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Open AccessPractical Hamiltonian learning with unitary dynamics and Gibbs states
We study the problem of learning the parameters for the Hamiltonian of a quantum many-body system, given limited access to the system. In this work, we build upon recent approaches to Hamiltonian learning via ...
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Open AccessA semi-agnostic ansatz with variable structure for variational quantum algorithms
Quantum machine learning—and specifically Variational Quantum Algorithms (VQAs)—offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, mate...
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Article
Open AccessOut-of-distribution generalization for learning quantum dynamics
Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural...
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Open AccessLong-time simulations for fixed input states on quantum hardware
Publicly accessible quantum computers open the exciting possibility of experimental dynamical quantum simulations. While rapidly improving, current devices have short coherence times, restricting the viable ci...
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Challenges and opportunities in quantum machine learning
At the intersection of machine learning and quantum computing, quantum machine learning has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, bi...
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Open AccessGeneralization in quantum machine learning from few training data
Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generaliz...
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Article
Open AccessVariational quantum eigensolver with reduced circuit complexity
The variational quantum eigensolver (VQE) is one of the most promising algorithms to find eigenstates of a given Hamiltonian on noisy intermediate-scale quantum devices (NISQ). The practical realization is lim...
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Open AccessNoise-induced barren plateaus in variational quantum algorithms
Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether noise on NISQ devices places fundamental limitations...
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Variational quantum algorithms
Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers, owing to the extremely high computational cost. Quantum ...
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Open AccessCost function dependent barren plateaus in shallow parametrized quantum circuits
Variational quantum algorithms (VQAs) optimize the parameters θ of a parametrized quantum circuit V(θ) to minimize a cost function C. While VQAs may enable practical applications of noisy quantum computers, they ...
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Open AccessVariational fast forwarding for quantum simulation beyond the coherence time
Trotterization-based, iterative approaches to quantum simulation (QS) are restricted to simulation times less than the coherence time of the quantum computer (QC), which limits their utility in the near term. ...
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Open AccessVariational consistent histories as a hybrid algorithm for quantum foundations
Although quantum computers are predicted to have many commercial applications, less attention has been given to their potential for resolving foundational issues in quantum mechanics. Here we focus on quantum ...
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Open AccessVariational quantum state diagonalization
Variational hybrid quantum-classical algorithms are promising candidates for near-term implementation on quantum computers. In these algorithms, a quantum computer evaluates the cost of a gate sequence (with s...
