arXiv subjects

Massimiliano Di Ventra

Publications and source records attributed to Massimiliano Di Ventra.

At least 37 records · Page 2Linked to original sources

arXiv2023

Nanofluidic logic with mechano-ionic memristive switches

While most neuromorphic systems are based on nanoscale electronic devices, nature relies on ions for energy-efficient information processing. Therefore, finding memristive nanofluidic devices is a milestone toward realizing electrolytic computers mimicking the brain down to its basic principles of operation. Here, we present a nanofluidic device designed for circuit scale in-memory processing that combines single-digit nanometric confinement and large entrance asymmetry. Our fabrication process is scalable while the device operates at the second timescale with a conductance ratio in the range 10-60. In-operando optical microscopy unveils the origin of memory, arising from the reversible formation of liquid blisters modulating the device conductance. The combination of features of these mechano-ionic memristive switches permits assembling logic circuits composed of two interactive devices and an ohmic resistor. These results open the way to design multi-component ionic machinery, such as nanofluidic neural networks, and implementing brain-inspired ionic computations.

cond-mat.soft↗

arXiv2023

Reconfigurable cascaded thermal neuristors for neuromorphic computing

While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, we explore an alternative route based on a new class of spiking oscillators we call thermal neuristors, which operate and interact solely via thermal processes. Utilizing the insulator-to-metal transition in vanadium dioxide, we demonstrate a wide variety of reconfigurable electrical dynamics mirroring biological neurons. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. This study establishes the foundation for scalable and energy-efficient thermal neural networks, fostering progress in brain-inspired computing.

cs.ET↗

arXiv2023

Self-averaging of digital memcomputing machines

Digital memcomputing machines (DMMs) are a new class of computing machines that employ non-quantum dynamical systems with memory to solve combinatorial optimization problems. Here, we show that the time to solution (TTS) of DMMs follows an inverse Gaussian distribution, with the TTS self-averaging with increasing problem size, irrespective of the problem they solve. We provide both an analytical understanding of this phenomenon and numerical evidence by solving instances of the 3-SAT (satisfiability) problem. The self-averaging property of DMMs with problem size implies that they are increasingly insensitive to the detailed features of the instances they solve. This is in sharp contrast to traditional algorithms applied to the same problems, illustrating another advantage of this physics-based approach to computation.

cs.ET↗

arXiv2023

Hardware implementation of digital memcomputing on small-size FPGAs

Memcomputing is a novel computing paradigm beyond the von-Neumann one. Its digital version is designed for the efficient solution of combinatorial optimization problems, which emerge in various fields of science and technology. Previously, the performance of digital memcomputing machines (DMMs) was demonstrated using software simulations of their ordinary differential equations. Here, we present the first hardware realization of a DMM algorithm on a low-cost FPGA board. In this demonstration, we have implemented a Boolean satisfiability problem solver. To optimize the use of hardware resources, the algorithm was partially parallelized. The scalability of the present implementation is explored and our FPGA-based results are compared to those obtained using a python code running on a traditional (von-Neumann) computer, showing one to two orders of magnitude speed-up in time to solution. This initial small-scale implementation is projected to state-of-the-art FPGA boards anticipating further advantages of the hardware realization of DMMs over their software emulation.

cs.ET↗

arXiv2023

Transformer Quantum State: A Multi-Purpose Model for Quantum Many-Body Problems

Inspired by the advancements in large language models based on transformers, we introduce the transformer quantum state (TQS): a versatile machine learning model for quantum many-body problems. In sharp contrast to Hamiltonian/task specific models, TQS can generate the entire phase diagram, predict field strengths with experimental measurements, and transfer such a knowledge to new systems it has never been trained on before, all within a single model. With specific tasks, fine-tuning the TQS produces accurate results with small computational cost. Versatile by design, TQS can be easily adapted to new tasks, thereby pointing towards a general-purpose model for various challenging quantum problems.

quant-ph↗

arXiv2023

Brain-like features of MemComputing machines

MemComputing is a new model of computation that exploits the non-equilibrium property-we call 'memory'-of any physical system to respond to external perturbations by keeping track of how it has reacted at previous times. Its digital, scalable version maps a finite string of symbols into a finite string of symbols. In this paper, I will discuss some analogies of the operation of MemComputing machines-in general, and digital in particular-with a few physical properties of the biological brain. These analogies could be a source of inspiration to improve on the design of these machines. In turn, they could suggest new directions of study in (computational) neuroscience.

cond-mat.dis-nn↗

arXiv2022

Efficient Quantum State Tomography with Mode-assisted Training

Neural networks (NNs) representing quantum states are typically trained using Markov chain Monte Carlo based methods. However, unless specifically designed, such samplers only consist of local moves, making the slow-mixing problem prominent even for extremely simple quantum states. Here, we propose to use mode-assisted training that provides global information via the modes of the NN distribution. Applied to quantum state tomography using restricted Boltzmann machines, this method improves the quality of reconstructed quantum states by orders of magnitude. The method is applicable to other types of NNs and may efficiently tackle problems previously unmanageable.

quant-ph↗

arXiv2022

A Finite-temperature Phase Transition for the Ising Spin-glass in $d\geq 2$

It is believed that the $\pm J$ Ising spin-glass does not order at finite temperatures in dimension $d=2$. However, using a graphical representation and a contour argument, we prove rigorously the existence of a finite-temperature phase transition in $d\geq 2$ with $T_c \geq 0.4$. In the graphical representation, the low-temperature phase allows for the coexistence of multiple infinite clusters each with a rigidly aligned spin-overlap state. These clusters correlate negatively with each other, and are entropically stable without breaking any global symmetry. They can emerge in most graph structures and disorder measures.

cond-mat.dis-nn↗

arXiv2022

MemComputing vs. Quantum Computing: some analogies and major differences

Quantum computing employs some quantum phenomena to process information. It has been hailed as the future of computing but it is plagued by serious hurdles when it comes to its practical realization. MemComputing is a new paradigm that instead employs non-quantum dynamical systems and exploits time non-locality (memory) to compute. It can be efficiently emulated in software and its path towards hardware is more straightforward. I will discuss some analogies between these two computing paradigms, and the major differences that set them apart.

cs.ET↗

arXiv2022

Kondo cooling in quantum impurity systems

The Peltier effect is the reverse phenomenon of the Seebeck effect, and has been observed experimentally in nanoscale junctions. However, despite its promising applications in local cooling of nanoelectronic devices, the role of strong electron correlations on such a phenomenon is still unclear. Here, by analyzing the thermoelectric properties of quantum impurity systems out of equilibrium, we unveil the essential role of electron-electron interactions and quantum resonant states in Peltier cooling, leading to the prediction of the Kondo cooling phenomenon. The existence of such Kondo cooling is validated by a reverse heat current and a lowered local temperature in a model junction. The discovery of this unconventional Peltier cooling offers a new approach toward nano-refrigeration, and highlights the unique role of strong electron correlations in nonequilibrium quantum systems.

cond-mat.mes-hall↗

arXiv2022

Custodial chiral symmetry in a Su-Schrieffer-Heeger electrical circuit with memory

Custodial symmetries are common in the Standard Model of particle physics. They arise when quantum corrections to a parameter are proportional to the parameter itself. Here, we show that a custodial symmetry of the chiral type is also present in a classical Su-Schrieffer-Heeger (SSH) electrical circuit with memory (memcircuit). In the absence of memory, the SSH circuit supports a symmetry-protected topological edge state. Memory induces nonlinearities that break chiral symmetry explicitly and spreads the state across the circuit. However, the resulting state is still protected against perturbations by the ensuing custodial chiral symmetry. These predictions can be verified experimentally and demonstrate the interplay between symmetry and memory.

cond-mat.mes-hall↗

arXiv2022

Antiferromagnetic parametric resonance driven by voltage-controlled magnetic anisotropy

Voltage controlled magnetic anisotropy (VCMA) is a low-energy alternative to manipulate the ferromagnetic state, which has been recently considered also in antiferromagnets (AFMs). Here, we theoretically demonstrate that VCMA can be used to excite linear and parametric resonant modes in easy-axis AFMs with perpendicular anisotropy, thus opening the way for an efficient electrical control of the Neel vector, and for detection of high-frequency dynamics. Our work leads to two key results: (i) VCMA parametric pumping experiences the so-called exchange enhancement of the coupling efficiency and, thus, is 1-2 orders of magnitude more efficient than microwave magnetic fields or spin-orbit-torques, and (ii) it also allows for zero-field parametric resonance, which cannot be achieved by other parametric pumping mechanisms in AFMs with out-of-plane easy axis. Therefore, we demonstrate that the VCMA parametric pumping is the most promising method for coherent excitation and manipulation of AFM order in perpendicular easy-axis AFMs.

cond-mat.mes-hall↗

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

Thousand-fold Increase in Plasmonic Light Emission via Combined Electronic and Optical Excitations

Surface plasmon enhanced processes and hot-carrier dynamics in plasmonic nanostructures are of great fundamental interest to reveal light-matter interactions at the nanoscale. Using plasmonic tunnel junctions as a platform supporting both electrically- and optically excited localized surface plasmons, we report a much greater (over 1000x) plasmonic light emission at upconverted photon energies under combined electro-optical excitation, compared with electrical or optical excitation separately. Two mechanisms compatible with the form of the observed spectra are interactions of plasmon-induced hot carriers and electronic anti-Stokes Raman scattering. Our measurement results are in excellent agreement with a theoretical model combining electro-optical generation of hot carriers through non-radiative plasmon excitation and hot-carrier relaxation. We also discuss the challenge of distinguishing relative contributions of hot carrier emission and the anti-Stokes electronic Raman process. This observed increase in above-threshold emission in plasmonic systems may open avenues in on-chip nanophotonic switching and hot carrier photocatalysis.

physics.optics↗

arXiv2021

Directed percolation and numerical stability of simulations of digital memcomputing machines

Digital memcomputing machines (DMMs) are a novel, non-Turing class of machines designed to solve combinatorial optimization problems. They can be physically realized with continuous-time, non-quantum dynamical systems with memory (time non-locality), whose ordinary differential equations (ODEs) can be numerically integrated on modern computers. Solutions of many hard problems have been reported by numerically integrating the ODEs of DMMs, showing substantial advantages over state-of-the-art solvers. To investigate the reasons behind the robustness and effectiveness of this method, we employ three explicit integration schemes (forward Euler, trapezoid and Runge-Kutta 4th order) with a constant time step, to solve 3-SAT instances with planted solutions. We show that, (i) even if most of the trajectories in the phase space are destroyed by numerical noise, the solution can still be achieved; (ii) the forward Euler method, although having the largest numerical error, solves the instances in the least amount of function evaluations; and (iii) when increasing the integration time step, the system undergoes a "solvable-unsolvable transition" at a critical threshold, which needs to decay at most as a power law with the problem size, to control the numerical errors. To explain these results, we model the dynamical behavior of DMMs as directed percolation of the state trajectory in the phase space in the presence of noise. This viewpoint clarifies the reasons behind their numerical robustness and provides an analytical understanding of the unsolvable-solvable transition. These results land further support to the usefulness of DMMs in the solution of hard combinatorial optimization problems.

cs.ET↗

arXiv2021

Non-equilibrium criticality and efficient exploration of glassy landscapes with memory dynamics

Spin glasses are notoriously difficult to study both analytically and numerically due to the presence of frustration and metastability. Their highly non-convex landscapes require collective updates to explore efficiently. Currently, most state-of-the-art algorithms rely on stochastic spin clusters to perform non-local updates, but such "cluster algorithms" lack general efficiency. Here, we introduce a non-equilibrium approach for simulating spin glasses based on classical dynamics with memory. By simulating various classes of 3d spin glasses (Edwards-Anderson, partially-frustrated, and fully-frustrated models), we find that memory dynamically promotes critical spin clusters during time evolution, in a self-organizing manner. This facilitates an efficient exploration of the low-temperature phases of spin glasses.

cond-mat.dis-nn↗

arXiv2021

Mode-Assisted Joint Training of Deep Boltzmann Machines

The deep extension of the restricted Boltzmann machine (RBM), known as the deep Boltzmann machine (DBM), is an expressive family of machine learning models which can serve as compact representations of complex probability distributions. However, jointly training DBMs in the unsupervised setting has proven to be a formidable task. A recent technique we have proposed, called mode-assisted training, has shown great success in improving the unsupervised training of RBMs. Here, we show that the performance gains of the mode-assisted training are even more dramatic for DBMs. In fact, DBMs jointly trained with the mode-assisted algorithm can represent the same data set with orders of magnitude lower number of total parameters compared to state-of-the-art training procedures and even with respect to RBMs, provided a fan-in network topology is also introduced. This substantial saving in number of parameters makes this training method very appealing also for hardware implementations.

cs.LG↗

arXiv2021

Effect of quantum resonances on local temperature in nonequilibrium open systems

Measuring local temperatures of open systems out of equilibrium is emerging as a novel approach to study the local thermodynamic properties of nanosystems. An operational protocol has been proposed to determine the local temperature by coupling a probe to the system and then minimizing the perturbation to a certain local observable of the probed system. In this paper, we first show that such a local temperature is unique for a single quantum impurity and the given local observable. We then extend this protocol to open systems consisting of multiple quantum impurities by proposing a local minimal perturbation condition (LMPC). The influence of quantum resonances on the local temperature is elucidated by both analytic and numerical results. In particular, we demonstrate that quantum resonances may give rise to strong oscillations of the local temperature along a multiimpurity chain under a thermal bias.

cond-mat.mes-hall↗