Search

X-ray polarization is a unique new probe of the particle acceleration in astrophysical jets made possible through the Imaging X-ray Polarimetry Explorer. Here we report on the first dense X-ray polarization monitoring campaign on the blazar Mrk 421. Our observations were accompanied by an even denser radio and optical polarization campaign. We find significant short-timescale variability in both X-ray polarization degree and angle, including a $\sim90^\circ$ angle rotation about the jet axis. We attribute this to random variations of the magnetic field, consistent with the presence of turbulence but also unlikely to be explained by turbulence alone. At the same time, the degree of lower-energy polarization is significantly lower and shows no more than mild variability. Our campaign provides further evidence for a scenario in which energy-stratified shock-acceleration of relativistic electrons, combined with a turbulent magnetic field, is responsible for optical to X-ray synchrotron emission in blazar jets., Comment: 23 pages, including 8 pages of appendices. 12 figures, 3 tables. Submitted to ApJ

Inspired by the recent success of machine-learned interatomic potentials for crystal structure prediction of the inorganic crystals, we present a methodology that exploits Moment Tensor Potentials and active learning (based on maxvol algorithm) to accelerate structure prediction of molecular crystals. Benzene and glycine are used as test systems. Interestingly, among obtained low energy structures of benzene we have found a peculiar polymeric benzene structure.

2D magnets have emerged as a class of materials highly promising for studies of quantum phenomena and applications in ultra-compact spintronics. Current research aims at design of 2D magnets with particular functional properties. A formidable challenge is to produce metallic monolayers: the material landscape of layered magnetic systems is strongly dominated by insulators; rare metallic magnets, such as Fe3GeTe2, become insulating as they approach the monolayer limit. Here, electron transport measurements demonstrate that the recently discovered 2D magnet GdAlSi - graphene-like AlSi layers coupled to layers of Gd atoms - remains metallic down to a single monolayer. Band structure analysis indicates the material to be an electride, which may stabilize the metallic state. Remarkably, the sheet conductance of 2D GdAlSi is proportional to the number of monolayers - a manifestation of scalable conductivity. The GdAlSi layers are epitaxially integrated with silicon, facilitating applications in electronics.

We present multiwavelength polarization measurements of the luminous blazar Mrk~501 over a 14-month period. The 2--8 keV X-ray polarization was measured with the Imaging X-ray Polarimetry Explorer (IXPE) with six 100-ks observations spanning from 2022 March to 2023 April. Each IXPE observation was accompanied by simultaneous X-ray data from NuSTAR, Swift/XRT, and/or XMM-Newton. Complementary optical-infrared polarization measurements were also available in the B, V, R, I, and J bands, as were radio polarization measurements from 4.85 GHz to 225.5 GHz. Among the first five IXPE observations, we did not find significant variability in the X-ray polarization degree and angle with IXPE. However, the most recent sixth observation found an elevated polarization degree at $>3\sigma$ above the average of the other five observations. The optical and radio measurements show no apparent correlations with the X-ray polarization properties. Throughout the six IXPE observations, the X-ray polarization degree remained higher than, or similar to, the R-band optical polarization degree, which remained higher than the radio value. This is consistent with the energy-stratified shock scenario proposed to explain the first two IXPE observations, in which the polarized X-ray, optical, and radio emission arises from different regions., Comment: Accepted for publication in ApJ

The discovery of the quantum tunnelling effect -- the transmission of particles through a high potential barrier -- was one of the most impressive achievements of quantum mechanics made in the 1920s. Responding to the contemporary challenges, I introduce a novel deep neural network (DNN) architecture that processes information using the effect of quantum tunnelling. I demonstrate the ability of the quantum tunnelling DNN (QT-DNN) to recognise optical illusions like a human. Hardware implementation of QT-DNN is expected to result in an inexpensive and energy-efficient neuromorphic chip suitable for applications in autonomous vehicles. The optical illusions recognition tests developed in this paper should lay foundations for cognitive benchmarking tasks for AI systems of the future, benefiting the fields of sociophysics and behavioural science.

Variability can be the pathway to understanding the physical processes in astrophysical jets, however, the high-cadence observations required to test particle acceleration models are still missing. Here we report on the first attempt to produce continuous, >24 hour polarization light curves of blazars using telescopes distributed across the globe and the rotation of the Earth to avoid the rising Sun. Our campaign involved 16 telescopes in Asia, Europe, and North America. We observed BL Lacertae and CGRaBS J0211+1051 for a combined 685 telescope hours. We find large variations in the polarization degree and angle for both sources in sub-hour timescales as well as a ~180 degree rotation of the polarization angle in CGRaBS J0211+1051 in less than two days. We compared our high-cadence observations to Particle-In-Cell magnetic reconnection and turbulent plasma simulations. We find that although the state of the art simulation frameworks can produce a large fraction of the polarization properties, they do not account for the entirety of the observed polarization behavior in blazar jets., Comment: 20 pages, 15 figures, 2 tables, accepted for publication in A&A. The data used in the paper are available here: https://doi.org/10.7910/DVN/IETSXS

Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of total power available onboard, thereby limiting the vehicle's range of functions and considerably reducing the distance the vehicle can travel on a single charge. Next-generation onboard AI systems need an even higher power since they collect and process even larger amounts of data in real time. This problem cannot be solved using the traditional computing devices since they become more and more power-consuming. In this review article, we discuss the perspectives of development of onboard neuromorphic computers that mimic the operation of a biological brain using nonlinear-dynamical properties of natural physical environments surrounding autonomous vehicles. Previous research also demonstrated that quantum neuromorphic processors (QNPs) can conduct computations with the efficiency of a standard computer while consuming less than 1% of the onboard battery power. Since QNPs is a semi-classical technology, their technical simplicity and low, compared with quantum computers, cost make them ideally suitable for application in autonomous AI system. Providing a perspective view on the future progress in unconventional physical reservoir computing and surveying the outcomes of more than 200 interdisciplinary research works, this article will be of interest to a broad readership, including both students and experts in the fields of physics, engineering, quantum technologies and computing., Comment: review article

Altermagnets have emerged as a class of materials combining certain ferromagnetic properties with zero net magnetization. This combination is highly promising for spintronics, especially if a material can be brought to a nanoscale size. However, experimental studies of the 2D limit of altermagnets and evolution of their properties with thickness are lacking. Here, we study epitaxial films on silicon of the Weyl altermagnet GdAlSi ranging from more than a hundred unit cells to a single unit cell. The films are synthesized by molecular beam epitaxy and, expectedly, do not show any discernible net magnetic moments. Electron transport studies reveal a remarkable transformation of the electron state with the film thickness. Thick films exhibit negative longitudinal magnetoresistance associated with the chiral anomaly but do not demonstrate altermagnetic properties in electron transport due to symmetry restrictions. In ultrathin films, a spontaneous anomalous Hall effect manifests itself, indicating a non-relativistic spin splitting in the electronic structure. The transformation is associated with crystal symmetry breaking accompanying the 3D-to-2D crossover. The work highlights the role of dimensionality in altermagnetism and provides a platform for studies of altermagnets aiming at ultra-compact spintronics.

Atomistic modeling is a widely employed theoretical method of computational materials science. It has found particular utility in the study of magnetic materials. Initially, magnetic empirical interatomic potentials or spin-polarized density functional theory (DFT) served as the primary models for describing interatomic interactions in atomistic simulations of magnetic systems. Furthermore, in recent years, a new class of interatomic potentials known as magnetic machine-learning interatomic potentials (magnetic MLIPs) has emerged. These MLIPs combine the computational efficiency, in terms of CPU time, of empirical potentials with the accuracy of DFT calculations. In this review, our focus lies on providing a comprehensive summary of the interatomic interaction models developed specifically for investigating magnetic materials. We also delve into the various problem classes to which these models can be applied. Finally, we offer insights into the future prospects of interatomic interaction model development for the exploration of magnetic materials.

We developed a method for fitting machine-learning interatomic potentials with magnetic degrees of freedom, namely, magnetic Moment Tensor Potentials (mMTP). The main feature of our method consists in fitting mMTP to magnetic forces (negative derivatives of energies with respect to magnetic moments) as obtained spin-polarized density functional theory calculations. We test our method on the bcc Fe-Al system with different compositions. Specifically, we calculate formation energies, equilibrium lattice parameter, and total cell magnetization. Our findings demonstrate an accurate correspondence between the values calculated with mMTP and those obtained by DFT at zero temperature. Additionally, using molecular dynamics, we estimate the finite-temperature lattice parameter and capture the cell expansion as was previously revealed in experiment. Furthermore, we demonstrate that fitting to magnetic forces increases the reliability of structure relaxation (or, equilibration), in the sense of ensuring that every relaxation run ends up with a successfully relaxed structure (the failure may otherwise be caused by falsely driving a configuration away from the region covered in the training set).

Transition-metal compounds represent a fascinating playground for exploring the intricate relationship between structural distortions, electronic properties, and magnetic behaviour, holding significant promise for technological advancements. Among these compounds, YBaCo$_4$O$_{7}$ (Y114) is attractive due to its manifestation of a ferrimagnetic component at low temperature intertwined with distortion effect due to the charge disproportionation on Co ions, exerting profound impact on its magnetic properties. In this perspective paper, we study the structural and magnetic intricacies of the Y114 crystal using a novel first-principles methodology. Traditionally, the investigation of such materials has relied heavily on computational modelling using density-functional theory (DFT) with the on-site Coulomb interaction correction $U$ (DFT+$U$) based on the Hubbard model (sometimes including Hund's exchange coupling parameter $J$, DFT+$U$+$J$) to unravel their complexities. Herein, we analysed the spurious effects of magnetic-moment delocalisation and spillover to non-magnetic ions in the lattice on electronic structure and magnetic properties of Y114. To overcome this problem we have applied constrained DFT (cDFT) based on the potential self-consistency approach, and comprehensively explore the Y114 crystal's characteristics in its ferrimagnetic order. We find that cDFT yields magnetic moments of Co ions much closer to the experimental values than Hubbard model with the parameters $U$ and $J$ fitted to reproduce experimental lattice constants. cDFT allows for an accurate prediction of magnetic properties using oxidation states of magnetic ions as well-defined parameters. Through this perspective, we not only enhance our understanding of the magnetic interactions in Y114 crystal, but also pave the way for future investigations into magnetic materials.

On the surface, behavioural science and physics seem to be two disparate fields of research. However, a closer examination of problems solved by them reveals that they are uniquely related to one another. Exemplified by the theories of quantum mind, cognition and decision-making, this unique relationship serves as the topic of this chapter. Surveying the current academic journal papers and scholarly monographs, we present an alternative vision of the role of quantum mechanics in the modern studies of human perception, behaviour and decision-making. To that end, we mostly aim to answer the 'how' question, deliberately avoiding complex mathematical concepts but developing a technically simple computational code that the readers can modify to design their own quantum-inspired models. We also present several practical examples of the application of the computation code and outline several plausible scenarios, where quantum models based on the proposed do-it-yourself model kit can help understand the differences between the behaviour of individuals and social groups., Comment: Book chapter. The computational codes and supplementary videos are available at https://github.com/IvanMaksymov/BehavDataSciCodes

Layered magnets are stand-out materials because of their range of functional properties that can be controlled by external stimuli. Regretfully, the class of such compounds is rather narrow, prompting the search for new members. Graphitization - stabilization of layered graphitic structures in the 2D limit - is being discussed for cubic materials. We suggest the phenomenon to extend beyond cubic structures; it can be employed as a viable route to a variety of layered materials. Here, the idea of graphitization is put into practice to produce a new layered magnet, GdAlSi. The honeycomb material, based on graphene-like layers AlSi, is studied both experimentally and theoretically. Epitaxial films of GdAlSi are synthesized on silicon; the critical thickness for the stability of the layered polymorph is around 20 monolayers. Notably, the layered polymorph of GdAlSi demonstrates ferromagnetism stemming from the open 4f-shells of Gd, in contrast to the non-layered, tetragonal polymorph. The ferromagnetism is further supported by electron transport measurements revealing negative magnetoresistance and the anomalous Hall effect. The results show that graphitization can be a powerful tool in the design of functional layered materials.

Physical reservoir computing (RC) is a machine learning algorithm that employs the dynamics of a physical system to forecast highly nonlinear and chaotic phenomena. In this paper, we introduce a quantum RC system that employs the dynamics of a probed atom in a cavity. The atom experiences coherent driving at a particular rate, leading to a measurement-controlled quantum evolution. The proposed quantum reservoir can make fast and reliable forecasts using a small number of artificial neurons compared with the traditional RC algorithm. We theoretically validate the operation of the reservoir, demonstrating its potential to be used in error-tolerant applications, where approximate computing approaches may be used to make feasible forecasts in conditions of limited computational and energy resources.

We present a study of the nearby low-metallicity dwarf galaxy IC 1613, focusing on the search for massive stars and related feedback processes, as well as for faint supernova remnants (SNR) in late stages of evolution. We obtained the deepest images of IC 1613 in the narrow-band H{\alpha}, He II and [S II] emission lines and new long-slit spectroscopy observations using several facilities (6-m BTA, 2.5m SAI MSU, and 150RTT telescopes), in combination with the multi-wavelength archival data from MUSE/VLT, VLA, XMM-Newton, and Swift/XRT. Our deep narrow-band photometry identifies several faint shells in the galaxy, and we further investigate their physical characteristics with the new long-slit spectroscopy observations and the archival multi-wavelength data. Based on energy balance calculations and assumptions about their possible nature, we propose that one of the shells is a possible remnant of a supernova explosion. We study five out of eight Wolf-Rayet (WR) star candidates previously published for this galaxy using the He ii emission line mapping, MUSE/VLT archival spectra, and new long-slit spectra. Our analysis discards the considered WR candidates and finds no new ones. We found P Cyg profiles in H{\alpha} line in two stars, which we classify as Luminous Blue Variable (LBV) star candidates. Overall, the galaxy IC 1613 may have a lower rate of WR star formation than previously suggested.

We investigated the ionised and atomic gas kinematics and excitation state in the central region of ongoing star formation of the nearby low-metallicity dwarf galaxy Sextans B. The analysis is based on the new observations performed in Ha emission line with high resolution ($R \sim 16000$) scanning Fabry-Perot interferometer at the 6-m BTA SAO RAS telescope, and on the long-slit spectral observations at the 9.2-m SALT and 2.5-m CMO SAI MSU telescopes. Strong non-circular gas motions detected in the studied regions probably resulted from the off-plane gas motions and impact of stellar feedback. We identified six regions of elevated Ha velocity dispersion, five of which exhibit asymmetric or two-component Ha line profiles. Three of these regions are young ($<1.1$ Myr) expanding ($V_{\rm exp} \sim 25-50\ {\rm km\ s^{-1}}$) superbubbles. We argue that at least three regions in the galaxy could be supernova remnants. We conclude that supernovae feedback is the dominant source of energy for superbubbles in Sextans B, which is expected for such a low metallicity, although we cannot rule out a strong impact of pre-supernova feedback for one superbubble., Comment: 16 pages, 6+6 figures, 4 tables. submitted to MNRAS after revision

Quantum-mechanical models of human cognition, opinion formation and decision-making have changed the way we understand and predict human behaviour in many practical situations, including political elections, financial decisions and international affairs. Yet, at present, such models overlook certain essential social aspects of human behaviour and self-identification. In this paper, we introduce a magnetism-inspired quantum-mechanical model of gender fluidity, a concept that challenges social norms across the globe. Addressing a number of independent suggestions made by members of the general public concerning a potential analogy between quantum superposition and non-binary self-identification, we explore new territories, demonstrating that physic of magnetism can help explain gender fluidity and similar social phenomena better than the traditional quantum-mechanical models of human cognition and perception. We anticipate that the proposed model can be used to analyse experimental datasets aimed to develop sexual orientation and gender identity legal definitions as well as to create artificial intelligence systems that can sensibly identify both binary and non-binary genders.

Hot Jupiters were many of the first exoplanets discovered in the 1990s, but in the decades since their discovery, the mysteries surrounding their origins remain. Here, we present nine new hot Jupiters (TOI-1855 b, TOI-2107 b, TOI-2368 b, TOI-3321 b, TOI-3894 b, TOI-3919 b, TOI-4153 b, TOI-5232 b, and TOI-5301 b) discovered by NASA's TESS mission and confirmed using ground-based imaging and spectroscopy. These discoveries are the first in a series of papers named the Migration and Evolution of giant ExoPlanets (MEEP) survey and are part of an ongoing effort to build a complete sample of hot Jupiters orbiting FGK stars, with a limiting Gaia $G$-band magnitude of 12.5. This effort aims to use homogeneous detection and analysis techniques to generate a set of precisely measured stellar and planetary properties that is ripe for statistical analysis. The nine planets presented in this work occupy a range of masses (0.55 Jupiter masses (M$_{\rm{J}}$) $<$ M$_{\rm{P}}$ $<$ 3.88 M$_{\rm{J}}$) and sizes (0.967 Jupiter radii (R$_{\rm{J}}$) $<$ R$_{\rm{P}}$ $<$ 1.438 R$_{\rm{J}}$) and orbit stars that range in temperature from 5360 K $<$ Teff $<$ 6860 K with Gaia $G$-band magnitudes ranging from 11.1 to 12.7. Two of the planets in our sample have detectable orbital eccentricity: TOI-3919 b ($e = 0.259^{+0.033}_{-0.036}$) and TOI-5301 b ($e = 0.33^{+0.11}_{-0.10}$). These eccentric planets join a growing sample of eccentric hot Jupiters that are consistent with high-eccentricity tidal migration, one of the three most prominent theories explaining hot Jupiter formation and evolution., Comment: 35 pages, 7 tables, and 14 figures. Submitted to AAS Journals on 2023 Dec 28

Reservoir computing (RC) systems can efficiently forecast chaotic time series using nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper we experimentally validate a physical RC system that substitutes the effect of randomness for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with a minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the `next-generation' improvement of the traditional RC algorithm., Comment: The Supplementary Video can be found here: https://youtu.be/Zwu3KEo8f00

Ambiguous optical illusions have been a paradigmatic object of fascination, research and inspiration in arts, psychology and video games. However, accurate computational models of perception of ambiguous figures have been elusive. In this paper, we design and train a deep neural network model to simulate the human's perception of the Necker cube, an ambiguous drawing with several alternating possible interpretations. Defining the weights of the neural network connection using a quantum generator of truly random numbers, in agreement with the emerging concepts of quantum artificial intelligence and quantum cognition we reveal that the actual perceptual state of the Necker cube is a qubit-like superposition of the two fundamental perceptual states predicted by classical theories. Our results will find applications in video games and virtual reality systems employed for training of astronauts and operators of unmanned aerial vehicles. They will also be useful for researchers working in the fields of machine learning and vision, psychology of perception and quantum-mechanical models of human mind and decision-making.

The Micius satellite is the pioneering initiative to demonstrate quantum teleportation, entanglement distribution, quantum key distribution (QKD), and quantum-secured communications experiments at the global scale. In this work, we report on the results of the 600-mm-aperture ground station design which has enabled the establishment of a quantum-secured link between the Zvenigorod and Nanshan ground stations using the Micius satellite. As a result of a quantum communications session, an overall sifted key of 2.5 Mbits and a total final key length of 310 kbits have been obtained. We present an extension of the security analysis of the realization of satellite-based QKD decoy-state protocol by taking into account the effect of the detection-efficiency mismatch for four detectors. We also simulate the QKD protocol for the satellite passage and by that validate our semi-empirical model for a realistic receiver, which is in good agreement with the experimental data. Our results pave the way to the considerations of realistic imperfection of the QKD systems, which are important in the context of their practical security., Comment: 8+2 pages, 5+2 figures

Paradoxical decision-making behaviours such as preference reversal often arise from imprecise or noisy human preferences. Harnessing the physical principle of magnetisation reversal in ferromagnetic nanostructures, we developed a model that closely reflects human decision-making dynamics. Tested against a spectrum of psychological data, our model adeptly captures the complexities inherent in individual choices. This blend of physics and psychology paves the way for fresh perspectives on understanding the imprecision of human decision-making processes, extending the reach of the current classical and quantum physical models of human behaviour and decision-making.

Recent advancements in quantum computing and quantum-inspired algorithms have sparked renewed interest in binary optimization. These hardware and software innovations promise to revolutionize solution times for complex problems. In this work, we propose a novel method for solving linear systems. Our approach leverages binary optimization, making it particularly well-suited for problems with large condition numbers. We transform the linear system into a binary optimization problem, drawing inspiration from the geometry of the original problem and resembling the conjugate gradient method. This approach employs conjugate directions that significantly accelerate the algorithm's convergence rate. Furthermore, we demonstrate that by leveraging partial knowledge of the problem's intrinsic geometry, we can decompose the original problem into smaller, independent sub-problems. These sub-problems can be efficiently tackled using either quantum or classical solvers. While determining the problem's geometry introduces some additional computational cost, this investment is outweighed by the substantial performance gains compared to existing methods., Comment: 17 pages, 4 figures

We propose a quantum-mechanical model that represents a human system of beliefs as quantised energy levels of a physical system. This model underscores a novel perspective on opinion dynamics, recreating a broad range of experimental and real-world data that exhibit an asymmetry of opinion radicalisation. In particular, the model demonstrates the phenomena of pronounced conservatism versus mild liberalism when individuals are exposed to opposing views, mirroring recent findings on opinion polarisation via social media exposure. Advancing this model, we establish a solid framework that integrates elements from physics, psychology, behavioural science, decision-making theory and philosophy, and also emphasise the inherent advantages of the quantum approach over traditional classical models. We also suggest a number of new directions for future research work on quantum-mechanical models of human cognition and decision-making.

This paper introduces a novel quantum-mechanical model that describes psychological phenomena using the analogy of a harmonic oscillator represented by an electron trapped in a potential well. Study~1 demonstrates the application of the proposed model to bistable perception of ambiguous figures (i.e., optical illusions), exemplified by the Necker cube. While prior research has theoretically linked quantum mechanics to psychological phenomena, in Study~2 we demonstrate a viable physiological connection between physics and bistable perception. To that end, the model draws parallels between quantum tunneling of an electron through a potential energy barrier and an eye blink, an action known to trigger perceptual reversals. Finally, we discuss the ability of the model to capture diverse optical illusions and other psychological phenomena, including cognitive dissonance.

More than 3.5 billion people live in rural areas, where water and water energy resources play an important role in ensuring sustainable and productive rural economies. This article reviews and critically analyses the recent advances in the field of analogue and reservoir computing that have been driven by unique physical properties and energy of water waves. It also demonstrates that analogue and reservoir computing hold the potential to bring artificial intelligence closer to people living outside large cities, thus enabling them to enjoy the benefits of novel technologies that already work in large cities but are not readily available and suitable for regional communities., Comment: Review article

The idea of creating magnetically controllable colloids whose rheological properties can be finely tuned on the nano- or micro-scale has caused a lot of experimental and theoretical effort. The latter resulted in systems whose building blocks are ranging between single magnetic nanoparticles to complexes of such nanoparticles bound together by various mechanisms. The binding can be either chemical or physical, reversible or not. One way to create a system that is physically bound is to let the precrosslinked supracolloidal magnetic polymers (SMPs) to cluster due to both magnetic and Van-der-Waals-type forces. The topology of the SMPs in this case can be used to tune both magnetic and rheological properties of the resulting clusters as we show in this work. We employ Molecular Dynamics computer simulations coupled with explicit solvent modelled by Lattice-Boltzmann method in order to model the behaviour of the clusters formed by chains, rings, X- and Y-shaped SMPs in a shear flow with externally applied magnetic field. We find that the shear stabilises the shape of the clusters not letting them extend in the direction of the field and disintegrate. The clusters that show the highest response to an applied field and higher shape stability are those made of Y- and X-like SMPs., Comment: 13 pages, 10 figures

Producing original and arranging existing musical outcomes is an art that takes years of learning and practice to master. Yet, despite the constant advances in the field of AI-powered musical creativity, production of quality musical outcomes remains a prerogative of the humans. Here we demonstrate that a single bubble in water can be used to produce creative musical outcomes, when it nonlinearly oscillates under an acoustic pressure signal that encodes a piece of classical music. The audio signal of the response of the bubble resembles an electric guitar version of the original composition. We suggest, and provide plausible theoretical supporting arguments, that this property of the bubble can be used to create physics-inspired AI systems capable of simulating human creativity in arrangement and composition of music.

Several theoretical works have shown that solitons -- waves that self-maintain constant shape and velocity as they propagate -- can be used as a physical computational reservoir, a concept where machine learning algorithms designed for digital computers are replaced by analog physical systems that exhibit nonlinear dynamical behaviour. Here we propose and experimentally validate a novel reservoir computing (RC) system that for the first time employs solitary-like (SL) waves propagating on the surface of a liquid film flowing over an inclined surface. We demonstrate the ability of the SL wave RC system (SLRC) to forecast chaotic time series and to successfully pass essential benchmark tests, including a memory capacity test and a Mackey-Glass model test.