Leveraging this reduction yields a non-interactive protocol for encrypted federated training of hybrid quantum--classical networks, which includes correctness proofs for aggregation and sign handling, plus a compilation lemma proving parameterised entanglers add only constant-factor overhead without altering the depth...
Marcel Mordarski, N. Mani, Arshad Patel et al.· 0 citations
The results suggest that, for highly periodic scientific monitoring time series, models incorporating strong seasonal or low-dimensional functional priors can match or outperform substantially more complex sequence architectures.
A scalable hybrid Quantum Diffusion Model is presented, based on a Discrete-Time Quantum Walk algorithm, executed on a real quantum device, to model the forward dynamics of the diffusion model, and its use for medical image analysis is evaluated.
Francesco Aldo Venturelli, Stefano Martina, Marco Parigi et al.· 0 citations
We propose a variational quantum implementation of self-attention (QSA)-the core operation in transformers and large language models-which predicts future elements of a sequence by forming overlap-weighted combinations of past data. At variance with previous approaches, our QSA realizes the required nonlinearity throug...
Alessio Pecilli, Matteo Rosati· 0 citations
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This work forms a Markov Decision Process for the problem and uses double deep Q-networks with Message Passing Neural Networks, experience replay buffers, and curriculum training to obtain policies, indicating a promising method for interpretable policy extraction for large quantum networks, where direct training becom...
L. Rode, Sumeet Khatri, Supartha Podder· 0 citations
A unified, learning-based selector that integrates these disparate stages into a cohesive framework that can be extended to simultaneously optimize multiple objectives -- such as fidelity, compilation time, and scheduling latency -- while dynamically adapting to circuit characteristics and device conditions is proposed...
Andre Youssefi, Ercüment Kaya, Minh Chung et al.· 0 citations
The framework connects benchmark performance to experimentally actionable diagnoses of reservoir encoding, measurement choice, classical representation, and shot allocation and introduces task-resolved Fisher spectroscopy, in which prediction targets define orthonormal score coordinates on the stationary distribution o...
Hybrid quantum-classical neural networks have emerged as a promising approach for leveraging quantum computing in machine learning while mitigating current hardware limitations. This paper presents Sim-HVQC, a hybrid Deep Quantum Neural Network that couples an adaptive, parameter-free SimAM weighting module with classi...
Context-sensitive behavior can be modeled by enriching an internal state, by allowing a response rule to access context directly, or by preserving a shared state while introducing an auxiliary criterion or control variable. This paper isolates an information-theoretic constraint on the third architecture. Let $C$ denot...
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.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
Microsoft Research Blog· microsoft.comOct 21, 2026
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
