Coherent Floquet quantum reservoirs for molecular property prediction

• URL: http://arxiv.org/abs/2609.11071v1
• Date: Thu, 10 Sep 2026 04:27:01 GMT
• Title: Coherent Floquet quantum reservoirs for molecular property prediction
• Abstract summary: Quantum reservoir computing (QRC) uses quantum dynamics to represent input histories for prediction through a trained classical readout.<n>We construct a DTC-based reservoir architecture to predict molecular properties from structural and dynamical observations.
• Score: 11.126126867103522
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum reservoir computing (QRC) uses quantum dynamics to represent input histories for prediction through a trained classical readout. Discrete time crystals (DTCs) exhibit robust subharmonic responses under periodic driving, and previous work has used their dynamics to construct DTC-QRC. Here we construct a DTC-based reservoir architecture to predict molecular properties from structural and dynamical observations. Coherent Floquet evolution processes local molecular graph events and surface-hopping frames, while controlled reset regulates the contribution of earlier inputs. Measurements at the end of each input sequence yield a feature vector of fixed dimension. Trained classical decoders use this vector for inhibitor-activity and blood--brain-barrier permeability classification and electronic-gap forecasting, while the reservoir parameters remain fixed during training. With matched input lengths and output widths, DTC-QRC outperforms echo-state networks on long-prefix graph classification and the studied ethene gap forecasting tasks. Dephasing lowers performance in both applications, consistent with a role for coherent propagation. Experiments on the Quafu superconducting quantum cloud platform show that pair observables retain task information under device noise. The architecture provides a common framework for molecular screening and time-resolved property prediction using quantum reservoir computing.

Related papers

• A Quantum Reservoir for Neurodynamical Forecasting [0.0]
  We introduce a quantum reservoir based on a transverse-field Ising model, combined with heterogeneous quantum measurements and regression parameters.<n>Results show that the quantum reservoir outperforms a classical counterpart overall.<n>To assess performance on biological signals, we evaluate QRC on simulated human electroencephalography (EEG) data with a parallel reservoir architecture.
  arXiv  Detail & Related papers  (2026-07-31T14:52:33Z)
• Digital Quantum Reservoir Computing for ATM Time Series Prediction [0.0]
  We investigate a digital quantum reservoir computing framework for multi-step forecasting of ATM cash demand time series.<n>The proposed approach uses parametrized four-qubit reservoirs with a fixed structure exploiting partial measurement and reset.<n>Experiments are performed with noiseless simulation, noise-aware emulation, and a real IQM Spark quantum processor.
  arXiv  Detail & Related papers  (2026-06-03T10:11:07Z)
• Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory [45.322818939044645]
  We aim to learn wavefunctions simulated by time-dependent density functional theory (TDDFT)<n>In real-time TDDFT, the electronic wavefunctions of a molecule evolve over time in response to an external excitation.<n>We propose OrbEvo, which learns to evolve the full electronic wavefunction coefficients across time steps.
  arXiv  Detail & Related papers  (2026-03-03T20:34:05Z)
• QRC-Lab: An Educational Toolbox for Quantum Reservoir Computing [0.0]
  Quantum Reservoir Computing (QRC) has emerged as a strong pa- radigm for Noisy Intermediate-Scale Quantum (NISQ) machine learning.<n>This paper introduces QRC-Lab, an open-source, modular Python framework designed to bridge the gap between theoretical quantum dynamics and applied machine learning work- flows.
  arXiv  Detail & Related papers  (2026-02-03T13:38:56Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning [45.92935470813908]
  Quantum computing reservoir (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems.<n>Recent studies indicate that perturbation quantums based on variational circuits remain susceptible to adversarials.<n>We investigate the first systematic evaluation of adversarial robustness in a QR based learning model.
  arXiv  Detail & Related papers  (2025-10-15T12:17:23Z)
• Quantum reservoir computing for predicting and characterizing chaotic maps [0.3966519779235704]
  Quantum reservoir computing has emerged as a promising paradigm for harnessing quantum systems to process temporal data efficiently.<n>We demonstrate the capability of this approach to predict and characterize chaotic dynamics in discrete nonlinear maps.<n>Results highlight quantum reservoir computing as a scalable and noise-resilient tool for modeling complex dynamical systems.
  arXiv  Detail & Related papers  (2025-09-15T15:55:59Z)
• Quantum QSAR for drug discovery [41.94295877935867]
  Quantitative Structure-Activity Relationship (QSAR) modeling is key in drug discovery.<n>This research proposes enhancing QSAR techniques through Quantum Support Vector Machines (QSVMs)<n>By using quantum data encoding and quantum kernel functions, we aim to develop more accurate and efficient predictive models.
  arXiv  Detail & Related papers  (2025-05-06T17:58:33Z)
• Temporally Consistent Koopman Autoencoders for Forecasting Dynamical Systems [38.36312939874359]
  We introduce the temporally consistent Koopman autoencoder (tcKAE)<n>tcKAE generates accurate long-term predictions even with limited and noisy training data.<n>We empirically demonstrate tcKAE's superior performance over state-of-the-art KAE models across a variety of test cases.
  arXiv  Detail & Related papers  (2024-03-19T00:48:25Z)
• Data-driven Quantum Dynamical Embedding Method for Long-term Prediction on Near-term Quantum Computers [6.1549556540537855]
  We introduce a data-driven method designed for time series prediction with quantum dynamical embedding (QDE)<n>Based on its independence of time series length, this method achieves depth-efficient quantum circuits.<n> Numerical simulations demonstrate the model's capability to predict not only wave signals but also more complex signals such as NARMA.
  arXiv  Detail & Related papers  (2023-05-25T12:12:49Z)
• When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing [75.75419308975746]
  This work proposes a vertical federated learning architecture based on variational quantum circuits to demonstrate the competitive performance of a quantum-enhanced pre-trained BERT model for text classification. Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
  arXiv  Detail & Related papers  (2022-02-17T09:55:21Z)
• Computing molecular excited states on a D-Wave quantum annealer [52.5289706853773]
  We demonstrate the use of a D-Wave quantum annealer for the calculation of excited electronic states of molecular systems. These simulations play an important role in a number of areas, such as photovoltaics, semiconductor technology and nanoscience.
  arXiv  Detail & Related papers  (2021-07-01T01:02:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.