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Variational Benchmarks for Quantum Many-Body Problems
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The continued development of computational approaches to many-body ground-state problems in physics and chemistry calls for a consistent way to assess its overall progress. In this work, we introduce a metric of variational accuracy, the V-score, obtained from the variational energy and its variance. We provide an extensive curated dataset of variational calculations of many-body quantum systems, identifying cases where state-of-the-art numerical approaches show limited accuracy, and future algorithms or computational platforms, such as quantum computing, could provide improved accuracy. The V-score can be used as a metric to assess the progress of quantum variational methods toward a quantum advantage for ground-state problems, especially in regimes where classical verifiability is impossible.
Forward citations
Cited by 2 Pith papers
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Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States
SineKAN, a Kolmogorov-Arnold network with sinusoidal activations, accurately represents ground states of 1D spin chains and outperforms RBM, LSTM, and MLP neural quantum states in the J1-J2 model.
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Pauli Propagation: A Computational Framework for Simulating Quantum Systems
Pauli propagation, a classical method that evolves Pauli operators through quantum circuits, is presented as a unified algorithmic framework together with the Julia package PauliPropagation.jl that implements it.
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