AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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603 papers
Hybrid quantum neural networks (QNNs) pair a classical feature extractor with a variational quantum circuit, and the effective dimension of the circuit's quantum Fisher information matrix (QFIM) is widely used to assess such models. This scalar is often interpreted as an indicator of trainability or accuracy, although...
It is proved that this single-qubit re-uploading (serial) circuit represents every function that a multi-qubit circuit with the same encoding budget with the same encoding budget can; it is found that this exchange of width for depth is not free in first-order training.
Preparing quantum states in bosonic qudits with SNAP--Displacement sequences requires both optimizing the gate parameters and deciding which Fock levels should be controlled by each SNAP gate. A fully parameterized ansatz assigns an independent phase to every Fock level in every gate, leading to a number of phases that...
Experiments with pretrained diffusion language models show that superposition-based generation can retain information from conditioning components not selected at the final measurement, while QIR improves LLM-assessed text quality when applied at an appropriate stage of generation.
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A regret lower bound is established, showing that the regret rates of Exponential Weights and the algorithm of Tseng et al. (2025) have optimal polynomial dependence on the dimension, which leads to a quantum state tomography algorithm that is sample optimal up to polylogarithmic factors in the sample size.
What connects minimal decision-making to contextual probability? We address this question through the Tug-of-War (TOW) principle, a resource-coupled mechanism for multi-armed bandit decision-making. Starting from classical probability, we show that when reward-learning functionality and specified information requiremen...
An evolutionary framework in which large language models propose circuit designs and an independent evaluator verifies and scores them is introduced and opens the door for evaluator-guided LLM co-design of new algorithmic structures for quantum computation.
It is proposed that fusion can improve prediction when classical and quantum representations capture complementary predictive relationships, and evaluated quantum representations for their complementary predictive contribution and matching the representation and fusion strategy to the target relationships.
This work developed an algorithmic framework for variational quantum kernels utilizing resource- efficient quantum kernels utilizing resource- efficient ansatze for complex classification tasks and introduced a parameter scaling technique to accelerate convergence.
Abstract The development of new drugs is a tedious, time-consuming, and expensive process for which the average costs are estimated to be up to around $2.5 billion. The first step in this long process is the design of the new drug, for which de novo drug design, assisted by artificial intelligence, has blossomed in rec...
This study serves as a proof-of-concept for the utility, effectiveness, and superior performance of 1D and 2D hyperbolic NQS ansatzes compared to conventional Euclidean NQS models in many-body quantum systems - particularly those exhibiting structural hierarchy, criticality, or a combination of both.
A scalable ML framework that efficiently learns from quantum many-body data by leveraging the exponential decay of correlations prevalent in noncritical systems is proposed, and it is rigorously proved that the GLQK substantially improves the sample complexity in the system size $n$ over the existing shadow kernel.
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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