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26th International Conference on Research in Computational Molecular Biology, RECOMB 2022

作者： Rahmani, Elior Jordan, Michael I. Yosef, Nir Department of Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA United States Department of Statistics University of California Berkeley BerkeleyCA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Chan-Zuckerberg Biohub San FranciscoCA United States Ragon Institute of MGH MIT and Harvard CambridgeMA United States

ISBN: (纸本)9783031047480

A major limitation in single-cell genomics is a lack of ability to conduct cost-effective population-level studies. As a result, much of the current research in single-cell genomics focuses on biological processes that are broadly conserved across individuals, such as cellular organization and tissue development. This limitation prevents us from studying the etiology of experimental or clinical conditions that may be inconsistent across individuals owing to molecular variation and a wide range of effects in the population. In order to address this gap, we developed "kernel of integrated single cells" (Keris), a novel model-based framework to inform the analysis of single-cell gene expression data with population-level effects of a condition of interest. By inferring cell-type-specific moments and their variation across conditions using large tissue-level bulk data representing a population, Keris allows us to generate testable hypotheses at the single-cell level that would otherwise require collecting single-cell data from a large number of donors. Within the Keris framework, we show how the combination of low-resolution, large bulk data with small but high-resolution single-cell data enables the identification of changes in cell-subtype compositions and the characterization of subpopulations of cells that are affected by a condition of interest. Using Keris we estimate linear and non-linear age-associated changes in cell-type expression in large bulk peripheral blood mononuclear cells (PBMC) data. Combining with three independent single-cell PBMC datasets, we demonstrate that Keris can identify changes in cell-subtype composition with age and capture cell-type-specific subpopulations of senescent cells. This demonstrates the promise of enhancing single-cell data with population-level information to study compositional changes and to profile condition-affected subpopulations of cells, and provides a potential resource of targets for future clinical interventions. ©

关键词： Cost effectiveness

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Deep-Neural-Network Discrimination of Multiplexed Superconducting-Qubit States

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Physical Review Applied 2022年第1期17卷 014024-014024页

作者： Benjamin Lienhard Antti Vepsäläinen Luke C.G. Govia Cole R. Hoffer Jack Y. Qiu Diego Ristè Matthew Ware David Kim Roni Winik Alexander Melville Bethany Niedzielski Jonilyn Yoder Guilhem J. Ribeill Thomas A. Ohki Hari K. Krovi Terry P. Orlando Simon Gustavsson William D. Oliver Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Quantum Engineering and Computing Group Raytheon BBN Technologies Cambridge Massachusetts 02138 USA MIT Lincoln Laboratory Lexington Massachusetts 02421 USA

Demonstrating a quantum computational advantage will require high-fidelity control and readout of multiqubit systems. As system size increases, multiplexed qubit readout becomes a practical necessity to limit the growth of resource overhead. Many contemporary qubit-state discriminators presume single-qubit operating conditions or require considerable computational effort, limiting their potential extensibility. Here, we present multiqubit readout using neural networks as state discriminators. We compare our approach to contemporary methods employed on a quantum device with five superconducting qubits and frequency-multiplexed readout. We find that fully connected feedforward neural networks increase the qubit-state-assignment fidelity for our system. Relative to contemporary discriminators, the assignment error rate is reduced by up to 25% due to the compensation of system-dependent nonidealities such as readout crosstalk, which is reduced by up to one order of magnitude. Our work demonstrates a potentially extensible building block for high-fidelity readout relevant to both near-term devices and future fault-tolerant systems.

关键词： Machine learning Measurement-based quantum computing Optoelectronics Quantum control Quantum information with solid state qubits Superconducting qubits Artificial neural networks Microwave techniques

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arXiv

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arXiv 2023年

作者： Nguyen, Quang Minh Nguyen, Lam M. Das, Subhro Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States IBM Research Thomas J. Watson Research Center Yorktown HeightsNY United States MIT-IBM Watson AI Lab IBM Research CambridgeMA United States

Multivariate time series (MTS) analysis prevails in real-world applications such as finance, climate science and healthcare. The various self-attention mechanisms, the backbone of the state-of-the-art Transformer-based models, efficiently discover the temporal dependencies, yet cannot well capture the intricate cross-correlation between different features of MTS data, which inherently stems from complex dynamical systems in practice. To this end, we propose a novel correlated attention mechanism, which not only efficiently captures feature-wise dependencies, but can also be seamlessly integrated within the encoder blocks of existing well-known Transformers to gain efficiency improvement. In particular, correlated attention operates across feature channels to compute cross-covariance matrices between queries and keys with different lag values, and selectively aggregate representations at the sub-series level. This architecture facilitates automated discovery and representation learning of not only instantaneous but also lagged cross-correlations, while inherently capturing time series auto-correlation. When combined with prevalent Transformer baselines, correlated attention mechanism constitutes a better alternative for encoder-only architectures, which are suitable for a wide range of tasks including imputation, anomaly detection and classification. Extensive experiments on the aforementioned tasks consistently underscore the advantages of correlated attention mechanism in enhancing base Transformer models, and demonstrate our state-of-the-art results in imputation, anomaly detection and classification. © 2023, CC BY-NC-ND.

关键词： Anomaly detection

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Improving the speed of variational quantum algorithms for quantum error correction

arXiv

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arXiv 2023年

作者： Zoratti, Fabio De Palma, Giacomo Kiani, Bobak Nguyen, Quynh T. Marvian, Milad Lloyd, Seth Giovannetti, Vittorio Scuola Normale Superiore PisaI-56126 Italy Department of Mathematics University of Bologna Bologna40126 Italy Department of Electrical Engineering and Computer Science MIT 77 Massachusetts Avenue CambridgeMA02139 United States Department of Electrical and Computer Engineering Center for Quantum Information and Control University of New Mexico United States Department of Mechanical Engineering MIT 77 Massachusetts Avenue CambridgeMA02139 United States Turing Inc. CambridgeMA02139 United States NEST Scuola Normale Superiore Istituto Nanoscienze-CNR PisaI-56126 Italy

We consider the problem of devising a suitable Quantum Error Correction (QEC) procedures for a generic quantum noise acting on a quantum circuit. In general, there is no analytic universal procedure to obtain the encoding and correction unitary gates, and the problem is even harder if the noise is unknown and has to be reconstructed. The existing procedures rely on Variational Quantum Algorithms (VQAs) and are very difficult to train since the size of the gradient of the cost function decays exponentially with the number of qubits. We address this problem using a cost function based on the Quantum Wasserstein distance of order 1 (QW1). At variance with other quantum distances typically adopted in quantum information processing, QW1 lacks the unitary invariance property which makes it a suitable tool to avoid to get trapped in local minima. Focusing on a simple noise model for which an exact QEC solution is known and can be used as a theoretical benchmark, we run a series of numerical tests that show how, guiding the VQA search through the QW1, can indeed significantly increase both the probability of a successful training and the fidelity of the recovered state, with respect to the results one obtains when using conventional approaches. © 2023, CC BY-SA.

关键词： Cost functions

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IMPROVING WORLD MODELS USING DEEP SUPERVISION WITH LINEAR PROBES

arXiv

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arXiv 2025年

作者： Zahorodnii, Andrii Department of Electrical Engineering and Computer Science MIT United States Department of Brain and Cognitive Sciences McGovern Institute for Brain Research MIT CambridgeMA United States

Developing effective world models is crucial for creating artificial agents that can reason about and navigate complex environments. In this paper, we investigate a deep supervision technique for encouraging the development of a world model in a network trained end-to-end to predict the next observation. While deep supervision has been widely applied for task-specific learning, our focus is on improving the world models. Using an experimental environment based on the Flappy Bird game, where the agent receives only LIDAR measurements as observations, we explore the effect of adding a linear probe component to the network’s loss function. This additional term encourages the network to encode a subset of the true underlying world features into its hidden state. Our experiments demonstrate that this supervision technique improves both training and test performance, enhances training stability, and results in more easily decodable world features – even for those world features which were not included in the training. Furthermore, we observe a reduced distribution drift in networks trained with the linear probe, particularly during high-variability phases of the game (flying between successive pipe encounters). Including the world features loss component roughly corresponded to doubling the model size, suggesting that the linear probe technique is particularly beneficial in compute-limited settings or when aiming to achieve the best performance with smaller models. These findings contribute to our understanding of how to develop more robust and sophisticated world models in artificial agents, paving the way for further advancements in this field. Copyright © 2025, The Authors. All rights reserved.

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Synchronous Detection of Cosmic Rays and Correlated Errors in Superconducting Qubit Arrays

arXiv

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arXiv 2024年

作者： Harrington, Patrick M. Li, Mingyu Hays, Max Van De Pontseele, Wouter Mayer, Daniel Pinckney, H. Douglas Contipelli, Felipe Gingras, Michael Niedzielski, Bethany M. Stickler, Hannah Yoder, Jonilyn L. Schwartz, Mollie E. Grover, Jeffrey A. Serniak, Kyle Oliver, William D. Formaggio, Joseph A. Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States MIT Lincoln Laboratory LexingtonMA02421 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

Quantum information processing at scale will require sufficiently stable and long-lived qubits, likely enabled by error-correction codes [1]. Several recent superconducting-qubit experiments [2-4], however, reported observing intermittent spatiotemporally correlated errors that would be problematic for conventional codes, with ionizing radiation being a likely cause. Here, we directly measured the cosmic-ray contribution to spatiotemporally correlated qubit errors. We accomplished this by synchronously monitoring cosmic-ray detectors and qubit energy-relaxation dynamics of 10 transmon qubits distributed across a 5×5×0.35 mm3 silicon chip. Cosmic rays caused correlated errors at a rate of 1/(592+48 -41 s), accounting for 17.1 ± 1.3% of all such events. Our qubits responded to essentially all of the cosmic rays and their secondary particles incident on the chip, consistent with the independently measured arrival flux. Moreover, we observed that the landscape of the superconducting gap in proximity to the Josephson junctions dramatically impacts the qubit response to cosmic rays. Given the practical difficulties associated with shielding cosmic rays [5], our results indicate the importance of radiation hardening-for example, superconducting gap engineering-to the realization of robust quantum error correction. Copyright © 2024, The Authors. All rights reserved.

关键词： Error correction

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Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach

arXiv

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arXiv 2021年

作者： Wang, Haoyue Ibrahim, Shibal Mazumder, Rahul MIT Operations Research Center United States MIT Department of Electrical Engineering and Computer Science United States MIT Sloan School of Management Operations Research Center MIT Center for Statistics United States

We explore computational aspects of maximum likelihood estimation of the mixture proportions of a nonparametric finite mixture model—a convex optimization problem with old roots in statistics and a key member of the modern data analysis toolkit. Motivated by problems in shape constrained inference, we consider structured variants of this problem with additional convex polyhedral constraints. We propose a new cubic regularized Newton method for this problem and present novel worst-case and local computational guarantees for our algorithm. We extend earlier work by Nesterov and Polyak to the case of a self-concordant objective with polyhedral constraints, such as the ones considered herein. We propose a Frank-Wolfe method to solve the cubic regularized Newton subproblem;and derive efficient solutions for the linear optimization oracles that may be of independent interest. In the particular case of Gaussian mixtures without shape constraints, we derive bounds on how well the finite mixture problem approximates the infinite-dimensional Kiefer-Wolfowitz maximum likelihood estimator. Experiments on synthetic and real datasets suggest that our proposed algorithms exhibit improved runtimes and scalability features over existing *** Codes 90C06, 90C25, 90C90, 62G07 Copyright © 2021, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Implementing a Synthetic Magnetic Vector Potential in a 2D Superconducting Qubit Array

arXiv

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arXiv 2024年

作者： Rosen, Ilan T. Muschinske, Sarah Barrett, Cora N. Chatterjee, Arkya Hays, Max DeMarco, Michael A. Karamlou, Amir H. Rower, David A. Das, Rabindra Kim, David K. Niedzielski, Bethany M. Schuldt, Meghan Serniak, Kyle Schwartz, Mollie E. Yoder, Jonilyn L. Grover, Jeffrey A. Oliver, William D. Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States MIT Lincoln Laboratory LexingtonMA02421 United States

Superconducting quantum processors are a compelling platform for analog quantum simulation due to the precision control, fast operation, and site-resolved readout inherent to the hardware. Arrays of coupled superconducting qubits natively emulate the dynamics of interacting particles according to the Bose-Hubbard model. However, many interesting condensed-matter phenomena emerge only in the presence of electromagnetic fields. Here, we emulate the dynamics of charged particles in an electromagnetic field using a superconducting quantum simulator. We realize a broadly adjustable synthetic magnetic vector potential by applying continuous modulation tones to all qubits. We verify that the synthetic vector potential obeys requisite properties of electromagnetism: a spatially-varying vector potential breaks time-reversal symmetry and generates a gaugeinvariant synthetic magnetic field, and a temporally-varying vector potential produces a synthetic electric field. We demonstrate that the Hall effect-the transverse deflection of a charged particle propagating in an electromagnetic field-exists in the presence of the synthetic electromagnetic field. Copyright © 2024, The Authors. All rights reserved.

关键词： Hubbard model

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International Conference on Smart Computing & Communications (ICSCC)

作者： Rushikesh Shaharao Gopal Sakarkar Abhijeet Khandagale Mahima Gaurihar Ramsha Shaikh Rupak Kadhare Agus Putu Abiyasa Department of Artificial Intelligence G. H. Raisoni College of Engineering Nagpur India Department of Computer Science & Applications Dr Vishwanath Karad MIT World Peace University Pune India Vruksh Ecosystem Foundation India Department of DIC(Data Science CSE(IoT) CSE(Cyber Security) G. H. Raisoni College of Engineering Nagpur India Ekatra Learning Inc. Mumbai India Department of Electrical Engineering Universitas Pendidikan Nasional Denpasar Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

Semantic Similarity, also known as Semantic Textual Similarity, is an NLP task that evaluates the relationship between texts using predefined metrics. It is crucial in domains like information retrieval and sentiment analysis. This research proposes a methodology to enhance learner performance by leveraging semantic similarity for multi-source information retrieval. The methodology involves four stages: text extraction from PDFs and web scraping from web pages, preprocessing, embedding, and semantic similarity computation. Various embedding techniques, such as word2vec, GloVe, Hugging Face embeddings, and BERT, are employed to vectorize text data. We compute cosine similarity between the query and sentences to find the top N matches. Our approach aims to retrieve relevant information efficiently, supporting learners in accessing information pertinent to their queries. The results demonstrate that our methodology outperforms traditional information retrieval methods in precision and speed. This approach can significantly benefit NLP and information retrieval, especially in improving learner performance.

关键词： Measurement Sentiment analysis Electronic learning Accuracy Semantics Education Web pages Information retrieval Faces

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Enhanced Detection of Fetal Pose in 3D MRI by Deep Reinforcement Learning with Physical Structure Priors on Anatomy 23rd

Enhanced Detection of Fetal Pose in 3D MRI by Deep Reinforce...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Molin Xu, Junshen Abaci Turk, Esra Grant, P. Ellen Golland, Polina Adalsteinsson, Elfar Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Childrens Hospital BostonMA United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA United States Harvard Medical School BostonMA United States Institute for Medical Engineering and Science MIT CambridgeMA United States

ISBN: (纸本)9783030597245

Fetal MRI is heavily constrained by unpredictable and substantial fetal motion that causes image artifacts and limits the set of viable diagnostic image contrasts. Current mitigation of motion artifacts is predominantly performed by fast, single-shot MRI and retrospective motion correction. Estimation of fetal pose in real time during MRI stands to benefit prospective methods to detect and mitigate fetal motion artifacts where inferred fetal motion is combined with online slice prescription with low-latency decision making. Current developments of deep reinforcement learning (DRL), offer a novel approach for fetal landmarks detection. In this task 15 agents are deployed to detect 15 landmarks simultaneously by DRL. The optimization is challenging, and here we propose an improved DRL that incorporates priors on physical structure of the fetal body. First, we use graph communication layers to improve the communication among agents based on a graph where each node represents a fetal-body landmark. Further, additional reward based on the distance between agents and physical structures such as the fetal limbs is used to fully exploit physical structure. Evaluation of this method on a repository of 3-mm resolution in vivo data demonstrates a mean accuracy of landmark estimation 10mm of ground truth as 87.3%, and a mean error of 6.9mm. The proposed DRL for fetal pose landmark search demonstrates a potential clinical utility for online detection of fetal motion that guides real-time mitigation of motion artifacts as well as health diagnosis during MRI of the pregnant mother. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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