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05/22/2023
On quantum backpropagation, information reuse, and cheating measurement collapse
The success of modern deep learning hinges on the ability to train neura...
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03/22/2023
The power and limitations of learning quantum dynamics incoherently
Quantum process learning is emerging as an important tool to study quant...
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03/16/2023
Challenges and Opportunities in Quantum Machine Learning
At the intersection of machine learning and quantum computing, Quantum M...
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01/30/2023
Improved machine learning algorithm for predicting ground state properties
Finding the ground state of a quantum many-body system is a fundamental ...
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12/12/2022
Hardware-efficient learning of quantum many-body states
Efficient characterization of highly entangled multi-particle systems is...
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10/26/2022
Learning to predict arbitrary quantum processes
We present an efficient machine learning (ML) algorithm for predicting a...
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10/13/2022
The Complexity of NISQ
The recent proliferation of NISQ devices has made it imperative to under...
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10/06/2022
Learning many-body Hamiltonians with Heisenberg-limited scaling
Learning a many-body Hamiltonian from its dynamics is a fundamental prob...
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04/28/2022
Foundations for learning from noisy quantum experiments
Understanding what can be learned from experiments is central to scienti...
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04/21/2022
Dynamical simulation via quantum machine learning with provable generalization
Much attention has been paid to dynamical simulation and quantum machine...
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04/21/2022
Out-of-distribution generalization for learning quantum dynamics
Generalization bounds are a critical tool to assess the training data re...
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12/01/2021
Revisiting dequantization and quantum advantage in learning tasks
It has been shown that the apparent advantage of some quantum machine le...
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12/01/2021
Quantum advantage in learning from experiments
Quantum technology has the potential to revolutionize how we acquire and...
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11/10/2021
Exponential separations between learning with and without quantum memory
We study the power of quantum memory for learning properties of quantum ...
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11/10/2021
A Hierarchy for Replica Quantum Advantage
We prove that given the ability to make entangled measurements on at mos...
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11/09/2021
Generalization in quantum machine learning from few training data
Modern quantum machine learning (QML) methods involve variationally opti...
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06/23/2021
Provably efficient machine learning for quantum many-body problems
Classical machine learning (ML) provides a potentially powerful approach...
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03/12/2021
Efficient estimation of Pauli observables by derandomization
We consider the problem of jointly estimating expectation values of many...
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01/07/2021
Information-theoretic bounds on quantum advantage in machine learning
We study the complexity of training classical and quantum machine learni...
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12/16/2020
Nearly tight Trotterization of interacting electrons
We consider simulating quantum systems on digital quantum computers. We ...
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11/03/2020
Power of data in quantum machine learning
The use of quantum computing for machine learning is among the most exci...
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07/13/2020
Mixed-state entanglement from local randomized measurements
We propose a method for detecting bipartite entanglement in a many-body ...
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02/18/2020
Predicting Many Properties of a Quantum System from Very Few Measurements
Predicting properties of complex, large-scale quantum systems is essenti...
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09/16/2019
Near-term quantum algorithms for linear systems of equations
Solving linear systems of equations is an essential component in science...
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08/23/2019
Predicting Features of Quantum Systems using Classical Shadows
Predicting features of complex, large-scale quantum systems is essential...
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10/06/2018
FlowQA: Grasping Flow in History for Conversational Machine Comprehension
Conversational machine comprehension requires a deep understanding of th...
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11/16/2017
