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.

Digital memcomputing machines (DMMs) have been designed to solve complex combinatorial optimization problems. Since DMMs are fundamentally classical dynamical systems, their ordinary differential equations (ODEs) can be efficiently simulated on modern computers. This provides a unique platform to study their performance under various conditions. An aspect that has received little attention so far is how their performance is affected by the numerical errors in the solution of their ODEs and the physical noise they would be naturally subject to if built in hardware. Here, we analyze these two aspects in detail by varying the integration time step (numerical noise) and adding stochastic perturbations (physical noise) into the equations of DMMs. We are particularly interested in understanding how noise induces a chaotic transition that marks the shift from successful problem-solving to failure in these systems. Our study includes an analysis of power spectra and Lyapunov exponents depending on the noise strength. The results reveal a correlation between the instance solvability and the sign of the ensemble averaged mean largest Lyapunov exponent. Interestingly, we find a regime in which DMMs with positive mean largest Lyapunov exponents still exhibit solvability. Furthermore, the power spectra provide additional information about our system by distinguishing between regular behavior (peaks) and chaotic behavior (broadband spectrum). Therefore, power spectra could be utilized to control whether a DMM operates in the optimal dynamical regime. Overall, we find that the qualitative effects of numerical and physical noise are mostly similar, despite their fundamentally different origin.

Halide perovskites have enabled major advances in optoelectronics, extending well beyond photovoltaics. Their mixed ionic-electronic conduction, once regarded as detrimental to device stability, is increasingly viewed as a functional degree of freedom for memory and neuromorphic-inspired devices, especially when coupled to external stimuli such as light. Specifically, single crystals are attractive models because they suppress grain-boundary effects and microstructural disorder that can mask intrinsic transport and interfacial mechanisms in polycrystalline films. Here, we report the growth of thin methylammonium lead iodide (MAPbI_3) single crystals by a space-confined method and their integration into planar two-terminal devices with directly deposited Ag contacts. At room temperature, the devices exhibit ultra-low dark currents (10^-13-10^-12 A) and negligible hysteresis in the dark. Under illumination, the current increases due to photogeneration and the I-V characteristics develop a pronounced polarity-dependent hysteresis and a threshold-like transition between two conductance states. Temperature-dependent dark measurements (300-400 K) show thermionically activated, contact-influenced transport and a weakly varying normalized hysteresis metric. Together with the back-to-back Schottky-diode analysis and control devices using more inert contact materials, these results support a transport model in which Ag/perovskite interfaces play a central role and the hysteretic response is influenced by coupled interfacial and ionic processes.

Digital Memcomputing machines (DMMs) are dynamical systems with memory (time non-locality) that have been designed to solve combinatorial optimization problems. Their corresponding ordinary differential equations depend on a few hyper-parameters that define both the system's relevant time scales and its phase-space geometry. Using numerical simulations on a prototypical DMM, we analyze the role of these physical parameters in engineering the phase space to either help or hinder the solution search by DMMs. We find that the DMM explores its phase space efficiently for a wide range of parameters, aided by the system-wide correlations in their fast degrees of freedom that emerge dynamically due to coupling with the (slow) memory degrees of freedom. In this regime, the time it takes for the system to find a solution scales well as the number of variables increases. When these hyper-parameters are chosen poorly, the system navigates its phase space far less efficiently. However, we find that, in many cases, collective behavior persists even when the phase-space exploration process is inefficient. This behavior only disappears if the memories are made to evolve as quickly as the fast degrees of freedom. This study points to the important role of memory and hyper-parameters in engineering the DMMs' phase space for optimal computational efficiency.

Neural manifolds refer to the low-dimensional shapes formed by populations of spiking neurons. This intrinsic population geometry can be physically emulated by electronic devices, facilitating near-training-free forecasting with exceptional energy efficiency.

Recent research has shown that memory, in the form of slow degrees of freedom, can induce a phase of long-range order (LRO) in locally-coupled fast degrees of freedom, producing power-law distributions of avalanches. In fact, such memory-induced LRO (MILRO) arises in a wide range of physical systems. Here, we show that MILRO can be transferred to coupled systems that have no memory of their own. As an example, we consider a stack of layers of spins with local feedforward couplings: only the first layer contains memory, while downstream layers are memory-free and locally interacting. Analytical arguments and simulations reveal that MILRO can indeed drag across the layers, enabling downstream layers to sustain intra-layer LRO despite having neither memory nor long-range interactions. This establishes a simple, yet generic mechanism for propagating collective activity through media without fine tuning to criticality, with testable implications for neuromorphic systems and laminar information flow in the brain cortex.

Synchronization is conventionally regarded as a mechanism for suppressing variability and enforcing order in coupled systems, from pendula and lasers to neurons and electronic oscillators. Here, we show that synchronization can also embed stochasticity at finer scales. We observe this phenomenon in thermally coupled VO2 neuristors, where robust in-phase synchronization at the microsecond scale coexists with spike onset fluctuations at the nanosecond scale, with no fixed leader. The coexistence of order and disorder originates from stochastic domain-level physics of the insulator-metal and metal-insulator transitions, where local variations in transition temperature drive cycle-to-cycle randomness in nucleation, percolation, and relaxation. A stochastic domain model reproduces this effect by generating synchronized spike trains with random lead-lag jitter, and experimental interspike interval statistics confirm the persistence of fine-scale variability despite macroscopic phase locking. These findings establish that synchronization and stochasticity can coexist within the same physical platform, revealing hidden disorder within collective order. This insight reframes synchronization as not purely deterministic, but as a universal context where microscopic variability can persist, with implications for electronics, cryptography, and the fundamental physics of order-disorder coexistence.

Time non-locality, or memory, is a non-equilibrium property shared by all physical systems. Here, we show that memory is sufficient to induce a phase of spatial long-range order (LRO) even if the system's primary dynamical variables are coupled locally. This occurs when the memory degrees of freedom have slower dynamics than the primary degrees of freedom. In addition, such an LRO phase is non-perturbative, and can be understood through the lens of a correlated percolation transition of the fast degrees of freedom mediated by memory. When the two degrees of freedom have comparable time scales, the length of the effective long-range interaction shortens. We exemplify this behavior with a model of locally coupled spins and a single dynamic memory variable, but our analysis is sufficiently general to suggest that memory could induce a phase of LRO in a much wider variety of physical systems.

Transition-metal (TM) atom-functionalized nanomaterials are promising candidates for hydrogen storage due to their ability to adsorb multiple hydrogen molecules through Kubas interactions. However, achieving efficient hydrogen desorption at ambient conditions remains a critical challenge for practical use. Here, we present a novel approach to modulate the desorption temperature of hydrogen in TM-intercalated bilayer graphene (BLG) using external mechanical forces. By employing first-principles density functional theory and thermodynamic occupancy probability calculations, we demonstrate that adjusting the interlayer distance allows for precise control over the interaction energy of H2, thereby facilitating its desorption at ambient conditions. Complete hydrogen desorption occurs when the interlayer distance is reduced below 4.7, 5.3, and 5.1 & Aring; for Sc-, Ti-, and V-intercalated BLG, respectively. Our findings suggest that external mechanical forces can effectively bring hydrogen occupancy to zero by minimizing charge transfer from the TM d-orbitals to H2 antibonding orbitals. Notably, while the total charge transferred from the TM atoms remains nearly constant at varying interlayer distances, its redistribution between the graphene layers and H2 fine-tunes the interaction strength. This approach can be extended to large interlayer distances, as supported by recent experiments on graphene oxide membranes [ACS Nano 12, 9309 (2018)]. Furthermore, recent experimental advances in noble gas and alkali-metal intercalation in BLG highlight the potential of this approach to overcome the long-standing challenge of high desorption temperatures in TM-functionalized layered nanomaterials.

In-memory computing is a promising alternative to traditional computer designs, as it helps overcome performance limits caused by the separation of memory and processing units. However, many current approaches struggle with unreliable device behavior, which affects data accuracy and efficiency. In this work, the authors present a new computing method that combines two types of operations-those based on electrical resistance and those based on voltage-within each memory cell. This design improves reliability and avoids the need for expensive current measurements. A new software tool also helps automate the design process, supporting highly parallel operations in dense two-dimensional memory arrays. The approach balances speed and space, making it practical for advanced computing tasks. Demonstrations include a digital adder and a key part of encryption module, showing both strong performance and accuracy. This work offers a new direction for reliable and efficient in-memory computing systems with real-world applications.

The "criticality hypothesis," based on observed scale-free correlations in neural activity, posits that the brain operates at a critical point of transition between two phases. However, the validity of this hypothesis is still debated. Here, employing a commonly used model of cortical dynamics, we find that a phase of long-range order (LRO) in neural activity may be induced by memory (time nonlocality) without invoking criticality. The corticaldynamics model contains fast and slow time scales that govern the neural and resource (memory) dynamics, respectively. When the resource dynamics are sufficiently slow, we observe an LRO phase, which manifests in avalanche size and duration probability distributions that are fit well by power laws. When the slow and fast time scales are no longer sufficiently distinct, LRO is destroyed. Since this LRO phase spans a wide range of parameters, it is robust against perturbations, unlike critical systems.

We show that a spinor traveling along a one-dimensional helical path develops a spin-orbit coupling as a result of the curvature of the path. We estimate the magnitude of the associated spin polarization and obtain values typical of many helical molecular structures that showcase the Chirality-induced Spin Selectivity (CISS) effect. We find that this chirality-induced spin-orbit coupling (χ-SOC), in conjunction with broken time-reversal symmetry, may be an important ingredient for the microscopic underpinning of the CISS phenomenon.

Digital MemComputing machines (DMMs), which employ nonlinear dynamical systems with memory (time non-locality), have proven to be a robust and scalable unconventional computing approach for solving a wide variety of combinatorial optimization problems. However, most of the research so far has focused on the numerical simulations of the equations of motion of DMMs. This inevitably subjects time to discretization, which brings its own (numerical) issues that would be otherwise absent in actual physical systems operating in continuous time. Although hardware realizations of DMMs have been previously suggested, their implementation would require materials and devices that are not so easy to integrate with traditional electronics. Addressing this, our study introduces a novel hardware design for DMMs, utilizing readily available electronic components. This approach not only significantly boosts computational speed compared to current models but also exhibits remarkable robustness against additive noise. Crucially, it circumvents the limitations imposed by numerical noise, ensuring enhanced stability and reliability during extended operations. This paves a new path for tackling increasingly complex problems, leveraging the inherent advantages of DMMs in a more practical and accessible framework.

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.

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.

In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore's Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed “thermal neuristors.” These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical behavior of networks of these neurons showcases a rich phase structure, tunable by adjusting the thermal coupling and input voltage. Notably, we identify phases exhibiting long-range order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.