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Transcript
Quantum Gibbs Samplers
Fernando G.S.L. Brandão
QuArC, Microsoft Research
Q-QuArC Retreat, 2015
Dynamical Properties
Hij
Hamiltonian:
State at time t:
Compute:
Expectation values:
Temporal correlations:
Quantum Simulators, Dynamical
Quantum Computer
Can simulate the dynamics of every multi-particle quantum system
Spin models
Fermionic and bosonic models
Topological quantum field theory
Quantum field theory
Quantum Chemestry
(Lloyd ‘96, …, Berry, Childs, Kothari ‘15)
(Bravyi, Kitaev ’00, …)
(Freedman, Kitaev, Wang ‘02)
(Jordan, Lee, Preskill ‘11)
(Hastings, Wecker, Bauer, Triyer ’14)
Quantum Simulators
Can simulate the dynamics of particular models
Optical Lattices
Ion Traps
Superconducting systems
…
Static Properties
Static Properties
Hij
Hamiltonian:
Static Properties
Hij
Hamiltonian:
Groundstate:
Thermal state:
Compute: local expectation values (e.g. magnetization),
correlation functions (e.g.
), …
Static Properties
Can we prepare groundstates?
Warning: in general hard, even for one-dimensional translational(…, Gottesman-Irani ‘09)
invariant models
Static Properties
Can we prepare groundstates?
Warning:I in general hard, even for one-dimensional translational(…, Gottesman-Irani ‘09)
invariant models
Method 1: Adiabatic Evolution; works if Δ ≥ n-c
H(sf)
H(si)
ψi
H(s)
ψs
H(s)ψs = E0,sψs
Δ := min Δ(s)
Method 2: Phase Estimation; works if can find a “simple” state |0>
such that
*
(Abrams, Lloyd ‘99)
Static Properties
Can we prepare thermal states?
Warning: NP-hard to estimate energy of general classical Gibbs states
Static Properties
Can we prepare thermal states?
Warning: NP-hard to estimate energy of general classical Gibbs states
Are quantum computers useful in some cases?
Plan
1. Classical Glauber dynamics
2. Mixing in space vs mixing in time
3. Quantum Master Equations
4. Mixing in space vs mixing in time for commuting quantum
systems
5. Approach to non-commuting
6. Potential Applications
Thermalization
Can we prepare thermal states?
Method 1: Couple to a bath of the right temperature and wait.
S
B
But size of environment might be huge. Maybe not efficient. No
guarantee Gibbs state will be reached
Method 2 (classical): Metropolis
Sampling
Consider e.g. Ising model:
Coupling to bath modeled by stochastic map Q
i
j
Metropolis Update:
The stationary state is the thermal (Gibbs) state:
Method 2 (classical): Metropolis
Sampling
Consider e.g. Ising model:
Coupling to bath modeled by stochastic map Q
i
j
Metropolis Update:
The stationary state is the thermal (Gibbs) state:
• (Metropolis et al ’53) “We devised a general method to calculate the
properties of any substance comprising individual molecules with
classical statistics”
• Example of Markov Chain Monte Carlo method.
Extremely useful algorithmic technique
Glauber Dynamics
A stochastic map R = eG is a Glauber dynamics for a (classical)
Hamiltonian if it’s generator G is local and the unique fixed point
of R is e-βH/Z(β) (+ detailed balance)
Ex: Metropolis, Heat-bath generator, ….
Glauber Dynamics
A stochastic map R = eG is a Glauber dynamics for a (classical)
Hamiltonian if it’s generator G is local and the unique fixed point
of R is e-βH/Z(β) (+ detailed balance)
Ex: Metropolis, Heat-bath generator, ….
When is Glauber dynamics effective for sampling from Gibbs state?
(Stroock, Zergalinski ’92;
Martinelli, Olivieri ’94, …)
Rapidly mixing
Glauber dynamics
Gibbs state with
finite correlation
length
(Proved only for hard core
mode and 2-spin antiferromagetic model)
(Sly ‘10)
Gibbs Sampling in P
(vs NP -hard)
Temporal Mixing
transition matrix
after t time steps
eigenprojectors
eigenvalues
Convergence time given by the gap Δ = 1- λ1:
Time of equilibration ≈ n/Δ
We have rapid mixing if Δ = constant
Spatial Mixing
Let
be the Gibbs state for
a model in the lattice V with
boundary conditions τ, i.e.
blue: V,
red: boundary
0 0 0 0 0 0 0 0 0 0 0 0
0
0
0
0
0
0
0
0
0
0
0 0 0 0 0 0 0 0 0 0 0 0
Ex. τ = (0, … 0)
Spatial Mixing
Let
be the Gibbs state for
a model in the lattice V with
boundary conditions τ, i.e.
blue: V,
red: boundary
0 0 0 0 0 0 0 0 0 0 0 0
0
0
0
0
0
0
0
0
0
0
0 0 0 0 0 0 0 0 0 0 0 0
Ex. τ = (0, … 0)
def: The Gibbs state has correlation
length ξ if for every f, g
f
g
Temporal Mixing <-> Spatial Mixing
(Stroock, Zergalinski ’92; Martinelli, Olivieri ’94, …) For every classical
Hamiltonian, the Gibbs state has finite correlation length if, and
only if, the Glauber dynamics has a finite gap
Temporal Mixing <-> Spatial Mixing
(Stroock, Zergalinski ’92; Martinelli, Olivieri ’94, …) For every classical
Hamiltonian, the Gibbs state has finite correlation length if, and
only if, the Glauber dynamics has a finite gap
Obs1: Same is true for the log-Sobolev constant of the system
Obs2: For many models, when correlation length diverges, gap is
exponentially small in the system size (e.g. Ising model)
Obs3: Any model in 1D, and any model in arbitrary dim. at high enough
temperature, has a finite correlation length
(connected to uniqueness of the phase, e.g. Dobrushin’s condition)
Temporal Mixing <-> Spatial Mixing
(Stroock, Zergalinski ’92; Martinelli, Olivieri ’94, …) For every classical
Hamiltonian, the Gibbs state has finite correlation length if, and
only if, the Glauber dynamics has a finite gap
Obs1: Same is true for the log-Sobolev constant of the system
Obs2: For many models, when correlation length diverges, gap is
exponentially small in the system size (e.g. Ising model)
Obs3: Any model in 1D, and any model in arbitrary dim. at high enough
temperature, has a finite correlation length
(connected to uniqueness of the phase, e.g. Dobrushin’s condition)
Does something similar hold in the quantum case?
1st step: Need a quantum version of Glauber dynamics…
Preparing Quantum Thermal States
(Terhal and diVincenzo ’00, …)
Simulate interaction of system with heat bath
no run-time estimate
(Poulin, Wocjan ’09, …) Grover-type speed-up for preparing Gibbs states
exponential run-time
Quantum metropolis: Quantum channel s.t. (i) can be
implemented efficiently on a quantum computer and (ii) has Gibbs state as
fixed point
no run-time estimate
(Temme et al ‘09)
Amplitude amplification applied to quantum metropolis:
Square-root speed-up on spectral gap.
no run-time estimate
(Yung, Aspuru-Guzik ‘10)
(Hastings ’08; Bilgin, Boixo ’10) Poly-time quantum/classical algorithm for
every 1D model
restricted to 1D
Quantum Master Equations
Canonical example: cavity QED
Lindblad Equation:
(most general Markovian and time homogeneous q. master equation)
Quantum Master Equations
Canonical example: cavity QED
Lindblad Equation:
(most general Markovian and time homogeneous q. master equation)
Generator completely positive tracepreserving map:
fixed point:
How fast does it converge? Determined by gap of of Lindbladian
Quantum Master Equations
Canonical example: cavity QED
Lindblad Equation:
Local master equations: L is k-local if all Ai act on at most k sites
Ai
(Kliesch et al ‘11) Time evolution of every k-local Lindbladian on n
qubits can be simulated in time poly(n, 2^k) in the circuit model
Davies Maps
Lindbladian:
Lindblad terms:
: spectral density
Davies Maps
Lindbladian:
Lindblad terms:
: spectral density
Hij
Sα (Xα, Yα, Zα)
Thermal state is the unique fixed point:
(satisfies q. detailed balance:
)
Why Davies Maps?
(Davies ‘74) Rigorous derivation in the
weak-coupling limit:
Coarse grain over time t ≈ λ-2 >> max(1/ (Ei – Ej + Ek - El))
(Ei: eigenvalues of H)
Interacting Ham.
Why Davies Maps?
(Davies ‘74) Rigorous derivation in the
Interacting Ham.
weak-coupling limit:
Coarse grain over time t ≈ λ-2 >> max(1/ (Ei – Ej + Ek - El))
(Ei: eigenvalues of H)
But: for n spin Hamiltonian H:
max(1/ (Ei – Ej + Ek - El)) = exp(O(n))
Consequence:
Sα(ω) are non-local (act on n qubits);
But for commuting Hamiltonian H, it is local
Gap
The relevant gap is given by
L2 weighted inner product:
Variance:
Gap equal to spectral gap of
Mixing time of order
Previous Results
Gap has been estimated for:
(Alicki, Fannes, Horodecki ‘08)
Λ = Ω(1) for 2D toric code
(Alicki, Horodecki, Horodecki, Horodecki ‘08)
Λ = exp(-Ω(n)) for 4D toric code
(Temme ‘14)
Λ > exp(- βε)/n, with ε the energy barrier, for stabilizer Hamiltonians
Equivalence of Clustering in Space and
Time for Quantum Commuting
thm For commuting Hamiltonians in a finite dimensional lattice, the
Davies generator has a constant gap if, and only if, the Gibbs state
satisfies strong clustering of correlations
(Kastoryano, B. 1409.3435)
Equivalence of Clustering in Space and
Time for Quantum Commuting
thm For commuting Hamiltonians in a finite dimensional lattice, the
Davies generator has a constant gap if, and only if, the Gibbs state
satisfies strong clustering of correlations
Strong Clustering holds true in:
• 1D at any temperature
• Any D at sufficiently high temperature
(critical T determined only by dim and interaction range)
Equivalence of Clustering in Space and
Time for Quantum Commuting
thm For commuting Hamiltonians in a finite dimensional lattice, the
Davies generator has a constant gap if, and only if, the Gibbs state
satisfies strong clustering of correlations
Strong Clustering holds true in:
• 1D at any temperature
• Any D at sufficiently high temperature
(critical T determined only by dim and interaction range)
Gives first polynomial-time quantum algorithm for preparing Gibbs states
of commuting models at high temperature.
Caveat: At high temperature cluster expansion works well for computing
local expectation values. (Open: How the two threshold T’s compare?)
Q advantage: we get the full Gibbs state
(e.g. could perform swap test of purifications of two Gibbs states. Good for anything?)
Strong Clustering
A
B
def: Strong clustering holds if there is
ξ>0 s.t. for every A and B and operator
f acting on
Conditional Covariance:
Conditional Expectation:
Xc : complement of X
d(X, Y) : distance between regions X and Y
Strong Clustering
A
B
Fact 1:
Fact 2: Strong clustering follows if
def: Strong clustering holds if there is
ξ>0 s.t. for every A and B and operator
f acting on
Strong Clustering -> Gap
We show that under the clustering condition:
Getting:
A
B
V : entire lattice
V0 : sublattice of size O(ξ)
Follows idea of a proof of the classical analogue for Glauber dynamics
(Bertini et al ‘00)
Key lemma: If
then
Gap -> Strong Clustering
Employs the following mapping between Liouvillians for commuting
Hamiltonians and local Hamiltonians on a larger space:
Apply the detectability lemma (Aharonov et al ‘10) to prove
gap -> strong clustering
(strengthening proof previous proof that gap -> clustering)
Non-Commuting Hamiltonians?
Davies Master Equation is Non-Local…
Question: Can it be implemented efficiently on a quantum computer?
Non-Commuting Hamiltonians?
Davies Master Equation is Non-Local… Beyond master equations:
def: Hamiltonian H satisfies local indistinguishability (LI)
if for every
Λ
A
B
Non-Commuting Hamiltonians?
Davies Master Equation is Non-Local… Beyond master equations:
def: Hamiltonian H satisfies local indistinguishability (LI)
if for every
thm Suppose H (local Ham in d dim) satisfies
LI and for everyregion A,
l
A
Bl
Then e-H/T/Z(T) can be created by a quantum
circuit of size exp(O(logd(n)))
Based on recent lower bound by Fawzi and Renner on conditional mutual
information
Non-Commuting Hamiltonians?
Davies Master Equation is Non-Local… Beyond master equations:
def: Hamiltonian H satisfies local indistinguishability (LI)
if for every
thm Suppose H (local Ham in d dim) satisfies
LI and for everyregion A,
l
A
Then e-H/T/Z(T) can be created by a quantum
circuit of size exp(O(logd(n)))
• When is the requirement on mutual information true?
(always true for commuting models)
• Can we improve to poly(n) time?
Bl
Applications of Q. Gibbs Samplers
1. Machine Learning? (Nate’s talk)
2. Quantum Algorithms for semidefinite programming?
O(d4log(1/ε))-time algorithm (interior point methods)
For sparse Ai, Y, is there a polylog(d) quantum algorithm?
(think of HHL quantum algorithm for linear equations)
Applications of Q. Gibbs Samplers
1. Machine Learning? (Nate’s talk)
2. Quantum Algorithms for semidefinite programming?
O(d4log(1/ε))-time algorithm (interior point methods)
For sparse Ai, Y, is there a polylog(d) quantum algorithm?
(think of HHL quantum algorithm for linear equations)
No, unless NP in BQP
Applications of Q. Gibbs Samplers
1. Machine Learning? (Nate’s talk)
2. Quantum Algorithms for semidefinite programming?
(Peng, Tangwongsan ‘12) For Ai > 0 (||A||<1), can reduce the problem to
polylog(d) evaluations of
(for λi < log(d)/ε)
Applications of Q. Gibbs Samplers
1. Machine Learning? (Nate’s talk)
2. Quantum Algorithms for semidefinite programming?
(Peng, Tangwongsan ‘12) For Ai > 0 (||A||<1), can reduce the problem to
polylog(d) evaluations of
(for λi < log(d)/ε)
Are there interesting choices of {Ai} for which quantum gives an
advantage to compute the Gibbs states above?