Transformer quantum state: A multipurpose model for quantum many-body problems
TL;DR: The transformer quantum state (TQS) as mentioned in this paper is a machine learning model for quantum many-body problems, which can generate the phase diagram, predict field strengths with experimental measurements, and transfer such knowledge to new systems it has never been trained on before, all within a single model.
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Abstract: Inspired by the advancements in large language models based on transformers, we introduce the transformer quantum state (TQS): a versatile machine learning model for quantum many-body problems. In sharp contrast to Hamiltonian/task specific models, TQS can generate the entire phase diagram, predict field strengths with experimental measurements, and transfer such a knowledge to new systems it has never been trained on before, all within a single model. With specific tasks, fine-tuning the TQS produces accurate results with small computational cost. Versatile by design, TQS can be easily adapted to new tasks, thereby pointing towards a general-purpose model for various challenging quantum problems.
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Variational Benchmarks for Quantum Many-Body Problems
09 Mar 2023
TL;DR: In this paper , the authors introduce a metric of variational accuracy, the V-score, obtained from the variational energy and its variance, which can be used as a metric to assess the progress of quantum variational methods towards quantum advantage for ground-state problems.
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Variational benchmarks for quantum many-body problems
Dian Wu,Riccardo Rossi,Filippo Vicentini,Nikita Astrakhantsev,Federico Becca,Xiaodong Cao,Juan Carrasquilla,Francesco Ferrari,Antoine Georges,Mohamed Hibat-Allah,Masatoshi Imada,Andreas M. Läuchli,Guglielmo Mazzola,Antonio Mezzacapo,Andrew J. Millis,Javier Robledo Moreno,Titus Neupert,Yusuke Nomura,Jannes Nys,Olivier Parcollet,Rico Pohle,Imelda Romero,M. Schmid,J. Maxwell Silvester,Sandro Sorella,Luca F. Tocchio,Lei Wang,Steven R. White,Alexander Wietek,Qi Yang,Yiqi Yang,Shiwei Zhang,Giuseppe Carleo +32 more
TL;DR: Researchers introduce the V-score metric to assess variational accuracy in many-body quantum systems, providing a curated dataset to identify limitations of current methods and potential areas for improvement with quantum computing or future algorithms.
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TL;DR: The contributions that language models are making in the effort to build quantum computers are highlighted and their future role in the race to quantum advantage is discussed.
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Adaptive Quantum State Tomography with Active Learning
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TL;DR: Active learning quantum state tomography significantly improves quantum state reconstruction with fewer measurements compared to traditional methods.
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From Architectures to Applications: A Review of Neural Quantum States
Hannah Lange,Alj Van De Walle,Atiye Abedinnia,Annabelle Bohrdt +3 more
TL;DR: This review explores neural quantum states (NQS) for simulating quantum many-body systems, overcoming exponential scaling by compressing state parameters, and discusses various NQS architectures and applications for ground, excited, and open system states.
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