arXiv subjects

Alexander G. Balanov

Publications and source records attributed to Alexander G. Balanov.

7 recordsLinked to original sources

arXiv2026

A Quantum Reservoir Computing Approach to Quantum Stock Movement Forecasting in Quantum-Invested Markets

We present a quantum reservoir computing (QRC) framework based on a small-scale quantum system comprising at most six interacting qubits, designed for nonlinear financial time-series forecasting. We apply the model to predict future daily closing trading volumes of 20 quantum-sector publicly traded companies over the period from April 11, 2020, to April 11, 2025, as well as minute-by-minute trading volumes during out-of-market hours on July 7, 2025. Our analysis identifies optimal reservoir parameters that yield stock trend (up/down) classification accuracies exceeding $86 \%$. Importantly, the QRC model is platform-agnostic and can be realized across diverse physical implementations of qubits, including superconducting circuits and trapped ions. These results demonstrate the expressive power and robustness of small-scale quantum reservoirs for modeling complex temporal correlations in financial data, highlighting their potential applicability to real-world forecasting tasks on near-term quantum hardware.

quant-ph↗

arXiv2026

Memorisation and forgetting in a learning Hopfield neural network: bifurcation mechanisms, attractors and basins

Despite explosive expansion of artificial intelligence based on artificial neural networks (ANNs), these are employed as "black boxes'', as it is unclear how, during learning, they form memories or develop unwanted features, including spurious memories and catastrophic forgetting. Much research is available on isolated aspects of learning ANNs, but due to their high dimensionality and non-linearity, their comprehensive analysis remains a challenge. In ANNs, knowledge is thought to reside in connection weights or in attractor basins, but these two paradigms are not linked explicitly. Here we comprehensively analyse mechanisms of memory formation in an 81-neuron Hopfield network undergoing Hebbian learning by revealing bifurcations leading to formation and destruction of attractors and their basin boundaries. We show that, by affecting evolution of connection weights, the applied stimuli induce a pitchfork and then a cascade of saddle-node bifurcations creating new attractors with their basins that can code true or spurious memories, and an abrupt disappearance of old memories (catastrophic forgetting). With successful learning, new categories are represented by the basins of newly born point attractors, and their boundaries by the stable manifolds of new saddles. With this, memorisation and forgetting represent two manifestations of the same mechanism. Our strategy to analyse high-dimensional learning ANNs is universal and applicable to recurrent ANNs of any form. The demonstrated mechanisms of memory formation and of catastrophic forgetting shed light on the operation of a wider class of recurrent ANNs and could aid the development of approaches to mitigate their flaws.

math.DS↗

arXiv2026

Scalable platform enabling reservoir computing with nanoporous oxide memristors for image recognition and time series prediction

Typical mammal brains have some form of random connectivity between neurons. Reservoir computing, a neural network approach, uses random weights within its processing layer along with built-in recurrent connections and short-term, fading memory, and is shown to be time and training efficient in processing spatiotemporal signals. Here we prepared a niobium oxide-based thin film memristor device with intrinsic structural in-homogeneity in the form of random nanopores and performed computational tasks of XOR operations, image recognition, and time series prediction and reconstruction. For the latter task we chose a complex three-dimensional chaotic Lorenz-63 time series. By applying three temporal voltage waveforms individually across the device and training the readout layer with electrical current signals from a three-output physical reservoir, we achieved satisfactory prediction and reconstruction accuracy in comparison to the case of no reservoir. This work highlights the potential for scalable, on-chip devices using all-oxide reservoir systems, paving the way for energy-efficient neuromorphic electronics dealing with time signals.

cond-mat.dis-nn↗

arXiv2026

Stochastic Dynamics of Diffusive Memristor Blocks for Neuromorphic Computing

Biological systems use neural circuits to integrate input information and produce outputs. Synaptic convergence, where multiple neurons converge their inputs onto a single downstream neuron, is common in natural neural circuits. However, understanding specific computations performed by such neural blocks and implementating them in hardware requires further research. This work focuses on synaptic convergence in a simplified circuit of three spiking artificial neurons based on diffusive memristors. Numerical modelling and experiments reveal input voltage combinations that enable targeted activation of spiking for specific neuron configurations. We analyse the statistical characteristics of spiking patterns and interpret them from a computational perspective. The numerical simulations match experimental measurements. Our findings contribute to development of universal functional blocks for neuromorphic systems.

cond-mat.mes-hall↗

arXiv2026

Topological Quenching of Noise in a Free-Running Moebius Microcomb

Microcombs require ultralow-noise repetition rates to enable next-generation applications in metrology, high-speed communications, microwave photonics, and sensing, where spectral purity is a central performance metric. Best-performing sources operate actively locked at "quiet points" in parameter space, fixed by device and material properties. Creating broad, low-noise operating regions with relaxed constraints-especially in simplified free-running architectures that avoid electronics-heavy control-remains an open challenge. Here, we demonstrate a symmetry-protected topological Möbius soliton molecule that enables intrinsically low phase noise in a fully free-running microcomb, operating without any external referencing or control. Using a microresonator-filtered laser, we implement a Möbius geometry via interleaved microcavity modes. Upon the formation of a topological Möbius soliton molecule, the free-running laser exhibits over 15 dB of phase-noise suppression across 10 Hz-10 kHz at a 100 GHz repetition rate, yielding -63 dBc/Hz phase noise at 1 kHz and an Allan deviation of 4x10^-10 at 10 s average time-without any external control. We show that the Möbius structure brings dynamic robustness to the comb, and we demonstrate a symmetry-protected topological regime that enables long-term drift-invariant operation. Our results establish a route to intrinsically noise-quenched microcombs operating in a fully free-running configuration, governed by internal physical principles and suitable for field-deployable, low-noise photonic systems.

physics.optics↗

arXiv2025

Minimal Quantum Reservoirs with Hamiltonian Encoding

We investigate a minimal architecture for quantum reservoir computing based on Hamiltonian encoding, in which input data is injected via modulation of system parameters rather than state preparation. This approach circumvents many of the experimental overheads typically associated with quantum machine learning, enabling computation without feedback, memory, or state tomography. We demonstrate that such a minimal quantum reservoir, despite lacking intrinsic memory, can perform nonlinear regression and prediction tasks when augmented with post-processing delay embeddings. Our results provide a conceptually and practically streamlined framework for quantum information processing, offering a clear baseline for future implementations on near-term quantum hardware.

quant-ph↗

arXiv2024

Stochastic Instabilities of the Diffusive Memristor

Recently created diffusive memristors have garnered significant research interest owing to their distinctive capability to generate a diverse array of spike dynamics which are similar in nature to those found in biological cells. This gives the memristor an opportunity to be used in a wide range of applications, specifically within neuromorphic systems. The diffusive memristor is known to produce regular, chaotic and stochastic behaviors which leads to interesting phenomena resulting from the interactions between the behavioral properties. The interactions along with the instabilities that lead to the unique spiking phenomena are not fully understood due to the complexities associated with examining the stochastic properties within the diffusive memristor. In this work, we analyze both the classical and the noise induced bifurcations that a set of stochastic differential equations, justified through a Fokker-Planck equation used to model the diffusive memristor, can produce. Finally, we replicate the results of the numerical stochastic threshold phenomena with experimentally measured spiking.

physics.app-ph↗