Designing quantum experiments with a genetic algorithm

Bayesian multiparameter quantum metrology with limited data

“…More concretely, if we recall that ¯ mse ≈ 0.168 in such scenario, and noticing that ¯ prior = π 2 /48 ≈ 0.206, then a single shot can improve our knowledge about (θ 1 , θ 2 ) by 18% with respect to the prior uncertainty, having defined the improvement as (¯ prior − ¯ mse )/¯ prior multiplied by 100%. Further examples of this notion of improvement can be found in [68].…”

A hybrid machine learning algorithm for designing quantum experiments

“…Explicitly calculating the fidelity the large number of times required to run our algorithm takes a surprisingly long time, whereas using our DNN we get a useful, albeit modest, speed-up. While in the work presented here the speed-up is only small, our results can be seen as a proof of principle, paving the way for much more demanding fitness functions, such as the Bayesian mean squared error [14,16], to be approximately evaluated in this way. Once the DNN has guided the search in the right direction, the fidelity is then used to provide the exact fitness function (see Section III A for the full description of our algorithm).…”

“…Many more states, operators and measurements can be included in this toolbox. As discussed in our paper that fully introduces our algorithm [14], the algorithm is design with flexibility in mind, so it is straightforward for more elements to be added (or removed) from the toolbox, depending on the available equipment or desired goal. But here we consider a simplified toolbox to discover whether such a limited toolbox of experimentally viable elements can still produce a range of quantum states to a high fidelity.…”

“…The core of our algorithm, named AdaQuantum [13] and introduced in [14] and [4], uses a genetic algorithm to search for optimal arrangements of quantum optics experimental equipment. Any given arrangement will output a quantum state of light, and the algorithm's task is to optimise the arrangement to find states with specific properties.…”

“…To assess the suitability of a given state, we require a fitness function that takes as input a quantum state and outputs a number -the fitness value -that quantifies whether the state has the properties we desire or not. Our previous works largely focused on quantum metrology, where our algorithm found quantum states with substantial improvements over the alternatives in the literature [4,14]. While in [4,14] the fitness function assessed the phase-measuring capabilities of the states, in this paper instead we look at producing a range of useful and interesting states (introduced below) to a high fidelity, and hence we use as our fitness function the fidelity to our target states.…”