Agentic artificial intelligence (AI) is shifting from decision support to autonomous coordination, challenging established assumptions about managerial authority. This qualitative conceptual paper synthesises recent literature in algorithmic management, organisational theory, and human-computer interaction to examine h...
Kwan Hong TAN· Zenodo (CERN European Organi...· 0 citations
Quantum sensing technologies offer transformative potential for ultra-sensitive biomedical sensing, yet their clinical translation remains constrained by classical noise limits and a reliance on macroscopic ensembles. We propose a unifying generational framework to organize the evolving landscape of quantum biosensors...
Xin Jin, Priyam Srivastava, Ronghe Wang et al.· 0 citations
The graybox framework integrates a physics-based system model with a data-driven description of experimental imperfections, achieving higher fidelity than purely analytical (whitebox) approaches while requiring fewer training resources than fully deep-learning blackbox models.
Akram Youssry, S. Todd, Patrick D. F. Murton et al.· arXiv.org· 1 citation
This work proposes a novel algorithm structured on Lie algebras for the approximate quantum state preparation problem, achieving high accuracy of up to 4 qubits in simulation, but also its current limitations with an increasing number of qubits.
Marco Mordacci, Giacomo Belli, Michele Amoretti· 4 citations
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For two causal structures with the same set of visible variables, one is said to observationally dominate the other if the set of distributions over the visible variables realizable by the first contains the set of distributions over the visible variables realizable by the second. Knowing such dominance relations is us...
Marina Maciel Ansanelli, Elie Wolfe, Robert W. Spekkens· 0 citations
A classical computational model based on quantum channel transformation, performed and calculated on a classical computer, possibly hardware-optimized is discussed.
Mikhail Gennadievich Belov, V. V. Dubov, Vadim Konstantinovich Ivanov et al.· Physical Review E· 2 citations
We develop a quantum approach to spectral feature extraction from the density of states (DOS) of a problem-dependent Hamiltonian, and apply it to machine learning on signed graphs. We propose to embed a signed graph as an Ising model instance with positive and negative interactions, and use the standardized moments of...
GenQAS is introduced, a tensor network-guided RL framework that combines a fixed matrix product state warm-start with prioritized generative replay and can mitigate sample starvation in quantum architecture search and support more resource efficient circuit discovery.
Akash Kundu, Amit Kumar Jaiswal, Sebastian Feld et al.· 0 citations
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-base...
Luo-Fei Wang, Da Zhang, Cong-Ren Wang et al.· 0 citations
The results show that the factorization underlying a quantum block encoding can itself provide sufficient classical structure even when sampling-and-query access to the composite matrix is unavailable, suggesting a classical sampler with prescribed accuracy and polynomially related runtime.
Natsuto Isogai, M. Murao, Hayata Yamasaki· 0 citations
A general learning-based framework that, given only an encoding circuit, constructs physical implementations of logical operations while enforcing structural properties such as transversality or shallow depth, and position VarEFTQC as a proof-of-concept framework for discovering hardware-adapted logical gadgets for ear...
Nico Meyer, Christopher Mutschler, Dominik Seuss et al.· Physical Review A· 3 citations
We determine the optimal sample complexity of low-rank quantum state tomography when each measurement may act jointly on at most $t$ samples. For sufficiently small $\varepsilon$, estimating an unknown state on $\mathbb{C}^d$ of rank at most $r$ to trace norm error $\varepsilon$ with constant success probability requir...
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
