Fisher symmetry
and the geometry of quantum states
Jonathan A. Gross , Ninnat Dangniam,
Carlton M. Caves
Center for Quantum Information and Control ,
University of New Mexico
Overview
Goal
Measure deviations from a reference quantum state with equal
precision in all directions
Background
How do deviations in different directions relate?
Different directions
You've never heard of the Millennium Falcon?…It's the ship
that made the Kessel Run in less than twelve parsecs.
We will use the concept of statistical distance to
naturally relate all the directions in quantum state space
Statistical distance
Quantify distance between two quantum states by how statistically
distinguishable they are
More distinguishable : further apart
Less distinguishable : closer together
Example: coin bias
d s ∼ d
p Δ
p
^
\begin{align}
ds\sim\frac{dp}{\Delta \hat{p}}
\end{align}
Statistical fluctuations
The fluctuations depend on the estimator used
The Cramér–Rao bound gives us an achievable lower
bound
E
[
( Δ
x
^
) 2
]
≥ 1 F
( x )
\begin{align}
\mathbb{E}\left[(\Delta\hat{x})^2\right]&\geq\frac{1}{F(x)}
\end{align}
This allows us to generally define our statistical distance in terms of the
Fisher information (FI) :
d s ∼ F
( x )
d x
ds\sim\sqrt{F(x)}dx
Fisher information
F ( x
) =
∑ ξ
Pr (
Ξ = ξ
| x
) (
d d x ln
Pr
( Ξ =
ξ
| x )
) 2
= ∑
ξ
( d d x
Pr
( Ξ =
ξ
| x )
) 2
Pr (
Ξ = ξ
| x
)
\begin{align}
F(x)&=\sum_\xi\operatorname{Pr}(\Xi=\xi|x)\left(\frac{d}{dx}\ln\,\operatorname{Pr}(\Xi=\xi|x)\right)^2 \\
&=\sum_\xi\frac{\left(\frac{d}{dx}\operatorname{Pr}(\Xi=\xi|x)\right)^2}{\operatorname{Pr}(\Xi=\xi|x)}
\end{align}
The FI quantifies the information
Ξ
\Xi
has about
x x
Multiple parameters
So far we've only discussed fluctuations in 1 "direction"
If you're trying to measure multiple parameters, the FI becomes a
matrix
F
α β
(
x )
= ∑ ξ
∂
∂ x α
Pr
( Ξ =
ξ
|
x )
∂ ∂
x β
Pr (
Ξ = ξ
|
x
) Pr
( Ξ =
ξ
|
x )
\begin{align}
F_{\alpha\beta}(\mathbf{x})&=\sum_\xi\frac{\frac{\partial}{\partial x^\alpha}\operatorname{Pr}(\Xi=\xi|\mathbf{x})
\frac{\partial}{\partial x^\beta}\operatorname{Pr}(\Xi=\xi|\mathbf{x})}{\operatorname{Pr}(\Xi=\xi|\mathbf{x})}
\end{align}
Diagonal elements are the FIs for individual parameters
This matrix can be used as a metric:
d
s ∼ d
x
T
F (
x ) d
x
ds\sim\sqrt{d\mathbf{x}^\mathsf{T}F(\mathbf{x})d\mathbf{x}}
Quantum mechanics
Random variable defined by a positive-operator-valued measure (POVM),
{ E
ξ }
\{E^\xi\}
Pr (
Ξ = ξ
|
x
) =
tr (
E ξ ρ
(
x )
) ∑
ξ E
ξ = I
\begin{align}
\operatorname{Pr}(\Xi=\xi|\mathbf{x})&=\operatorname{tr}(E^\xi\rho(\mathbf{x})) & \sum_\xi E^\xi=I
\end{align}
We write the FI generated by a particular POVM as
C
α β
(
x )
= ∑ ξ
tr
( E
ξ
∂
ρ ∂
x α
|
x )
tr
( E
ξ
∂
ρ ∂
x β
|
x )
tr
( E
ξ ρ
(
x )
)
\begin{align}
C_{\alpha\beta}(\mathbf{x})&=\sum_\xi\frac{
\operatorname{tr}\left(E^\xi\left.\frac{\partial\rho}{\partial x^\alpha}\right\vert_\mathbf{x}\right)
\operatorname{tr}\left(E^\xi\left.\frac{\partial\rho}{\partial x^\beta}\right\vert_\mathbf{x}\right)}
{\operatorname{tr}\left(E^\xi\rho(\mathbf{x})\right)}
\end{align}
Quantum Fisher information
d s
v
∼ max
{
E ξ
}
v
T
C (
x )
v
=
v
T
Q (
x
)
v
\begin{align}
ds_\mathbf{v}&\sim\max_{\{E^\xi\}}\sqrt{\mathbf{v}^\mathsf{T}C(\mathbf{x})\mathbf{v}}
\hphantom{=\sqrt{\mathbf{v}^\mathsf{T}Q(\mathbf{x})\mathbf{v}}\,\,\,\,}
\end{align}
S. L. Braunstein and C. M. Caves,
Phys. Rev. Lett.
72 , 3439 (1994)
Quantum Fisher information
d s
v
∼ max
{
E ξ
}
v
T
C (
x )
v
=
v
T
Q (
x
)
v
\begin{align}
ds_\mathbf{v}&\sim\max_{\{E^\xi\}}\sqrt{\mathbf{v}^\mathsf{T}C(\mathbf{x})\mathbf{v}}
\hphantom{=\sqrt{\mathbf{v}^\mathsf{T}Q(\mathbf{x})\mathbf{v}}\,\,\,\,}
\end{align}
S. L. Braunstein and C. M. Caves,
Phys. Rev. Lett.
72 , 3439 (1994)
Quantum Fisher information
d s
v
∼ max
{
E ξ
}
v
T
C (
x )
v
=
v
T
Q (
x
)
v
\begin{align}
ds_\mathbf{v}&\sim\max_{\{E^\xi\}}\sqrt{\mathbf{v}^\mathsf{T}C(\mathbf{x})\mathbf{v}}
\hphantom{=\sqrt{\mathbf{v}^\mathsf{T}Q(\mathbf{x})\mathbf{v}}\,\,\,\,}
\end{align}
S. L. Braunstein and C. M. Caves,
Phys. Rev. Lett.
72 , 3439 (1994)
Quantum Fisher information
d s
v
∼ max
{
E ξ
}
v
T
C (
x )
v
=
v
T
Q (
x
)
v
\begin{align}
ds_\mathbf{v}&\sim\max_{\{E^\xi\}}\sqrt{\mathbf{v}^\mathsf{T}C(\mathbf{x})\mathbf{v}}
\hphantom{=\sqrt{\mathbf{v}^\mathsf{T}Q(\mathbf{x})\mathbf{v}}\,\,\,\,}
\end{align}
S. L. Braunstein and C. M. Caves,
Phys. Rev. Lett.
72 , 3439 (1994)
Quantum Fisher information
d s
v
∼ max
{
E ξ
}
v
T
C (
x )
v
= v
T
Q (
v
) d
x
\begin{align}
ds_\mathbf{v}&\sim\max_{\{E^\xi\}}\sqrt{\mathbf{v}^\mathsf{T}C(\mathbf{x})\mathbf{v}}
=\sqrt{\mathbf{v}^\mathsf{T}Q(\mathbf{v})d\mathbf{x}}
\end{align}
S. L. Braunstein and C. M. Caves,
Phys. Rev. Lett.
72 , 3439 (1994)
State-space geometry
Consider equatorial plane of the Bloch ball
As in the simplex case, Euclidean distance doesn't reflect statistical
distance
State-space geometry
Just like the simplex is naturally a semicircle, the Bloch ball is
naturally the upper hemisphere of
S
3 S^3
Simultaneous measurement
QFI gives bounds for individual parameter estimation
The Gill–Massar bound gives us simultaneous bounds
on multiple parameters for a
d
d
-dimensional system
tr
( Q
− 1 C
)
≤ d − 1
Q α
β C
α
β
≤ d − 1
\begin{align}
\operatorname{tr}\left(Q^{-1}C\right)&\leq d-1 \\
Q^{\alpha\beta}C_{\alpha\beta}&\leq d-1
\end{align}
Saturated iff
{ E
ξ }
\{E^\xi\}
is rank-1
R. D. Gill and S. Massar,
Phys. Rev. A
61 , 042312 (2000)
Gill–Massar bound
Through change of parameters we can set
Q
= I Q=I
In this natural parametrization
tr C
≤ d − 1
\begin{align}
\operatorname{tr}C&\leq d-1
\end{align}
If we measured all parameters with quantum-limited accuracy
tr C
= {
2 d − 2
p
u r
e d
2 − 1
g
e n
e r
a l
\begin{align}
\operatorname{tr}C&=\begin{cases}
2d-2 & \mathrm{pure} \\ d^2-1 & \mathrm{general}
\end{cases}
\end{align}
Gill–Massar bound
Fisher symmetry
Can we saturate bound and locally measure all parameters with the same
fraction of the quantum-limited accuracy?
C (
x )
= {
1 2 Q (
x
)
p u
r e
1 d + 1
Q (
x )
g
e n
e r
a l
\begin{align}
C(\mathbf{x})&=\begin{cases}
\frac{1}{2}Q(\mathbf{x}) & \mathrm{pure} \\
\frac{1}{d+1}Q(\mathbf{x}) & \mathrm{general}
\end{cases}
\end{align}
We call a measurement with this property a Fisher-symmetric
measurement (FSM)
Fisher symmetry
Fisher symmetry
Pure states
Li et al. have shown FSMs to exist for pure states in all
dimensions and given an explicit construction
N. Li, C. Ferrie, and C. M. Caves,
arXiv:1507.06904
Mixed states
A symmetric informationally complete POVM (SIC-POVM) is a FSM at the
maximally mixed state, provided it exists
E ξ
∝ Π
ξ
tr (
Π ξ
Π η
) =
c
o n
s t
. ξ
≠ η
\begin{align}
E^\xi&\propto\Pi^\xi \\
\operatorname{tr}\left(\Pi^\xi\Pi^\eta\right)&=\mathrm{const.} & \xi&\neq\eta
\end{align}
Mixed states
For qubits, can explicitly calculate the FSM for an arbitrary mixed
state
E ξ
= |
ϕ ξ
⟩ ⟨
ϕ ξ
|
|
ϕ 0
⟩ =
1 2 − z
| 0
⟩
|
ϕ j
⟩ =
1 3
( 1
− z 2 − z
| 0
⟩ + e
i φ
j
| 1
⟩
)
\begin{align}
E^\xi&=\left\vert\phi_\xi\middle\rangle\middle\langle\phi_\xi\right\vert \\
\left\vert\phi_0\right\rangle&=\frac{1}{\sqrt{2-z}}\left\vert0\right\rangle \\
\left\vert\phi_j\right\rangle&=\frac{1}{\sqrt{3}}\left(
\sqrt{\frac{1-z}{2-z}}\left\vert0\right\rangle+
e^{i\varphi_j}\left\vert1\right\rangle \right)
\end{align}
POVM
| ϕ
j ⟩
= 1 3
( 1
− z 2 −
z
| 0
⟩ +
e i φ
j
| 1
⟩
)
POVM
| ϕ
j ⟩
= 1 3
( 1
− z 2 −
z
| 0
⟩ +
e i φ
j
| 1
⟩
)
POVM
| ϕ
j ⟩
= 1 3
( 1
− z 2 −
z
| 0
⟩ +
e i φ
j
| 1
⟩
)
Back to the coin
POVM
| ϕ
j ⟩
= 1 3
( 1
− z 2 −
z
| 0
⟩ +
e i φ
j
| 1
⟩
)
POVM
| ϕ
j ⟩
= 1 3
( 1
− z 2 −
z
| 0
⟩ +
e i φ
j
| 1
⟩
)
POVM
| ϕ
j ⟩
= 1 3
( 1
− z 2 −
z
| 0
⟩ +
e i φ
j
| 1
⟩
)
Higher dimensions
SIC-POVMs are hard to find, so it looks like FSMs are going to be hard to
find (if they exist at all)
Easier problem (?) : given a SIC-POVM, can one transform it
to be a FSM at an arbitrary state?
Since FSMs are intimately related to the metric, parallel
transport recommends itself as a metric-dependent means of
transforming objects on state space
Parallel transport
Need to associate operators with vectors/covectors
ρ (
x
) = I
/ d +
x α X
α
∂ ∂
x α
→ ∂
ρ ∂
x α =
X α
δ
β
α =:
tr (
X
~
α X
β )
d x α
→
X
~
α
\begin{align}
\rho(\mathbf{x})&=I/d+x^\alpha X_\alpha &
\frac{\partial}{\partial x^\alpha}&\rightarrow
\frac{\partial\rho}{\partial x^\alpha}=X_\alpha \\
\delta^\alpha_{\quad\beta}&=:\operatorname{tr}\left(\tilde{X}^\alpha X_\beta\right) &
dx^\alpha&\rightarrow\tilde{X}^\alpha
\end{align}
POVM elements naturally correspond to covectors
C
α β
(
x )
= ∑ ξ
tr
( E
ξ
∂
ρ ∂
x α
|
x )
tr
( E
ξ
∂
ρ ∂
x β
|
x )
tr
( E
ξ ρ
(
x )
)
\begin{align}
C_{\alpha\beta}(\mathbf{x})&=\sum_\xi\frac{
\operatorname{tr}\left(E^\xi\left.\frac{\partial\rho}{\partial x^\alpha}\right\vert_\mathbf{x}\right)
\operatorname{tr}\left(E^\xi\left.\frac{\partial\rho}{\partial x^\beta}\right\vert_\mathbf{x}\right)}
{\operatorname{tr}\left(E^\xi\rho(\mathbf{x})\right)}
\end{align}
Preliminary results
Naïve parallel transport does not work (doesn't preserve positivity or rank
of POVM elements)
X
~ z
= σ z
− z I
\begin{align}
\tilde{X}^z&=\sigma_z-zI
\end{align}
Where does this leave us?
What we've done so far
Future directions
Several possible things to try moving forward
Try a different metric
Try manipulating more natural objects
Attack the problem perturbatively
Thanks!
Special thanks to Adrian, Jim, Qi, Rick, Matt, Travis, and Ninnat