Tensors: From Entanglement to Computational Complexity
Matthias Christandl (Copenhagen & MIT) Peter Vrana (Budapest) and Jeroen Zuiddam (Amsterdam->IAS) arXiv:1709.0781, Proc. STOC’18
Tensors: From Entanglement to Computational Complexity Matthias - - PowerPoint PPT Presentation
Tensors: From Entanglement to Computational Complexity Matthias Christandl (Copenhagen & MIT) Peter Vrana (Budapest) and Jeroen Zuiddam (Amsterdam->IAS) arXiv:1709.0781, Proc. STOC18 Outline Two motivations Resource theory of
Matthias Christandl (Copenhagen & MIT) Peter Vrana (Budapest) and Jeroen Zuiddam (Amsterdam->IAS) arXiv:1709.0781, Proc. STOC’18
1 1 1 1 1 1
State of a classical system (3 bits) 1 1 1 1 1 1
State of a quantum system (3 qubits)
e0 = ✓ 1 ◆ e1 = ✓ 0 1 ◆
d
i,j,k=1
1 1
r
i=1
Greenberger-Horne-Zeilinger
1 1 1 1 1 1
1 1 1 Local trans- formation: Flip first bit Local trans- formation: Flip first qubit
✓ 1 1 ◆
Linear combination of slices
e0 ⊗ e0 ⊗ e0 + e1 ⊗ e0 ⊗ e1 e0 ⊗ e0 ⊗ e0 + e0 ⊗ e1 ⊗ e1 e0 ⊗ e0 ⊗ e0 + e1 ⊗ e1 ⊗ e0
Einstein-Podolsky-Rosen (EPR)-state
e0 ⊗ e0 ⊗ e0 + e1 ⊗ e1 ⊗ e1
Greenberger-Horne-Zeilinger GHZ-state
e0 ⊗ e0 ⊗ e0
unentangled state
e0 ⊗ e0 ⊗ e1 + e0 ⊗ e1 ⊗ e0 + e1 ⊗ e0 ⊗ e0
W-state
Mamu(d) : M(d) × M(d) → M(d) (A, B) 7! A · B M(d) = algebra of d × d complex matrices
d3 multiplications
bilinear
d x = d
Mamu(d) : M(d) × M(d) × M(d)∗ → C (A, B, C) 7! trA · B · C Mamu(d) =
d
X
i,j,k=1
eij ⊗ ejk ⊗ eki eij = ei ⊗ ej
=
d
X
i,j,k=1
(ei ⊗ ej) ⊗ (ej ⊗ ek) ⊗ (ek ⊗ ei)
EPR states
e± := e0 ± e1
e00 ⊗ e00 ⊗ e00 + e11 ⊗ e11 ⊗ e11 e01 ⊗ e10 ⊗ e00 + e10 ⊗ e01 ⊗ e11 e01 ⊗ e11 ⊗ e10 + e10 ⊗ e00 ⊗ e01 e00 ⊗ e01 ⊗ e10 + e11 ⊗ e10 ⊗ e01
Do you like Strassen’s decomposition? Then you might want to look at some tensor surgery next!
arXiv:1606.04085
=e−1 ⊗ e1+ ⊗ e00 + e1+ ⊗ e00 ⊗ e−1 + e00 ⊗ e−1 ⊗ e1+ − e−0 ⊗ e0+ ⊗ e11 − e0+ ⊗ e11 ⊗ e−0 − e11 ⊗ e−0 ⊗ e0+ + (e00 + e11) ⊗ (e00 + e11) ⊗ (e00 + e11)
r
i=1
valuable resource free
= min{r : t =
r
X
i=1
αi ⊗ βi ⊗ γi}
G = GL(d) × GL(d) × GL(d)
(e0 + ✏e1)⊗3 − e⊗3 = ✏(e0 ⊗ e0 ⊗ e1 + e0 ⊗ e1 ⊗ e0 + e1 ⊗ e0 ⊗ e0) + O(✏2)
✏ 7! 0
GHZ state W state
e0 ⊗ e0 ⊗ e0 + e1 ⊗ e1 ⊗ e1 e0 ⊗ e0 ⊗ e1 + e0 ⊗ e1 ⊗ e0 + e1 ⊗ e0 ⊗ e0
f=Cayley hyperdeterminant
G covariant polynomial f : f(t) 6= f(t0)
be happy with partial information
t0
C ∈
⊗ Cd t0 ∈ Cd ⊗ Cd ⊗ Cd
normalised
t0
A ∈ Cd ⊗
λA = singular values (t0
A)2
t0
B ∈ · · ·
λB = singular values (t0
B)2
λC = singular values (t0
C)2
=spectrum of reduced density operator
Ch-Mitchison, Klyachko, Daftuar-Hayden (2004) based in part on Kirwan GHZ =all W EPR product Walter-Doran-Gross-Ch, Sawicki-Oszmaniec-Kus (2010) based on Brion
14
marginal polytope
– not in W-polytope – Then must be in GHZ-class!
Headline headline Subline sublkdjfksdfdf lkdsjfdkjfdf
λ(3)
max
1 λ(1)
max
λ(2)
max
0.5 1 1
C
ρ
max
λ(2)
max
λ(3)
max
.
# boxes=degree
G covariant polynomial f : f(t) 6= f(t0)
λ
Sn acts GL(d) acts
=:Pλ
= X
i
viv∗
i
! t⊗n = X
i
v∗
i fi(t)
Mulmuley-Sohoni, Strassen, Bürgisser-Ikenmeyer, …
G covariant polynomial f : f(t) 6= f(t0)
GHZ =all W EPR product
14
✓4 8, 3 8, 1 8 ◆
.
Pλt⊗n 6= 0
Kronecker = marginal polytope
gλ 6= 0
Source Encoder Storage Decoder
Source Encoder Storage Decoder Source Source
d = 2n
ei = ei1i2···in = ei1 ⊗ ei2 ⊗ · · · ⊗ ein
d
X
i=1
ei ⊗ ei =
2
X
i1=1
ei1 ⊗ ei1 ! ⊗
2
X
i2=1
ei2 ⊗ ei2 ! ⊗ · · · ⊗
2
X
in=1
ein ⊗ ein !
= (e0 ⊗ e0 + e1 ⊗ e1)⊗n
d
X
i=1
ei ⊗ ei ⊗ ei = (e0 ⊗ e0 ⊗ e0 + e1 ⊗ e1 ⊗ e1)⊗n
d
X
i,j,k=1
eij ⊗ ejk ⊗ eki = @
2
X
i,j,k=1
eij ⊗ ejk ⊗ eki 1 A
⊗n
= h2i⊗n
= Mamu(2)⊗n Mamu(d) =
hdi =
d3 multiplications
…, Coppersmith-Winograd Strassen
d x = d
r
i=1
n→∞ R(t⊗n)
1 n
n→∞ Q(t⊗n)
1 n
˜ R(Mamu(2)) = 2ω
F(hri) = r F(s ⊗ s0) = F(s) · F(s0) F(s ⊕ s0) = F(s) + F(s0)
F
F
easy
difficult every F is an obstruction Asymptotic analogue of completeness of invariants for degeneration
under restriction
F(s) ≥ F(s0) for all s ≥ s0
– Compact space worth of them – 3 Gauge points: ranks of slicings – Construction of others open since ’80s
– Oblique tensor – Strassen’s support functionals
θ = (θA, θB, θC) probability distribution e.g. θA = θB = θC = 1 3
Eθ(t) := max
λ∈∆(t){θAH(λA) + θBH(λB) + θCH(λC)}
Fθ(t) := 2Eθ(t)
Measures distance to origin (relative entropy distance)
14
h ✓1 3 ◆ ≈ 0.92
1
E( 1
3 , 1 3 , 1 3 )
2 3 quantum functionals entanglement polytope
Eθ(t) := max
λ∈∆(t){θAH(λA) + θBH(λB) + θCH(λC)}
Fθ(t) := 2Eθ(t)
easy, since polytope gets smaller under restriction quantum functional gets smaller easy, since polytope of unit tensor contains uniform point similar to multiplicativity, see paper
F(hri) = r
Fθ(t) := 2Eθ(t)
Entanglement polytope: Reduced density matrices
Entanglement polytopes: Invariant-theoretic
Eθ(t ⊗ t0) = Eθ(t) + Eθ(t0)
– If complete, then
– Reproves recent results – Characterise slice-rank
ω = 2
14
GHZ =all W EPR product
t ≥ t0 if (a ⊗ b ⊗ c) t = t0 for some matrices a, b, c t & t0 if t⌦n+o(n) ≥ t0⌦n
Eθ(t) := max
λ∈∆(t){θAH(λA) + θBH(λB) + θCH(λC)}
Fθ(t) := 2Eθ(t)
If all, then ω = 2
x =
1 1