arXiv subjects

Gian Luca Giorgi

Publications and source records attributed to Gian Luca Giorgi.

At least 19 recordsLinked to original sources

arXiv2026

Analog neutral-atom for in-memory processing in quantum reservoir computing

Quantum Reservoir Computing (QRC) exploits the rich dynamics of quantum many-body systems to process time-dependent information with high-dimensional state spaces. While neutral atom arrays offer a scalable platform for this paradigm, realizing intrinsic temporal memory without relying on external classical buffering requires precise control over the relaxation dynamics of the system. We address the necessity of specific non-unitary dynamics for enforcing fading memory while overcoming the lack of separability. This study simulates and empirically identifies the specific dynamical regimes required to maximize the computational capacity of the reservoir, addressing the amount of dissipation, the effectiveness of lying at the edge of quantum chaos, and that of time-multiplexing compared to the scaling of the physical size of the system. Here we show that controlled dissipation is strictly necessary to induce echo state property, fading memory, and separability, in neutral atom arrays. Computational performance increases at the edge of quantum chaos. The counter-intuitive residual memory provided by the unital phase damping channel is justified by introducing a model based on pure outputs. The consequent implementation enables the solution of complex non-linear tasks, such as the Mackey-Glass time series, with as few as $N=5$ atoms. We establish a rigorous framework for engineering dissipative quantum reservoirs required for intrinsic temporal processing on near-term quantum devices.

quant-ph↗

arXiv2026

Gaussian quantum reservoir computing with a hybrid cavity magnomechanical system

We propose a quantum reservoir computing framework based on a hybrid cavity magnomechanical system in the linearized Gaussian regime. The reservoir combines microwave-cavity, magnon, and mechanical degrees of freedom, and is extended by an auxiliary cavity acting as an input port, with time-dependent signals encoded in its detuning. The covariance matrix of the quadrature fluctuations provides the features for a trained linear readout. Using linear-memory, nonlinearmemory, and parity-check benchmarks, we find strong temporal memory together with more limited nonlinear processing, whose balance is controlled by the reservoir evolution time, and we show that intermode correlations substantially enhance the information accessible to the readout. The same architecture reconstructs a time-dependent signal encoded in the auxiliary-cavity detuning, with an accuracy governed by the interplay between the internal couplings and the encoding strength, and robust against Gaussian detuning noise. Accounting for finite measurement statistics reveals a trade-off between encoding strength and the precision of the covariance estimation, so that the optimal encoding depends on the available measurement budget. These results establish hybrid cavity magnomechanical systems as a promising platform for continuous-variable quantum reservoir computing with potential applications in signal probing.

quant-ph↗

arXiv2026

Temporal information processing on a 4,500-qubit quantum annealer

Quantum machine learning could uncover statistical structure beyond the reach of classical models, but this requires quantum models large and expressive enough to be useful and cheap enough to read out. Most approaches optimize many quantum parameters and are thus limited by expensive training loops. Here we report a quantum machine-learning model implemented on a programmable superconducting quantum annealer that processes temporal data at large scale using up to 4,500 qubits-the largest quantum machine-learning experiment performed to date. Following the quantum reservoir computing paradigm, the untrained native many-body dynamics generated by reverse annealing is directly used to process temporal data. We prove that the interactions produced during annealing are indispensable-a non-interacting reservoir retains no memory of its input. We evaluate our model experimentally on standard memory benchmarks and demonstrate that it can successfully forecast chaotic time series. These results establish quantum annealers as a scalable platform for large-scale quantum machine learning.

quant-ph↗

arXiv2026

Unraveling-Dependent Metastability in Monitored Quantum Systems and Associative Memories

Metastability in open quantum systems is usually inferred from spectral separation in the Liouvillian, which governs unconditional, ensemble-averaged dynamics. We show that this diagnosis is incomplete at the level of individual quantum trajectories: conditioned realizations of the same unconditional dynamics can bypass, transiently access, or operationally preserve a metastable memory, depending on the monitored channel and the observed record. We demonstrate these mechanisms in a driven-dissipative nonlinear oscillator realizing a quantum associative memory, by comparing spectra, target fidelities, and phase-space distributions. The non-Hermitian dynamics obtained by post-selecting on the absence of detected events supports metastable retrieval, but with a distinct long-time fate: normalization selects the least-decaying mode of the non-Hermitian spectrum rather than the addressed memory branch. In contrast, stochastic jump trajectories can preserve retrieval over extended times when the post-measurement update remains compatible with the coherent memory structure. Thus trajectory-level metastability is not determined by the averaged generator alone; it requires compatibility between the monitored channel, the measurement record, and the metastable manifold.

quant-ph↗

arXiv2026

Energetic Cost of Temporal Information Processing in Quantum Reservoirs

Quantum reservoir computing offers a promising route toward energy-efficient machine learning by processing temporal information with minimal training overhead. Yet, the physical principles linking its energetic cost to computational performance remain largely unexplored. Here we show that, in an interacting spin reservoir, information encoding and information processing are governed by distinct physical mechanisms. In the weak interacting regime, we derive an analytical expression for the average (switching) work, showing that the energetic cost of encoding new inputs is determined by the local response of the reservoir units. In contrast, interactions primarily redistribute the encoded information, generating memory and nonlinear features while only weakly affecting the work. This separation produces opposite correlations between energetic cost and performance for representative linear and nonlinear benchmark tasks. Our results identify the switching work as the energetic signature of information encoding and clarify when energetic efficiency and computational performance are compatible.

quant-ph↗

arXiv2026

General theory of monitored Quantum Reservoir Computing

Quantum reservoir computing (QRC) provides a powerful framework for processing temporal data using quantum dynamics, but incorporating measurements into the reservoir remains a fundamental challenge and distinctive feature with respect to classical settings. The induced back-action can vary from a source of disturbance to a computational resource, as measurement deeply modifies the dynamics underlying temporal processing. Existing approaches have treated specific monitoring schemes independently, missing the common physical principles governing online quantum reservoirs. Here we develop a general theory of monitored quantum reservoir computing based on indirect quantum measurements, which unifies projective, weak, partial, and dissipative monitoring protocols within a single operational framework. Measurement back-action can serve as a controllable resource, providing the effective dissipation and non-unital dynamics required for successful QRC, even when the underlying unmonitored evolution is unsuitable. We derive general criteria under which monitored dynamics satisfy the echo-state property, fading memory, and input separability, including a necessary and sufficient condition for emergent strict contractivity. By comparing different monitoring schemes under a common reference dynamics, we show that these protocols are not interchangeable parameterizations to be optimized for peak performance, but rather constitute qualitatively distinct routes to computational capability, each enabled by the interplay between information extraction and measurement-induced disturbance -- a trade-off that can be further shaped through time multiplexing. Our results provide a unified theoretical foundation for online monitored quantum reservoir computing and establish quantum measurement engineering as a systematic approach for designing reservoir architectures across different quantum platforms.

quant-ph↗

arXiv2026

Non-Markovianity and memory enhancement in Quantum Reservoir Computing

Featuring memory of past inputs is a fundamental requirement for machine learning models processing time-dependent data. In quantum reservoir computing, all architectures proposed so far rely on Markovian dynamics, which, as we prove, inherently lead to an exponential decay of past information, thereby limiting long-term memory capabilities. We demonstrate that non-Markovian dynamics can overcome this limitation, enabling extended memory retention. By analytically deriving memory bounds and supporting our findings with numerical simulations, we show that non-Markovian reservoirs can outperform their Markovian counterparts, particularly in tasks that require a coexistence of short- and long-term correlations. We introduce an embedding approach that allows a controlled transition from Markovian to non-Markovian evolution, providing a path for practical implementations. Our results establish quantum non-Markovianity as a key resource for enhancing memory in quantum machine learning architectures, with broad implications in quantum neural networks.

quant-ph↗

arXiv2026

Temporal processing of quantum states with hybrid quantum-classical reservoirs

A distinctive feature of Quantum Reservoir Computing (QRC) is the ability to directly embed quantum input states into the reservoir dynamics. However, the resulting output is fundamentally linear for a single input state, preventing QRC from naturally computing nonlinear functionals such as purity or entropy. We overcome this limitation with a quantum-classical hybrid architecture combining a qubit quantum reservoir with a classical echo state network (ESN), allowing both nonlinear functional approximation and effective temporal processing. We systematically study performance under two information regimes: full-tomography and partial information (single-axis measurements), with the latter demonstrating that the hybrid system outperforms its standalone components in both linear and nonlinear tasks due to the enhanced information retrieval provided by the quantum reservoir. Building on these results, we apply an online monitoring protocol that explicitly accounts for measurement back-action and finite measurement ensembles, enabling a realistic assessment of performance under experimental conditions. These results establish hybrid quantum-classical reservoir computing (HRC) architectures as a practical and scalable route for enhanced quantum machine learning on near-term qubit hardware.

quant-ph↗

arXiv2026

Benchmarking Quantum Extreme Learning based on Gaussian Boson Sampling

Reservoir models offer a hardware-efficient learning paradigm for noisy intermediate-scale quantum devices by exploiting untrained quantum dynamics as a fixed feature map and restricting optimization to a simple classical readout layer. We propose a quantum extreme learning machine implemented using gaussian boson sampling and an encoding strategy that achieves high classification accuracy while reducing optical resource requirements. Classical inputs are jointly encoded in the squeezing parameters and in the interferometer unitary, enabling sampling-based, highly nonlinear feature maps while leveraging large-scale GBS output statistics, which are conjectured to be classically intractable. We systematically compare multiple families of quantum features accessible in the same setup and find that photon-number sampling probabilities provide the best performance, consistent with their higher effective feature dimensionality. Finally, we benchmark against classical nonlinear baselines and analyse robustness under noisy scenarios, showing competitive performance with fewer trainable parameters and indicating practical promise for near-term photonic implementations.

quant-ph↗

arXiv2026

Quantum reservoir computing in Jaynes-Cummings models: Nonlinear memory and time-series prediction

We investigate quantum reservoir computing (QRC) using a hybrid qubit-boson system described by the Jaynes-Cummings (JC) Hamiltonian and its dispersive limit (DJC). These models provide high-dimensional Hilbert spaces and intrinsic nonlinear dynamics, making them powerful substrates for temporal information processing. We systematically benchmark both reservoirs through linear and nonlinear memory tasks, demonstrating that they exhibit an unusual superior nonlinear over linear memory capacity. We further test their predictive performance on the Mackey-Glass time series, a widely used benchmark for chaotic dynamics, and show comparable forecasting ability. We also investigate how memory and prediction accuracy vary with reservoir parameters, and show the role of higher-order bosonic observables and time multiplexing in enhancing expressivity, even in minimal spin-boson configurations. Our results establish JC- and DJC-based reservoirs as versatile platforms for time-series processing and as elementary units that overcome the setting of equivalent qubit pairs and offer pathways toward tunable, high-performance quantum machine learning architectures.

quant-ph↗

arXiv2026

Memory-enhanced quantum extreme learning machines for characterizing non-Markovian dynamics

We use a Quantum Extreme Learning Machine for characterizing and estimating parameters of quantum dynamics generated by a tunable collision model. The input to the learning protocol consists of quantum states produced by successive system environment interactions, while the reservoir is implemented as a disordered many body quantum system evolving under a fixed Hamiltonian. We systematically explore how extending the QELM feature space, through the inclusion of temporal information and additional observables, affects estimation performance. Our results demonstrate that temporal extensions of the feature vector consistently and significantly enhance estimation accuracy relative to the baseline protocol. Notably, incorporating memory from earlier time steps yields the most substantial and robust improvements, whereas extensions based solely on additional observables offer only marginal gains. Crucially, the advantage conferred by temporal memory becomes increasingly pronounced as the dynamics become more strongly non Markovian, indicating that environmental memory effects serve as a constructive resource for learning.

quant-ph↗

arXiv2026

Learning functions of quantum states with distributed architectures

Distributed architectures are gaining prominence in quantum machine learning as a means to overcome hardware limitations and enable scalable quantum information processing. In this context, we analyze the design and performance of distributed Quantum Extreme Learning Machine (QELM) architectures for learning functions of quantum states directly from data, restricting measurements to easily implementable projective measurements in the computational basis. The aim is to determine which schemes can effectively recover specific properties of input quantum states, including both linear and nonlinear features, while also quantifying the resource requirements in terms of measurements and reservoir dimensionality. We compare standard three-layer QELM with a spatially multiplexed architecture composed of multiple independent three-layer units for linear (quantum) tasks, showing a linear reduction in resource requirements per unit. For nonlinear properties, the study examines the multiple-injection architecture and introduces a novel distributed design that incorporates entanglement between subsystems within a spatially multiplexed framework, evaluating its performance through the reconstruction of complex nonlinear quantities such as polynomial targets, Rényi entropy, and entanglement measures. Our results demonstrate that the distributed design enables the reconstruction of higher-order nonlinearities by increasing the number of interacting subsystems with reduced resources, rather than increasing the size of an individual reservoir, providing a scalable and hardware-efficient route to quantum property learning.

quant-ph↗

arXiv2025

Experimental memory control in continuous variable optical quantum reservoir computing

Quantum reservoir computing (QRC) offers a promising framework for online quantum-enhanced machine learning tailored to temporal tasks, yet practical implementations with native memory capabilities remain limited. Here, we demonstrate an optical QRC platform based on deterministically generated multimode squeezed states, exploiting both spectral and temporal multiplexing in a fully continuous-variable (CV) setting, and enabling controlled fading memory. Data is encoded via programmable phase shaping of the pump in an optical parametric process and retrieved through mode-selective homodyne detection. Real-time memory is achieved through feedback using electro-optic phase modulation, while long-term dependencies are achieved via spatial multiplexing. This architecture with minimal post-processing performs nonlinear temporal tasks, including parity checking and chaotic signal forecasting, with results corroborated by a high-fidelity Digital Twin. We show that leveraging the entangled multimode structure significantly enhances the expressivity and memory capacity of the quantum reservoir. This work establishes a scalable photonic platform for quantum machine learning, operating in CV encoding and supporting practical quantum-enhanced information processing.

quant-ph↗

arXiv2025

Entanglement estimation of Werner states with a quantum extreme learning machine

Quantum Extreme Learning Machines (QELMs) have emerged as a potent tool for various quantum information processing tasks. We present a QELM protocol for estimating the amount of entanglement in Werner states. The protocol requires the generation of a sequence of random Werner states, which are then combined with a reservoir state and evolved using an Ising Hamiltonian. A set of observables based on the Bloch basis is constructed and employed to train the system to recognize unseen features. To assess the protocol's robustness, noise is introduced into the input states, and the system's performance under these noisy conditions is analyzed. Additionally, the influence of the magnetic field parameter within the Ising Hamiltonian on the estimation accuracy is investigated.

quant-ph↗

arXiv2025

Chiral cat code: Enhanced error correction induced by higher-order nonlinearities

We introduce a Schrödinger chiral cat qubit, a novel bosonic quantum code generalizing Kerr cat qubits that exploits higher-order nonlinearities. Compared to a standard Kerr cat, the chiral cat qubit allows additional correction of bit-flip errors within the Hilbert space of a single bosonic oscillator. Indeed, this code displays optical bistability, i.e., the simultaneous presence of multiple long-lived states. Two of them define the code space and two define an error space. Thanks to the chiral structure of the phase space of this system, the error space can be engineered to ``capture'' bit flip events in the code space (a bit-flip trap), without affecting the quantum information stored in the system. Therefore, it is possible to perform detection and correction of errors. We demonstrate how this topological effect can be particularly efficient in the presence of large dephasing. We provide concrete examples of the performance of the code and show the possibility of applying quantum operations rapidly and efficiently. Beyond the interest in this single technological application, our work demonstrates how the topology of phase space can enhance the performance of bosonic codes.

quant-ph↗

arXiv2025

Exponential concentration and symmetries in Quantum Reservoir Computing

Quantum reservoir computing (QRC) is an emerging framework for near-term quantum machine learning that offers in-memory processing, platform versatility across analogue and digital systems, and avoids typical trainability challenges such as barren plateaus and local minima. The exponential number of independent features of quantum reservoirs opens the way to a potential performance improvement compared to classical settings. However, this exponential scaling can be hindered by exponential concentration, where finite-ensemble noise in quantum measurements requires exponentially many samples to extract meaningful outputs, a common issue in quantum machine learning. In this work, we go beyond static quantum machine learning tasks and address concentration in QRC for time-series processing using quantum-scrambling reservoirs. Beyond discussing how concentration effects can constrain QRC performance, we demonstrate that leveraging Hamiltonian symmetries significantly suppresses concentration, enabling robust and scalable QRC implementations. We illustrate our approach with concrete examples, including an established QRC design.

quant-ph↗

arXiv2025

Unveiling hidden features of the Kitaev model through a complex-network analysis

We introduce a density matrix-based network analysis to explore the ground state of the Kitaev chain, uncovering previously hidden structural and entanglement features. This approach successfully identifies the critical point associated with the topological phase transition and reveals a singular point where the ground state exhibits uniform, nonzero entanglement between all fermion pairs, corresponding to a fully connected network structure. We provide an analytical explanation for this singular behavior and establish a connection to the concept of ground state factorization observed in spin chains. Moreover, we analyze the open chain scenario and observe characteristic symmetry changes in the ground state corresponding to Majorana zero modes. While complex network theory has been employed in the study of quantum systems before, we demonstrate that it can uncover fundamentally new physical insights.

quant-ph↗

arXiv2025

Liouvillian skin effect in quantum neural networks

In the field of dissipative systems, the non-Hermitian skin effect has generated significant interest due to its unexpected implications. A system is said to exhibit a skin effect if its properties are largely affected by the boundary conditions. Despite the burgeoning interest, the potential impact of this phenomenon on emerging quantum technologies remains unexplored. In this work, we address this gap by demonstrating that quantum neural networks can exhibit this behavior and that skin effects, beyond their fundamental interest, can also be exploited in computational tasks. Specifically, we show that the performance of a given complex network used as a quantum reservoir computer is dictated solely by the boundary conditions of a dissipative line within its architecture. The closure of one (edge) link is found to drastically change the performance in time-series processing, proving the possibility of exploiting skin effects for machine learning.

quant-ph↗