fernando g.s.l. brand ão eth zürich princeton ee department, march 2013
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Fernando G.S.L. Brand ão ETH Zürich Princeton EE Department, March 2013. Monogamy of Quantum Entanglement. Simulating quantum is hard. More than 25% of DoE supercomputer power is devoted to simulating quantum physics Can we get a better handle on this simulation problem?. - PowerPoint PPT PresentationTRANSCRIPT
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Monogamy of Quantum Entanglement
Fernando G.S.L. BrandãoETH Zürich
Princeton EE Department, March 2013
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Simulating quantum is hard
More than 25% of DoE supercomputer power is devoted
to simulating quantum physics
Can we get a better handle on this simulation problem?
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Simulating quantum is hard, secrets are hard to conceal
More than 25% of DoE supercomputer power is devoted
to simulating quantum physics
Can we get a better handle on this simulation problem?
All current cryptography is based on unproven hardness
assumptions
Can we have better security guarantees for our secrets?
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Quantum Information Science…
… gives a path for solving both problems.
But it’s a long journey
QIS is at the crossover of computer science,
engineering and physics
Physics CS
Engineering
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The Two Holy Grails of QIS
Quantum Computation:
Use of engineered quantum systems for performing
computation
Exponential speed-ups over classical computing
E.g. Factoring (RSA)Simulating quantum systems
Quantum Cryptography:
Use of engineered quantum systems for secret key
distribution
Unconditional security based solely on the correctness of
quantum mechanics
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The Two Holy Grails of QIS
Quantum Computation:
Use of well controlled quantum systems for performing
computations.
Exponential speed-ups over classical computing
Quantum Cryptography:
Use of engineered quantum systems for secret key
distribution
Unconditional security based solely on the correctness of
quantum mechanics
State-of-the-art: +-5 qubits computer
Can prove (with high probability) that 15 = 3 x 5
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The Two Holy Grails of QIS
Quantum Computation:
Use of well controlled quantum systems for performing
computations.
Exponential speed-ups over classical computing
Quantum Cryptography:
Use of well controlled quantum systems for secret key
distribution
Unconditional security based solely on the correctness of
quantum mechanics
State-of-the-art: +-5 qubits computer
Can prove (with high probability) that 15 = 3 x 5
State-of-the-art: +-100 km
Still many technological challenges
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While waiting for a quantum computer
…how can a theorist help?
Answer 1: figuring out what to do with a quantum computer and the fastest way of building one(quantum communication, quantum algorithms, quantum fault tolerance, quantum simulators, …)
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While waiting for a quantum computer
…how can a theorist help?
Answer 1: figuring out what to do with a quantum computer and the fastest way of building one(quantum communication, quantum algorithms, quantum fault tolerance, quantum simulators, …)
Answer 2: finding applications of QIS independent of building a quantum computer
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While waiting for a quantum computer
…how can a theorist help?
Answer 1: figuring out what to do with a quantum computer and the fastest way of building one(quantum communication, quantum algorithms, quantum fault tolerance, quantum simulators, …)
Answer 2: finding applications of QIS independent of building a quantum computer
This talk
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Quantum Mechanics I
States: norm-one vector in Cd:
Dynamics: L2 norm preserving linear maps, unitary matrices: UUT = I
Observation: To any experiment with d outcomes we associate d PSD matrices {Mk} such that ΣkMk=I Born’s rule:
probability theory based on L2 norm instead of L1
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Quantum Mechanics II
Mixed States: PSD matrix of unit trace:
Dynamics: linear operations mapping density matrices to density matrices (unitaries + adding ancilla + ignoring subsystems)
Born’s rule:
probability theory based on PSD matrices
Dirac notation reminder:
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Quantum EntanglementPure States:
If , it’s separable
otherwise, it’s entangled.
Mixed States:
If , it’s separable
otherwise, it’s entangled.
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A Physical Definition of Entanglement
LOCC: Local quantum Operations and Classical Communication
Separable states can be created by LOCC:
Entangled states cannot be created by LOCC: non-classical correlations
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Quantum FeaturesSuperposition principle uncertainty principle
interference non-locality
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Monogamy of Entanglement
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Monogamy of Entanglement
Maximally Entangled State:
Correlations of A and B in cannot be shared:
If ρABE is is s.t. ρAB = , then ρABE =
Only knowing that the quantum correlations of A and B are very strong one can infer A and B are not correlated with anything else. Purely quantum mechanical phenomenon!
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Quantum Key Distribution…… as an application of entanglement monogamy
- Using insecure channel Alice and Bob share entangled state ρAB
- Applying LOCC they distill
(Bennett, Brassard 84, Ekert 91)
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Quantum Key Distribution…… as an application of entanglement monogamy
- Using insecure channel Alice and Bob share entangled state ρAB
- Applying LOCC they distill
Let πABE be the state of Alice, Bob and the Eavesdropper. Since πAB = , πAB = . Measuring A and B in the computational basis give 1 bit of secret key.
(Bennett, Brassard 84, Ekert 91)
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OutlineEntanglement Monogamy
How general is the concept? Can we make it quantitative? Are there other applications/implications?
1. Detecting Entanglement and Sum-Of-Squares Hierarchy
2. Accuracy of Mean-Field Approximation
3. Other Applications
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Problem 1: For M in H(Cd) (d x d matrix) compute
Very Easy!
Problem 2: For M in H(Cd Cl), compute
Quadratic vs Biquadratic Optimization
Next:Best known algorithm (and best hardness result) using
ideas from Quantum Information Theory
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Problem 1: For M in H(Cd) (d x d matrix) compute
Very Easy!
Problem 2: For M in H(Cd Cl), compute
Quadratic vs Biquadratic Optimization
Next:Best known algorithm using monogamy of entanglement
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The Separability Problem • Given
is it entangled?
• (Weak Membership: WSEP(ε, ||*||)) Given ρAB determine if it is separable, or ε-away from SEP
SEP Dε
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The Separability Problem • Given
is it entangled?
• (Weak Membership: WSEP(ε, ||*||)) Given ρAB determine if it is separable, or ε-away from SEP
SEP Dε
Frobenius norm
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The Separability Problem • Given
is it entangled?
• (Weak Membership: WSEP(ε, ||*||)) Given ρAB determine if it is separable, or ε-away from SEP
SEP Dε
Frobenius norm
LOCC norm
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The Separability Problem • Given
is it entangled?
• (Weak Membership: WSEP(ε, ||*||)) Given ρAB determine if it is separable, or ε-away from SEP
• Dual Problem: Optimization over separable states
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The Separability Problem • Given
is it entangled?
• (Weak Membership: WSEP(ε, ||*||)) Given ρAB determine if it is separable, or ε-away from SEP
• Dual Problem: Optimization over separable states
• Relevance: Entanglement is a resource in quantum cryptography, quantum communication, etc…
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Previous Work
When is ρAB entangled? - Decide if ρAB is separable or ε-away from separable
Beautiful theory behind it (PPT, entanglement witnesses, etc)
Horribly expensive algorithms
State-of-the-art: 2O(|A|log|B|) time complexity
Same for estimating hSEP (no better than exaustive search!)
dim of A
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Hardness Results
When is ρAB entangled? - Decide if ρAB is separable or ε-away from separable
(Gurvits ’02, Gharibian ’08, …) NP-hard with ε=1/poly(|A||B|)
(Harrow, Montanaro ‘10) No exp(O(log1-ν|A|log1-μ|B|)) time algorithm with ε=Ω(1) and any ν+μ>0 for unless ETH fails
ETH (Exponential Time Hypothesis): SAT cannot be solved in 2o(n) time(Impagliazzo&Paruti ’99)
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Quasipolynomial-time Algorithm
(B., Christandl, Yard ’11) There is an algorithm with run time exp(O(ε-2log|A|log|B|)) for WSEP(||*||, ε)
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Quasipolynomial-time Algorithm
(B., Christandl, Yard ’11) There is an algorithm with run time exp(O(ε-2log|A|log|B|)) for WSEP(||*||, ε)
Corollary: Solving WSEP(||*||, ε) is not NP-hard for ε = 1/polylog(|A||B|), unless ETH fails
Contrast with:
(Gurvits ’02, Gharibian ‘08) Solving WSEP(||*||, ε) NP-hard for ε = 1/poly(|A||B|)
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Quasipolynomial-time Algorithm
(B., Christandl, Yard ’11) For M in 1-LOCC, can compute hSEP(M) within additive error ε in time exp(O(ε-2log|A|log|B|))
M in 1-LOCC:
Contrast with(Harrow, Montanaro ’10) No exp(O(log1-ν|A|log1-μ|B|)) algorithm for hSEP(M) with ε=Ω(1), for separable M
M in SEP:
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Algorithm: SoS Hierarchy
Polynomial optimization over hypersphereSum-Of-Squares (Parrilo/Lasserre) hierarchy: gives sequence of SDPs that approximate hSEP(M)
- Round k takes time dim(M)O(k)
- Converge to hSEP(M) when k -> ∞
SoS is the strongest SDP hierarchy known for polynomial optimization (connections with SoS proof system, real algebraic geometric, Hilbert’s 17th problem, …)
We’ll derive SoS hierarchy by a quantum argument
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Algorithm: SoS Hierarchy
Polynomial optimization over hypersphereSum-Of-Squares (Parrilo/Lasserre) hierarchy: gives sequence of SDPs that approximate hSEP(M)
- Round k SDP has size dim(M)O(k)
- Converge to hSEP(M) when k -> ∞
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Algorithm: SoS Hierarchy
Polynomial optimization over hypersphereSum-Of-Squares (Parrilo/Lasserre) hierarchy: gives sequence of SDPs that approximate hSEP(M)
- Round k SDP has size dim(M)O(k)
- Converge to hSEP(M) when k -> ∞
SoS is the strongest SDP hierarchy known for polynomial optimization (connections with SoS proof system, real algebraic geometric, Hilbert’s 17th problem, …)
We’ll derive SoS hierarchy exploring ent. monogamy
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Classical Correlations are Shareable Given separable state
Consider the symmetric extension
Def. ρAB is k-extendible if there is ρAB1…Bk s.t for all j in [k], tr\ Bj (ρAB1…Bk) = ρAB
A
B1B2B3B4
Bk
…
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Monogamy Defines Entanglement(Stormer ’69, Hudson & Moody ’76, Raggio & Werner ’89)ρAB separable iff ρAB is k-extendible for all k
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SoS as optimization over k-extensions
(Doherty, Parrilo, Spedalieri ‘01) k-level SoS SDP for hSEP(M) is equivalent to optimization over k-extendible states(plus PPT (positive partial transpose) test):
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How close to separable are k-extendible states?
(Koenig, Renner ‘04) De Finetti Bound
If ρAB is k-extendible
Improved de Finetti Bound (B., Christandl, Yard ’11)
If ρAB is k-extendible:
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How close to separable are k-extendible states?
(Koenig, Renner ‘04) De Finetti Bound
If ρAB is k-extendible
Improved de Finetti Bound (B., Christandl, Yard ’11)
If ρAB is k-extendible:
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How close to separable are k-extendible states?
Improved de Finetti Bound
If ρAB is k-extendible:
k=2ln(2)ε-2log|A| rounds of SoS solves WSEP(ε) with a SDP of size
|A||B|k = exp(O(ε-2log|A| log|B|))SEP Dε
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Proving…
Proof is information-theoreticMutual Information: I(A:B)ρ := H(A) + H(B) – H(AB) H(A)ρ := -tr(ρ log(ρ))
Improved de Finetti Bound
If ρAB is k-extendible:
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Proving…
Proof is information-theoreticMutual Information: I(A:B)ρ := H(A) + H(B) – H(AB) H(A)ρ := -tr(ρ log(ρ))
Let ρAB1…Bk be k-extension of ρAB
log|A| > I(A:B1…Bk) = I(A:B1)+I(A:B2|B1)+…+I(A:Bk|B1…Bk-1)
For some l<k: I(A:Bl|B1…Bl-1) < log|A|/k
(chain rule)
Improved de Finetti Bound
If ρAB is k-extendible:
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Proving…
Proof is information-theoreticMutual Information: I(A:B)ρ := H(A) + H(B) – H(AB) H(A)ρ := -tr(ρ log(ρ))
Let ρAB1…Bk be k-extension of ρAB
log|A| > I(A:B1…Bk) = I(A:B1)+I(A:B2|B1)+…+I(A:Bk|B1…Bk-1)
For some l<k: I(A:Bl|B1…Bl-1) < log|A|/k
(chain rule)
What does it imply?
Improved de Finetti Bound
If ρAB is k-extendible:
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Quantum Information?Nature isn't classical, dammit,
and if you want to make a simulation of Nature, you'd
better make it quantum mechanical, and by golly it's a wonderful problem, because it
doesn't look so easy.
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Quantum Information?Nature isn't classical, dammit,
and if you want to make a simulation of Nature, you'd
better make it quantum mechanical, and by golly it's a wonderful problem, because it
doesn't look so easy.
Bad news• Only definition I(A:B|C)=H(AC)
+H(BC)-H(ABC)-H(C)• Can’t condition on quantum
information.• I(A:B|C)ρ ≈ 0 doesn’t imply ρ is
approximately separablein 1-norm (Ibinson, Linden, Winter ’08)
Good news• I(A:B|C) still defined• Chain rule, etc. still hold• I(A:B|C)ρ=0 implies ρ is
separable (Hayden, Jozsa, Petz, Winter‘03)
information theory
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Obs: I(A:B|E)≥0 is strong subadditivity inequality (Lieb, Ruskai ‘73)
Chain rule:
2log|A| > I(A:B1…Bk) = I(A:B1)+I(A:B2|B1)+…+I(A:Bk|B1…Bk-1)
For ρAB k-extendible:
Proving the Bound
Thm (B., Christandl, Yard ’11)
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Conditional Mutual Information Bound
• Coding Theory Strong subadditivity of von Neumann entropy as state redistribution rate (Devetak, Yard ‘06)
• Large Deviation Theory Hypothesis testing for entanglement (B., Plenio ‘08)
( )
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hSEP equivalent to
1. Injective norm of 3-index tensors
2. Minimum output entropy quantum channel, Optimal acceptance probability in QMA(2), ….
4. 2->q norms of projectors:
(||*||2->q for q > 2 are hypercontractive norms: useful in Markov Chain Monte Carlo (rapidly mixing condition), etc)
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Quantum Bound on SoS Implies
1. For n x n matrix A can compute with O(log(n)ε-3) rounds of SoS a number x s.t.
2. nO(ε) -level SoS solves ε-Small Set Expansion Problem (closely related to unique games). Alternative algorithm to (Arora, Barak, Steurer ’10)
3. Open Problem: Improvement in bound (additive -> multiplicative error) would solve SSE in exp(O(log2(n))) time.
Can formulate it as a quantum information-theoretic problem
(Barak, B., Harrow, Kelner, Steurer, Zhou ‘12)
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Quantum Bound on SoS Implies
1. For n x n matrix A can compute with O(log(n)ε-3) rounds of SoS a number x s.t.
2. nO(ε) -level SoS solves ε-Small Set Expansion Problem (closely related to unique games). Alternative algorithm to (Arora, Barak, Steurer ’10)
3. Open Problem: Improvement in bound (additive -> multiplicative error) would solve SSE in exp(O(log2(n))) time.
Can formulate it as a quantum information-theoretic problem
(Barak, B., Harrow, Kelner, Steurer, Zhou ‘12)
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Quantum Bound on SoS Implies
1. For n x n matrix A can compute with O(log(n)ε-3) rounds of SoS a number x s.t.
2. nO(ε) -level SoS solves ε-Small Set Expansion Problem (closely related to unique games). Alternative algorithm to (Arora, Barak, Steurer ’10)
3. Open Problem: Improvement in bound (additive -> multiplicative error) would solve SSE in exp(O(log2(n))) time.
Can formulate it as a quantum information-theoretic problem
(Barak, B., Harrow, Kelner, Steurer, Zhou ‘12)
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OutlineEntanglement Monogamy
How general is the concept? Can we make it quantitative? Are there other applications/implications?
1. Detecting Entanglement and Sum-Of-Squares Hierarchy
2. Accuracy of Mean Field Approximation
3. Other applications
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Constraint Satisfaction Problems vs Local Hamiltonians
k-arity CSP:
Variables {x1, …, xn}, alphabet Σ
Constraints:
Assignment:
Unsat :=
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Constraint Satisfaction Problems vs Local Hamiltonians
k-arity CSP:
Variables {x1, …, xn}, alphabet Σ
Constraints:
Assignment:
Unsat :=
k-local Hamiltonian H:
n qudits in
Constraints:
qUnsat := E0 : min eigenvalue
H1
qudit
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C. vs Q. Optimal AssignmentsFinding optimal assignment of CSPs can be hard
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C. vs Q. Optimal AssignmentsFinding optimal assignment of CSPs can be hard
Finding optimal assignment of quantum CSPs can be even harder
(BCS, Laughlin states for FQHE,…)
Main difference: Optimal Assignment can be a highly entangled state (unit vector in )
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Optimal Assignments:Entangled States
Non-entangled state:
e.g.
Entangled states:
e.g.
To describe a general entangled state of n spins requires exp(O(n)) bits
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How Entangled?
Given bipartite entangled state
the reduced state on A is mixed:
The more mixed ρA, the more entangled ψAB:
Quantitatively: E(ψAB) := S(ρA) = -tr(ρA log ρA)
Is there a relation between the amount of entanglement in the ground-state and the computational complexity of the model?
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Mean-Field……consists in approximating groundstate by a product state
is a CSP
Successful heuristic in Quantum Chemistry (e.g. Hartree-Fock) Condensed matter (e.g. BCS theory)
Folklore: Mean-Field good when Many-particle interactions Low entanglement in state
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Mean-Field Good When(B., Harrow ‘12) Let H be a 2-local Hamiltonian on qudits with interaction graph G(V, E) and |E| local terms.
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(B., Harrow ‘12) Let H be a 2-local Hamiltonian on qudits with interaction graph G(V, E) and |E| local terms.
Let {Xi} be a partition of the sites with each Xi having m sites.
X1
X3X2
m < O(log(n))
Mean-Field Good When
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(B., Harrow ‘12) Let H be a 2-local Hamiltonian on qudits with interaction graph G(V, E) and |E| local terms.
Let {Xi} be a partition of the sites with each Xi having m sites. Then there are products states ψi in Xi s.t.
deg(G) : degree of GEi : expectation over Xi
S(Xi) : entropy of groundstate in Xi
X1
X3X2
m < O(log(n))
Mean-Field Good When
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Approximation in terms of degree
1-D
2-D
3-D
∞-D
…shows mean field becomes exact in high dim
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Approximation in terms of degree
Relevant to the quantum PCP problem:
(Aharonov, Arad, Landau, Vazirani ’08; Freedman, Hastings ‘12; Bravyi, DiVincenzo, Loss, Terhal ’08, Aaronson ‘08)
Is approximating e0(H) to constant accuracy quantum NP-hard?
It shows that quantum generalizations of PCP theorem and parallel repetition cannot both be true (assuming “quantum NP”= QMA ≠ NP)
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Approximation in terms of average entanglement
Mean-field works well if entanglement of groundstate satisfies a subvolume law:
Connection of amount of entanglement in groundstate and computational complexity of the model
X1
X3X2
m < O(log(n))
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Approximation in terms of average entanglement
Systems with low entanglement are expected to be simpler
So far only precise in 1D:
Area law for entanglement -> Succinct classical description (MPS)Here:Good: arbitrary lattice, only subvolume law
Bad: Only mean energy approximated well
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New Algorithms for Quantum Hamiltonians
Following same approach we also obtain polynomial time algorithms for approximating the groundstate energy of
1. Planar Hamiltonians, improving on (Bansal, Bravyi, Terhal ‘07)2. Dense Hamiltonians, improving on (Gharibian, Kempe ‘10)3. Hamiltonians on graphs with low threshold rank, building on (Barak, Raghavendra, Steurer ‘10)
In all cases we prove that a product state does a good job and use efficient algorithms for CSPs.
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Proof Idea: Monogamy of Entanglement
Cannot be highly entangled with too many neighbors
Entropy S(Xi) quantifies how entangled it can be
Proof uses information-theoretic techniques (chain rule of conditional mutual information, informationally complete measurements, etc) to make this intuition precise
Inspired by classical information-theoretic ideas for bounding convergence of SoS hierarchy for CSPs (Tan, Raghavendra ’10; Barak, Raghavendra, Steurer ‘10)
Xi
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OutlineEntanglement Monogamy
How general is the concept? Can we make it quantitative? Are there other applications/implications?
1. Detecting Entanglement and Sum-Of-Squares Hierarchy
2. Accuracy of Mean Field Approximation
3. Other applications
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Other Applications
(B., Horodecki ‘12) Proving entanglement area law from exponential decay of correlations for 1D states
(Almheiri, Marolf, Polchinski, Sully ’12) Black hole firewall controversy
(Vidick, Vazirani ’12; Bartett, Colbeck, Kent ’12; …) Device independent key distribution
(Vidick and Ito ‘12) Entangled multiple proof systems: NEXP in MIP*
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ConclusionsEntanglement Monogamy is a distinguishing feature of quantum correlations(quantum engineering)It’s at the heart of quantum cryptography(convex optimization) Can be used to detect entanglement efficiently and understand Sum-Of-Squares hierarchy better(computational physics)Can be used to bound efficiency of mean-field approximation (quantum many-body physics)Can be used to prove area law from exponential decay of correlations
QIT useful to bound efficiency of mean-field theory
- Cornering quantum PCP - Poly-time algorithms for planar and dense Hamiltonians
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Thank you!