[
  {
    "id": "rodenasruiz2026quantum",
    "type": "article",
    "title": "Quantum Solvers for Nonlinear Matrix Equations in Quantum Chemistry",
    "author": [
      {
        "family": "Rodenas-Ruiz",
        "given": "Pablo"
      },
      {
        "family": "Zhao",
        "given": "Andrew"
      },
      {
        "family": "Lee",
        "given": "Joonho"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2026,
          5
        ]
      ]
    },
    "container-title": "arXiv preprint (quant-ph, physics.chem-ph)",
    "URL": "https://arxiv.org/abs/2605.16189",
    "abstract": "We present a quantum algorithm for solving algebraic Riccati equations, with applications to quantum-chemical random-phase approximation (RPA) and higher-order RPA theories. Our method block-encodes stabilizing Riccati solutions via Riesz projectors onto invariant subspaces of an associated non-normal matrix, implemented using contour-integral resolvents and quantum singular value transformations. Applied to m-particle, m-hole RPA, our algorithm yields a block-encoding of the amplitude solution and estimates the electronic correlation-energy density with it. Under localized-orbital sparsity assumptions, the end-to-end cost scales linearly with system size and polynomially with excitation rank m, suggesting an exponential advantage in m over plausible classical local-correlation heuristics. More broadly, this work provides a framework for quantum algorithms for nonlinear matrix equations in quantum chemistry and opens a possible route toward developing quantum algorithms for coupled-cluster theory.",
    "keyword": "quantum algorithms, quantum chemistry, algebraic Riccati equations, block encodings",
    "note": "arXiv:2605.16189"
  },
  {
    "id": "claudon2026quantum",
    "type": "article-journal",
    "title": "Quantum Circuits for the Metropolis–Hastings Algorithm",
    "author": [
      {
        "family": "Claudon",
        "given": "Baptiste"
      },
      {
        "family": "Rodenas-Ruiz",
        "given": "Pablo"
      },
      {
        "family": "Piquemal",
        "given": "Jean-Philip"
      },
      {
        "family": "Monmarché",
        "given": "Pierre"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2026,
          7
        ]
      ]
    },
    "container-title": "Journal of Physics A: Mathematical and Theoretical",
    "volume": "59",
    "issue": "30",
    "page": "305304",
    "ISSN": "1751-8121",
    "publisher": "IOP Publishing",
    "DOI": "10.1088/1751-8121/ae8983",
    "URL": "https://doi.org/10.1088/1751-8121/ae8983",
    "abstract": "Szegedy's quantization of a reversible Markov chain provides a quantum walk whose spectral gap is quadratically larger than that of the classical walk. Quantum computers are therefore expected to provide a speedup of Metropolis-Hastings (MH) simulations. Existing generic methods to implement the quantum walk require coherently computing the transition probabilities of the underlying Markov kernel. However, reversible computing methods require a number of qubits that scales with the complexity of the computation. This overhead is undesirable in near-term fault-tolerant quantum computing, where few logical qubits are available. In this work, we present a Szegedy quantum walk construction which follows the classical proposal-acceptance logic, and does not require further reversible computing methods. We also compare this construction with an alternative to Szegedy's approach which also provides a quadratic gap amplification. Since each step of the quantum walks uses a constant number of proposal and acceptance steps, we expect the end-to-end quadratic speedup to hold for MH Markov Chain Monte-Carlo simulations.",
    "keyword": "quantum walks, Metropolis-Hastings algorithm, Markov chain Monte Carlo, fault-tolerant quantum computing",
    "note": "arXiv:2506.11576"
  },
  {
    "id": "cspedessarrias2025mmhsd",
    "type": "paper-conference",
    "title": "MM-HSD: Multi-Modal Hate Speech Detection in Videos",
    "author": [
      {
        "family": "Céspedes-Sarrias",
        "given": "Berta"
      },
      {
        "family": "Collado-Capell",
        "given": "Carlos"
      },
      {
        "family": "Rodenas-Ruiz",
        "given": "Pablo"
      },
      {
        "family": "Hrynenko",
        "given": "Olena"
      },
      {
        "family": "Cavallaro",
        "given": "Andrea"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2025,
          10
        ]
      ]
    },
    "container-title": "Proceedings of the 33rd ACM International Conference on Multimedia",
    "page": "2546–2555",
    "publisher": "ACM",
    "DOI": "10.1145/3746027.3754558",
    "URL": "https://doi.org/10.1145/3746027.3754558",
    "abstract": "While hate speech detection (HSD) has been extensively studied in text, existing multi-modal approaches remain limited, particularly in videos. As modalities are not always individually informative, simple fusion methods fail to fully capture inter-modal dependencies. Moreover, previous work often omits relevant modalities such as on-screen text and audio, which may contain subtle hateful content and thus provide essential cues, both individually and in combination with others. In this paper, we present MM-HSD, a multi-modal model for HSD in videos that integrates video frames, audio, and text derived from speech transcripts and from frames (i.e. on-screen text) together with features extracted by Cross-Modal Attention (CMA). We are the first to use CMA as an early feature extractor for HSD in videos, to systematically compare query/key configurations, and to evaluate the interactions between different modalities in the CMA block. Our approach leads to improved performance when on-screen text is used as a query and the rest of the modalities serve as a key. Experiments on the HateMM dataset show that MM-HSD outperforms state-of-the-art methods on M-F1 score (0.874), using concatenation of transcript, audio, video, on-screen text, and CMA for feature extraction on raw embeddings of the modalities. The code is available at https://github.com/idiap/mm-hsd",
    "keyword": "hate speech detection, multi-modal fusion, cross-modal attention, video analysis",
    "note": "arXiv:2508.20546. Equal contribution: Berta Céspedes-Sarrias, Carlos Collado-Capell, Pablo Rodenas-Ruiz."
  },
  {
    "id": "rodenasruiz2025benchmarking",
    "type": "paper-conference",
    "title": "Benchmarking Fine-Tuned RNA Language Models for Intronic Branch Point Prediction",
    "author": [
      {
        "family": "Rodenas-Ruiz",
        "given": "Pablo"
      },
      {
        "family": "Saadat",
        "given": "Ali"
      },
      {
        "family": "Tran",
        "given": "Timothy T."
      },
      {
        "family": "Müller Smedt",
        "given": "Oliver"
      },
      {
        "family": "Zhang",
        "given": "Peng"
      },
      {
        "family": "Fellay",
        "given": "Jacques"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2025
        ]
      ]
    },
    "container-title": "ICLR 2025 MLGenX Workshop (Tiny Papers track)",
    "URL": "https://openreview.net/forum?id=Q15Dg5lQou",
    "abstract": "Accurate prediction of RNA branch points is critical for understanding splicing mechanisms and identifying variants that may lead to genetic diseases. Despite their biological importance, few computational methods have been developed for reliably identifying branch points. In this work, we fine-tune several RNA language models for branch point prediction. The top-performing model, ERNIE-RNA, achieved an F1 score of 0.811, a sequence accuracy of 0.790, and an average precision score of 0.868, outperforming previous leading models. These results showcase the potential of RNA-specific language models in capturing the subtle sequence features relevant to splicing. Our findings suggest that extended training and hyperparameter tuning could yield additional performance gains, positioning this study as a strong baseline for future research in RNA splicing.",
    "keyword": "RNA language models, branch point prediction, RNA splicing, model fine-tuning",
    "note": "Equal contribution: Pablo Rodenas-Ruiz, Ali Saadat, Timothy T. Tran, Oliver Müller Smedt."
  }
]