Search Results for author: Alexander Zlokapa

Found 8 papers, 4 papers with code

Bayesian Interpolation with Deep Linear Networks

no code implementations29 Dec 2022 Boris Hanin, Alexander Zlokapa

For any training dataset, network depth, and hidden layer widths, we find non-asymptotic expressions for the predictive posterior and Bayesian model evidence in terms of Meijer-G functions, a class of meromorphic special functions of a single complex variable.

Bayesian Inference Learning Theory +1

Fault-Tolerant Neural Networks from Biological Error Correction Codes

no code implementations25 Feb 2022 Alexander Zlokapa, Andrew K. Tan, John M. Martyn, Ila R. Fiete, Max Tegmark, Isaac L. Chuang

It has been an open question in deep learning if fault-tolerant computation is possible: can arbitrarily reliable computation be achieved using only unreliable neurons?

Open-Ended Question Answering

A quantum algorithm for training wide and deep classical neural networks

1 code implementation19 Jul 2021 Alexander Zlokapa, Hartmut Neven, Seth Lloyd

Given the success of deep learning in classical machine learning, quantum algorithms for traditional neural network architectures may provide one of the most promising settings for quantum machine learning.

BIG-bench Machine Learning Quantum Machine Learning

A deep learning model for noise prediction on near-term quantum devices

2 code implementations21 May 2020 Alexander Zlokapa, Alexandru Gheorghiu

We present an approach for a deep-learning compiler of quantum circuits, designed to reduce the output noise of circuits run on a specific device.

Quantum Physics

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

no code implementations25 Mar 2020 Xiangyang Ju, Steven Farrell, Paolo Calafiura, Daniel Murnane, Prabhat, Lindsey Gray, Thomas Klijnsma, Kevin Pedro, Giuseppe Cerati, Jim Kowalkowski, Gabriel Perdue, Panagiotis Spentzouris, Nhan Tran, Jean-Roch Vlimant, Alexander Zlokapa, Joosep Pata, Maria Spiropulu, Sitong An, Adam Aurisano, Jeremy Hewes, Aristeidis Tsaris, Kazuhiro Terao, Tracy Usher

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision.

Instrumentation and Detectors High Energy Physics - Experiment Computational Physics Data Analysis, Statistics and Probability

Quantum adiabatic machine learning with zooming

1 code implementation13 Aug 2019 Alexander Zlokapa, Alex Mott, Joshua Job, Jean-Roch Vlimant, Daniel Lidar, Maria Spiropulu

The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a new class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks.

BIG-bench Machine Learning Quantum Machine Learning

Charged particle tracking with quantum annealing-inspired optimization

no code implementations13 Aug 2019 Alexander Zlokapa, Abhishek Anand, Jean-Roch Vlimant, Javier M. Duarte, Joshua Job, Daniel Lidar, Maria Spiropulu

At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for analysis are expected to face challenges due to scaling with track density.

Combinatorial Optimization

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