arXiv subjects

Ariel Norambuena

Publications and source records attributed to Ariel Norambuena.

At least 19 recordsLinked to original sources

arXiv2026

Quantum Magic Reveals CP Phases Invisible to Entanglement in Spin-0 Decays

All standard scalar quantum-information measures -- concurrence, negativity, entanglement entropy, the optimized CHSH bound, and quantum Fisher information -- are CP-blind in ideal \\ spin-0 $\to f\bar f$ decays because the two-qubit spin state is maximally entangled for every CP angle. We show that stabilizer magic, fixed in the physical Pauli frame of spin analysis, escapes this blind spot: the stabilizer R\'enyi entropy admits an exact closed form, vanishing at CP-definite and Clifford phases and peaking at maximal non-Clifford mixing. Two experimentally accessible, magic-inspired CP witnesses follow; the linear amplitude is $14.3\times$ more efficient than its quartic counterpart and reaches discovery-level sensitivity at the HL-LHC for $H\to\tau^+\tau^-$.

quant-ph↗

arXiv2026

Learning spectral density functions in open quantum systems

Spectral density functions quantify how environmental modes couple to quantum systems and govern their open dynamics. Inferring such frequency-dependent functions from time-domain measurements is an ill-conditioned inverse problem. Here, we use exactly solvable spin-boson models with pure-dephasing and amplitude-damping channels to reconstruct spectral density functions from noisy simulated data. First, we introduce a parameter estimation approach based on machine learning regressors to infer Lorentzian and Ohmic-like spectral density parameters, quantifying robustness to noise. Second, we show that a cosine transform inversion yields a physics-consistent spectral prior estimation, which is refined by a constrained neural network enforcing positivity and correct asymptotic behaviour. Our neural network framework robustly reconstructs structured spectral densities by filtering simulated noisy signals and learning general functional dependencies.

quant-ph↗

arXiv2026

Geometric control of maximal entanglement via bound states in the continuum

Bound states in the continuum (BiCs) convert dissipative open systems into effectively closed quantum subspaces through destructive interference. We show that two identical giant atoms coupled to a one-dimensional waveguide support BICs that coincide with maximally entangled atomic states. Most importantly, entanglement is predominantly determined by the geometric design; the ratio of intra-atomic connection lengths fixes the concurrence, while the propagation phase between atoms selects a family of Bell-like states. We further analyze the dynamical stability of these maximally entangled BICs under exact time evolution, revealing a clear hierarchy of robustness against parameter perturbations. Our results establish an analytical bridge between symmetry, geometry, entanglement, and BICs in giant-atom waveguide platforms.

quant-ph↗

arXiv2025

Contact temporal network during motility induced phase separation

Motility-induced phase separation (MIPS) is a paradigmatic non-equilibrium transition in active matter, determined by the Péclet number and packing fraction. We investigate the single-phase and phase-separated regimes of MIPS using a complex network approach, where networks are constructed from particle interactions over finite time windows. In the single-phase (gas-like) regime, the degree distributions $P(k)$ exhibit Gaussian behavior and resemble those of random graphs. Plotting the location and height of the $P(k)$ peak reveals a universal curve across different Péclet numbers at fixed packing fraction. In the phase-separated regime, we analyze the dense and dilute phases independently. The $P(k)$ distributions unveil distinct collective dynamics, including caging in the dense phase and the emergence of active solid-like structures at longer times. Clustering coefficients and average path lengths in both phases stabilize rapidly, indicating that short simulations are sufficient to capture essential network features. Overall, our results show that network metrics expose both universal and phase-specific aspects of active matter dynamics. Notably, we identify distinct and previously unreported topological structures arising in the dense and dilute phases within the MIPS regime.

physics.bio-ph↗

arXiv2025

Memory effects in pulsed optomechanical systems

Memory, understood as time non-locality, is a fundamental property of any physical system, whether classical or quantum, and has important applications in a wide variety of technologies. In the context of quantum technologies, systems with memory can be used in quantum information, communication, and sensing. Here, we demonstrate that cavity optomechanical systems driven by a pulsed laser can operate as programmable quantum memory elements. By engineering the adiabatic and non-adiabatic pulses, particularly the Gaussian and sinusoidal, we induce and control diverse memory phenomena such as dynamical hysteresis, quantized phononic transitions, and distinct energy-storing responses. Within a mean-field approach, we derive the analytical and numerical criteria under which the photonic and phononic observables manifest the memory effects in strongly driven regimes. The memory effects are quantified through a dimensionless geometric form factor, which provides a versatile metric to characterize the memory efficiency. Our protocol is readily compatible with the current optomechanical platforms, highlighting the new possibilities for advanced memory functionalities in quantum technologies.

quant-ph↗

arXiv2025

Entropy, entanglement and susceptibility of three qubits near quantum criticality

This work investigates a system of three entangled qubits within the XXX model, subjected to an external magnetic field in the $z$-direction and incorporating an anisotropy term along the $y$-axis. We explore the thermodynamics of the system by calculating its magnetic susceptibility and analyzing how this quantity encodes information about entanglement. By deriving rigorous bounds for susceptibility, we demonstrate that their violation serves as an entanglement witness. Our results show that anisotropy enhances entanglement, extending the temperature range over which it persists. Additionally, by tracing over the degrees of freedom of two qubits, we examine the reduced density matrix of the remaining qubits and find that its entropy under the influence of the magnetic field can be mapped to an effective thermal bath at $(B,K) > 0$ K.

cond-mat.stat-mech↗

arXiv2024

Magnetic and phonon-induced effects on the non-Markovian dynamics of a single solid-state defect

The electron-phonon interaction is one of the most fundamental mechanisms in condensed matter physics. Phonons can induce memory effects in solid-state platforms when localized electronic states interact with lattice vibrations in non-unitary dynamical maps. In this work, we demonstrate how single-mode and structured phonon environments can give rise to non-Markovian dynamics of an individual negatively charged silicon-vacancy center in diamond. Using trace distance as a quantifier via numerical simulations and theoretical calculations, we identify the physical conditions for emerging and understanding non-Markovian behavior in diverse scenarios. Most importantly, we investigate the influence of magnetic fields (longitudinal and transverse), phonon couplings, Fock states, and temperature to understand how these factors influence memory effects in this solid-state device.

quant-ph↗

arXiv2024

Effects of Magnetic Anisotropy on 3-Qubit Antiferromagnetic Thermal Machines

This study investigates the anisotropic effects on a system of three qubits with chain and ring topology, described by the antiferromagnetic Heisenberg XXX model subjected to a homogeneous magnetic field. We explore the Stirling and Otto cycles and find that easy-axis anisotropy significantly enhances engine efficiency across all cases. At low temperatures, the ring configuration outperforms the chain on both work and efficiency during the Stirling cycle. Additionally, in both topologies, the Stirling cycle achieves Carnot efficiency with finite work at quantum critical points. In contrast, the quasistatic Otto engine also reaches Carnot efficiency at these points but yields no useful work. Notably, the Stirling cycle exhibits all thermal operational regimes engine, refrigerator, heater, and accelerator unlike the quasistatic Otto cycle, which functions only as an engine or refrigerator.

cond-mat.stat-mech↗

arXiv2024

Emerging double power-law through dynamical complex networks in motility-induced phase separation in Active Brownian Particles

We investigate the behavior of active Brownian particles (ABP) within a temporal complex network framework approach. We focused on the node degree distribution, average path length, and average clustering coefficient across the Péclet number and packing fraction region. In the single phase or gas region, particle interactions mirror a random graph, and the average number of unique interactions display a non-monotonic behavior with the packing fraction. As we ventured toward the Motility-Phase Separation (MIPS) frontier, a hybrid distribution with a time-evolving pattern emerged, combining Gaussian and power law components. Moreover, we discovered a double power-law distribution in the phase-separated region, with two characteristic slopes symbolizing the emerging gas and solid regions. Our approach involved various numerical and theoretical analyses to capture the role of the packing fraction and Péclet number in the topological properties of the dynamical complex network that arises during the ABP dynamics. These findings provide a robust understanding of a phase transition between two states and how this transition manifests as a random to scale-free behavior.

physics.bio-ph↗

arXiv2024

Quantum kernels for classifying dynamical singularities in a multiqubit system

Dynamical quantum phase transition is a critical phenomenon involving out-of-equilibrium states and broken symmetries without classical analogy. However, when finite-sized systems are analyzed, dynamical singularities of the rate function can appear, leading to a challenging physical characterization when parameters are changed. Here, we report a quantum support vector machine (QSVM) algorithm that uses quantum Kernels to classify dynamical singularities of the rate function for a multiqubit system. We illustrate our approach using $N$ long-range interacting qubits subjected to an arbitrary magnetic field, which induces a quench dynamics. Inspired by physical arguments, we introduce two different quantum Kernels, one inspired by the ground state manifold and the other based on a single state tomography. Our accuracy and adaptability results show that this quantum dynamical critical problem can be efficiently solved using physically inspiring quantum Kernels. Moreover, we extend our results for the case of time-dependent fields, quantum master equation, and when we increase the number of qubits.

quant-ph↗

arXiv2023

Sequential epidemic spread between agglomerates of self-propelled agents in one dimension

Motile organisms can form stable agglomerates such as cities or colonies. In the outbreak of a highly contagious disease, the control of large-scale epidemic spread depends on factors like the number and size of agglomerates, travel rate between them, and disease recovery rate. While the emergence of agglomerates permits early interventions, it also explains longer real epidemics. In this work, we study the spread of susceptible-infected-recovered epidemics in one-dimensional spatially-structured systems. By working in one dimension, we mimic microorganisms in narrow channels and establish a necessary foundation for future investigation in higher dimensions. We employ a model of self-propelled particles which spontaneously form multiple clusters. As the rate of stochastic reorientation decreases, clusters become larger and less numerous. Besides examining the time evolution averaged over many epidemics, we show how the final number of ever-infected individuals depends non-trivially on single-individual parameters. In particular, the number of ever-infected individuals first increases with the reorientation rate since particles escape sooner from clusters and spread the disease. For higher reorientation rate, travel between clusters becomes too diffusive and the clusters too small, decreasing the number of ever-infected individuals.

q-bio.PE↗

arXiv2022

Engineering non-Markovianity from defect-phonon interactions

Understanding defect-phonon interactions in solid-state devices is crucial for improving our current knowledge of quantum platforms. In this work, we develop first-principles calculations for a defect composed of two spin-$1/2$ particles that interact with phonon modes in a one-dimensional lattice. We follow a bottom-up approach that begins with a dipolar magnetic interaction to ultimately derive the spectral density function and time-local master equation that describes the open dynamics of the defect. We provide theoretical and numerical analysis for the non-Markovian features of the defect-phonon dynamics induced by a pure dephasing channel acting on the Bell basis. Finally, we analyze two measures of non-Markovianity based on the canonical rates and Coherence, shedding more light on the role of the spectral density function and temperature; and envisioning experimental realizations.

quant-ph↗

arXiv2022

Estimating the degree of non-Markovianity using variational quantum circuits

Several applications of quantum machine learning (QML) rely on a quantum measurement followed by training algorithms using the measurement outcomes. However, recently developed QML models, such as variational quantum circuits (VQCs), can be implemented directly on the state of the quantum system (quantum data). Here, we propose to use a qubit as a probe to estimate the degree of non-Markovianity of the environment. Using VQCs, we find an optimal sequence of qubit-environment interactions that yield accurate estimations of the degree of non-Markovianity for the amplitude damping, phase damping, and the combination of both models. We introduce a problem-based ansatz that optimizes upon the probe qubit and the interaction time with the environment. This work contributes to practical quantum applications of VQCs and delivers a feasible experimental procedure to estimate the degree of non-Markovianity.

quant-ph↗

arXiv2022

Kernel-based quantum regressor models learn non-Markovianity

Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlap between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum dataset. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlap between quantum states. We show that our models deliver accurate predictions that are comparable with the fully classical models.

quant-ph↗

arXiv2022

Physics-informed neural networks for quantum control

Quantum control is a ubiquitous research field that has enabled physicists to delve into the dynamics and features of quantum systems, delivering powerful applications for various atomic, optical, mechanical, and solid-state systems. In recent years, traditional control techniques based on optimization processes have been translated into efficient artificial intelligence algorithms. Here, we introduce a computational method for optimal quantum control problems via physics-informed neural networks (PINNs). We apply our methodology to open quantum systems by efficiently solving the state-to-state transfer problem with high probabilities, short-time evolution, and using low-energy consumption controls. Furthermore, we illustrate the flexibility of PINNs to solve the same problem under changes in physical parameters and initial conditions, showing advantages in comparison with standard control techniques.

quant-ph↗

arXiv2022

Dynamical quantum phase transition in diamond: applications in quantum metrology

Nonequilibrium dynamics is a paramount scenario for studying quantum systems. The emergence of new features with no equilibrium counterpart, such as dynamical quantum phase transition (DQPT), has attracted wide attention. In this work, we depart from the well known Ising model and showcase an experimentally accessible configuration of a negatively charged Nitrogen-Vacancy center that interacts with nearby Carbon-13 nuclear spins. We provide new insights into this system in the context of DQPT. We show that nuclear spins undergo DQPT by appropriately choosing the relation between the transverse and longitudinal components of an external magnetic field. Furthermore, we can steer the DQPT via a time-dependent longitudinal magnetic field and apply this control to enhance the estimation of the coupling strength between the nuclear spins. Moreover, we propose a novel quenched dynamics that originates from the rotation of the central electron spin, which controls the DQPT relying on the anisotropy of the hyperfine coupling.

quant-ph↗

arXiv2021

Polariton-based quantum memristors

Information processing and storing by the same physical system is emerging as a promising alternative to traditional computing platforms. In turn, this requires the realization of elementary units whose memory content can be easily tuned and controlled. Here, we introduce a polariton-based quantum memristor where the memristive nature arises from the inter-cavity polariton exchange and is controlled by a time-varying atom-cavity detuning. A dynamical hysteresis is characterized by the fluctuations in the instantaneous polariton number, where the history information is encoded into a dynamical phase. Using a Lindblad master equation approach, we find that features of the quantum memristor dynamics, such as the area and circulation of the hysteresis loop, showcase a kind of "plasticity" controlled by quantum state initialization. This makes this quantum memristor very versatile for a wide range of applications

quant-ph↗

arXiv2021

Probabilistic magnetometry with two-spin system in diamond

Solid-state magnetometers like the Nitrogen-Vacancy center in diamond have been of paramount importance for the development of quantum sensing with nanoscale spatial resolution. The basic protocol is a Ramsey sequence, that imprints an external static magnetic field into phase of the quantum sensor, which is subsequently readout. In this work we show that the hyperfine coupling between the Nitrogen-Vacancy and a nearby Carbon-13 can be used to set a post-selection protocol that leads to an enhancement of the sensitivity under realistic experimental conditions. We found that for an isotopically purified sample the detection of weak magnetic fields in the $μ$T range can be achieved with a sensitivity of few nTHz$^{-1/2}$ at cryogenic temperature ($4$ K), and $0.1$ $μ$THz$^{-1/2}$ at room temperature.

quant-ph↗