Dynamical learning of a photonics quantum-state engineering process

Suprano, Alessia; Zia, Danilo; Polino, Emanuele; Giordani, Taira; Innocenti, Luca; Ferraro, Alessandro; Paternostro, Mauro; Spagnolo, Nicolò; Sciarrino, Fabio

doi:10.1117/1.ap.3.6.066002

Cited by 18 publications

(12 citation statements)

References 85 publications

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“…Most notably, a hybrid, quantumclassical reinforcement learning algorithm was shown to find the optimal state preparations and measurements that maximize nonlocality [54]. Furthermore, hybrid optimization techniques have been used to maximize nonlocality in simple photonic systems [55,56]. Our VQO methods have many similarities with these previous approaches, thus, we expect VQO to show similar success.…”

Section: Introductionmentioning

confidence: 78%

“…5 where qubits are rotated about the y-axis. In fact, hybrid optimization techniques on photonic systems have previously demonstrated the ability to maximize the violation of the CHSH inequality [55,56]. Hence, our VQO framework could be extended to similar photonic implementations of n-local networks [65][66][67][68][69].…”

Section: Adapting Vqo To Quantum Network Hardwarementioning

confidence: 99%

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Variational Quantum Optimization of Nonlocality in Noisy Quantum Networks

Doolittle¹,

Bromley²,

Killoran³

et al. 2022

Preprint

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The inherent noise and complexity of quantum communication networks leads to challenges in designing quantum network protocols using classical methods. To address this issue, we develop a variational quantum optimization framework that simulates quantum networks on quantum hardware and optimizes the network using differential programming techniques. We use our hybrid framework to optimize nonlocality in noisy quantum networks. On the noisy IBM quantum computers, we demonstrate our framework's ability to maximize quantum nonlocality. On a classical simulator with a static noise model, we investigate the noise robustness of quantum nonlocality with respect to unital and nonunital channels. In both cases, we find that our optimization methods can reproduce known results, while uncovering interesting phenomena. When unital noise is present we find numerical evidence suggesting that maximally entangled state preparations yield maximal nonlocality. When nonunital noise is present we find that nonmaximally entangled states can yield maximal nonlocality. Thus, we show that variational quantum optimization is a practical design tool for quantum networks in the near-term. In the long-term, our variational quantum optimization techniques show promise of scaling beyond classical approaches and can be deployed on quantum network hardware to optimize quantum communication protocols against their inherent noise.

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Section: Introductionmentioning

confidence: 78%

Section: Adapting Vqo To Quantum Network Hardwarementioning

confidence: 99%

Variational Quantum Optimization of Nonlocality in Noisy Quantum Networks

Doolittle¹,

Bromley²,

Killoran³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…5, the setup is composed of three blocks containing a series of waveplates, acting on the coin state, followed by a q-plate. The latter is a device composed of a birefringent and inhomogeneous material capable of modify the photons' OAM conditionally on their polarization [95], and is thus suitable to engineer nontrivial OAM states [52,96].…”

Section: B Experimental Implementationmentioning

confidence: 99%

Regression of high dimensional angular momentum states of light

Zia¹,

Checchinato²,

Suprano³

et al. 2022

Preprint

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The Orbital Angular Momentum (OAM) of light is an infinite-dimensional degree of freedom of light with several applications in both classical and quantum optics. However, to fully take advantage of the potential of OAM states, reliable detection platforms to characterize generated states in experimental conditions are needed. Here, we present an approach to reconstruct input OAM states from measurements of the spatial intensity distributions they produce. To obviate issues arising from intrinsic symmetry of Laguerre-Gauss modes, we employ a pair of intensity profiles per state projecting it only on two distinct bases, showing how this allows to uniquely recover input states from the collected data. Our approach is based on a combined application of dimensionality reduction via principal component analysis, and linear regression, and thus has a low computational cost during both training and testing stages. We showcase our approach in a real photonic setup, generating up-to-four-dimensional OAM states through a quantum walk dynamics. The high performances and versatility of the demonstrated approach make it an ideal tool to characterize high-dimensional states in quantum information protocols.

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“…Other black-box algorithms based on coherent light measurements always require high phase stability during the scan in the input and output sections, [31][32][33] thus making them not viable for optical setups with in-fiber connections, which are nevertheless typical for integrated photonic devices. The last classes we mention are machine-learning algorithms, 34,35 which need large sets of data to learn the correct transformation. Very recently, Kuzmin et al 36 simulated the application of a supervised-learning strategy for the calibration of a reconfigurable interferometer and experienced an unfavorable scaling of the training set size with the number of modes in the black-box scenario.…”

Section: Introductionmentioning

confidence: 99%

Characterization of multimode linear optical networks

et al. 2023

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Multimode optical interferometers represent the most viable platforms for the successful implementation of several quantum information schemes that take advantage of optical processing. Examples range from quantum communication and sensing, to computation, including optical neural networks, optical reservoir computing, or simulation of complex physical systems. The realization of such routines requires high levels of control and tunability of the parameters that define the operations carried out by the device. This requirement becomes particularly crucial in light of recent technological improvements in integrated photonic technologies, which enable the implementation of progressively larger circuits embedding a considerable amount of tunable parameters. We formulate efficient procedures for the characterization of optical circuits in the presence of imperfections that typically occur in physical experiments, such as unbalanced losses and phase instabilities in the input and output collection stages. The algorithm aims at reconstructing the transfer matrix that represents the optical interferometer without making any strong assumptions about its internal structure and encoding. We show the viability of this approach in an experimentally relevant scenario, defined by a tunable integrated photonic circuit, and we demonstrate the effectiveness and robustness of our method. Our findings can find application in a wide range of optical setups, based on both bulk and integrated configurations.

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Dynamical learning of a photonics quantum-state engineering process

Cited by 18 publications

References 85 publications

Variational Quantum Optimization of Nonlocality in Noisy Quantum Networks

Variational Quantum Optimization of Nonlocality in Noisy Quantum Networks

Regression of high dimensional angular momentum states of light

Characterization of multimode linear optical networks

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