arXiv subjects

Matteo Rosati

Publications and source records attributed to Matteo Rosati.

At least 19 recordsLinked to original sources

arXiv2026

Certification of Network Quantum Sensing

The distribution of quantum sensors on quantum networks is a key enabler of quantum technologies in interferometry, gravimetry, timekeeping, biological monitoring, and beyond. Yet, guaranteeing the security of these distributed sensors over noisy, insecure networks remains a formidable challenge. Previous efforts to combine quantum metrology and cryptography have encountered an apparently unavoidable tension, proposing bounds for security which are only loosely tied to the achievable measurement performance. Here we introduce a quantum remote sensing protocol that can rigorously certify privacy and integrity of the estimation. By employing offline bilateral Pauli-twirling, our approach forces the effective quantum channel into a Bell-diagonal form, independently of the attack. Surprisingly, this also preserves metrological sensitivity without introducing additional experimental overhead. Relying solely on public communication alongside an insecure quantum link, the protocol enables legitimate users to exactly quantify their estimation error relative to an eavesdropper controlling the channels. We experimentally demonstrate this framework by estimating an optical phase using entangled photons, observing that the users' precision consistently surpasses the eavesdropper's capabilities across a broad parameter regime. By unifying quantum cryptography and metrology, our results provide a practical pathway to achieve simultaneous quantum-limited precision and rigorous information security in real-world quantum networks.

quant-ph↗

arXiv2026

Quantum Attention by Overlap Interference: Predicting Sequences from Classical and Many-Body Quantum Data

We propose a variational quantum implementation of self-attention (QSA), the core operation in transformers and large language models, which predicts future elements of a sequence by forming overlap-weighted combinations of past data. At variance with previous approaches, our QSA realizes the required nonlinearity through interference of state overlaps and returns a Renyi-1/2 cross-entropy loss directly as the expectation value of an observable, avoiding the need to decode amplitude-encoded predictions into classical logits. Furthermore, QSA naturally accommodates a constrained, trainable data-embedding that ties quantum state overlaps to data-level similarities. We find a gate complexity dominant scaling O(T d^2) for QSA, versus O(T^2 d) classically, suggesting an advantage in the practical regime where the sequence length T dominates the embedding size d. In simulations, we show that our QSA-based quantum transformer learns sequence prediction on classical data and on many-body transverse-field Ising quantum trajectories, establishing trainable attention as a practical primitive for quantum dynamical modeling.

quant-ph↗

arXiv2025

Private Remote Phase Estimation over a Lossy Quantum Channel

Private remote quantum sensing (PRQS) aims at estimating a parameter at a distant location by transmitting quantum states on an insecure quantum channel, limiting information leakage and disruption of the estimation itself from an adversary. Previous results highlighted that one can bound the estimation performance in terms of the observed noise. However, if no assumptions are placed on the channel model, such bounds are very loose and severely limit the estimation. We propose and analyse a PRQS using, for the first time to our knowledge, continuous-variable states in the single-user setting. Assuming a typical class of lossy attacks and employing tools from quantum communication, we calculate the true estimation error and privacy of our protocol, both in the asymptotic limit of many channel uses and in the finite-size regime. Our results show that a realistic channel-model assumption, which can be validated with measurement data, allows for a much tighter quantification of the estimation error and privacy for all practical purposes.

quant-ph↗

arXiv2025

Photon-starved polarimetry via functional classical shadows

Polarimetry and optical imaging techniques face challenges in photon-starved scenarios, where the low number of detected photons imposes a trade-off between image resolution, integration time, and sample sensitivity. Here we introduce a quantum-inspired method, functional classical shadows, for reconstructing a polarization profile in the low photon-flux regime. Our method harnesses correlations between neighbouring datapoints, based on the recent realisation that machine learning can estimate multiple physical quantities from a small number of non-identical samples. This is applied to the experimental reconstruction of polarization as a function of the wavelength. Although the quantum formalism helps structuring the problem, our approach suits arbitrary intensity regimes.

quant-ph↗

arXiv2025

Controlling Sloppiness in Two-Phase Estimation with a Tunable Weak Measurement

The description of complex systems requires a progressively larger number of parameters. However, in practice, it often happens that a small subset of parameters suffices to describe the dynamics of the system itself: these combinations are usually referred to as \textit{stiff} combinations. In turn, the remaining combinations, called \textit{sloppy}, only play a minor role in the dynamics of the system, hence provide little information on it. While this effect can reduce model complexity, it can also limit the estimation precision when the stiff and sloppy combinations are unknown to the experimenter, and one is forced to estimate the potentially sloppy model parameters. We explored how such a sloppy behavior can be controlled and counteracted via quantum weak measurements in the estimation of two sequential phases. We showed that the introduction of a weak measurement of variable strength in-between the two phases allows to switch from a fully sloppy setup to a fully determined one where both phases can be estimated with quantum-limited precision. Our work provides an important insight of sloppiness detection in quantum systems, with promising applications in quantum metrology and imaging, as well as to quantum security and quantum monitoring.

quant-ph↗

arXiv2025

Probabilistic pulse-position modulation for classical communication on quantum channels

Classical communication over lossy quantum channels is an essential topic in quantum information theory, with practical implications for optical-fiber and free-space communications. Multi-phase Hadamard codes, based on coherent-state Binary Phase-Shift Keying (BPSK) modulation and decoded using vacuum-or-pulse (VP) detectors, offer a promising approach for achieving high communication rates while relying only on linear optics and single-photon detectors (SPDs). However, their performance does not reach the ultimate Holevo limit. In this work, we propose a generalization of Hadamard codes that distributes the signal across multiple modes with optimized probabilities, rather than concentrating it in a single mode, dubbed probabilistic pulse-position modulation (PPPM). We derive an achievable communication rate, demonstrating that our PPPM can outperform traditional Hadamard codes in certain intermediate energy regimes.

quant-ph↗

arXiv2025

Honest-binding quantum bit commitment from separable operations

Bit commitment is a fundamental cryptographic primitive and a cornerstone for numerous two-party cryptographic protocols, including zero-knowledge proofs. However, it has been proven that unconditionally secure bit commitment, both classical and quantum, is impossible. In this work, we demonstrate that imposing a restriction on the committing party to perform only separable operations enables secure quantum bit commitment schemes. Specifically, we prove that in any perfectly hiding bit commitment protocol, an honestly-committing party limited to separable operations will be detected with high probability if they attempt to alter their commitment. To illustrate our findings, we present an example protocol.

quant-ph↗

arXiv2024

Private Communication over a Bosonic Compound Channel

It is a common belief that quantum key distribution systems are the one and only information-theoretically secure physical layer security protocol that enables secure data transmission without a need for the legitimate parties to have any channel knowledge. It is also known that this high security profile results in severe rate restrictions for the parties utilizing the quantum key distribution systems. This observation raises the question of whether quantifying the level of ignorance of the legitimate parties with regard to the channel parameters may enable us to navigate the large gray zone between insecure but highly performant systems on the one side and perfectly secure but highly non-performant systems on the other side. Indeed, by proving a capacity formula for the bosonic compound wiretap channel using the binary phase shift keying alphabet, we are able to quantify in this work exactly how channel uncertainty penalizes data transmission rates.

quant-ph↗

arXiv2023

Optical decoder learning for fiber communication at the quantum limit

Quantum information theory predicts that communication technology can be enhanced by using quantum signals to transfer classical bits. In order to fulfill this promise, the message-carrying signals must interact coherently at the decoding stage via a joint-detection receiver (JDR), whose realization with optical technologies remains an outstanding open problem to date. We introduce a supervised-learning framework for the systematic discovery of new JDR designs based on parametrized photonic integrated circuits. Our framework relies on the synthesis of a training set comprising quantum codewords and the corresponding classical message label; the codewords are processed by the JDR circuit and, after photo-detection, produce a guess for the label. The circuit parameters are then updated by minimizing a suitable loss function, reaching an optimal JDR design for that specific architecture. We showcase our method with coherent-state codes for the pure-loss bosonic channel, modelling optical-fiber and space communication, with a circuit architecture comprising linear optics, squeezing and threshold photo-detectors. We train JDR circuits for several code families, varying energy and code-size. We discover optical JDR circuit setups for maximum-size codes and small message-length that offer up to a $3$-fold enhancement in the bit decoding rate with respect to the optimal single-symbol receiver, and less than $7\%$-away from the theoretically optimal decoder, for which an explicit design is missing to date. Furthermore, the discovered receivers surpass previous JDR designs both in terms of bit decoding and bit transmission rate. Finally, we observe that the best-performing codes are those which can be mapped, via the JDR's optical processing, to modulations with different energy levels on different symbols, making the message symbols more distinguishabile via photo-detection.

quant-ph↗

arXiv2023

Probing LLMs for Joint Encoding of Linguistic Categories

Large Language Models (LLMs) exhibit impressive performance on a range of NLP tasks, due to the general-purpose linguistic knowledge acquired during pretraining. Existing model interpretability research (Tenney et al., 2019) suggests that a linguistic hierarchy emerges in the LLM layers, with lower layers better suited to solving syntactic tasks and higher layers employed for semantic processing. Yet, little is known about how encodings of different linguistic phenomena interact within the models and to what extent processing of linguistically-related categories relies on the same, shared model representations. In this paper, we propose a framework for testing the joint encoding of linguistic categories in LLMs. Focusing on syntax, we find evidence of joint encoding both at the same (related part-of-speech (POS) classes) and different (POS classes and related syntactic dependency relations) levels of linguistic hierarchy. Our cross-lingual experiments show that the same patterns hold across languages in multilingual LLMs.

cs.CL↗

arXiv2023

Fundamental precision limits of fluorescence microscopy: a new perspective on MINFLUX

In the past years, optical fluorescence microscopy (OFM) made steady progress towards increasing the localisation precision of fluorescent emitters in biological samples. The high precision achieved by these techniques has prompted new claims, whose rigorous validation is an outstanding problem. For this purpose, local estimation theory (LET) has emerged as the most used mathematical tool. We establish a novel multi-parameter estimation framework that captures the full complexity of single-emitter localisation in an OFM experiment. Our framework relies on the fact that there are other unknown parameters alongside the emitter's coordinates, such as the average number of photons emitted (brightness), that are correlated to the emitter position, and affect the localisation precision. The increasing complexity of a multi-parameter approach allows for a more accountable assessment of the precision. We showcase our method with MINFLUX microscopy, the OFM approach that nowadays generates images with the best resolution. Introducing the brightness as an unknown parameter, we shed light on features that remain obscure in the conventional approach: the precision can be increased only by increasing the brightness, (i.e., illumination power or exposition time), whereas decreasing the beam separation offers limited advantages. We demonstrate that the proposed framework is a solid and general method for the quantification of single-emitter localisation precision for any OFM approach on equal footing, evaluating the localization precision of stimulated emission depletion (STED) microscopy and making a comparison with MINFLUX microscopy.

physics.optics↗

arXiv2022

Learning quantum processes without input control

We introduce a general statistical learning theory for processes that take as input a classical random variable and output a quantum state. Our setting is motivated by the practical situation in which one desires to learn a quantum process governed by classical parameters that are out of one's control. This framework is applicable, for example, to the study of astronomical phenomena, disordered systems and biological processes not controlled by the observer. We provide an algorithm for learning with high probability in this setting with a finite amount of samples, even if the concept class is infinite. To do this, we review and adapt existing algorithms for shadow tomography and hypothesis selection, and combine their guarantees with the uniform convergence on the data of the loss functions of interest. As a by-product we obtain sufficient conditions for performing shadow tomography of classical-quantum states with a number of copies which depends on the dimension of the quantum register, but not on the dimension of the classical one. We give concrete examples of processes that can be learned in this manner, based on quantum circuits or physically motivated classes, such as systems governed by Hamiltonians with random perturbations or data-dependent phase-shifts.

quant-ph↗

arXiv2022

A learning theory for quantum photonic processors and beyond

We consider the tasks of learning quantum states, measurements and channels generated by continuous-variable (CV) quantum circuits. This family of circuits is suited to describe optical quantum technologies and in particular it includes state-of-the-art photonic processors capable of showing quantum advantage. We define classes of functions that map classical variables, encoded into the CV circuit parameters, to outcome probabilities evaluated on those circuits. We then establish efficient learnability guarantees for such classes, by computing bounds on their pseudo-dimension or covering numbers, showing that CV quantum circuits can be learned with a sample complexity that scales polynomially with the circuit's size, i.e., the number of modes. Our results show that CV circuits can be trained efficiently using a number of training samples that, unlike their finite-dimensional counterpart, does not scale with the circuit depth.

quant-ph↗

arXiv2022

[Re] Badder Seeds: Reproducing the Evaluation of Lexical Methods for Bias Measurement

Combating bias in NLP requires bias measurement. Bias measurement is almost always achieved by using lexicons of seed terms, i.e. sets of words specifying stereotypes or dimensions of interest. This reproducibility study focuses on the original authors' main claim that the rationale for the construction of these lexicons needs thorough checking before usage, as the seeds used for bias measurement can themselves exhibit biases. The study aims to evaluate the reproducibility of the quantitative and qualitative results presented in the paper and the conclusions drawn thereof. We reproduce most of the results supporting the original authors' general claim: seed sets often suffer from biases that affect their performance as a baseline for bias metrics. Generally, our results mirror the original paper's. They are slightly different on select occasions, but not in ways that undermine the paper's general intent to show the fragility of seed sets.

cs.CL↗

arXiv2022

Performance of Coherent Frequency-Shift Keying for Classical Communication \\ on Quantum Channels

We evaluate the performance of coherent frequency-shift keying (CFSK) alphabets for communication on quantum channels. We show that, contrarily to what previously thought, the square-root-measurement (SRM) is sub-optimal for discriminating CFSK states. Furthermore, we compute the maximum information transmission rate of the CFSK alphabet, observing that it employs at least as many frequency modes as the signal states, and compare it with standard phase-shift-keying. Finally, we introduce a discretized CFSK alphabet with higher mode-efficiency, exhibiting comparable error-probability performance with respect to CFSK and better rate performance. Our results suggest the existence of a tradeoff between the CFSK reduced error-probability and its mode efficiency.

quant-ph↗

arXiv2022

Reinforcement-learning calibration of coherent-state receivers on variable-loss optical channels

We study the problem of calibrating a quantum receiver for optical coherent states when transmitted on a quantum optical channel with variable transmissivity, a common model for long-distance optical-fiber and free/deep-space optical communication. We optimize the error probability of legacy adaptive receivers, such as Kennedy's and Dolinar's, on average with respect to the channel transmissivity distribution. We then compare our results with the ultimate error probability attainable by a general quantum device, computing the Helstrom bound for mixtures of coherent-state hypotheses, for the first time to our knowledge, and with homodyne measurements. With these tools, we first analyze the simplest case of two different transmissivity values; we find that the strategies adopted by adaptive receivers exhibit strikingly new features as the difference between the two transmissivities increases. Finally, we employ a recently introduced library of shallow reinforcement learning methods, demonstrating that an intelligent agent can learn the optimal receiver setup from scratch by training on repeated communication episodes on the channel with variable transmissivity and receiving rewards if the coherent-state message is correctly identified.

quant-ph↗

arXiv2022

Operating Fiber Networks in the Quantum Limit

We consider all-optical network evolution from a quantum perspective. We show that a use of optimal quantum receivers allows an estimated $55\%$ decrease in energy consumption of all-optical amplifiers in network configurations that are typical today. We then compare data transmission capacities of quantum receivers with today's technology operating within the boundaries set by Shannon. We find that quantum receiver technology allows for a logarithmic scaling of the system capacity with the baud-rate, while Shannon-type systems are limited by the transmit power. Thus a natural quantum limit of classical data transmission emerges. Based on the above findings we argue for a new approach to optical communication network design, wherein in-line amplifiers are replaced by novel fiber supporting high spectral bandwidth to allow for noiseless data transmission in the quantum limit.

quant-ph↗

arXiv2021

Compound Channel Capacities under Energy Constraints and Application

Compound channel models offer a simple and straightforward way of analyzing the stability of decoder design under model variations. With this work we provide a coding theorem for a large class of practically relevant compound channel models. We give explicit formulas for the cases of the Gaussian classical-quantum compound channels with unknown noise, unknown phase and unknown attenuation. We show analytically how the classical compound channel capacity formula motivates nontrivial choices of the displacement parameter of the Kennedy receiver. Our work demonstrates the value of the compound channel model as a method for the design of receivers in quantum communication.

cs.IT↗