2014/10/31 by J. K. Freericks, B. K. Nikolic, B. K. Nikolić +1
Computer Science · Mathematics · Physics and Astronomy · #Big data #Hilbert space #Non-equilibrium thermodynamics #Perspective (graphical) #Quantum #Quantum Computing Algorithms and Architecture #Quantum computer #Quantum many-body systems #Space (punctuation) #Statistical Mechanics and Entropy #cond-mat.stat-mech #cond-mat.str-el #cs.CC #cs.CE #math-ph #math.MP
paper · pdf · doi:10.1142/s0217979214300217
published as Int J. Mod. Phys. B 28, 1430021 (2014) · 33 pages, 7 figures, invited review for Int. J. Mod. Phys. B; published version with additional references
openalex publication_date 2014/12/04 · arxiv created 2014/12/09 · arxiv updated 2014/12/11 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
Generating big data pervades much of physics. But some problems, which we call extreme data problems, are too large to be treated within big data science. The nonequilibrium quantum many-body problem on a lattice is just such a problem, where the Hilbert space grows exponentially with system size and rapidly becomes too large to fit on any computer (and can be effectively thought of as an infinite-sized data set). Nevertheless, much progress has been made with computational methods on this problem, which serve as a paradigm for how one can approach and attack extreme data problems. In addition, viewing these physics problems from a computer-science perspective leads to new approaches that can be tried to solve more accurately and for longer times. We review a number of these different ideas here.