2019/11/08 by Ali Hamed Moosavian, Moosavian, Ali Hamed, James R. Garrison +3 · 12 citations
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Bottleneck #Computer science #FOS: Physical sciences #Field (mathematics) #Geometry #Heuristic #Lattice (music) #Mathematics #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph) #Quantum algorithm #Quantum and electron transport phenomena #Quantum annealing #Quantum computer #Quantum field theory #Quantum many-body systems #Quantum mechanics #Quantum simulator #Scaling #Statistical physics #Theoretical computer science #Theoretical physics #quant-ph
paper · pdf · doi:10.48550/arxiv.1911.03505
published in arXiv (Cornell University) (Cornell University) · 10 pages, 6 figures
arxiv created 2019/11/08 · openalex publication_date 2019/11/08 · arxiv updated 2019/11/12 · openalex created_date 2019/11/22 · openalex updated_date 2026/07/28
Answering whether quantum computers can efficiently simulate quantum field theories has both theoretical and practical motivation. From the theoretical point of view, it answers the question of whether a hypothetical computer that utilizes quantum field theory would be more powerful than other quantum computers. From the practical point of view, when reliable quantum computers are eventually built, these algorithms can help us better understand the underlying physics that govern our world. In the best known quantum algorithms for simulating quantum field theories, the time scaling is dominated by initial state preparation. In this paper, we exclusively focus on state preparation and present a heuristic algorithm that can prepare the vacuum of fermionic systems in more general cases and more efficiently than previous methods. With our method, state preparation is no longer the bottleneck, as its runtime has the same asymptotic scaling with the desired precision as the remainder of the simulation algorithm. We numerically demonstrate the effectiveness of our proposed method for the 1+1 dimensional Gross-Neveu model.