arXiv subjects

Chiara Macchiavello

Publications and source records attributed to Chiara Macchiavello.

At least 19 recordsLinked to original sources

arXiv2026

Optimal Dynamic Cooling of Multiple Qubits

We solve the closed-system problem of cooling $M$ qubits, selected from $N$ identical thermal qubits, to the lowest common local temperature allowed by unitarity. The optimal protocol consists of two conceptually distinct steps. First, a passive rearrangement assigns the largest eigenvalues of the initial state to target sectors of lowest Hamming weight, thereby minimizing the total target energy. Second, a target-only complex-Hadamard transformation within each fixed-Hamming-weight subspace equalizes the one-qubit target marginals without changing any target-sector probability or the total energy. Consequently, imposing a common local temperature costs neither cooling depth nor additional work: the constrained optimum coincides with the unconstrained passive minimum for every $N>M$ and every initial temperature. The complex-Hadamard correction may nevertheless be costly at the circuit level. We therefore derive an exact arithmetic criterion for when the same optimum can be attained by a temperature-independent computational-basis permutation alone, and exhaustively classify the resulting finite-size islands of feasibility for $M+2\leq N\leq 128$. At isolated temperatures, further optimal permutations can arise through numerical cancellations between different thermal eigenvalue shells. These alternative realizations may reduce implementation complexity, but they cannot improve the cooling curve already attained by the universal protocol. We also derive the exact cooling curve, prove that at least two ancillary qubits are necessary and sufficient for nontrivial cooling, and show that joint many-target cooling can strictly outperform parallel single-target strategies.

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arXiv2026

Quantum optical shallow networks

Classical shallow networks are universal approximators. Given a sufficient number of neurons, they can reproduce any continuous function to arbitrary precision, with a resource cost that scales linearly in both the input size and the number of trainable parameters. In this work, we present a quantum optical protocol that implements a shallow network with an arbitrary number of neurons. Both the input data and the parameters are encoded into single-photon states. Leveraging the Hong-Ou-Mandel effect, the network output is determined by the coincidence rates measured when the photons interfere at a beam splitter, with multiple neurons prepared as a mixture of single-photon states. Remarkably, once trained, our model requires constant optical resources regardless of the number of input features and neurons.

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arXiv2026

Sparse positive maps on qutrits with exact nondecomposability thresholds and PPT-entanglement transitions

We study a family of sparse positive maps on qutrits for which positivity, decomposability, and PPT entanglement can all be analysed explicitly. The block structure of the associated Choi matrices reduces positivity to a Hermitian biquadratic form and leads to exact positivity boundaries for three representative parametric families. For the same families we determine the exact transition between decomposable and non-decomposable maps and construct associated PPT states of two classes. The first consists of witness-adapted deformations naturally tied to the non-decomposability analysis. The second consists of analytically tractable families whose full PPT-entangled branch is detected by fixed positive maps, yielding exact thresholds between separability and bound entanglement. For the trace-preserving subclass, we further compare positivity with a recent eigenvalue bound for 2-positive maps, thereby making the gap between positivity and higher-order positivity fully explicit within this family.

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arXiv2026

Generating Non-Decomposable Maps with Differentiable Semidefinite Programming

Positive maps that are not decomposable are a key resource in entanglement theory because they can detect bound entangled states, yet systematic methods for constructing them remain limited. We introduce an optimization framework based on differentiable semidefinite programming (SDP) for generating positive non-decomposable maps under flexible structural constraints on their Choi matrices. The method combines SDP-based certificates of non-decomposability and positivity with gradient-based optimization, enabling a systematic search over maps with different input and output dimensions. Within this framework, we generate previously unknown numerical examples, identify a parametrized family of maps arising from masked Choi matrices, and construct real non-decomposable maps. We further show that the same approach can be adapted to explore open questions in quantum information theory, including the PPT square conjecture and recently proposed eigenvalue bounds for 2-positive trace-preserving maps.

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arXiv2026

Shake before use: universal enhancement of quantum thermometry by unitary driving

Quantum thermometry aims at determining temperature with ultimate precision in the quantum regime. Standard equilibrium approaches, limited by the Quantum Fisher Information given by static energy fluctuations, lose sensitivity outside a fixed temperature window. Non-equilibrium strategies have therefore been recently proposed to overcome these limits, but their advantages are typically model-dependent or tailored for a specific purpose. This Letter establishes a general, model-independent result showing that any temperature-dependent unitary driving applied to a thermalized probe enhances its quantum Fisher information with respect to its equilibrium value. Such information gain is expressed analytically through a positive semi-definite kernel of information currents that quantify the flow of statistical distinguishability. Our results, together with an analysis of the relation between information gain and control cost, are benchmarked on a driven spin-$1/2$ thermometer, furthermore showing that resonant modulations remarkably restore the quadratic-in-time scaling of the Fisher information and allow to shift the sensitivity peak across arbitrary temperature ranges.

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arXiv2026

High-dimensional quantum key distribution rates for multiple measurement bases

We investigate the advantages of high-dimensional encoding for a quantum key distribution protocol. In particular, we address a BBM92-like protocol where the dimension of the systems can be larger than two and more than two mutually unbiased bases (MUBs) can be employed. Indeed, it is known that, for a system whose dimension $d$ is a prime or the power of a prime, up to $d+1$ MUBs can be found. We derive an analytic expression for the asymptotic key rate when $d+1$ MUBs are exploited and show the effects of using different numbers of MUBs on the performance of the protocol. Then, we move to the non-asymptotic case and optimize the finite key rate against collective and coherent attacks for generic dimension of the systems and all possible numbers of MUBs. In the finite-key scenario, we find that, if the number of rounds is small enough, the highest key rate is obtained by exploiting three MUBs, instead of $d+1$ as one may expect.

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arXiv2026

Divide et impera: hybrid multinomial classifiers from quantum binary models

We investigate how to combine a collection of quantum binary models into a multinomial classifier. We employ a hybrid approach, adopting strategies like one-vs-one, one-vs-rest and a binary decision tree. We benchmark each method, by emphasizing their computational overhead and their impact on the quantum advantage. By comparison against a classical binary model (generalized using the same approach), we show that the decision tree represents a cost-effective solution, achieving similar accuracies to other methods with an overhead at most logarithmic in the total number of classes.

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arXiv2026

Quantum Optical Neuron for Image Classification via Multiphoton Interference

The rapid growth of machine learning is increasingly constrained by the energy and bandwidth limits of classical hardware. Optical and quantum technologies offer an alternative route, enabling high-dimensional, parallel information processing directly in the physical layer, particularly suited for imaging tasks. In this context, quantum photonic platforms provide both a natural mechanism for computing inner products and a promising path to energy-efficient inference in photon-limited regimes. Here, we experimentally demonstrate a camera-free quantum-optical images classifier that performs inference directly at the measurement layer using Hong-Ou-Mandel (HOM) interference of spatially programmable single photons. Two-photon coincidences directly report the overlap between an input image mode and a learned template, replacing pixel-resolved acquisition with a single global measurement. We realize both a single-perceptron quantum optical neuron and a two-neuron shallow network, achieving high accuracy on benchmark datasets with strong robustness to experimental noise and minimal hardware complexity. With a fixed measurement budget, performance remains insensitive to input resolution, demonstrating intrinsic robustness to the number of pixels, which would be impossible in a classical framework. This approach paves the way toward neuromorphic quantum photonic processors capable of extracting task-relevant information directly from HOM interference, with promising applications in remote object recognition, low-signal sensing, and photon-starved biological microscopy.

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arXiv2026

Quantum Hypergraph States: A Review

Quantum hypergraph states extend the well-studied class of graph states by taking into account multi-qubit interactions through hyperedges. They provide a powerful framework to represent a family of quantum states with genuine multipartite entanglement. In this review, we provide a compact overview of the formal structure, entanglement characteristics, and operational relevance of hypergraph states in quantum information theory. We begin by introducing their mathematical foundations and generalizations of the stabilizer formalism. A central focus is placed on their entanglement properties, including the classification under local unitary (LU) and stochastic local operations with classical communication (SLOCC), the quantification of multipartite entanglement, and detection techniques via entanglement witnesses. We also explore other nonclassical features of hypergraph states, such as contextuality and genuine multipartite nonlocality, derived from stabilizer-based Bell-type inequalities. Additional attention is given to the role of hypergraph states in error correction, and as a computational resource in measurement-based quantum computation (MBQC), and to their non-stabilizer character - quantified via resource-theoretic measures of quantum magic. Finally we review their generalization to higher dimensions, i.e. to qudits and continuous variables.

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arXiv2025

Quantum frequency resampling

In signal processing, resampling algorithms can modify the number of resources encoding a collection of data points. Downsampling reduces the cost of storage and communication, while upsampling interpolates new data from limited one, e.g. when resizing a digital image. We present a toolset of quantum algorithms to resample data encoded in the probabilities of a quantum register, using the quantum Fourier transform to adjust the number of high-frequency encoding qubits. We discuss advantage over classical resampling algorithms.

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arXiv2025

Complementarity-based complementarity: the choice of mutually unbiased observables shapes quantum uncertainty relations

Quantum uncertainty relations impose fundamental limits on the joint knowledge that can be acquired from complementary observables: perfect knowledge of a quantum state in one basis implies maximal indetermination in all other mutually unbiased bases (MUBs). Uncertainty relations derived from joint properties of the MUBs are generally assumed to be uniform, irrespective of the specific observables chosen within a set. In this work, we demonstrate instead that the uncertainty relations can depend on the choice of observables. Through both experimental observation and numerical methods, we show that selecting different sets of three MUBs in a 5-dimensional quantum system results in distinct uncertainty bounds, i.e. in varying degrees of complementarity, in terms of both entropy and variance.

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arXiv2025

Learning Time-Varying Gaussian Quantum Lossy Channels

Time-varying quantum channels are essential for modeling realistic quantum systems with evolving noise properties. Here, we consider Gaussian lossy channels varying from one use to another and we employ neural networks to classify, regress, and forecast the behavior of these channels from their Choi-Jamiolkowski states. The networks achieve at least 87% of accuracy in distinguishing between non-Markovian, Markovian, memoryless, compound, and deterministic channels. In regression tasks, the model accurately reconstructs the loss parameter sequences, and in forecasting, it predicts future values, with improved performance as the memory parameter approaches 1 for Markovian channels. These results demonstrate the potential of neural networks in characterizing and predicting the dynamics of quantum channels.

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arXiv2025

Experimental entropic uncertainty relations in dimensions three to five

We provide experimental validation of tight entropic uncertainty relations for the Shannon entropies of observables with mutually unbiased eigenstates in high dimensions. In particular, we address the cases of dimensions $d = 3$, $4$ and $5$ and consider from $2$ to $d+1$ mutually unbiased bases. The experiment is based on pulsed frequency bins measured with a multi-output quantum pulse gate, which can perform projective measurements on a complete high-dimensional basis in the time-frequency domain. Our results fit the theoretical predictions: the bound on the sum of the entropies is never violated and is saturated by the states that minimize the uncertainty relations.

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arXiv2025

High-dimensional quantum key distribution with resource-efficient detection

While quantum key distribution (QKD) based on two-dimensional (qubit) encoding is a mature, field-tested technology, its performance is lacking for many cryptographic applications. High-dimensional encoding for QKD enables increased achievable key rates and robustness as compared to the standard qubit-based systems. However, experimental implementations of such systems are more complicated, expensive, and require careful security analysis as they are less common. In this work we present a proof of principle high-dimensional time-phase BB84 QKD experiment using only one single-photon detector per measurement basis. We employ the temporal Talbot effect to detect QKD symbols in the control basis, and show experimentally-obtained simplistic key rates for the two-dimensional and four-dimensional case, including in an urban fiber network. We present a comparison of a simplistic secret key rate obtained from a standard security proof with the one derived from a recently devised proof using a tunable beam splitter to display security issues stemming from asymmetric detection efficiencies in the two bases. Our results contribute to the discussion of the benefits of high-dimensional encoding and highlight the impact of security analysis on the achievable QKD performance.

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arXiv2025

Bosonic two-stroke heat engines with polynomial nonlinear coupling

We study the thermodynamics of two-stroke heat engines where two bosonic modes $a$ and $b$ are coupled by the general nonlinear interaction $V_θ = \exp {(θa^{\dagger n}b^m -θ^* a^n b^{\dagger m})}$. By adopting the two-point measurement scheme we retrieve the distribution of the stochastic work, and hence the relative fluctuations of the extracted work up to the second order in the coupling $θ$. We identify the optimal interactions providing large average work with small fluctuations in the operational regime of the heat engine. Then, we consider the specific cases $n=2$, $m=1$ and $n=1$, $m=2$ up to the fourth order in $θ$. We optimize the average work and the signal-to-noise ratio over the frequencies of the bosonic modes and the temperatures of the reservoirs. Finally, we determine the thermodynamic uncertainty relations for these processes in relation with the order of the expansion of the unitary interaction $V_θ$.

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arXiv2025

Quantum optical classifier with superexponential speedup

Classification is a central task in deep learning algorithms. Usually, images are first captured and then processed by a sequence of operations, of which the artificial neuron represents one of the fundamental units. This paradigm requires significant resources that scale (at least) linearly in the image resolution, both in terms of photons and computational operations. Here, we present a quantum optical pattern recognition method for binary classification tasks. It classifies objects without reconstructing their images, using the rate of two-photon coincidences at the output of a Hong-Ou-Mandel interferometer, where both the input and the classifier parameters are encoded into single-photon states. Our method exhibits the behaviour of a classical neuron of unit depth. Once trained, it shows a constant $\mathcal{O}(1)$ complexity in the number of computational operations and photons required by a single classification. This is a superexponential advantage over a classical artificial neuron.

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arXiv2025

Detecting Markovianity of Quantum Processes via Recurrent Neural Networks

We present a novel methodology utilizing Recurrent Neural Networks (RNNs) to classify Markovian and non-Markovian quantum processes, leveraging time series data derived from Choi states. The model exhibits exceptional accuracy, surpassing 95%, across diverse scenarios, including dephasing and Pauli channels in an arbitrary basis, generalized amplitude damping dynamics, and even in the presence of noise. Additionally, the developed model shows efficient forecasting capabilities for the analyzed time series data. These results suggest the potential of RNNs in discerning and predicting the Markovian nature of quantum processes.

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arXiv2025

Quantum Key Distribution with Basis-Dependent Detection Probability

Quantum Key Distribution (QKD) is a promising technology for secure communication. Nevertheless, QKD is still treated with caution in certain contexts due to potential gaps between theoretical models and actual QKD implementations. A common assumption in security proofs is that the detection probability at the receiver, for a given input state, is independent of the measurement basis, which might not always be verified and could lead to security loopholes. This paper presents a security proof for QKD protocols that does not rely on the above assumption and is thus applicable in scenarios with detection probability mismatches, even when induced by the adversary. We demonstrate, through simulations, that our proof can extract positive key rates for setups vulnerable to large detection probability mismatches. This is achieved by monitoring whether an adversary is actively exploiting such vulnerabilities, instead of considering the worst-case scenario as in previous proofs. Our work highlights the importance of accounting for basis-dependent detection probabilities and provides a concrete solution for improving the security of practical QKD systems.

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