QUANTUM GIBBS SAMPLERS: THE COMMUTATIVE CASE M. J. Kastoryano, F. - - PowerPoint PPT Presentation

quantum gibbs samplers the commutative case
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QUANTUM GIBBS SAMPLERS: THE COMMUTATIVE CASE M. J. Kastoryano, F. - - PowerPoint PPT Presentation

QUANTUM GIBBS SAMPLERS: THE COMMUTATIVE CASE M. J. Kastoryano, F. G. S. L. Brandao QIP 2015, Sydney Tuesday, February 10, 15 MOTIVATION Simulation of systems in Analysis of thermal equilibrium thermalization in nature Can we say anything


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SLIDE 1

QUANTUM GIBBS SAMPLERS: THE COMMUTATIVE CASE

  • M. J. Kastoryano, F. G. S. L. Brandao

QIP 2015, Sydney

Tuesday, February 10, 15

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SLIDE 2

MOTIVATION

Simulation of systems in thermal equilibrium Analysis of thermalization in nature

Can we say anything about the difficulty of simulating a state, just from the state? Does nature always prepare “easy states” efficiently?

Tuesday, February 10, 15

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SLIDE 3

MOTIVATION

Rate of convergence Correlations in Gibbs state Main structural theorem:

Characterizes the thermodynamically trivial phase

Tuesday, February 10, 15

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SLIDE 4

SETTING

Finite lattice system

Λ

Finite local dimension Bounded, local and commuting Hamiltonian

[hZ, hY ] = 0, ∀Z, Y

HA = X

Z∈A

hZ

A ⊂ Λ

A

Non-commutative spaces:

ρ ∝ e−βHΛ

Global Gibbs state:

hf, giρ = tr[ρ1/2f †ρ1/2g]

||f||p

p,ρ = tr[|ρ1/2pfρ1/2p|p]

Lp inner product

norm

Lp Lp

Def: Gibbs samplers are primitive semigroups with Gibbs state as unique stationary state

hX

Tuesday, February 10, 15

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SLIDE 5

GIBBS SAMPLERS

Davies generators

LA(f) = X

α(j),j∈A,ω

gα(j)(ω)(Sα(j)(ω)fS†

α(j)(ω) − 1

2{Sα(j)(ω)S†

α(j)(ω)})

Finite system weakly coupled to a markovian thermal bath

A ⊂ Λ

Bath autocorolation fcn Jump operators: between eigenstates of H Properties: Locally reversible: Completely positive

hf, LA(g)iρ = hLA(f), giρ

S B, β

Local (same locality as H)

Tuesday, February 10, 15

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SLIDE 6

GIBBS SAMPLERS

Heat-bath generators local projection onto Gibbs state

LA(f) = X

k∈A

k(f) − f

γk = (trk[ρ])−1/2ρ1/2

k(f) = trk[γkfγ† k]

Only depends on properties

  • f the state.

is a conditional expectation

A

Properties: Locally reversible: Completely positive

hf, LA(g)iρ = hLA(f), giρ

Local (same locality as H)

Tuesday, February 10, 15

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SLIDE 7

RELAXATION TIME

VarA = ||f − Eρ

A(f)||2 2,ρ

λA = inf

f∈AΛ

hf, LA(f)iρ VarA(f)

We want to estimate how rapidly the sampler converges to the Gibbs state Trace norm bound: Mixing time:

||etL() − ⇢||1 ≤ ✏

⌧ ≥ log(||⇢−1||/✏)

  • ||ρ−1|| ≤ eo(|Λ|)

τ ∝ |Λ|/λΛ

Reduces to estimating the gap! where

Tuesday, February 10, 15

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SLIDE 8

CLUSTERING

Def: weak clustering

Cov(f, g) ≤ c||f||2,ρ||g||2,ρe−d(Σf ,Σg)/ξ

Σf

Σg

Λ

d(Σf, Σg)

Cov(f, g) = hf hfiρ, g hgiρiρ

Def: strong clustering

CovA∪B(EA(f), EB(f)) ≤ c||f||2

2,ρe−w(A∩B)/ξ

CovA∪B(f, g) = hf EA(f), g EB(g)iρ

Λ

A ∩ B

Ac

Bc

w(A ∩ B)

different norm

Tuesday, February 10, 15

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SLIDE 9

=

weak clustering local indistinguishability strong clustering Equivalence breaks down for quantum systems!

Λ A A

DLR THEORY (CLASSICAL)

Tuesday, February 10, 15

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SLIDE 10

MAIN THEOREM

is gapped satisfies strong clustering

L L

Tuesday, February 10, 15

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SLIDE 11

PROOF OUTLINE

Prop: If the Gibbs state satisfies strong clustering, then

CovA∪B(EA(f), EB(f)) ≤ ✏||f||2

2,ρ

Assume

w(A ∩ B) ≈ √ L

w(A) ≈ w(B) ≈ L VarA∪B(f) ≤ (1 + ✏)(VarA(f) + VarB(f))  (1 + ✏)(−1

A hf, LA(f)iρ + −1 B hf, LB(f)iρ)

 (1 + ✏)−1

A,B(hf, LA∪B(f)iρ + hf, LA∩B(f)iρ)

can eliminate this term by averaging Thus we get:

λ(2L) ≈ λ(L)

since

VarA∪B(f) ≤ (1 + ✏)(VarA(f) + VarB(f))

✏ ≤ ce−

√ L/ξ

applying iteratively completes the proof

Λ

A ∩ B

Ac

Bc

w(A ∩ B)

Tuesday, February 10, 15

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SLIDE 12

PROOF OUTLINE

Map Liouvillian onto FF Hamiltonian Use the detectability lemma Can invoke the theory of FF gaped Hamiltonians By constructing an approximate projector

Πl ≈ E = EinEout

it is not difficult to show that

||ˆ EAˆ EB − ˆ EA∪B|| ≤ e−lλ/ξ

Tuesday, February 10, 15

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SLIDE 13

MAIN THEOREM

is gapped satisfies strong clustering

L L

Tuesday, February 10, 15

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SLIDE 14

APPLICATIONS

In 1D strong and weak clustering are equivalent In 1D Gibbs samplers are always gapped

Boundaries can be removed in 1D One can use MPS methods in 1D

Beyond a universal critical temperature Gibbs samplers are gapped

Note: cannot use Araki’s result!

Tuesday, February 10, 15

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SLIDE 15

OUTLOOK

Extend the results to get Log-Sobolev bounds Consider what this means for topological order at non-zero temperature What can we say about the non- commuting case?

Tuesday, February 10, 15

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SLIDE 16

THANK YOU FOR YOUR ATTENTION!

Tuesday, February 10, 15