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603 papers
This work proposes QFWP-ANO, a novel architecture which employs a classical hypernetwork to dynamically program VQC parameters and/or non-local observables conditioned on each input, and establishes input-conditioned ANO as an effective approach for enhancing QNNs.
The results identify information required for recovery selection, establish conditional guarantees against harmful updates, and quantify the recovery improvements forgone through conservative acceptance.
QT is introduced, a Quantum-inspired Transformer for vision tasks with three components: angle-inspired encoding that maps image tokens to learned trigonometric Hilbert-space features analogous to quantum rotation-based state encoding; self-attention over these periodic features, inducing a classical cosine kernel appr...
QIQCBench is introduced, a benchmark of $49$ expert-authored tasks spanning multiple layers including calibration and control, error correction and compilation, sensing and networking, that reveals wide variation in verified performance across frontier agentic systems.
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A family of shallow circuits using Hadamards and controlled-Phase gates, HP-$L$ circuits, are constructed that prove preserve shift invariance and the $\mathcal{O}(n)$ HP-$1$ QFT in the numerical implementation of Shor's algorithm.
LLM-learned shuttling compilation is feasible, and it is shown how far it currently reaches, and preliminary evidence of cross-architecture generalization is shown.
L Langevin simulated bifurcation (LSB) is established as a fast and accurate Boltzmann sampler and key insights are provided that enable practical applications of fast non-MCMC sampling methods via efficient temperature estimation with CEM.
This work studies the simplified setting of reinforcement learning with a uniform generative model in which uniformly random state-action samples are available, and provides finite sample guarantees for classical kernelized Fitted Q-Iteration, with classical kernels designed to match the inductive bias of particular pa...
This work evaluates a 10-qubit hybrid quantum-classical variational circuit for paraphrase detection across three benchmarks: MRPC, Quora Question Pairs, and adversarial PAWS to constitute the first systematic parameter-matched multi-benchmark empirical validation of hybrid variational circuits for NLP.
Automatic Hindi pregroup assignment is feasible and can reduce reliance on manual annotation in future multilingual QNLP pipelines, and suffix/morphology features improve karaka-token accuracy but not overall performance.
Continuous-variable systems enable key quantum technologies in computation, communication, and sensing. Bosonic Gaussian states emerge naturally in various such applications, including gravitational-wave and dark-matter detection. A fundamental question is how to characterize an unknown bosonic Gaussian state from as f...
These results establish exact spin elimination as a controlled trade between active-spin capacity and interaction complexity and show that removing spins while allowing more complex interactions can fit larger problems within the same spin budget and improve optimization.
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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