A quantum reservoir processing framework that captures a broad range of physical computing models with quantum systems and it is numerically demonstrated that exponential concentration can still exist even with a physical reservoir such as an Ising model whenever the reservoir operates in a quantum-chaotic phase.
Recursive state estimation often executes approximate numerical solutions inside a feedback loop, where highly accurate local steps do not guarantee better overall results. For a fixed linear Kalman model, we characterize when a correction within a prescribed subspace and norm budget can meet a local admissibility tole...
Yanjun Ji, D. Willsch, Orkun Şensebat et al.· 0 citations
This work establishes a general quantum score-matching framework with end-to-end theoretical guarantees for quantum states and achieves information-theoretically optimal sample complexity in the high-temperature regime for Hamiltonians with bounded locality and interaction degree.
Quantum cloud computing, delivered through the quantum-as-a-service (QaaS) model, provides access to quantum computing resources. However, applying uniform time-based pricing across fundamentally heterogeneous quantum resources significantly complicates task orchestration, particularly when addressing the tradeoff betw...
An N. H. Phan, Dang van Huynh, Muhammad Usman et al.· 0 citations
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Compared with five different classical transfer learning methods, the proposed QBPM architecture demonstrated its efficiency as an alternative to classical approaches by achieving higher classification accuracy and comparable execution time while utilizing fewer circuit parameters.
Emine Akpinar, Murat Oduncuoglu· arXiv.org· 0 citations
A fundamental question in physics is: When does classical behavior emerge from quantum systems? Bosonic Gaussian states provide a natural setting to explore this quantum-classical boundary, as they capture both the classical field behavior and the intrinsic quantum nature of light. Here, we address this problem from a...
Senrui Chen, A. A. Mele, Francesco Anna Mele et al.· 0 citations
We construct the first type of non-Euclidean non-autoregressive neural quantum state (NQS) in the form of the hyperbolic Restricted Boltzmann Machine (HRBM), which is studied in the variational Monte-Carlo (VMC) setting of the Quantum Sherrington-Kirkpatrick (QSK) model whose ground state exhibits volume-law entangleme...
BOPS is presented, a generative model based on Schrodinger bridges, using a custom denoiser architecture, that learns a transformation from a source circuit into an equivalent optimized circuit, opening up the quantum compilation stack to learned optimization along multiple axes.
L. Hofstetter, Lia Yeh, Prakash Murali· 0 citations
Results demonstrate that a trainable quantum receiver can recover task-relevant semantic information from noise-distorted quantum states and maintain high classification performance, highlighting a distinction between physical-state recovery and semantic-feature recovery.
Melek Krichen, Nikhitha Nunavath, R. Bassoli et al.· 0 citations
We present Hybrid Quantum Root Cause Analysis (HQ-RCA), an industrially grounded workflow for root cause analysis in banking IT operations, built on a hybrid Quantum Graph Neural Network (QGNN): the classical backbone of DynEdge (the IceCube neutrino-reconstruction GNN, which we call standalone DynEdge), with its class...
Antonio Greco, Riccardo Paoletti, Roberto Cappuccio et al.· 0 citations
Together, these contributions show how learned representations, statistical control, and hardware constraints shape the exchange between quantum computing and machine learning.
Language models can be adapted by changing the computations applied to individual tokens. Quantum circuits offer one such approach, but evaluating wider circuits inside a large model can be computationally demanding. Here we introduce HyperQ, which adds token-conditioned quantum residual branches to a frozen masked-dif...
Xiaoqiang Wang, Mengyang Xiong, Jun Dai et al.· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
Biology doesn't operate in silos, and neither should the AI representation of it. Quine is an early-stage research effort to create a multimodal world model of biology. By connecting insights across biological scales and modalities, Quine helps scientists computationally search a space far larger than intuition allows and prioritize hypotheses before they reach the lab. Experimental results provide important feedback, helping researchers sharpen future research directions. The post Introducing Q…
