Dominique SUGNY
Laboratoire Interdisciplinaire Carnot de Bourgogne, Dijon
(France).
Nottingham, Thursday, 22th January 2015
Quantum Cybernetics and Control
Recent advances in the control
of spin systems
Collaborations: Gr. B. Bonnard (Mathematics), Gr. S. Glaser (NMR),
M. Lapert, E. Assémat, L. Van Damme.
Optimal control of quantum systems
Quantum Optimal Control
Optimal control theory Quantum Dynamics
- Collaboration between mathematicians, physicists, chemists…
- Open-loop quantum optimal control
Electronics
Economy
Biology
Quantum physics…
Control theory has a large number of
applications:
Lev Pontryagin 1908-1988
The modern history of optimal control
The mathematical framework of optimal control is based on the
Pontryagin Maximum Principle: Generalization of the Euler-
Lagrange principle for controlled systems - 1960
Pontryagin Maximum Principle
The optimal control problem is described through a classical Hamiltonian
dynamics. The PMP only gives a necessary condition of optimality.
ft
dtuxFMin
tuxFx
0
0 ),(
),,(
),(),,( 00 uxFptuxFpH
Adjoint states:
),( 0pp
Hamiltonian equations and maximization condition:
vvtpxHutpxH )],,,,(max[),,,(
Boundary conditions:
0)()( ptporxtx fff
Terminal cost:
))(( ftx
Optimal control of quantum systems
Geometric (analytic) and numerical methods:
Geometric approach if
Numerical approach if
1N
1N
Ex: Pi-pulse
Population transfert in a two-level quantum system
T
dttu0
)(
Geometry: Analytical and geometric methods to solve the
optimal equations
Numerics: Iterative algorithms used to solve the optimal
equations (Krotov, GRAPE)
Optimal control of a two-level dissipative quantum system
Generalization of the standard pi-pulse to the dissipative case
We consider a two-level quantum system whose dynamics is
ruled by the Lindblad equation.
xuyuTzz
zuTyy
zuTxx
yx
x
y
1
2
2
/)1(
/
/
Bloch equation:
Bloch ball: 1222 zyx
Condition on the relaxation parameters: 12 20 TT
A geometric structure: The magic plane
sin
sincos
coscos
rz
ry
rx
)cossin(tan
cossinsincos
)sin1
(cos
sinsincos
21
1
2
12
2
xy
yx
uu
uuTrT
T
r
TT
rr
We introduce the spherical coordinates:
We cannot directly control the r-coordinate
Ref.: D. J. Tannor and A. Bartana, J. Phys. Chem. A 103, 10359 (1999)
A basic geometric structure: The magic plane
If there is no bound on the control fields:
We can move in an arbitrarily short time along the Bloch
sphere.
How to control the dynamics along the radial direction ?
112
sincos2cossincos2
T
r
TT
r
d
rd
What are the characteristic points of the Bloch sphere ?
0r Steady-state ellipsoid
][/ rMinMax Other geometric structure?
Another geometric structure: The magic plane
z-axis:
)212
(112
222
22
T
z
TT
z
zyx
yx
d
rd
Maximum shrinking if:
][/ rMinMax
The magic plane
)(2 12
20
TT
Tzz
00 zz
Maximum shrinking for any
point of the plane
The magic plane and the Steady-state ellipsoid
The magic plane belongs to
the Bloch ball if:
11 0 z
Time-optimal control of the saturation process
The goal is to reach in minimum time the center of the Bloch ball
On the magic plane, the
control fields satisfy:
xuyuT
zz yx
1
)1(0
Optimal synthesis in the unbounded case
This result can be generalized to any target state
Generalization of a Pi-pulse to a two-level dissipative quantum
system
Ref.: PRA 88, 033407 (2013)
Experimental implementation in NMR
Time-optimal solution versus Inversion solution
Gain of 60% in the
control duration
Ref.: PRL 104, 083001 (2010)
Generalization to the control of non-linear dynamics
The radiation damping interaction in NMR:
2
1
2
/)1(
/
kyyuTzz
kyzzuTyy
x
x
Optimal synthesis without the magic plane
Overlap curve in
blue and green
Switching
curve in red
Ref.: PRA 87, 043417 (2013), JCP 134, 054103 (2011)
Generalization to the control of two uncoupled spins
)1()1(
1
)1()1(
)1()1(
2
)1()1(
/)1(
/
yuTzz
zuTyy
x
x
)2()2(
1
)2()2(
)2()2(
2
)2()2(
/)1(
/
yuTzz
zuTyy
x
x
Optimization of the saturation contrast in MRI:
][
0
2)2(2)2(
)1()1(
zyMax
zy
Goal:
Desoxygeneated blood Oxygeneated blood
Ref.: IEEE 57, 1857 (2012)
Experimental implementation in MRI
Geometry of the sample
Application of the optimal sequence
(optimization with GRAPE)
GOCT gives the physical limit of the process
(80% of the limit with GRAPE)
Ref.: Sci. Rep. 2, 589 (2012)
Generalization to the optimization of the signal to noise ratio
Ref.: PRA90, 023411 (2014), JCP (2015) to be published
Cyclic process repeated N times to
improve the SNR in NMR
The initial and the final states (the
same steady state) are not known.
Optimization of the steady state and
of the control field.
p
N
T
My
Q
1
)(Figure of Merit:
)()(
,
N
TM
N
TMQMaxMaxQMax
pp
Two-step optimization process:
We prove the optimality of the
Ernst angle solution in the
unbounded case: Bang pulse ])[arccos(exp
1
)(
T
TdE
Geometric optimal control in quantum computation
Time-optimal control of SU(2) quantum operations
Definition of the optimal control
problem
UtHUi )(yyxxz StStStH )()()(
2
0
22 )()(
)2(1:)(
tt
SUUtU
yx
f
Use of the Euler angles coordinates
Description in terms of the PMP
Examples of projected
optimal trajectories:
zyz iaSibSiaS
f
zf
eeeUb
SiUa
:)(
]exp[:)(
0
Ref.: PRA88, 043422 (2013)
Geometric optimal control in quantum computation
Time-optimal control of a chain of three coupled spins
Definition of the optimal control
problem
UtHUi )(yzzzz StuSSJSSJtH 232232112 )(22)(
ft 0:)(
Only a four-dimensional control space is considered:
CNOT gate in the original space (Khaneja et al, PRA 2002)
The Pontryagin Hamiltonian describes the free rotation of a
three-dimensional rigid body (Euler top):
1,,1
222
3221
3
2
3
2
2
2
1
2
1
IIk
I
I
L
I
L
I
LH
Ref.: PRA90, 013409 (2014), Poster L. Van Damme
Conclusion and outlook
Reference on GOCT:
Geometric OCT:
A powerful tool for simple
quantum systems
We can combine the advantages
of the two approaches
Numerical OCT:
A powerful tool for complex
quantum systems
Open PhD position in Dijon (september 2014)
Optimal control of spin systems with applications in MRI
ANR-DFG Research program: