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603 papers
Reporting the Fisher geometry of a trained variational quantum model is routine; quoting the shot budget that would establish it is not. Certifying an empirical Fisher matrix to relative Frobenius error $\varepsilon$ under coordinate-wise parameter shift costs $\Theta(B p^{2} V/(\varepsilon^{2} G))$ circuit executions,...
Generative augmentation is a standard remedy for label scarcity in physiological signal classification, but existing quantum generative models start from uninformative noise, ignoring class structure that is already available. We propose Conditional Quantum Flow Matching (CQFM): a single 306-parameter circuit, conditio...
Quantum transfer learning (QTL) is often evaluated by replacing a classical classifier with a fixed variational quantum head, but this hides a key question: when is the quantum branch actually useful? We propose QSTAR: Quantum Selective Transfer with Adaptive Routing, a selective QTL framework that keeps high-confidenc...
GRPO-QPS is introduced, a target-preserving framework in which GRPO learns proposal behavior and an exact Metropolis correction preserves the posterior after training, which combines target-preserving Bayesian inference with broad gains over learned transport baselines and a sampling advantage when efficient exploratio...
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Tensorizing a neural network involves reshaping some or all of its dense weight matrices into higher-order tensors and approximating them using low-rank tensor network decompositions. This technique has shown promise as a model compression strategy for large-scale neural networks. However, despite encouraging empirical...
The results establish, at the experimental level, automated logical reasoning as a viable task for near-term quantum processors and provide a concrete pathway toward quantum-enhanced symbolic intelligence.
This work provides a conceptual foundation for governance-aware, adaptable, and AI-first software engineering methods by proposing a metamodel that treats autonomy not as a fixed property of an actor, but as a derived, situation-dependent authority assignment determined by task, context, and collaboration pattern.
A hypothetical test which can be effective even for small datasets, based on the theoretical foundation of kernel-based tests using maximum mean discrepancy, is constructed, called MMD-FUSE, and a novel hybrid testing strategy that fuses classical and quantum kernels is proposed.
Reduced structured errors are observed that are visually correlated with oscillatory components on several scientific benchmarks, suggesting that geometric preconditioning combined with curriculum training is a practical approach for stabilizing quantum regression.
This paper uses reinforcement learning and importance sampling to outperform previous work at all scales and reduces the logical error rate by 25.9\% and 71.7\% on average, respectively.
This paper forms the compression of large language models (LLMs) by optimally deleting transformer blocks (``block removal'') as a constrained binary optimization (CBO) problem that can be mapped to a physical system (Ising glass), whose energies are a strong proxy for downstream model performance.
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...
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
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…
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
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.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
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