1 / 24

On the spectrum of Hamiltonians in finite dimensions

On the spectrum of Hamiltonians in finite dimensions. Roberto Oliveira. Joint with David DiVincenzo and Barbara Terhal @ IBM Watson. Paraty, August 14 th 2007. In one slide: Ground state energy is hard. Bulk of the spectrum is Gaussian and universal. The setup.

sheri
Download Presentation

On the spectrum of Hamiltonians in finite dimensions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. On the spectrum of Hamiltonians in finite dimensions Roberto Oliveira Joint with David DiVincenzo and Barbara Terhal @ IBM Watson. Paraty, August 14th 2007.

  2. In one slide:Ground state energy is hard.Bulk of the spectrum is Gaussian and universal.

  3. The setup • H = Hamiltonian on a set V of N spin ½ particles. • Spec(H) = {0·1·2…}. • We will assume Tr(H)=0. • 2(H) = “variance” = Tr(H2)/2N • Write H = X½ V HX , • where HX acts on the spins in X. Can assume Tr(HXHY)=0 if X Y.

  4. E.g.: Ising with transverse field H = (bond terms) + (site terms) bond term H{i,j} = J xx[i,j] i j k site term H{k} = h z[k] H=k H{k} + i~jH{i,j}

  5. E.g.: Ising with transverse field face term HF H = (bond terms) + (site terms) bond term H{i,j} = J xx[i,j] i j k site term H{k} = h z[k] H=k H{k} + i~jH{i,j} + FHF

  6. Dimensionality assumption • 9metric  and d, C, c, >0 such that: Radius R cRd·X½B(x,R)2(HX)·CRd X½ B(x,R)||HX||·CRd center x X ½  B(x,R)2(HX)·CRd- B(x,R) = {v2 V : (v,x)· R}

  7. E.g. nearest neighbor in Zd Radius R • L1 norm • ¼ Rd terms inside • ¼ Rd-1 at the boundary

  8. Gaussian spectrum • Plot a histogram of Spec(H/(H)). • with small but fixed bin size b>0. That will approach • a Gaussian as N adiverges, for fixed parameters.

  9. A bit more formally • There is a probability measure on the line given by H: • m=H= 2-N2 Spec(H)  • We show that this measure is approximately normal in the sense that for all a<b, as N grows: • H (a(H),b(H)) !(2)-1/2sab exp(-t2/2)dt

  10. Even more formally • Recall: strength inside ball ¼Rd,strength across boundary ¼ Rd-, with C,c extra parameteres. • Then for all a<b, • |H [a(H),b(H)]-(2)-1/2sab exp(-t2/2)dt| • · D(C,c,d,) (Diam())-d/8

  11. A bit less formally again • Inside ¼ Rd, boundary¼ Rd-. Radius R H/(H)[x,y] ¼ (2)-1/2sxy exp(-t2/2)dt center x B(x,R) = {v2 V : (v,x)· R}

  12. A simple case • We will now explain a special case of the Theorem. • Nearest neighbor interactions on a n x n patch of the planar square lattice (N=n2 spins). • Also assume that all terms in the Hamiltonian have norm of constant order.

  13. Back to that old slide H = (bond terms) + (site terms) bond term H{i,j} = J xx[i,j] i j k site term H{k} = h z[k] H=k H{k} + i~jH{i,j}

  14. What do we do? • Main idea: ignore terms acting on red lines (i.e. treat non-interacting systems). Then put them back in via a perturbation argument.

  15. Omitting the interactions • G = s Gs, subsystems might have high dimension. • How does one compute the global spectrum? Answer: convolution of the individual spectral distributions. • By the usual Central Limit Theorem, G/(G) is approximately Gaussian as long as some conditions are satisfied.

  16. Which conditions? • Many terms in the sum. • The influence of any given term is small. • m¿ n1/2 suffices.

  17. Putting red lines back in

  18. Next step • Recall that we have. • H/(H) = 2-N2 Spec(H) /(H) • We know that G/(G) is approx. gaussian. • G/(G) = 2-N2 Spec(G) /(G) • We will show that (H-G) is small. • By a perturbation theory argument, this implies that H/(H)¼ G/(G).

  19. (H-G) is small • Variance¼ # of qubits • Total # of qubits ¼ n2 • Qubits on red lines ¼ m(n/m)2 = n2/m • ) (H-G)2·(H)2/m, small if mÀ 1

  20. To conclude • Take some 1¿ m¿ n1/2 (e.g. m=n1/3). • Then G/(G) is approx. Gaussian. • Also H/(H)¼ G/(G). So we are done. • The key step: m x m boxes have m2 vertices but only ¼ m vertices on their boundaries.

  21. General case • Inside ¼ Rd, boundary¼ Rd-. Radius R H/(H)[a,b] ¼ (2)-1/2sab exp(-t2/2)dt center x B(x,R) = {v2 V : (v,x)· R}

  22. General result: proof sketch • Main idea: break the system into subparts with small total boundary strength. • Treat isolated systems via standard CLT. • Put the boundary back in via perturbation theory.

  23. Conclusions • Spectral distribution is approximately Gaussian with std. deviation ¼ N1/2. • This is universal for quantum spin systems in finite dimensional structures when long-range interactions decay fast enough.

  24. Further work • Bounds are actually weak for many problems. Are there better bounds for specific systems? • Fermions? Bosons? • Applications?

More Related