arXiv subjects

G. Massimo Palma

Publications and source records attributed to G. Massimo Palma.

At least 19 recordsLinked to original sources

arXiv2026

A general estimation framework for continuous-variable systems

We show that informational completeness, while sufficient to have a bijection between ideal measurement probabilities and quantum states, does not guarantee statistically stable reconstruction from finite measurement data. To address this problem, we develop a general estimation theory for continuous-variable systems in which stable reconstructibility is characterized by the POVM effects forming a measurement frame. Informational completeness is therefore necessary, but not sufficient, for stable reconstruction. Our framework is based on measurement frames in a $σ$-regularized operator geometry, where the reference state $σ$ encodes prior information about relevant features of the measured states. For any fixed measurement scheme, observables may be inaccessible, weakly reconstructible only through estimators with divergent variance, or stably reconstructible by finite-variance unbiased estimators. The relevant regime is determined by the range of the POVM synthesis operator. Our framework provides practical methods for constructing estimators and gives an operational interpretation of singular quasiprobability distributions, including the Glauber-Sudarshan $P$ representation: quasiprobabilities act as unbiased estimators for associated observables, and their singularities reflect a pathological feature of the corresponding measurement: its lack of loewr frame bound. We furthermore show how this formalism naturally provides operational regularization procedures tied to prior information. Overall, our framework provides a unified view of continuous-variable tomography, quasiprobability representations, and classical-shadow estimation.

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arXiv2026

Multimode-cavity picture of non-Markovian waveguide QED

We introduce a picture to describe and intrepret waveguide-QED problems in the non-Markovian regime of long photonic retardation times resulting in delayed coherent feedback. The framework is based on an intuitive spatial decomposition of the waveguide into blocks. Among these, the block directly coupled to the atoms embodies an effective lossy multimode cavity leaking into the rest of the waveguide, in turn embodying an effective white-noise bath. The dynamics can be approximated by retaining only a finite number of cavity modes which grows with the time delay. This description captures the atomic as well as the field's dynamics, even with many excitations, in both emission and scattering processes. As an application, we show that the recently identified non-Markovian steady states can be understood by retaining very few or even only one cavity modes.

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arXiv2026

Entanglement percolation in random quantum networks

Entanglement percolation aims at generating maximal entanglement between any two nodes of a quantum network by utilizing strategies based solely on local operations and classical communication between the nodes. As it happens in classical percolation theory, the topology of the network is crucial, but also the entanglement shared between the nodes of the network. In a network of identically partially entangled states, the network topology determines the minimum entanglement needed for percolation. In this work, we generalize the protocol to scenarios where the initial entanglement shared between any two nodes of the network is not the same but has some randomness. In such cases, we find that for classical entanglement percolation, only the average initial entanglement is relevant. In contrast, the quantum entanglement percolation protocol (within the q-swap framework) degrades under these more realistic conditions as the width of the distribution increases, suggesting that Random CEP may become the optimal LOCC strategy in sufficiently heterogeneous quantum networks.

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arXiv2026

Efficient classical training of model-free quantum photonic reservoir

Model-independent estimation of the properties of quantum states is a central challenge in quantum technologies, as experimental imperfections, drifts, and imprecise models of the actual quantum dynamics inevitably hinder accurate reconstructions. Here, we introduce a training strategy for photonic quantum extreme learning machines in which both the learning stage and the optimization of the measurement settings are performed entirely with classical light, while inference is carried out on genuinely quantum states. The protocol is based on the identity between the normalized output intensities following the evolution of coherent states through a linear optical reservoir, and the output statistics obtained with separable input quantum states. Building on this correspondence, we implemented a model-free, gradient-based optimization of the reservoir measurement projection directly on experimental data, without relying on a prior model of the device transformation. We experimentally show that the resulting classical-to-quantum transfer enables accurate reconstruction of single-qubit Pauli observables for previously unseen single-photon states, and extends to the estimation of a two-qubit entanglement witness for arbitrary bipartite states. Beyond demonstrating a qualitatively distinct form of out-of-distribution generalization across the classical-to-quantum boundary, our results identify a practical route to fast, adaptive, and resource-efficient training of photonic quantum learning devices.

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arXiv2026

Boosting Thermodynamic Efficiency with Quantum Coherence of Phaseonium Atoms

We present a realistic implementation of a quantum engine powered by a phaseonium gas of coherently prepared three-level atoms -- where quantum coherence acts as a thermodynamic resource. Using a collision model framework for phaseonium-cavity interactions and cavity optomechanics, we construct a full engine cycle based on two non-thermal reservoirs, each characterized by coherence-induced effective temperatures. This configuration enhances the efficiency of a simple optomechanical engine operating beyond standard thermal paradigms. We further address scalability by coupling a second cavity in cascade configuration, where the same phaseonium gas drives both cycles. Our results demonstrate the operational viability of coherence-driven quantum engines and their potential for future thermodynamic applications.

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arXiv2025

Quantum reservoir computing for photonic entanglement witnessing

Accurately estimating properties of quantum states, such as entanglement, while essential for the development of quantum technologies, remains a challenging task. Standard approaches to property estimation rely on detailed modeling of the measurement apparatus and a priori assumptions on their working principles. Even small deviations can greatly affect reconstruction accuracy and prediction reliability. Here, we demonstrate that quantum reservoir computing embodies a powerful alternative for witnessing quantum entanglement and, more generally, estimating quantum features from experimental data. We leverage the orbital angular momentum of photon pairs as an ancillary degree of freedom to enable informationally complete single-setting measurements of their polarization. Our approach does not require fine-tuning or refined knowledge of the setup, at the same time outperforming conventional approaches. It automatically adapts to noise and imperfections while avoiding overfitting, ensuring robust reconstruction of entanglement witnesses and paving the way to the assessment of quantum features of experimental multiparty states.

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arXiv2025

The non-stabilizerness cost of quantum state estimation

We study the non-stabilizer resources required to achieve informational completeness in single-setting quantum state estimation scenarios. We consider fixed-basis projective measurements preceded by quantum circuits acting on $n$-qubit input states, allowing ancillary qubits to increase retrievable information. We prove that when only stabilizer resources are allowed, these strategies are always informationally equivalent to projective measurements in a stabilizer basis, and therefore never informationally complete, regardless of the number of ancillas. We then show that incorporating $T$ gates enlarges the accessible information. Specifically, we prove that at least ${2n}/{\log_2 3}$ such gates are necessary for informational completeness, and that $2n$ suffice. We conjecture that $2n$ gates are indeed both necessary and sufficient. Finally, we unveil a tight connection between entanglement structure and informational power of measurements implemented with $t$-doped Clifford circuits. Our results recast notions of ``magic'' and stabilizerness - typically framed in computational terms - into the setting of quantum metrology.

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arXiv2025

Experimental property-reconstruction in a photonic quantum extreme learning machine

Recent developments have led to the possibility of embedding machine learning tools into experimental platforms to address key problems, including the characterization of the properties of quantum states. Leveraging on this, we implement a quantum extreme learning machine in a photonic platform to achieve resource-efficient and accurate characterization of the polarization state of a photon. The underlying reservoir dynamics through which such input state evolves is implemented using the coined quantum walk of high-dimensional photonic orbital angular momentum, and performing projective measurements over a fixed basis. We demonstrate how the reconstruction of an unknown polarization state does not need a careful characterization of the measurement apparatus and is robust to experimental imperfections, thus representing a promising route for resource-economic state characterisation.

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arXiv2025

On the emergence of quantum Darwinism and pointer states for non-commuting evolutions

Quantum systems achieve objectivity by redundantly encoding information about themselves into the surrounding environment, through a mechanism known as quantum Darwinism. When this happens, observes measure the environment and infer the system to be in one of its pointer states. We study the emergence of objectivity whenever the Hamiltonian of the system and the interaction Hamiltonian between system and environment do not commute, a condition which is thought to be incompatible with the presence of pointer states. We show that, not only non-commuting evolutions allow for the emergence of objective states, but it is possible to give a more relaxed definition of pointer states, that is always well defined whenever there is redundancy of information, and coincides with the usual one for commuting evolutions.

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arXiv2025

Exoplanetary atmospheres retrieval via a quantum extreme learning machine

The study of exoplanetary atmospheres traditionally relies on forward models to analytically compute the spectrum of an exoplanet by fine-tuning numerous chemical and physical parameters. However, the high-dimensionality of parameter space often results in a significant computational overhead. In this work, we introduce a novel approach to atmospheric retrieval leveraging on quantum extreme learning machines (QELMs). QELMs are quantum machine learning techniques that employ quantum systems as a black box for processing input data. In this work, we propose a framework for extracting exoplanetary atmospheric features using QELMs, employing an intrinsically fault-tolerant strategy suitable for near-term quantum devices, and we demonstrate such fault tolerance with a direct implementation on IBM Fez. The QELM architecture we present shows the potential of quantum computing in the analysis of astrophysical datasets and may, in the near-term future, unlock new computational tools to implement fast, efficient, and more accurate models in the study of exoplanetary atmospheres.

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arXiv2025

Speeding up thermalization and quantum state preparation through engineered quantum collisions

We realize fast thermalization and state preparation of a single mode cavity field using a collision model-like approach, where a sequence of qubits or three level system ancillae, sequentially interacting with the field, is engineered with a genetic algorithm approach. In contrast to optimal control techniques, there is no time-dependent system Hamiltonian control deployed and, in contrast to reservoir engineering, the engineered full system-environment dynamics is optimized, and the target is not a steady state. We prove a significant speed up in thermalization - for which we show that diagonal qubit ancilla states are sufficient - and in the preparation of coherent states. We demonstrate the preparation of squeezed states and of highly non-Gaussian states. Our work offers a new alternative for fast preparation of cavity states that lays in between optimal Hamiltonian control and reservoir engineering, and gives further insights on the resources needed to realize or speed up the preparation of such states.

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arXiv2025

Quantum simulations of complex systems

In this review we give a brief overview of quantum simulation as applied to the study of complex systems. In particular, we cover the basic ideas of quantum simulation, neuromorphic computation, the Sachdev-Ye-Kitaev model, as well as applications to quantum batteries.

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arXiv2025

State estimation with quantum extreme learning machines beyond the scrambling time

Quantum extreme learning machines (QELMs) leverage untrained quantum dynamics to efficiently process information encoded in input quantum states, avoiding the high computational cost of training more complicated nonlinear models. On the other hand, quantum information scrambling (QIS) quantifies how the spread of quantum information into correlations makes it irretrievable from local measurements. Here, we explore the tight relation between QIS and the predictive power of QELMs. In particular, we show efficient state estimation is possible even beyond the scrambling time, for many different types of dynamics -- in fact, we show that in all the cases we studied, the reconstruction efficiency at long interaction times matches the optimal one offered by random global unitary dynamics. These results offer promising venues for robust experimental QELM-based state estimation protocols, as well as providing novel insights into the nature of QIS from a state estimation perspective.

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arXiv2024

Quantum Extreme Learning of molecular potential energy surfaces and force fields

Quantum machine learning algorithms are expected to play a pivotal role in quantum chemistry simulations in the immediate future. One such key application is the training of a quantum neural network to learn the potential energy surface and force field of molecular systems. We address this task by using the quantum extreme learning machine paradigm. This particular supervised learning routine allows for resource-efficient training, consisting of a simple linear regression performed on a classical computer. We have tested a setup that can be used to study molecules of any dimension and is optimized for immediate use on NISQ devices with a limited number of native gates. We have applied this setup to three case studies: lithium hydride, water, and formamide, carrying out both noiseless simulations and actual implementation on IBM quantum hardware. Compared to other supervised learning routines, the proposed setup requires minimal quantum resources, making it feasible for direct implementation on quantum platforms, while still achieving a high level of predictive accuracy compared to simulations. Our encouraging results pave the way towards the future application to more complex molecules, being the proposed setup scalable.

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arXiv2024

Heating and cooling processes via phaseonium-driven dynamics of cascade systems

The search for strategies to harness the temperature of quantum systems is one of the main goals in quantum thermodynamics. Here we study the dynamics of a system made of a pair of quantum harmonic oscillators, represented by single-mode cavity fields, interacting with a thermally excited beam of phaseonium atoms, which act as ancillas. The two cavities are arranged in a cascade configuration, so that the second cavity interacts with phaseonium atoms only after their interaction with the first one. We provide exact closed dynamics of the first cavity for arbitrarily long interaction times. We highlight the role played by the characteristic coherence phase of phaseonium atoms in determining the steady states of the cavity fields as well as that of the ancillas. Also, we show how the second cavity follows a non-Markovian evolution due to interactions with the "used" ancillary atoms, that enables information exchange with the first cavity. Adjusting the parameters of the phaseonium atoms, we can determine the final stable temperature reached by the cavities. In this way, the cavities can be heated up as well as cooled down. These results provide useful insights towards the use of different types of ancillas for thermodynamic cycles in cavity QED scenarios.

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arXiv2024

The importance of using the averaged mutual information when quantifying quantum objectivity

In the context of quantum objectivity, a standard way to quantify the classicality of a state is via the mutual information between a system and different fractions of its environment. Many of the tools developed in the relevant literature to quantify quantum objectivity via quantum mutual information rely on the assumption that information about the system leaks symmetrically into its environment. In this work, we highlight the importance of taking this assumption into account, and in particular, analyse how taking non-averaged quantum mutual information as a quantifier of quantum objectivity can be severely misleading whenever information about the system is encoded into the environment in a non-homogeneous way. On the other hand, the averaged mutual information always provides results with a clear operative interpretation.

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arXiv2023

An operational definition of quantum information scrambling

Quantum information scrambling (QIS) is a characteristic feature of several quantum systems, ranging from black holes to quantum communication networks. While accurately quantifying QIS is crucial to understanding many such phenomena, common approaches based on the tripartite information have limitations due to the accessibility issues of quantum mutual information, and do not always properly take into consideration the dependence on the encoding input basis. To address these issues, we propose a novel and computationally efficient QIS quantifier, based on a formulation of QIS in terms of quantum state discrimination. We show that the optimal guessing probability, which reflects the degree of QIS induced by an isometric quantum evolution, is directly connected to the accessible min-information, a generalized channel capacity based on conditional min-entropy, which can be cast as a convex program and thus computed efficiently. By applying our proposal to a range of examples with increasing complexity, we illustrate its ability to capture the multifaceted nature of QIS in all its intricacy.

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arXiv2023

Shadow tomography on general measurement frames

We provide a new perspective on shadow tomography by demonstrating its deep connections with the general theory of measurement frames. By showing that the formalism of measurement frames offers a natural framework for shadow tomography -- in which ``classical shadows'' correspond to unbiased estimators derived from a suitable dual frame associated with the given measurement -- we highlight the intrinsic connection between standard state tomography and shadow tomography. Such perspective allows us to examine the interplay between measurements, reconstructed observables, and the estimators used to process measurement outcomes, while paving the way to assess the influence of the input state and the dimension of the underlying space on estimation errors. Our approach generalizes the method described in [H.-Y. Huang {\it et al.}, Nat. Phys. 16, 1050 (2020)], whose results are recovered in the special case of covariant measurement frames. As an application, we demonstrate that a sought-after target of shadow tomography can be achieved for the entire class of tight rank-1 measurement frames -- namely, that it is possible to accurately estimate a finite set of generic rank-1 bounded observables while avoiding the growth of the number of the required samples with the state dimension.

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