Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 10822 publications
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For many practical applications of quantum computing, the slowest and most costly steps involve coherently accessing classical data. We help address this challenge by applying mass production techniques, which can sometimes allow us to perform operations many times in parallel for a cost that is comparable to a single execution[1-3]. We combine existing mass-production results with modern approaches for loading classical data using ``quantum read-only memory.'' We show that quantum mass production techniques offer no benefit when we consider a cost model that focuses purely on the number of non-Clifford gates. However, analyzing the constant factors in a more nuanced cost model, we find that it may be possible to obtain a reduction in cost of an order or magnitude or more for a variety reasonably-sized fault-tolerant quantum algorithms. We present several applications of quantum mass-production techniques beyond naive parallelization, including a strategy for reducing the cost of serial calls to the same data loading step.
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AI coding assistants are rapidly becoming integral to modern software development. A key challenge in this space is the continual need to migrate and modernize codebases in response to evolving software ecosystems. Traditionally, such migrations have relied on rule-based systems and human intervention. With the advent of powerful large language models (LLMs), AI-driven agentic frameworks offer a promising alternative—but their effectiveness remains underexplored. In this paper, we introduce FreshBrew, a novel benchmark for evaluating AI-based agentic frameworks on project-level Java migrations. We benchmark several such frameworks, powered by state-of-the-art LLMs, and compare their performance against established rule-based tools. Our evaluation of AI agents on this benchmark of 228 repositories shows that the top-performing model, Gemini 2.5 Flash, can successfully migrate 56.5% of projects to JDK 17. Our empirical analysis reveals novel insights into the critical strengths and limitations of current agentic approaches, offering actionable insights into their real-world applicability. By releasing FreshBrew publicly upon acceptance, we aim to facilitate rigorous, reproducible evaluation and catalyze progress in AI-driven codebase modernization.
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Perceptual Audio Coding: A 40-Year Historical Perspective
Juergen Herre
Schuyler Quackenbush
Minje Kim
2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2025)
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In the history of audio and acoustic signal processing perceptual audio coding has certainly excelled as a bright success story by its ubiquitous deployment in virtually all digital media devices, such as computers, tablets, mobile phones, set-top-boxes, and digital radios. From a technology perspective, perceptual audio coding has undergone tremendous development from the first very basic perceptually driven coders (including the popular mp3 format) to today’s full-blown integrated coding/rendering systems. This paper provides a historical overview of this research journey by pinpointing the pivotal development steps in the evolution of perceptual audio coding. Finally, it provides thoughts about future directions in this area.
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On Design Principles for Private Adaptive Optimizers
Abhradeep Guha Thakurta
Arun Ganesh
Privacy-Preserving Machine Learning Workshop 2025 (2025) (to appear)
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The spherical noise added to gradients in differentially private (DP) training undermines the performance of adaptive optimizers like AdaGrad and Adam, and hence many recent works have proposed algorithms to address this challenge. However, the empirical results in these works focus on simple tasks and models and the conclusions may not generalize to model training in practice. In this paper we survey several of these variants, and develop better theoretical intuition for them as well as perform empirical studies comparing them. We find that a common intuition of aiming for unbiased estimates of second moments of gradients in adaptive optimizers is misguided, and instead that a simple technique called scale-then-privatize (which does not achieve unbiased second moments) has more desirable theoretical behaviors and outperforms all other variants we study on a small-scale language model training task. We additionally argue that scale-then-privatize causes the noise addition to better match the application of correlated noise mechanisms which are more desirable to use in practice.
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We study the problem of learning the optimal item pricing for a unit-demand buyer with independent item values, and we have query access to the underlying value distributions. We consider two common query model in the literature: the “sample complexity” model where we can obtain a sample of each item value, and the “pricing query complexity” model where we can set a price to each item and obtain a binary signal on whether the sampled value of the item is greater than our proposed price. In this work we give nearly tight sample complexity and pricing query complexity of the problem.
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Probing non-equilibrium topological order on a quantum processor
Melissa Will
Tyler Cochran
Bernhard Jobst
Norhan Eassa
Michael Knap
Adam Gammon-Smith
Frank Pollmann
Nature, 645 (2025), 348–353
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Out-of-equilibrium phases in many-body systems constitute a new paradigm in quantum matter—they exhibit dynamical properties that may otherwise be forbidden by equilibrium thermodynamics. Among these non-equilibrium phases are periodically driven (Floquet) systems, which are generically difficult to simulate classically because of their high entanglement. Here we realize a Floquet topologically ordered state on an array of superconducting qubits. We image the characteristic dynamics of its chiral edge modes and characterize its emergent anyonic excitations. Devising an interferometric algorithm allows us to introduce and measure a bulk topological invariant to probe the dynamical transmutation of anyons for system sizes up to 58 qubits. Our work demonstrates that quantum processors can provide key insights into the thus-far largely unexplored landscape of highly entangled non-equilibrium phases of matter.
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Tighter Privacy Analysis for Truncated Poisson Sampling
Arun Ganesh
(2025)
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We give a new privacy amplification analysis for truncated Poisson sampling, a Poisson sampling variant that truncates a batch if it exceeds a given maximum batch size.
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Correlated Error Bursts in a Gap-Engineered Superconducting Qubit Array
John Mark Kreikebaum
Leigh Martin
Vlad Kurilovich
Wojtek Mruczkiewicz
Lara Faoro
Gabrielle Roberts
Alex Opremcak
Alec Eickbusch
Igor Aleiner
Yu Chen
arXiv (2025)
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One of the roadblocks towards the implementation of a fault-tolerant superconducting quantum processor is impacts of ionizing radiation with the qubit substrate. Such impacts temporarily elevate the density of quasiparticles (QPs) across the device, leading to correlated qubit error bursts. The most damaging errors – T1 errors – stem from QP tunneling across the qubit Josephson junctions (JJs). Recently, we demonstrated that this type of error can be strongly suppressed by engineering the profile of superconducting gap at the JJs in a way that prevents QP tunneling. In this work, we identify a new type of correlated error that persists in the presence of gap engineering. We observe that impacts shift the frequencies of the affected qubits, and thus lead to correlated phase errors. The frequency shifts are systematically negative, reach values up to 3 MHz, and last for ~1 ms. We provide evidence that the shifts originate from the QP-qubit interactions in the JJ region. Further, we experimentally demonstrate these correlated phase errors are detrimental to the performance of quantum error correction protocols.
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PROTECT: A Framework to Foster Digital Resilience for Youth Navigating Technology-Facilitated Abuse
Diana Freed
Natalie Bazarova
Dan Cosley
Patrick Gage Kelley
Social Sciences Journal, 14(6) (2025)
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Youth are increasingly exposed to a broad range of technology-facilitated abuse that challenges their safety and well-being. Building on previous work that examined youth help-seeking behaviors, coping strategies, threats they encounter, and the social support systems around them, we articulate a framework— called PROTECT—Problem recognition, Reaching out, Organizing support, Training, Engaging experts, Continuous support, and Tackling safety measures—which integrates existing models of support, help-seeking, and digital skills to offer a high-level, structured approach to adults who serve as a support system to youth navigate technology-facilitated abuse. The framework unpacks social and contextual dynamics that influence help-seeking behaviors, providing a foundation for educators, advocates, health professionals, developers and other adult stakeholders to design and develop trauma-informed, timely interventions to promote resilience.
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Modern deep learning algorithms use variations of gradient descent as their main learning methods. Gradient descent can be understood as the simplest Ordinary Differential Equation (ODE) solver; namely, the Euler method applied to the gradient flow differential equation. Since Euler, many ODE solvers have been devised that follow the gradient flow equation more precisely and more stably. Runge-Kutta (RK) methods provide a family of very powerful explicit and implicit high-order ODE solvers. However, these higher-order solvers have not found wide application in deep learning so far. In this work, we evaluate the performance of higher-order RK solvers when applied in deep learning, study their limitations, and propose ways to overcome these drawbacks. In particular, we explore how to improve their performance by naturally incorporating key ingredients of modern neural network optimizers such as preconditioning, adaptive learning rates, and momentum.
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We introduce efficient differentially private (DP) algorithms for several linear algebraic tasks, including solving linear equalities over arbitrary fields, linear inequalities over the reals, and computing affine spans and convex hulls. As an application, we obtain efficient DP algorithms for learning halfspaces and affine subspaces. Our algorithms addressing equalities are strongly polynomial, whereas those addressing inequalities are weakly polynomial. Furthermore, this distinction is inevitable: no DP algorithm for linear programming can be strongly polynomial-time efficient.
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Most contextual bandit algorithms assume that rewards are bounded uniformly, or with a high probability for each context and action. Furthermore, the theoretical regret, as well as empirical performance of most prevailing methods scales polynomially with this reward scale. In this work, we use ideas from robust mean estimation to design contextual bandit algorithms where the regret has only a mild logarithmic scaling on the reward scale, with a polynomial dependence on the second moment rather than the maximum reward value. Our algorithm is based on Catoni's mean estimator, is applicable to arbitrary non-linear function classes, and we present both variance aware and variance agnostic versions of our approach.
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Matryoshka Model Learning for Improved Elastic Student Models
Cho-Jui Hsieh
Chetan Verma
Inderjit Dhillon
Xin Liu
Wen Chen
Ngot Bui
Yang Zhang
2025
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Production machine learning models in the industry are often devel-oped with a primary focus on maximizing model quality. However,these models must ultimately operate within the resource con-straints of their serving infrastructure, including limitations on com-pute, memory and bandwidth. The rapid evolution of serving hard-ware, particularly with advancements in accelerator technology,necessitates periodic retraining to leverage newer, more efficientinfrastructure. This cyclical retraining process is resource-intensive,demanding significant model development time and incurring sub-stantial training costs. This challenge is further amplified by thetrend towards increasingly complex models, which inherently re-quire greater computational resources for training and deployment.While prior work has explored techniques like supernet sub-modelextraction to address training efficiency, a critical gap remains: theefficient generation of a spectrum of high-quality models froman existing production model, a common requirement in diverseindustrial applications. To bridge this gap, we introduce a novel ap-proach leveraging a "Teaching Assistant" (TA) model, derived froma given production model (referred to as the Student model). Wedemonstrate that through co-training the Student and TA modelswith Matryoshka structure while using online distillation, we notonly enhance the Student model’s performance but also enable theflexible creation of a model family offering a compelling trade-offbetween model quality and model size.
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Improving simulation-based origin-destination demand calibration using sample segment counts data
Arwa Alanqary
Yechen Li
The 12th Triennial Symposium on Transportation Analysis conference (TRISTAN XII), Okinawa, Japan (2025)
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This paper introduces a novel approach to demand estimation that utilizes partial observations of segment-level track counts. Building on established simulation-based demand estimation methods, we present a modified formulation that integrates sample track counts as a regularization term. This approach effectively addresses the underdetermination challenge in demand estimation, moving beyond the conventional reliance on a prior OD matrix. The proposed formulation aims to preserve the distribution of the observed track counts while optimizing the demand to align with observed path-level travel times. We tested this approach on Seattle's highway network with various congestion levels. Our findings reveal significant enhancements in the solution quality, particularly in accurately recovering ground truth demand patterns at both the OD and segment levels.
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Multi-turn Function-calling via Graph-based Execution and Translation
Kai-Wei Chang
Ke Jiang
Jindong Gu
Fan Yin
2025
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We propose a principled method to synthesize high-quality multi-turn function calling trajectories to align large language model (LLM)-based agents. We start with iteratively building function calling graph and defining node operations to increase its complexity. This enables us to construct reliable reference. Then, based on the synthesized function calling graph, we adopt back-and-forth translation to first construct multi-turn user queries and then, fill in the function arguments with information in the query. We sample positive trajectories that distill the function graph reference and negative trajectories that contrast with the positive trajectories in targeted loss patterns in multi-turn scenarios. Training with the positive trajectories with supervised fine-tuning and preference optimization against negative trajectories, we obtain 67.42 on BFCL and 71.7 on ToolQuery with an open-sourced model with 14B parameters, surpassing the performance of strong proprietary models like o1.
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