A hybrid machine learning algorithm for designing quantum experiments
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RESEARCH ARTICLE
A hybrid machine learning algorithm for designing quantum experiments L. O’Driscoll1 · R. Nichols1 · P. A. Knott1 Received: 11 December 2018 / Accepted: 28 February 2019 © The Author(s) 2019
Abstract We introduce a hybrid machine learning algorithm for designing quantum optics experiments to produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schr¨odinger cat states and cubic phase states, all to a fidelity of over 96%. Here, we specifically focus on designing realistic experiments, and hence all of the algorithm’s designs only contain experimental elements that are available with current technology. The core of our algorithm is a genetic algorithm that searches for optimal arrangements of the experimental elements, but to speed up the initial search, we incorporate a neural network that classifies quantum states. The latter is of independent interest, as it quickly learned to accurately classify quantum states given their photon number distributions. Keywords Machine learning · Genetic algorithm · Artificial intelligence · Quantum state engineering · Quantum optics As artificial intelligence (AI) and machine learning develop, their range of applicability continues to grow. They are now being utilised in the fast-growing field of quantum machine learning (Dunjko and Briegel 2017; Biamonte et al. 2017; Schuld et al. 2015), with one particular application demonstrating that AI is an effective tool for designing quantum physics experiments (Knott 2016; Krenn et al. 2016; Melnikov et al. 2018; Arrazola et al. 2019; Sabapathy et al. 2018). In this vein, here we introduce a hybrid algorithm that designs and optimises quantum optics experiments for producing a range of useful quantum states, including Schr¨odinger cat states (Ourjoumtsev et al. 2007; Huang et al. 2015; Etesse et al. 2015) and cubic phase states (Gottesman et al. 2001).
P. A. Knott
[email protected] 1
Centre for the Mathematics and Theoretical Physics of Quantum Non-Equilibrium Systems (CQNE), School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
The core of our algorithm, named AdaQuantum1 and introduced in (Nichols et al. 2018) and (Knott 2016), 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 (Nichols et al. 2018; Knott 2016). While in Nichols et al. (2018) and Knott (2016) the fitness function assessed
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