Deep learning-enhanced variational Monte Carlo method for quantum many-body physics
Li Yang, Zhaoqi Leng, Guangyuan Yu, Ankit Patel, Wen-Jun Hu, Han Pu
Abstract
The authors construct and develop an optimization scheme to train a deep convolutional neural network to represent many-body wave function. The paper explores its performance by applying the network to find the ground state of an SU(N) spin-chain Hamiltonian using variational quantum Monte Carlo.
Topics & Concepts
PhysicsQuantum Monte CarloHamiltonian (control theory)Variational Monte CarloMonte Carlo methodStatistical physicsQuantumGround stateHybrid Monte CarloArtificial neural networkConstruct (python library)Variational methodScheme (mathematics)Quantum mechanicsComputer scienceQuantum computerMonte Carlo method in statistical physicsApplied mathematicsWave functionConvolutional neural networkQuantum networkMonte Carlo molecular modelingVariational analysisQuantum algorithmQuantum annealingDeep neural networksTheoretical physicsNumerical analysisQuantum systemMathematicsQuantum stateState (computer science)AlgorithmQuantum technologyQuantum simulatorOpen quantum systemQuantum many-body systemsMachine Learning in Materials ScienceQuantum Computing Algorithms and Architecture