σ-VQE uses low-depth circuits and an energy-selective cost function to preferentially prepare quantum many-body scar states on NISQ devices.
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Resource estimates for quantum simulation of pionless and pionful nuclear lattice EFTs, including time evolution and energy estimation, with new error bounds from symmetries and locality yielding orders-of-magnitude improvements for the pionless case.
Physics-informed quantum neural networks trained on noisy measurements can construct nontrivial decision boundaries to classify quantum states via order parameters and are suited for NISQ hardware due to links with Markovian open many-body systems.
citing papers explorer
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$\sigma$-VQE: Excited-state preparation of quantum many-body scars with shallow circuits
σ-VQE uses low-depth circuits and an energy-selective cost function to preferentially prepare quantum many-body scar states on NISQ devices.
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Quantum Algorithms for Simulating Nuclear Effective Field Theories
Resource estimates for quantum simulation of pionless and pionful nuclear lattice EFTs, including time evolution and energy estimation, with new error bounds from symmetries and locality yielding orders-of-magnitude improvements for the pionless case.
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Getting large-scale quantum neural networks ready for quantum hardware
Physics-informed quantum neural networks trained on noisy measurements can construct nontrivial decision boundaries to classify quantum states via order parameters and are suited for NISQ hardware due to links with Markovian open many-body systems.