arXiv subjects

Gabriel Margiani

Publications and source records attributed to Gabriel Margiani.

9 recordsLinked to original sources

arXiv2026

Activated switching between coexisting limit cycles

Noise-activated switching between coexisting stable states is a fundamental mechanism underlying stochastic dynamics in systems ranging from chemical reactions to neural networks. While this phenomenon is well understood for stationary attractors, it remains largely unexplored for limit cycles, whose periodic motion cannot be described by a static potential landscape. Here we experimentally demonstrate activated switching between two coexisting limit-cycle attractors in a driven nonlinear system of coupled resonators. Specifically, we introduce controlled fluctuations to directly observe the rare stochastic transitions between two limit cycles and measure their dependence on noise intensity and driving strength. The measured switching rates are well described by a large-deviation theory, which replaces the conventional activation barrier by the action along the most probable transition path. Our results extend the concept of activated dynamics from stationary to limit-cycle attractors and establish a framework for modeling stochastic transitions between limit cycles in driven-dissipative systems.

math.DS↗

arXiv2026

Nonequilibrium Kramers Turnover in a Kerr Parametric Oscillator

Activation processes govern noise-induced switching between long-lived states. In an equilibrium double well, the thermally activated switching rate exhibits a prefactor with a nonmonotonic dependence on environmental coupling, a foundational crossover known as Kramers turnover. Here, we demonstrate a Kramers turnover analogue in a Kerr parametric oscillator, a driven-dissipative nonlinear system featuring two stable phase states. First, we analytically establish turnover physics in this out-of-equilibrium setting. There, the strong physical correlation between the activation barrier and intrinsic damping fundamentally obscures the underlying turnover physics. To overcome this limitation, we rescale the rotating-frame dynamics and introduce a tunable effective friction controlled entirely by the parametric drive. This rescaling comes at the cost of a concurrent rescaling of the effective temperature. Exploiting this simultaneous scaling, we leverage the effective temperature to extract the turnover directly from temperature-dependent observations. Subsequently, measuring noise-induced phase slips in a micro-electromechanical device, we observe a distinct crossover in the prefactor's temperature dependence. Our results unambiguously isolate the out-of-equilibrium turnover regime and highlight that the competition between dissipation and fluctuations profoundly shapes activation dynamics also beyond equilibrium.

cond-mat.mes-hall↗

arXiv2025

A network of parametrically driven silicon nitride mechanical membranes

Networks of nonlinear resonators offer a promising platform for analog computing and the emulation of complex systems. However, realizing such networks remains challenging, as it requires resonators with high quality factors, individual frequency tunability, and strong inter-resonator coupling. In this work, we present a system that meets all these criteria. Our system is based on metallized silicon nitride membranes that are coupled via their common substrate and controlled capacitively via electrodes. We demonstrate individual frequency tuning and strong parametric driving of each membrane. Notably, we tune membrane frequencies through avoided crossings and demonstrate tunability of the coupled membrane's parametric response. This platform provides a scalable and controllable setting for exploring collective phenomena, dynamical phase transitions, nonlinear topology, and analog computing.

cond-mat.mes-hall↗

arXiv2025

Three Strongly Coupled Kerr Parametric Oscillators Forming a Boltzmann Machine

Coupled Kerr parametric oscillators (KPOs) are a promising resource for classical and quantum analog computation, for example to find the ground state of Ising Hamiltonians. Yet, the state space of strongly coupled KPO networks is very involved. As such, their phase diagram sometimes features either too few or too many states, including some that cannot be mapped to Ising spin configurations. This complexity makes it challenging to find and meet the conditions under which an analog optimization algorithm can be successful. Here, we demonstrate how to use three strongly coupled KPOs as a simulator for an Ising Hamiltonian, and estimate its ground state using a Boltzmann sampling measurement. While fully classical, our work is directly relevant for quantum systems operating on coherent states.

physics.class-ph↗

arXiv2024

Slow and fast topological dynamical phase transitions in a Duffing resonator driven by two detuned tones

The combination of a strong pump and a weak probe has been widely applied to investigate both optical and nanomechanical devices. Such pump-probe measurements allows for the exploration of nonlinear dynamics, driven by the large pump tone, by measuring the system response to a probe tone. In contrast, here we report on the dynamics of a mechanical Duffing resonator driven with a combination of two large tones at different frequencies. Our results indicate the presence of various distinct regimes with very different dynamics. We systematically investigate the impact of the relative strength and detuning between the two drives on the dynamical response. This provides an illustrative example of dynamical phase transitions in out-of-equilibrium systems.

cond-mat.mes-hall↗

arXiv2024

Hamiltonian reconstruction via ringdown dynamics

Many experimental techniques aim at determining the Hamiltonian of a given system. The Hamiltonian describes the system's evolution in the absence of dissipation, and is often central to control or interpret an experiment. Here, we theoretically propose and experimentally demonstrate a method for Hamiltonian reconstruction from measurements over a large area of phase space, overcoming the main limitation of previous techniques. A crucial ingredient for our method is the presence of dissipation, which enables sampling of the Hamiltonian through ringdown-type measurements. We apply the method to a driven-dissipative system -- a parametric oscillator -- observed in a rotating frame, and reconstruct the (quasi-)Hamiltonian of the system. Furthermore, we demonstrate that our method provides direct experimental access to the so-called symplectic norm of the stationary states of the system, which is tied to the particle- or hole-like nature of excitations of these states. In this way, we establish a method to unveil qualitative differences between the fluctuations around stabilized minima and maxima of the nonlinear out-of-equilibrium stationary states. Our method constitutes a versatile approach to characterize a wide class of driven-dissipative systems.

cond-mat.mes-hall↗

arXiv2023

A biased Ising model using two coupled Kerr parametric oscillators with external force

Networks of coupled Kerr parametric oscillators (KPOs) are a leading physical platform for analog solving of complex optimization problems. These systems are colloquially known as ``Ising machines''. We experimentally and theoretically study such a network under the influence of an external force. The force breaks the collective phase-parity symmetry of the system and competes with the intrinsic coupling in ordering the network configuration, similar to how a magnetic field biases an interacting spin ensemble. Specifically, we demonstrate how the force can be used to control the system, and highlight the crucial role of the phase and symmetry of the force. Our work thereby provides a method to create Ising machines with arbitrary bias, extending even to exotic cases that are impossible to engineer in real spin systems.

physics.class-ph↗

arXiv2022

Deterministic and stochastic sampling of two coupled Kerr parametric oscillators

The vision of building computational hardware for problem optimization has spurred large efforts in the physics community. In particular, networks of Kerr parametric oscillators (KPOs) are envisioned as simulators for finding the ground states of Ising Hamiltonians. It was shown, however, that KPO networks can feature large numbers of unexpected solutions that are difficult to sample with the existing deterministic (i.e., adiabatic) protocols. In this work, we experimentally realize a system of two classical coupled KPOs, and we find good agreement with the predicted mapping to Ising states. We then introduce a protocol based on stochastic sampling of the system, and we show how the resulting probability distribution can be used to identify the ground state of the corresponding Ising Hamiltonian. This method is akin to a Monte Carlo sampling of multiple out-of-equilibrium stationary states and is less prone to become trapped in local minima than deterministic protocols.

cond-mat.mes-hall↗

arXiv2021

Extracting the lifetime of a synthetic two-level system

The Kerr Parametric Oscillator (KPO) is a nonlinear resonator system that is often described as a synthetic two-level system. In the presence of noise, the system switches between two states via a fluctuating trajectory in phase space, instead of following a straight path. The presence of such fluctuating trajectories makes it hard to establish a precise count or even a useful definition, of the "lifetime" of the state. Addressing this issue, we compare several rate counting methods that allow to estimate a lifetime for the levels. In particular, we establish that a peak in the Allan variance of fluctuations can also be used to determine the levels' lifetime. Our work provides a basis for characterizing KPO networks for simulated annealing where an accurate determination of the state lifetime is of fundamental importance.

cond-mat.mes-hall↗