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Found 7 papers, 5 papers with code
Quantum approximate optimization via learning-based adaptive optimization
Quantum approximate optimization algorithm (QAOA), one of the most representative quantum-classical hybrid algorithms, is designed to solve combinatorial optimization problems by transforming the discrete optimization problem into a classical optimization problem over continuous circuit parameters.
Neural Predictor based Quantum Architecture Search
For instance, a key component of VQAs is the design of task-dependent parameterized quantum circuits (PQCs) as in the case of designing a good neural architecture in deep learning.
Neural Architecture Search
Quantum Physics
Differentiable Quantum Architecture Search
Hereby, we propose a general framework of differentiable quantum architecture search (DQAS), which enables automated designs of quantum circuits in an end-to-end differentiable fashion.
Quantum Physics
Automatic Differentiable Monte Carlo: Theory and Application
Differentiable programming has emerged as a key programming paradigm empowering rapid developments of deep learning while its applications to important computational methods such as Monte Carlo remain largely unexplored.
Automatic Differentiation for Complex Valued SVD
In this note, we report the back propagation formula for complex valued singular value decompositions (SVD).
Strong and Weak Many-Body Localizations
Many aspects of many-body localization (MBL), including dynamic classification of MBL phases, remain elusive.
Disordered Systems and Neural Networks Quantum Gases Statistical Mechanics Strongly Correlated Electrons Quantum Physics
Universal properties of many-body localization transitions in quasiperiodic systems
Precise nature of MBL transitions in both random and quasiperiodic (QP) systems remains elusive so far.
Strongly Correlated Electrons Disordered Systems and Neural Networks Quantum Gases Quantum Physics
