A Novel Hybrid Quantum Reservoir Computing (nHQRC) for Phase Transition Detection in Non-Equilibrium Dynamical Systems

Abstract:The analysis of highly non-linear stochastic data within non-equilibrium dynamical systems requires computational frameworks capable of detecting latent phase transitions before systemic structural breakdowns occur. Traditional Variational Quantum Algorithms (VQAs) are frequently bottlenecked by vanishing gradients, the barren plateau problem, and prohibitive training overheads. In this paper, we propose a novel Hybrid Quantum Reservoir Computing (nHQRC) framework, which bypasses these limitations by employing a frozen, disordered Transverse-Field Ising Model (TFIM) to project time-dependent stochastic driving forces into an exponentially large Hilbert space. To resolve the physical phase multi-wrapping vulnerabilities present in baseline quantum reservoir models, we introduce a lookahead-free, pre-amplification manifold scaling technique. Multi-qubit configurations are genetically optimized to the "edge of chaos," while quantum state tracking is performed by extracting von Neumann entropy ($S$) and exact mixed-state Quantum Fisher Information (QFI) to act as leading entanglement witnesses. Utilizing these quantum triggers as boundary constraints, trajectory predictions are constructed via a generative Stochastic Schrödinger Bridge (SSB) readout. By subjecting the quantum reservoir to an 8-dimensional non-stationary stochastic driving field, the framework significantly improves systemic drift-to-diffusion efficiency ($\eta$) and actively arrests maximum trajectory decay (MTD) by over 13% compared to standard classical benchmarks. This establishes a robust, $\mathcal{O}(1)$ temporal overhead blueprint for near-term quantum regime detection and macroscopic subsystem stabilization.

Quantum Physics (quant-ph); Machine Learning (cs.LG); Computational Finance (q-fin.CP); Statistical Finance (q-fin.ST)

arXiv:2607.16281 [quant-ph]

(or arXiv:2607.16281v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2607.16281 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.