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 10390 publications
Rapid Initial-State Preparation for the Quantum Simulation of Strongly Correlated Molecules
Dominic Berry
Yu Tong
Alec White
Tae In Kim
Lin Lin
Seunghoon Lee
Garnet Chan
PRX Quantum, 6 (2025), pp. 020327
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Studies on quantum algorithms for ground-state energy estimation often assume perfect ground-state preparation; however, in reality the initial state will have imperfect overlap with the true ground state. Here, we address that problem in two ways: by faster preparation of matrix-product-state (MPS) approximations and by more efficient filtering of the prepared state to find the ground-state energy. We show how to achieve unitary synthesis with a Toffoli complexity about 7 × lower than that in prior work and use that to derive a more efficient MPS-preparation method. For filtering, we present two different approaches: sampling and binary search. For both, we use the theory of window functions to avoid large phase errors and minimize the complexity. We find that the binary-search approach provides better scaling with the overlap at the cost of a larger constant factor, such that it will be preferred for overlaps less than about 0.003. Finally, we estimate the total resources to perform ground-state energy estimation of Fe-S cluster systems, including the FeMo cofactor by estimating the overlap of different MPS initial states with potential ground states of the FeMo cofactor using an extrapolation procedure. With a modest MPS bond dimension of 4000, our procedure produces an estimate of approximately 0.9 overlap squared with a candidate ground state of the FeMo cofactor, producing a total resource estimate of 7.3e10 Toffoli gates; neglecting the search over candidates and assuming the accuracy of the extrapolation, this validates prior estimates that have used perfect ground-state overlap. This presents an example of a practical path to prepare states of high overlap in a challenging-to-compute chemical system.
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Preventing Network Bottlenecks: Accelerating Datacenter Services with Hotspot-Aware Placement for Compute and Storage
Hamid Bazzaz
Yingjie Bi
Weiwu Pang
Minlan Yu
Ramesh Govindan
Chloe Tsai
Chris DeForeest
Charlie Carver
Jan Kopański
2025
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Datacenter network hotspots, defined as links with persistently high utilization, can lead to performance bottlenecks.In this work, we study hotspots in Google’s datacenter networks. We find that these hotspots occur most frequently at ToR switches and can persist for hours. They are caused mainly by bandwidth demand-supply imbalance, largely due to high demand from network-intensive services, or demand exceeding available bandwidth when compute/storage upgrades outpace ToR bandwidth upgrades. Compounding this issue is bandwidth-independent task/data placement by data-center compute and storage schedulers. We quantify the performance impact of hotspots, and find that they can degrade the end-to-end latency of some distributed applications by over 2× relative to low utilization levels. Finally, we describe simple improvements we deployed. In our cluster scheduler, adding hotspot-aware task placement reduced the number of hot ToRs by 90%; in our distributed file system, adding hotspot-aware data placement reduced p95 network latency by more than 50%. While congestion control, load balancing, and traffic engineering can efficiently utilize paths for a fixed placement, we find hotspot-aware placement – placing tasks and data under ToRs with higher available bandwidth – is crucial for achieving consistently good performance.
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LLM-based Lossless Text Simplification and its Effect on User Comprehension and Cognitive Load
Theo Guidroz
Diego Ardila
Jimmy Li
Adam Mansour
Paul Jhun
Nina Gonzalez
Xiang Ji
Mike Sanchez
Miguel Ángel Garrido
Divyansh Choudhary
Jay Hartford
Georgina Xu
Henry Serrano
Yifan Wang
Jeff Shaffer
Eric (Yifan) Cao
Sho Fujiwara
Peggy Bui
arXiv (2025)
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Information on the web, such as scientific publications and Wikipedia, often surpasses users' reading level. To help address this, we used a self-refinement approach to develop a LLM capability for minimally lossy text simplification. To validate our approach, we conducted a randomized study involving 4563 participants and 31 texts spanning 6 broad subject areas: PubMed (biomedical scientific articles), biology, law, finance, literature/philosophy, and aerospace/computer science. Participants were randomized to viewing original or simplified texts in a subject area, and answered multiple-choice questions (MCQs) that tested their comprehension of the text. The participants were also asked to provide qualitative feedback such as task difficulty. Our results indicate that participants who read the simplified text answered more MCQs correctly than their counterparts who read the original text (3.9% absolute increase, p<0.05). This gain was most striking with PubMed (14.6%), while more moderate gains were observed for finance (5.5%), aerospace/computer science (3.8%) domains, and legal (3.5%). Notably, the results were robust to whether participants could refer back to the text while answering MCQs. The absolute accuracy decreased by up to ~9% for both original and simplified setups where participants could not refer back to the text, but the ~4% overall improvement persisted. Finally, participants' self-reported perceived ease based on a simplified NASA Task Load Index was greater for those who read the simplified text (absolute change on a 5-point scale 0.33, p<0.05). This randomized study, involving an order of magnitude more participants than prior works, demonstrates the potential of LLMs to make complex information easier to understand. Our work aims to enable a broader audience to better learn and make use of expert knowledge available on the web, improving information accessibility.
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Shadow Hamiltonian Simulation
Rolando Somma
Robbie King
Tom O'Brien
Nature Communications, 16 (2025), pp. 2690
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Simulating quantum dynamics is one of the most important applications of quantum computers. Traditional approaches for quantum simulation involve preparing the full evolved state of the system and then measuring some physical quantity. Here, we present a different and novel approach to quantum simulation that uses a compressed quantum state that we call the "shadow state". The amplitudes of this shadow state are proportional to the time-dependent expectations of a specific set of operators of interest, and it evolves according to its own Schrödinger equation. This evolution can be simulated on a quantum computer efficiently under broad conditions. Applications of this approach to quantum simulation problems include simulating the dynamics of exponentially large systems of free fermions or free bosons, the latter example recovering a recent algorithm for simulating exponentially many classical harmonic oscillators. These simulations are hard for classical methods and also for traditional quantum approaches, as preparing the full states would require exponential resources. Shadow Hamiltonian simulation can also be extended to simulate expectations of more complex operators such as two-time correlators or Green's functions, and to study the evolution of operators themselves in the Heisenberg picture.
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Understanding fine-grained temporal dynamics is crucial in egocentric videos, where continuous streams capture frequent, close-up interactions with objects. In this work, we bring to light that current egocentric video question-answering datasets often include questions that can be answered using only few frames or commonsense reasoning, without being necessarily grounded in the actual video. Our analysis shows that state-of-the-art Multi-Modal Large Language Models (MLLMs) on these benchmarks achieve remarkably high performance using just text or a single frame as input. To address these limitations, we introduce EgoTempo, a dataset specifically designed to evaluate temporal understanding in the egocentric domain. EgoTempo emphasizes tasks that require integrating information across the entire video, ensuring that models would need to rely on temporal patterns rather than static cues or pre-existing knowledge. Extensive experiments on EgoTempo show that current MLLMs still fall short in temporal reasoning on egocentric videos, and thus we hope EgoTempo will catalyze new research in the field and inspire models that better capture the complexity of temporal dynamics. Dataset and code are available at https://github.com/google-research-datasets/egotempo.git.
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Participatory AI Considerations for Advancing Racial Health Equity
Andrea G. Parker
Jatin Alla
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI) (2025) (to appear)
Validation of Quantum Elliptic Curve Point Addition Circuits
(2025) (to appear)
Preview abstract
Specific quantum algorithms exist to—in theory—
break elliptic curve cryptographic protocols. Implementing these
algorithms requires designing quantum circuits that perform elliptic curve arithmetic. To accurately judge a cryptographic protocol’s resistance against future quantum computers, researchers
figure out minimal resource-count circuits for performing these
operations while still being correct. To assure the correctness of
a circuit, it is integral to restore all ancilla qubits used to their
original states. Failure to do so could result in decoherence of the
computation’s final result. Through rigorous classical simulation
and unit testing, I surfaced four inconsistencies in the state-ofthe-art quantum circuit for elliptic curve point addition where
the circuit diagram states the qubits are returned in the original
(|0⟩) state, but the intermediate values are not uncomputed. I
provide fixes to the circuit without increasing the leading-order
gate cost.
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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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Supporting the Digital Safety of At-Risk Users: Lessons Learned from 9+ Years of Research and Training
Tara Matthews
Patrick Gage Kelley
Lea Kissner
Andreas Kramm
Andrew Oplinger
Andy Schou
Stephan Somogyi
Dalila Szostak
Jill Woelfer
Lawrence You
Izzie Zahorian
ACM Transactions on Computer-Human Interaction, 32(3) (2025), pp. 1-39
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Creating information technologies intended for broad use that allow everyone to participate safely online—which we refer to as inclusive digital safety—requires understanding and addressing the digital-safety needs of a diverse range of users who face elevated risk of technology-facilitated attacks or disproportionate harm from such attacks—i.e., at-risk users. This article draws from more than 9 years of our work at Google to understand and support the digital safety of at-risk users—including survivors of intimate partner abuse, people involved with political campaigns, content creators, youth, and more—in technology intended for broad use. Among our learnings is that designing for inclusive digital safety across widely varied user needs and dynamic contexts is a wicked problem with no “correct” solution. Given this, we describe frameworks and design principles we have developed to help make at-risk research findings practically applicable to technologies intended for broad use and lessons we have learned about communicating them to practitioners.
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AI as a Catalyst for Educational Equity: Addressing Global Teacher Shortages and Learning Disparities
International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCERT) (2025)
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The global education system is grappling with a critical shortage of teachers, threatening the achievement of universal quality education. This article examines how artificial intelligence (AI) technologies can revolutionize educational access and equity by addressing these systemic challenges. Through a comprehensive article analysis of AI-enabled solutions, including personalized learning mechanisms, virtual tutoring systems, and intelligent content distribution platforms, the article explores the transformative potential of these technologies in democratizing education. The article investigates the implementation of AI across established educational platforms, examining their effectiveness in providing adaptive learning experiences, breaking down language barriers, and ensuring cultural relevance. The article demonstrates that strategic AI integration can significantly impact learning outcomes while helping to bridge the global teacher shortage gap. The article also addresses critical implementation challenges, providing policy recommendations and resource allocation frameworks for successful AI adoption in education systems worldwide. This article analysis contributes to the growing body of knowledge on educational technology by offering practical insights into how AI can be leveraged to create more inclusive, effective, and accessible learning environments, ultimately advancing the goal of quality education for all.
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Triaging mammography with artificial intelligence: an implementation study
Sarah M. Friedewald
Sunny Jansen
Fereshteh Mahvar
Timo Kohlberger
David V. Schacht
Sonya Bhole
Dipti Gupta
Scott Mayer McKinney
Stacey Caron
David Melnick
Mozziyar Etemadi
Samantha Winter
Alejandra Maciel
Luca Speroni
Martha Sevenich
Arnav Agharwal
Rubin Zhang
Gavin Duggan
Shiro Kadowaki
Atilla Kiraly
Jie Yang
Basil Mustafa
Krish Eswaran
Shravya Shetty
Breast Cancer Research and Treatment (2025)
Preview abstract
Purpose
Many breast centers are unable to provide immediate results at the time of screening mammography which results in delayed patient care. Implementing artificial intelligence (AI) could identify patients who may have breast cancer and accelerate the time to diagnostic imaging and biopsy diagnosis.
Methods
In this prospective randomized, unblinded, controlled implementation study we enrolled 1000 screening participants between March 2021 and May 2022. The experimental group used an AI system to prioritize a subset of cases for same-visit radiologist evaluation, and same-visit diagnostic workup if necessary. The control group followed the standard of care. The primary operational endpoints were time to additional imaging (TA) and time to biopsy diagnosis (TB).
Results
The final cohort included 463 experimental and 392 control participants. The one-sided Mann-Whitney U test was employed for analysis of TA and TB. In the control group, the TA was 25.6 days [95% CI 22.0–29.9] and TB was 55.9 days [95% CI 45.5–69.6]. In comparison, the experimental group's mean TA was reduced by 25% (6.4 fewer days [one-sided 95% CI > 0.3], p<0.001) and mean TB was reduced by 30% (16.8 fewer days; 95% CI > 5.1], p=0.003). The time reduction was more pronounced for AI-prioritized participants in the experimental group. All participants eventually diagnosed with breast cancer were prioritized by the AI.
Conclusions
Implementing AI prioritization can accelerate care timelines for patients requiring additional workup, while maintaining the efficiency of delayed interpretation for most participants. Reducing diagnostic delays could contribute to improved patient adherence, decreased anxiety and addressing disparities in access to timely care.
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Intuitively, the more complex a software system is, the harder it is to maintain. Statistically, it is not clear which complexity measures correlate with maintenance effort; in fact, it is not even clear how to objectively measure maintenance burden, so that developers’ sentiment and intuition can be supported by numbers. Without effective complexity and maintenance measures, it remains difficult to objectively monitor maintenance, control complexity, or justify refactoring. In this paper, we report a large-scale study of 1200+ projects written in C++ and Java from Google LLC. In this study, we collected three categories of measures: (1) architectural complexity, measured using propagation cost (PC), decoupling level (DL), and structural anti-patterns; (2) maintenance activity, measured using the number of changes, lines of code (LOC) written, and active coding time (ACT) spent on feature-addition vs. bug-fixing, and (3) developer sentiment on complexity and productivity, collected from 7200 survey responses. We statistically analysed the correlations among these measures and obtained significant evidence of the following findings: 1) the more complex the architecture is (higher propagation cost, more instances of anti-patterns), the more LOC is spent on bug-fixing, rather than adding new features; 2) developers who commit more changes for features, spend more lines of code on features, or spend more time on features also feel that they are less hindered by technical debt and complexity. To the best of our knowledge, this is the first large-scale empirical study establishing the statistical correlation among architectural complexity, maintenance activity, and developer sentiment. The implication is that, instead of solely relying upon developer sentiment and intuitions to detect degraded structure or increased burden to evolve, it is possible to objectively and continuously measure and monitor architectural complexity and maintenance difficulty, increasing feature delivery efficiency by reducing architectural complexity and anti-patterns.
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GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning
Sahiti Yerramilli
Nilay Pande
Rynaa Grover
Jayant Tamarapalli
(2025)
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This paper introduces GeoChain, a large-scale benchmark for evaluating step-by-step geographic reasoning in multimodal large language models (MLLMs). Leveraging 1.46 million Mapillary street-level images, GeoChain pairs each image with a 21-step chain-of-thought (CoT) question sequence (over 30 million Q&A pairs). These sequences guide models from coarse attributes to fine-grained localization across four reasoning categories - visual, spatial, cultural, and precise geolocation - annotated by difficulty. Images are also enriched with semantic segmentation (150 classes) and a visual locatability score. Our benchmarking of contemporary MLLMs (GPT-4.1 variants, Claude 3.7, Gemini 2.5 variants) on a diverse 2,088-image subset reveals consistent challenges: models frequently exhibit weaknesses in visual grounding, display erratic reasoning, and struggle to achieve accurate localization, especially as the reasoning complexity escalates. GeoChain offers a robust diagnostic methodology, critical for fostering significant advancements in complex geographic reasoning within MLLMs.
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Despite the advent of legislation such as the General Data Protection Regulation (GDPR) with its associated "Right to be Forgotten" (RTBF), few, if any, studies have measured user reactions to realistic edge cases with public-interest content. Surveying both users covered by and excluded from RTBF, this vignette-based survey experiment sought to better understand how users think of delisting content from search engine results and what factors influence user perceptions. While leaving information accessible in search engine results generally leads to warmer feelings towards those search engines than delisting it, we find that users do prefer different outcomes
depending on contextual elements specific to given cases. We also find that whether a country has active RTBF legislation does seem to be associated with both knowledge and attitudes about RTBF, but is unlikely to explain all of it. These results indicate a complex context around removing public-interest content from search engines’ results; it is essential that experts sensitive to local context perform the review in order to ensure that removal requests are handled in a way that meets users’ expectations.
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Linear Elastic Caching via Ski Rental
Todd Lipcon
The biennial Conference on Innovative Data Systems Research (2025)
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In this work we study the Linear Elastic Caching problem, where the goal is to minimize the total cost of a cache inclusive of not just its misses, but also its memory footprint integrated over time. We demonstrate a theoretical connection to the classic ski rental problem and propose a practical algorithm that combines online caching algorithms with ski rental policies. We also introduce a lightweight machine learning-based algorithm for ski rental that is optimized for production workloads and is easy to integrate within existing database systems. Evaluations on both production workloads in Google Spanner and publicly available traces show that the proposed elastic caching approach can significantly reduce the total cache cost compared to traditional fixed-size cache policies.
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