Estimation of physical quantities is at the core most scientific research, and use quantum devices promises to enhance its performances. In real scenarios, it fundamental consider that resources are limited, Bayesian adaptive estimation represents a powerful approach efficiently allocate, during process, all available resources. However, this framework relies on precise knowledge system model, retrieved with fine calibration, results often computationally experimentally demanding. We...

Recent developments have led to the possibility of embedding machine learning tools into experimental platforms address key problems, including characterization properties quantum states. Leveraging on this, we implement a extreme in photonic platform achieve resource-efficient and accurate polarization state photon. The underlying reservoir dynamics through which such input evolves is implemented using coined walk high-dimensional orbital angular momentum performing projective measurements...

Quantum sensors emerged among quantum technologies as the ones with promising potential applications in near future. This perspective reviews two leading sensing platforms and their advancements toward biological applications: light sources color centers diamonds. light, including squeezed states N00N states, allows enhanced phase measurements by surpassing classical shot noise limits. advantage can be exploited several contexts, enabling improved resolution sensitivity, which are...

Phase estimation is the most investigated protocol in quantum metrology, but its performance affected by presence of noise, also form imperfect state preparation. Here we discuss how to address this scenario using a multiparameter approach, which noise associated parameter be measured at same time as phase. We present an experiment two-photon states, and apply our setup investigating optical activity fructose solutions. Finally, illustrate scaling laws attainable precisions with number...

Introducing quantum sensors as a solution to real world problems demands reliability and controllability outside of laboratory conditions. Producers operators ought be assumed have limited resources readily available for calibration, yet, they should able trust the devices. Neural networks are almost ubiquitous similar tasks classical sensors: here we show applications this technique calibrating photonic sensor. This is based on set training data, collected only relying probe states, hence...

Calibration of sensors is a fundamental step to validate their operation. This can be demanding task, as it relies on acquiring detailed modelling the device, aggravated by its possible dependence upon multiple parameters. Machine learning provides handy solution this issue, operating mapping between parameters and device response, without needing additional specific information functioning. Here we demonstrate application Neural Network based algorithm for calibration integrated photonic...

Relevant metrological scenarios involve the simultaneous estimation of multiple parameters. The fundamental ingredient to achieve quantum-enhanced performance is based on use appropriately tailored quantum probes. However, reaching ultimate resolution allowed by physical laws requires nontrivial strategies from both a theoretical and practical point view. A crucial tool for this purpose application adaptive learning techniques. Indeed, provide flexible approach obtain optimal...

Abstract Variational quantum metrology represents a powerful tool to optimize estimation strategies, resulting particularly beneficial for multiparameter problems that often suffer from limitations due the curse of dimensionality and computational complexity. To overcome these challenges, we develop variational approach able efficiently multiphase sensor. Leveraging reconfigurability an integrated photonic device, implement hybrid quantum-classical feedback loop enhance performances. The...

Abstract The last decades saw a huge rise of artificial intelligence (AI) as powerful tool to boost industrial and scientific research in broad range fields. AI photonics are developing promising two‐way synergy: on the one hand, approaches can be used control number complex linear nonlinear photonic processes, both classical quantum regimes; other pave way for new class platforms accelerate AI‐tasks. This review provides reader with fundamental notions machine learning (ML) neural networks...

Abstract Adopting quantum resources for parameter estimation discloses the possibility to realize sensors operating at a sensitivity beyond standard limit. Such an approach promises reach fundamental Heisenberg scaling as function of employed N in process. Although previous experiments demonstrated precision approaching Heisenberg-limited performances, reaching such regime wide range remains hard accomplish. Here, we show method that suitably allocates available permitting them same power...

Traditional quantum metrology assesses precision using the figures of merit continuous-valued parameter estimation. Recently, digital estimation was introduced: it evaluates performance information-theoretically by quantifying number significant bits parameter, redefining key benchmarks like Heisenberg bound. Here, we report first experimental realization a Quantum Analog-to-Digital Converter for metrology, that takes an input continuous and outputs bit string, advanced photonic platform,...

The characterization of quantum features in large Hilbert spaces is a crucial requirement for testing protocols. In the continuous variable encoding, homodyne tomography requires an amount measurement that increases exponentially with number involved modes, which practically makes protocol intractable even few modes. Here, we introduce new technique, based on machine learning artificial neural networks, allows us to directly detect negativity Wigner function multimode states. We test...

Abstract The classification of big data usually requires a mapping onto new clusters which can then be processed by machine learning algorithms means more efficient and feasible linear separators. Recently, Lloyd et al. have advanced the proposal to embed classical into quantum ones: these live in complex Hilbert space where they get split linearly separable clusters. Here, ideas are implemented engineering two different experimental platforms, based on optics ultra‐cold atoms, respectively,...

The quest for precision in parameter estimation is a fundamental task different scientific areas. relevance of this problem thus provided the motivation to develop methods application quantum resources protocols. Within context, Bayesian offers complete framework optimal metrology techniques, such as adaptive However, use approach requires extensive computational resources, especially multiparameter estimations that represent typical operational scenario sensors. Hence, requirement...

Achieving quantum-enhanced performances when measuring unknown quantities requires developing suitable methodologies for practical scenarios, which include noise and the availability of a limited amount resources. Here, we report on optimization substandard quantum limit Bayesian multiparameter estimation in scenario where subset parameters describes unavoidable processes an experimental photonic sensor. We explore how changes depending are either interest or treated as nuisance ones. Our...

Enzymes are essential to maintain organisms alive. Some of the reactions they catalyze associated with a change in reagents chirality, hence their activity can be tracked by using optical means. However, illumination affects enzyme activity: challenge is operate at low-intensity regime avoiding loss sensitivity. Here we apply quantum phase estimation real-time measurement invertase enzymatic activity. Control probe level demonstrates potential for reducing invasiveness optimized sensitivity...

Quantum photonics has demonstrated its potential for enhanced sensing. Current sources of quantum light states tailored to measuring allow one monitor phenomena evolving on timescales second order. These are characteristic product accumulation in chemical reactions technological interest, particular those involving chiral compounds. Here we adopt a multiparameter approach investigate the dynamic process sucrose acid hydrolysis as test bed such applications. The estimation is made robust by...

The superposition of quantum states lies at the heart physics and has been recently found to serve as a versatile resource for information protocols, defining notion coherence. In this contribution, we report on implementation its complementary concept, coherence from measurements. By devising an accessible criterion which holds true in any classical statistical theory, demonstrate that noncommutative measurements violate constraint, rendering it possible perform operational assessment...

Recent works leverage export data to assess country production structure and ultimately relative competitiveness. These mostly rely only on the exported part of total output for reasons availability, homogeneity, quality. Here we use World Input-Output Database (WIOD), which offers cross-country harmonized that accounts both domestic export, investigate what extent is a proxy production. We find mirrors remarkably well manufacturing sectors or related physical goods. Conversely, this...

Frequency correlations are a versatile and powerful tool which can be exploited to perform spectral analysis of objects whose direct measurement might unfeasible. This is achieved through so-called ghost spectrometer, that implemented with quantum classical resources alike. While there some known advantages associated either choice, an their metrological capabilities has not yet been performed. Here we report on the comparison between spectrometer. We estimation transmittivity bandpass...

The emergence of realistic properties is a key problem in understanding the quantum-to-classical transition. In this respect, measurements represent way to interface quantum systems with macroscopic world: these can be driven weak regime, where reduced back-action imparted by choosing meter states able extract different amounts information. Here we explore implications such measurement for variation two-level pre- and post-measurement, extend our investigations case open implementing measurements.

Nonequilibrium states of quantum systems in contact with thermal baths help distinguishing between environments different temperatures or statistics. We extend these studies to a more generic problem that consists discriminating two disparate constituents at unequal temperatures. Notably there exist temperature regimes which the presence coherence initial state preparation is beneficial for discrimination capability. also find nonequilibrium are not universally optimal and detail conditions...

Quantum parameter estimation offers solid conceptual grounds for the design of sensors enjoying quantum advantage. This is realised not only by means hardware supporting and exploiting properties, but data analysis has its impact relevance, too. In this respect, Bayesian methods have emerged as an effective elegant solution, with perk incorporating naturally availability a priori information. article we present evaluation multiple phase estimation, assessed based on bounds that work beyond...

In this work, we demonstrate the use of stimulated emission tomography to characterize a hyperentangled state generated by spontaneous parametric downconversion in cw-pumped source. particular, consider generation states consisting photon pairs entangled polarization and path. These results extend capability beyond degree freedom technique study higher dimension Hilbert spaces.

Abstract We characterize the energetic footprint of a two-qubit quantum gate from perspective non-equilibrium thermodynamics. experimentally reconstruct statistics energy and entropy fluctuations following implementation controlled-unitary gate, linking them to performance itself phenomenology Landauer’s principle at single-quantum level. Our work thus addresses cost operating circuits, problem that is crucial for grounding upcoming technologies.