the algorithm to find quantum states that still improve over the optimal Gaussian state with realistic levels of noise. This will open up experimental and technological work in using exotic non-Gaussian states for quantum-enhanced phase measurements. Finally, we use the Bayesian mean square error to look beyond the regime of validity of the QFI, finding quantum states with precision enhancements over the alternatives even when the experiment operates in the regime of limited data.