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Physical Review Research

Machine-learning-based device-independent certification of quantum networks

Nicola d'Alessandro, Beatrice Polacchi, George Moreno, Emanuele Polino, Rafael Chaves, Iris Agresti, Fabio Sciarrino

Published Apr 10, 2023• 5 citations
Publisher

Abstract

Witnessing nonclassical behavior is a crucial ingredient in quantum information processing. For that, one has to optimize the quantum features a given physical setup can give rise to, which is a hard computational task currently tackled with semidefinite programming, a method limited to linear objective functions and that becomes prohibitive as the complexity of the system grows. Here, we propose an alternative strategy, which exploits a feedforward artificial neural network to optimize the correlations compatible with arbitrary quantum networks. A remarkable step forward with respect to existing methods is that it deals with nonlinear optimization constraints and objective functions, being applicable to scenarios featuring independent sources and nonlinear entanglement witnesses. Furthermore, it offers a significant speedup in comparison with other approaches, thus allowing to explore previously inaccessible regimes. We also extend the use of the neural network to the experimental realm, a situation in which the statistics are unavoidably affected by imperfections, retrieving device-independent uncertainty estimates on Bell-like violations obtained with independent sources of entangled photon states. In this way, this work paves the way for the certification of quantum resources in networks of growing size and complexity.

DOI
10.1103/physrevresearch.5.023016

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Witnessing nonclassical behavior is a crucial ingredient in quantum information processing. For that, one has to optimize the quantum features a given physical setup can give rise to, which is a hard computational task currently tackled with semidefinite programming, a method limited to linear objective functions and that becomes prohibitive as the complexity of the system grows. Here, we propose an alternative strategy, which exploits a feedforward artificial neural network to optimize the correlations compatible with arbitrary quantum networks. A remarkable step forward with respect to existing methods is that it deals with nonlinear optimization constraints and objective functions, being applicable to scenarios featuring independent sources and nonlinear entanglement witnesses. Furthermore, it offers a significant speedup in comparison with other approaches, thus allowing to explore previously inaccessible regimes. We also extend the use of the neural network to the experimental realm, a situation in which the statistics are unavoidably affected by imperfections, retrieving device-independent uncertainty estimates on Bell-like violations obtained with independent sources of entangled photon states. In this way, this work paves the way for the certification of quantum resources in networks of growing size and complexity.
Device-independent protocols will play a crucial role in the future of quantum networks. The properties of network settings lend themselves to the novel concept of network non-locality. Detecting it is a challenging task due to the independence of multiple, causally separated sources, which renders the set of network local behaviors non-convex and forces the use of nonlinear inequalities or semi-definite programs. We propose a linear programming framework for demonstrating network nonlocality using linear optics and single-photon sources for an arbitrary network. Furthermore, we investigate natural network topologies where the number of sources is smaller than the number of parties, allowing a more efficient use of resources. The resulting linear program depends only on observed probabilities and a tunable experimental parameter, and can be solved more efficiently than current semi-definite program approaches. Our method provides a systematic, device-independent witness for network nonlocality, allowing us to explore the range of networks which demonstrate network nonlocality and work towards scalable certification in complex quantum networks.
Bell's theorem shows that quantum theory is incompatible with local hidden-variable models. In recent years, research on nonlocality has moved beyond Bell's original scenario to quantum networks, where multiple independent sources distribute physical systems among distant parties, giving rise to correlations certified by nonlinear rather than standard Bell inequalities. Here, we introduce a self-guided protocol that variationally optimizes each party's measurement to maximize the violation of a network Bell inequality, with the violation evaluated efficiently at each step via local Pauli classical shadows and the search driven by the Complex Simultaneous Perturbation Stochastic Approximation (CSPSA) algorithm. Once converged, the measurement settings it returns are implemented directly and the inequality is re-evaluated without shadows. This two-stage structure separates a device-dependent search from a certificate that depends only on the observed statistics and on the causal structure of the network. We validate the protocol by numerically simulating it on the triangle network using the Wagon-Wheel inequality, recovering the violation achieved by the Fritz distribution, and extending the certification to non-maximally entangled and noisy states.
In the past few years, quantum computing and machine learning fostered rapid developments in their respective areas of application, introducing new perspectives on how information processing systems can be realized and programmed. The rapidly growing field of quantum machine learning aims at bringing together these two ongoing revolutions. Here, we first review a series of recent works describing the implementation of artificial neurons and feedforward neural networks on quantum processors. We then present an original realization of efficient individual quantum nodes based on variational unsampling protocols. We investigate different learning strategies involving global and local layerwise cost functions, and we assess their performances also in the presence of statistical measurement noise. While keeping full compatibility with the overall memory-efficient feedforward architecture, our constructions effectively reduce the quantum circuit depth required to determine the activation probability of single neurons upon input of the relevant data-encoding quantum states. This suggests a viable approach toward the use of quantum neural networks for pattern classification on near-term quantum hardware.
Network nonlocality extends Bell nonlocality to settings with multiple independent sources and parties. Certifying it in quantum information processing tasks requires suitable witnesses. However, in contrast to local correlations, the set of network-local correlations is non-convex. This non-convexity makes certifying network nonlocality a highly non-trivial task. Existing approaches involve leveraging network-specific properties, or inflation-based methods whose constraints grow combinatorially in the number of local variables. In this work, we introduce a linear programming witness for network nonlocality built from five classes of linear constraints. These classes are network-agnostic, although the explicit forms of the constraints must be tailored to a specific network's structure. We use the procedure to construct network nonlocality witnesses for a family of ring networks and certify network nonlocality for a concrete example, relying only on observed probabilities and a tunable experimental parameter. Our work advances the search for efficient witnesses to certify network nonlocality across diverse quantum network architectures.
While quantum networks have been extensively studied as a natural extension of the standard Bell scenario with a richer correlation structure, general constructions of nonlinear Bell inequalities with self-testing properties are still largely lacking. In this work, we present a general method for constructing such inequalities in the simplest network scenario, in which two independent sources distribute bipartite quantum states to three spatially separated observers. These inequalities allow for arbitrary numbers of binary measurements and are maximally violated by maximally entangled states of the corresponding local dimensions together with sets of pairwise anticommuting Clifford observables. Importantly, their maximal quantum values can be determined analytically, which makes them particularly promising for device-independent applications. In particular, we prove that these Bell inequalities can be used to device-independently certify the underlying quantum network, including both the quantum states produced by the sources and the observables measured by all parties. To the best of our knowledge, this is the first self-testing result for quantum networks that relies solely on the maximal violation of a nonlocality witness.
We present a method to certify the entanglement of all entangled quantum states in a device-independent way. This is achieved by placing the state in a quantum network and constructing a correlation inequality based on an entanglement witness for the state. Our method is device independent, in the sense that entanglement can be certified from the observed statistics alone, under minimal assumptions on the underlying physics. Conceptually, our results borrow ideas from the field of self-testing to bring the recently introduced measurement-device-independent entanglement witnesses into the fully device-independent regime.