quant-ph · 2026-06-30 · No. 39

Quantum Physics, 2026-06-30.

4 new papers in quant-ph. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

4 entries
  1. 01

    Bridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular Simulations

    Anurag K. S. V., Ashish Kumar Patra, Manas Mukherjee, Ruchika Bhat, Sai Shankar P., Rahul Maitra, Jaiganesh G

    quant-ph · cs.LG · physics.chem-ph

    Calculation of binding energies for protein-ligand molecular systems requires accurate treatment of the electronic structure, a quantum chemistry problem that scales exponentially on classical hardware, while current quantum hardware remains too noisy for the required circuit depths. This report presents a hybrid quantum-classical workflow performed on the Fujitsu FX700 ideal state-vector simulator using QARP that addresses two structural...

    arxiv.org/abs/2606.30551 · PDF

  2. 02

    Staged Hybridisation for Visual Quantum Reinforcement Learning via Knowledge Distillation

    Javier Lazaro, Juan-Ignacio Vazquez, Pablo Garcia-Bringas

    quant-ph · cs.LG

    Visual environments are a demanding setting for quantum reinforcement learning (QRL): high-dimensional observations, unstable RL optimisation, and constrained variational quantum circuits (VQCs) are difficult to train jointly. This paper studies knowledge distillation (KD) as a staged hybridisation strategy for visual QRL. Instead of training a hybrid visual agent end-to-end from pixels, we first train a classical visual teacher, freeze its...

    arxiv.org/abs/2606.30520 · PDF

  3. 03

    Learning the structure of open quantum systems

    Laura Lewis, Ewin Tang, John Wright

    quant-ph · cs.DS · cs.LG

    We design an algorithm for learning the coefficients of an $n$-qubit constant-local Lindbladian to $\varepsilon$ error with $O(g d^2 \log(n) / \varepsilon^2)$ total evolution time, where $g$ is the single-site energy and $d$ is the (approximate) degree of the interaction graph. Though Lindbladians present new challenges not present in the special case of Hamiltonians, our algorithm achieves the suite of desiderata attained by state-of-the-art...

    arxiv.org/abs/2606.30358 · PDF

  4. 04

    RiverONE: Generating Knowledge-Intensive VLM by Simulated Quantum Machines

    Xindian Ma, Xinyu Long, Yefei Zhang, Yanchen Liu, Xianghao Li, Yufu Wen, Yike Hu, Yuedong Zhu, Zeyang Ma, Wen Qin,...

    quant-ph · cs.AI

    Quantum computing provides a powerful paradigm for representing and transforming high-dimensional information through superposition, entanglement, and measurement-induced nonlinear features. While current quantum hardware is not yet practical for direct large-scale vision-language model (VLM) inference, simulated quantum computation can be used during model construction to generate structured parameters for compact classical AI systems. We...

    arxiv.org/abs/2606.29966 · PDF

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