Adaptive two-phase estimation on a photonic integrated device

Efficient adaptive multiphase estimation has been demonstrated experimentally on an integrated three-arm interferometer injected by single photons. Bayesian learning and Sequential Monte Carlo approximation have been employed as machine learning tools to achieve this goal

Experimental learning of quantum states

The number of parameters describing a quantum state is well known to grow exponentially with the number of particles. This scaling limits our ability to characterize and simulate the evolution of arbitrary states to systems, with no more than a few qubits. However, from a computational learning theory perspective, it can be shown that quantum states can be approximately learned using a number of measurements growing linearly with the number of qubits. Here, we experimentally demonstrate this linear scaling in optical systems with up to 6 qubits. Our results highlight the power of the computational learning theory to investigate quantum information, provide the first experimental demonstration that quantum states can be "probably approximately learned" with access to a number of copies of the state that scales linearly with the number of qubits, and pave the way to probing quantum states at new, larger scales

Quantum key distribution with entangled photons generated on demand by a quantum dot

Quantum key distribution-exchanging a random secret key relying on a quantum mechanical resource-is the core feature of secure quantum networks. Entanglement-based protocols offer additional layers of security and scale favorably with quantum repeaters, but the stringent requirements set on the photon source have made their use situational so far. Semiconductor-based quantum emitters are a promising solution in this scenario, ensuring on-demand generation of near-unity-fidelity entangled photons with record-low multiphoton emission, the latter feature countering some of the best eavesdropping attacks. Here, we use a coherently driven quantum dot to experimentally demonstrate a modified Ekert quantum key distribution protocol with two quantum channel approaches: both a 250-m-long single-mode fiber and in free space, connecting two buildings within the campus of Sapienza University in Rome. Our field study highlights that quantum-dot entangled photon sources are ready to go beyond laboratory experiments, thus opening the way to real-life quantum communication

Daylight entanglement-based quantum key distribution with a quantum dot source

Entanglement-based quantum key distribution can enable secure communication in trusted node-free networks and over long distances. Although implementations exist both in fiber and in free space, the latter approach is often considered challenging due to environmental factors. Here, we implement a quantum communication protocol during daytime for the first time using a quantum dot source. This technology presents advantages in terms of narrower spectral bandwidth-beneficial for filtering out sunlight-and negligible multiphoton emission at peak brightness. We demonstrate continuous operation over the course of three days, across an urban 270 m-long free-space optical link, under different light and weather conditions

Exclusivity graph approach to Instrumental inequalities

Instrumental variables allow the estimation of cause and effect relations even in presence of unobserved latent factors, thus providing a powerful tool for any science wherein causal inference plays an important role. More recently, the instrumental scenario has also attracted increasing attention in quantum physics, since it is related to the seminal Bell’s theorem and in fact allows the detection of even stronger quantum effects, thus enhancing our current capabilities to process information and becoming a valuable tool in quantum cryptography. In this work, we further explore this bridge between causality and quantum theory and apply a technique, originally developed in the field of quantum foundations, to express the constraints implied by causal relations in the language of graph theory. This new approach can be applied to any causal model containing a latent variable. Here, by focusing on the instrumental scenario, it allows us to easily reproduce known results as well as obtain new ones and gain new insights on the connections and differences between the instrumental and the Bell scenarios

Experimental adaptive Bayesian estimation of multiple phases with limited data

Achieving ultimate bounds in estimation processes is the main objective of quantum metrology. In this context, several problems require measurement of multiple parameters by employing only a limited amount of resources. To this end, adaptive protocols, exploiting additional control parameters, provide a tool to optimize the performance of a quantum sensor to work in such limited data regime. Finding the optimal strategies to tune the control parameters during the estimation process is a non-trivial problem, and machine learning techniques are a natural solution to address such task. Here, we investigate and implement experimentally an adaptive Bayesian multiparameter estimation technique tailored to reach optimal performances with very limited data. We employ a compact and flexible integrated photonic circuit, fabricated by femtosecond laser writing, which allows to implement different strategies with high degree of control. The obtained results show that adaptive strategies can become a viable approach for realistic sensors working with a limited amount of resources

Ab initio experimental violation of Bell inequalities

The violation of a Bell inequality is the paradigmatic example of device-independent quantum information: The nonclassicality of the data is certified without the knowledge of the functioning of devices. In practice, however, all Bell experiments rely on the precise understanding of the underlying physical mechanisms. Given that, it is natural to ask: Can one witness nonclassical behavior in a truly black-box scenario? Here, we propose and implement, computationally and experimentally, a solution to this ab initio task. It exploits a robust automated optimization approach based on the stochastic Nelder-Mead algorithm. Treating preparation and measurement devices as black boxes, and relying on the observed statistics only, our adaptive protocol approaches the optimal Bell inequality violation after a limited number of iterations for a variety photonic states, measurement responses, and Bell scenarios. In particular, we exploit it for randomness certification from unknown states and measurements. Our results demonstrate the power of automated algorithms, opening a venue for the experimental implementation of device-independent quantum technologies

Experimental learning of quantum states

The number of parameters describing a quantum state is well known to grow exponentially with the number of particles. This scaling limits our ability to characterize and simulate the evolution of arbitrary states to systems, with no more than a few qubits. However, from a computational learning theory perspective, it can be shown that quantum states can be approximately learned using a number of measurements growing linearly with the number of qubits. Here, we experimentally demonstrate this linear scaling in optical systems with up to 6 qubits. Our results highlight the power of the computational learning theory to investigate quantum information, provide the first experimental demonstration that quantum states can be “probably approximately learned” with access to a number of copies of the state that scales linearly with the number of qubits, and pave the way to probing quantum states at new, larger scales

Integrated-optics circuits for validation of non-classicality

Contrarily to the classical physics picture, according to quantum mechanics the observable properties of the objects do not yield defined values, until a measurement is performed. The measurement outcome depends indeed also on the set of observables that is being measured. Such a fundamental aspect of Nature is named quantum contextuality and it has been studied in several experimental systems, including single particles. Interestingly, it was recently suggested that even the non-classical power of quantum computing originates from contextuality [4]. Therefore, it is highly relevant to find experimental evidence of this aspect in technological platforms that may be adopted in future quantum computing devices, such as integrated photonics

Experimental test of quantum causal influences

Since Bell’s theorem, it is known that local realism fails to explain quantum phenomena. Bell inequality violations manifestly show the incompatibility of quantum theory with classical notions of cause and effect. As recently found, however, the instrumental scenario—a pivotal tool in causal inference—allows for nonclassicality signatures going beyond this paradigm. If we are not limited to observational data and can intervene in our setup, then we can witness quantum violations of classical bounds on the causal influence among the involved variables even when no Bell-like violation is possible. That is, through interventions, the quantum behavior of a system that would seem classical can be demonstrated. Using a photonic setup—faithfully implementing the instrumental causal structure and switching between observation and intervention run by run—we experimentally witness such a nonclassicality. We also test quantum bounds for the causal influence, showing that they provide a reliable tool for quantum causal modeling