The surface states of 3D topological insulators possess geometric structures that imprint distinctive signatures on electronic transport. A prime example is the Berry curvature, which controls electric frequency doubling via a higher order moment, called Berry curvature triple. In addition to the Berry curvature, topological surface states are expected to exhibit a nontrivial quantum metric, which plays a key role in governing nonlinear magnetotransport. However, its manifestation has yet to be experimentally observed in 3D topological insulators. Here, we provide evidence for a nonlinear response activated by the quantum metric of the topological surface states of Sb$_2$Te$_3$. We measure a time-reversal odd, nonlinear magnetoresistance that is independent of temperature and disorder below 30 K and is thus of intrinsic geometrical origin. Our measurements demonstrate the existence of quantum geometry-induced transport in topological phases of matter and provide strategies for designing novel functionalities in topological devices.

Ultrastrong coupling may allow faster operations for the development of quantum technologies at the expenses of increased sensitivity to new kind of intrinsic errors. We study state transfer in superconducting circuit QED architectures in the ultrastrong coupling regime. Using optimal control methods we find a protocol resilient to the main source of errors, coming from the interplay of the dynamical Casimir effect with cavity losses.

We introduce and validate a machine-learning assisted quantum sensing protocol to classify spatial and temporal correlations of classical noise affecting two ultrastrongly coupled qubits. We consider six distinct classes of Markovian and non-Markovian noise. Leveraging the sensitivity of a coherent population transfer protocol under three distinct driving conditions, the various forms of noise are discriminated by only measuring the final transfer efficiencies. Our approach achieves $\gtrsim 86\%$ accuracy in classification providing a near-perfect discrimination between Markovian and non-Markovian noise. The method requires minimal experimental resources, relying on a simple driving scheme providing three inputs to a shallow neural network with no need of measuring time-series data or real-time monitoring. The machine-learning data analysis acquires information from non-idealities of the coherent protocol highlighting how combining these techniques may significantly improve the characterization of quantum-hardware.

Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges. We hope that this Roadmap will be a useful resource to readers outside this field, for those who are just entering the field, and for those who are well established in the neuromorphic community. https://doi.org/10.1088/2634-4386/ac4a83

Ideal photonic quantum memories can store arbitrary pulses of light with unit efficiency. This requires operating in the adiabatic regime, where pulses have a duration much longer than the bandwidth of the memory. In the non-adiabatic regime of short pulses, memories are therefore imperfect, and information is always lost. We theoretically investigate the bandwidth limitations for setups based on individual atoms, or ensembles thereof, confined inside optical cavities. We identify an effective strategy for optimizing the efficiencies of the storage and retrieval process regardless of the duration of the pulses. Our protocol is derived almost completely analytically and attains efficiencies better than or comparable to those obtained by numerical optimization. Furthermore, our results provide an improved understanding of the performance of quantum memories in several regimes. When considering pulses defined on an infinite time interval, the shapes can be divided into two categories, depending on their asymptotic behaviours. If the intensity of the pulse increases with time slower than or as an exponential function, then the storage efficiency is only limited by the pulse width. For pulses defined on a finite interval, on the other hand, the efficiency is determined by the shape at the beginning of the storage or, correspondingly, at the end of the retrieval process.

Data show that the presence of women in quantum science is affected by a number of detriments and their percentage decreases even further for higher positions. Beyond data, from our shared personal experiences as female tenured quantum physics professors, we believe that the current model of scientific leadership, funding, and authority fails to represent many of us. It is time for a real change that calls for a different kind of force and for the participation of everyone. Women for quantum calls for a joint effort and aims with this initiative to contribute to such a transformation.

We analyze the efficiency of protocols for adiabatic quantum state transfer assisted by an engineered reservoir. The target dynamics is a quantum trajectory in the Hilbert space and is a fixed point of a time-dependent master equation in the limit of adiabatic dynamics. We specialize to quantum state transfer in a qubit and determine the optimal schedule for a class of time-dependent Lindblad equations. The speed limit on state transfer is extracted from a physical model of a qubit coupled to a reservoir, from which the Lindblad equation is derived in the Born-Markov limit. Our analysis shows that the resulting efficiency is comparable to the efficiency of the optimal unitary dynamics. Numerical studies indicate that reservoir-engineered protocols could outperform unitary protocols outside the regime of the Born-Markov master equation, namely, when correlations between the qubit and reservoir become relevant. Our study contributes to the theory of shortcuts to adiabaticity for open quantum systems and to the toolbox of protocols of the NISQ era.

Fundamental issues of 1/f noise in quantum nanoscience are reviewed starting from basic statistical noise processes. Fundamental noise models based on two-level systems (TLS) are described. We emphasize the importance of TLSs in materials parameter fluctuations, such as dielectric constant. The present understanding of 1/f noise in superconducting quantum interferometers and in single electron devices is summarized. For coherent quantum nanoscience, we introduce superconducting qubits and the relation between decoherence and 1/f noise using the filter function formulation. We also clarify the qubit noise spectroscopy and emphasize the importance of materials with reduced 1/f noise for future quantum coherent nanodevices.

Spiking Neural Networks (SNNs) can unleash the full power of analog Resistive Random Access Memories (RRAMs) based circuits for low power signal processing. Their inherent computational sparsity naturally results in energy efficiency benefits. The main challenge implementing robust SNNs is the intrinsic variability (heterogeneity) of both analog CMOS circuits and RRAM technology. In this work, we assessed the performance and variability of RRAM-based neuromorphic circuits that were designed and fabricated using a 130\,nm technology node. Based on these results, we propose a Neuromorphic Hardware Calibrated (NHC) SNN, where the learning circuits are calibrated on the measured data. We show that by taking into account the measured heterogeneity characteristics in the off-chip learning phase, the NHC SNN self-corrects its hardware non-idealities and learns to solve benchmark tasks with high accuracy. This work demonstrates how to cope with the heterogeneity of neurons and synapses for increasing classification accuracy in temporal tasks.

For semiconductor device fabrication, Pulsed Laser Annealing (PLA) offers significant advantages over conventional thermal processes. Notably, it can provide ultrafast (~ns) and high temperature profiles ($>1000^\circ$C). When the maximum temperature exceeds the melting point, a solid-liquid phase transition is observed, immediately followed by rapid recrystallization. This unique annealing mechanism gives raises questions about dopant diffusion and residual defects, in not only in the recrystallized region, but also just below it. As power devices require micrometer-sized junctions, high laser energy densities are needed, which were proved to promote the incorporation of complex impurities from the surface and the creation of defects at the liquid/solid interface. This paper reports on the impact of laser annealing at high energy densities (up to 8.0 J/cm$^2$) on the leakage current, using Schottky and PN diodes, and DLTS measurements. Various laser annealing conditions were used: energy densities between 1.7 and 8.0 J/cm$^2$ with 1 to 10 pulses. Our results suggest that the liquid and solid solubility of vacancies in silicon are fixed by the maximum temperature reached, so to the energy density. Increasing the number of laser pulses allows, not only to reach this maximum vacancy concentration but also to promote their diffusion towards the surface. Concomitantly, the in-diffusion of complex impurities inside the melted region allows the coupling between both defect types to create trap centers, responsible for the degradation of the leakage current.

The Atom-Calibrated Basis-set Extrapolation (ACBE) method is introduced as a robust approach for extrapolating MP2 correlation energies from small basis sets. Unlike conventional extrapolation techniques, ACBE incorporates system- and environment-specific parameters to enhance predictive accuracy, effectively mitigating errors associated with finite basis sets. Evaluated using the aug-cc-pwCV$n$Z basis set family across a diverse set of molecular systems, including first- and second-row species, ACBE consistently delivers reliable energy estimates, even when double- and triple-zeta basis sets are employed. These results highlight the computational efficiency of the method, making it a promising option for large MP2 studies.

Human brain processes sensory information in real-time with extraordinary efficiency compared to the possibilities of current artificial computing systems. It operates as a complex nonlinear system, composed of interacting dynamic units - neurons and synapses - that processes data-streams as time goes by, i.e. through time, using time as an internal self-standing variable. Here we report on a memristor-based compact chaotic circuit included in a computing architecture that can process information through time. We realized a hardware memristive version of the formally simplest chaotic circuit that, thanks to the nonlinearity of the nonvolatile memristor device, evolves with complex dynamics in response to a driving signal. The circuit is used in a single-node reservoir computing scheme to demonstrate nonlinear classification tasks and the processing of data streams through time. These results demonstrate that a simple memristor-based chaotic circuit has the potential to operate as a nonlinear dynamics-based computing system and to process temporal information through time.

Range-separated hybrid functionals (RSH) with ``ionization energy'' and/or ``optimal tuning'' of the screening parameter have proven to be among the most practical and accurate approaches for describing excited-state properties across a wide range of systems, including condensed matter. However, this method typically requires multiple self-consistent calculations and can become computationally expensive and unstable, particularly for extended systems. In this work, we propose a very simple and efficient alternative approach to determine the screening parameter for RSH based solely on the total electron density of the system and the compressibility sum rule of density functional theory (DFT). This effective screening parameter achieves remarkable accuracy, particularly for charge-transfer excitations, surpassing the performance of previously suggested alternatives. Because it relies only on the electron density, the proposed approach is physically transparent and highly practical to automate DFT calculations in large and complex systems, including bulk solids, where ``tuning'' is not possible.

Three-dimensional topological insulators possess topologically protected surface states with spin-momentum locking, which enable spin-charge-current interconversion (SCI) by the inverse Edelstein effect (IEE). However, it remains experimentally challenging to separate the surface-related IEE from the bulk-type inverse spin Hall effect (ISHE). Here, we search for distinct time-domain signatures of the two SCI phenomena in a $\mathcal{F}$|TI model stack of a ferromagnetic-metal layer $\mathcal{F}$ (Co and Fe) and a topological-insulator layer TI (Bi$_2$Te$_3$, SnBi$_2$Te$_4$ and Bi$_{1-x}$Sb$_x$ with $x$ = 0.15 and 0.3), where the focus is on Bi$_2$Te$_3$. A femtosecond laser pulse serves to induce a transient spin voltage $\mu_s^{\mathcal{F}}$ in $\mathcal{F}$ and, thus, drive an ultrafast spin current out of $\mathcal{F}$. SCI results in a transverse charge current with a sheet density $I_c$ that is detected by sampling the emitted terahertz electric field. Analysis of the dynamics of $I_c(t)$ vs time $t$ relative to $\mu_s^{\mathcal{F}}(t)$ reveals two components with distinct time scales: (i) a quasi-instantaneous response and (ii) a longer-lived response with a relaxation time of 270 fs, which is independent of the chosen $\mathcal{F}$ material. Component (i) is consistently ascribed to the ISHE. In contrast, we interpret component (ii) as a signature of interfacial spin accumulation and the IEE at the $\mathcal{F}$/Bi$_2$Te$_3$ interface, with a fraction of < 10^{-2} of the incident spins participating. This assignment is fully consistent with respect to its dynamics and magnitude. We rate other possible signal contributions, such as spin trapping in intermediate states, as less likely. Our results show that the femtosecond dynamics of photocurrents provide important insights into the mechanisms of spin transport and SCI in $\mathcal{F}$|TI stacks.

A qubit-oscillator junction connecting as a series two bosonic heat baths at different temperatures can display heat valve and diode effects. In particular, the rectification can change in magnitude and even in sign, implying an inversion of the preferential direction for the heat current with respect to the temperature bias. We perform a systematic study of these effects in a circuit QED model of qubit-oscillator system and find that the features of current and rectification crucially depend on the qubit-oscillator coupling. While at small coupling, transport occurs via a resonant mechanism between the sub-systems, in the ultrastrong coupling regime the junction is a unique, highly hybridized system and the current becomes largely insensitive to the detuning. Correspondingly, the rectification undergoes a change of sign. In the nonlinear transport regime, the coupling strength determines whether the current scales sub- or super-linearly with the temperature bias and whether the rectification, which increases in magnitude with the bias, is positive or negative. We also find that steady-state coherence largely suppresses the current and enhances rectification. An insight on these behaviors with respect to changes in the system parameters is provided by analytical approximate formulas.

Short ballistic graphene Josephson junctions sustain superconducting current with a non-sinusoidal current-phase relation up to a critical current threshold. The current-phase relation, arising from proximitized superconductivity, is gate-voltage tunable and exhibits peculiar skewness observed in high quality graphene superconductors heterostructures with clean interfaces. These properties make graphene Josephson junctions promising sensitive quantum probes of microscopic fluctuations underlying transport in two-dimensions. We show that the power spectrum of the critical current fluctuations has a characteristic $1/f$ dependence on frequency, $f$, probing two points and higher correlations of carrier density fluctuations of the graphene channel induced by carrier traps in the nearby substrate. Tunability with the Fermi level, close to and far from the charge neutrality point, and temperature dependence of the noise amplitude are clear fingerprints of the underlying material-inherent processes. Our results suggest a roadmap for the analysis of decoherence sources in the implementation of coherent devices by hybrid nanostructures.

Light-matter interaction and understanding the fundamental physics behind is essential for emerging quantum technologies. Solid-state devices may explore new regimes where coupling strengths are "ultrastrong", i.e., comparable to the energies of the subsystems. New exotic phenomena occur the common root of many of them being the fact that the entangled vacuum contains virtual photons. They herald the lack of conservation of the number of excitations which is the witness of ultrastrong coupling breaking the U(1) symmetry. Despite more than a decade of research, the detection of ground-state virtual photons still awaits demonstration. In this work, we recognize the "conspiring" set of experimental challenges and show how to overcome them, thus providing a solution to this long-standing problem. We find that combining a superinductor-based unconventional "light fluxonium" qudit and coherent control yields a highly efficient, faithful, and selective conversion of virtual photons into real ones. This enables their detection with resources available to present-day quantum technologies.

We address estimation of the minimum length arising from gravitational theories. In particular, we provide bounds on precision and assess the use of quantum probes to enhance the estimation performances. At first, we review the concept of minimum length and show how it induces a perturbative term appearing in the Hamiltonian of any quantum system, which is proportional to a parameter depending on the minimum length. We then systematically study the effects of this perturbation on different state preparations for several 1-dimensional systems, and we evaluate the Quantum Fisher Information in order to find the ultimate bounds to the precision of any estimation procedure. Eventually, we investigate the role of dimensionality by analysing the use of two-dimensional square well and harmonic oscillator systems to probe the minimal length. Our results show that quantum probes are convenient resources, providing potential enhancement in precision. Additionally, our results provide a set of guidelines to design possible future experiments to detect minimal length.

We address estimation of the minimum length arising from gravitational theories. In particular, we provide bounds on precision and assess the use of quantum probes to enhance the estimation performances. At first, we review the concept of minimum length and show how it induces a perturbative term appearing in the Hamiltonian of any quantum system, which is proportional to a parameter depending on the minimum length. We then systematically study the effects of this perturbation on different state preparations for several 1-dimensional systems, and we evaluate the Quantum Fisher Information in order to find the ultimate bounds to the precision of any estimation procedure. Eventually, we investigate the role of dimensionality by analysing the use of two-dimensional square well and harmonic oscillator systems to probe the minimal length. Our results show that quantum probes are convenient resources, providing potential enhancement in precision. Additionally, our results provide a set of guidelines to design possible future experiments to detect minimal length.