Quantum
computation,
randomness and chaos
Quantum mechanics has had an
enormous technological and societal impact. To grasp this point,
it is
sufficient to cite the invention of the
transistor, perhaps the most remarkable among the countless
other
applications of quantum mechanics. It is also easy to see the
enormous
impact of computers on everyday life. The importance of
computers is
such that it is appropriate to say that we are now living in the
information age. This information revolution became possible
thanks to
the invention of the transistor, that is, thanks to the synergy
between
computer science and quantum physics.
Today this synergy offers completely new opportunities and
promises
exciting advances in both fundamental science and technological
application. We are referring here to the fact that quantum mechanics can be used
to
process and transmit information [1]
Miniaturization
provides us
with an intuitive way of understanding why, in the near future,
quantum
laws will become important for computation. The electronics
industry
for computers grows hand-in-hand with the decrease in size of
integrated circuits. This miniaturization is necessary to
increase
computational power, that is, the number of floating-point
operations
per second (flops) a computer can perform. The progress in
the
miniaturization process may be quantified empirically in Moore's
law:
the number of transistors on a single integrated-circuit chip
doubles
approximately
every 18-24 months. Extrapolating Moore's law, one would
estimate that
around the year 2020 we shall reach the atomic size for storing
a
single bit of
information. At that point, quantum effects will become
unavoidably
dominant.
Therefore, quantum physics sets fundamental limitations on the
size of
the circuit components.
The first question under debate is whether it would be more
convenient
to push the silicon-based transistor to its physical limits or
instead
to
develop alternative devices, such as quantum dots,
single-electron
transistors or molecular switches. A common feature of all these
devices is that they are at the nanometre length scale, and
therefore
quantum effects play a crucial role.
So far, we have talked about quantum switches that could
substitute
silicon-based transistors and possibly be connected together to
execute
classical algorithms based on Boolean logic. In this
perspective,
quantum effects are simply unavoidable corrections that must be
taken
into
account owing to the nanometre size of the switches. A quantum
computer
represents a radically different challenge: the aim is to build
a machine based on quantum
logic,
that is, a machine that can process the information and perform
logic
operations in agreement with the laws of quantum mechanics.
Development of quantum algorithms for
dynamical systems
A relevant class of quantum algorithms is the simulation of
dynamical systems.
We have proposed [2] a quantum algorithm which uses the
number of
qubits in an optimal way and efficiently simulates a physical
model
with rich and complex dynamics described by the quantum sawtooth
map.
We have demonstrated that complex dynamics could be simulated
already
with less than 10 qubits, while 40 qubits would allow one to make
computations inaccessible to present-day supercomputers.
Husimi functions for the
sawtooth map in action-angle variables, for n=6 (top left), n=9
(top
right), n=16 qubits (bottom left) and classical
Poincare
section (bottom right).
Quantum
computation
of dynamical localization
Dynamical localization is one of the most interesting phenomena
that characterize the quantum behavior of classically chaotic
systems:
quantum interference effects suppress classical diffusion leading
to
exponentially localized wave functions.
We have shown [3] that quantum computers can simulate efficiently
the
quantum localization of classical chaos. The speed up with respect
to
classical computation is
quadratic. The localization effect was studied experimentally by
the
group of David Cory at MIT by emulating the dynamics if the
quantum sawtooth map in the perturbative regime on a three-qubit
nuclear magnetic resonance quantum information processor (see M.K.
Henry, J. Emerson, R. Martinez, and D.G. Cory, Phys. Rev. A 74,
062317
(2006)).
More recently, we have shown [4] that dynamical localization of
the quantum sawtooth map can be simulated on actual, small-scale
quantum processors. Our results demonstrate that quantum computing
of dynamical localization may become a convenient tool for
evaluating advances in quantum hardware performances.
Dynamical localization in the
sawtooth map model simulated using six qubits
Dynamical localization simulated in real (IBMQ) quantum
processors
Effects
of
imperfections on the stability of quantum computation
We have studied the effect of
static imperfections in the quantum computer
hardware on
the stability of quantum computation.
We have found that a reliable quantum computation is possible
up
to a time scale which is polynomial in the number of qubits.
The errors generated by these imperfections are more
dangerous
than the errors of random noise in gate operations [2].
Husimi functions for the sawtooth map in action-angle
variables,
in the presence of static imperfections, for n=6 (top left), n=9
(top
right), n=16 qubits (bottom left) and classical
Poincare
section (bottom right) in the presence of round-off errors.
Dynamics of entanglement in quantum
computers with imperfections
Objective: understand the
time
scales for the stability of entanglement (a key resource for
quantum
computation and information) under decoherence and imperfection
effects.
We have studied [5] the evolution of the entanglement of
formation
between two nearest neighbor qubits in a lattice, which
initially are
maximally entangled or separable.
We have characterized three regimes:
a) Perturbative regime: the entanglement is stable against
imperfections
b) Crossever regime: imperfections degrade the concurrence of
an
initially entangled pair but can also drive a significant
entanglement
generation
c) Ergodic regime: a pair of qubits becomes entangled with
the
rest of the lattice and the concurrence of the pair drops to
zero
The stability of the entanglement of formation in an operating
quantum
computer has been investigated in [6].
We have discussed behavior of entanglement across a transition
to chaos
in [7].
Concurrence saturation values
for
different number of qubits, starting from a Bell state (left) or
a
separable state (right)
Entanglement, Bell's inequalities, randomness and the
classical limit
Since
random states carry a lot of entanglement and entanglement
has no analogue in classical mechanics,
one can conclude that random states are highly
non-classical. On the
other hand, for chaotic map the classical limit can be
recovered when
the dimension of the Hilbert space diverges, and (ergodic)
random
states in a way mimic classical microcanonical density.
How can we
reconcile this apparent contradiction? We have considered
[8] the detection of
entanglement for
random states by means of witness
operators. While the entanglement content of
random pure states
is almost maximal, we have shown that, due to the
complexity of such
states, the detection of their entanglement is difficult.
Moreover, the
entanglement detection probability drops exponentially
when considering
mixtures of random states. Our results can be used to
explain the
emergence of classicality in coarse
grained quantum chaotic dynamics. We also
explored the violation of Bell's inequalities in the limit
of high-dimensional systems [9], naturally arising when
exploring the quantum-to-classical transition.
Schematic drawing of entanglement witnesses, see the
review paper
[10]
Robust and efficient generator of multipartite entanglement
Quantum chaotic maps can
efficiently generate pseudo-random states carrying almost
maximal
multipartite entanglement, as characterized by the probability
distribution of bipartite entanglement between all possible
bipartitions of the system. We have shown [11] that such
multipartite
entanglement is robust, in the sense that, when realistic noise
is
considered, distillable entanglement of bipartitions remains
almost
maximal up to a noise strength that drops only polynomially with
the
number of qubits.
Stability border for
distillable
entanglement of bipartitions
References
[1] G. Benenti, G. Casati, D. Rossini and G. Strini,
Principles of Quantum Computation and
Information (A comprehensive textbook) (World Scientific,
Singapore, 2019).
[2] G. Benenti, G. Casati, S. Montangero and D.L.
Shepelyansky, Efficient quantum
computing of complex
dynamics, Phys. Rev. Lett. 87,
227901 (2001).
[3] G. Benenti, G. Casati, S. Montangero and D.L.
Shepelyansky, Dynamical
localization simulated on a
few-qubit quantum computer, Phys. Rev. A 67, 052312 (2003).
[4] A. Pizzamiglio, S. Y. Chang, M. Bondani, S. Montangero, D.
Gerace and G. Benenti, Dynamical localization simulated on
actual quantum hardware, Entropy 23, 654 (2021).
[5] S. Montangero, G. Benenti and R. Fazio, Dynamics of entanglement in quantum
computers with imperfections, Phys. Rev. Lett. 91, 187901 (2003).
[6] D. Rossini, G. Benenti and G. Casati, Entanglement echoes in quantum
computation,
Phys. Rev. A 69, 052317
(2004).
[7] C. Mejia-Monasterio, G. Benenti, G.G. Carlo and G. Casati, Entanglement across a transition to
quantum chaos, Phys. Rev. A 71,
062324 (2005).
[8] M. Znidaric, T. Prosen, G. Benenti and G. Casati, Detecting entanglement of random
states
with an entanglement witness, J. Phys. A. 40, 13787 (2007).
[9] W. Weiss, G. Benenti, G. Casati, I. Guarneri, T.
Calarco, M. Paternostro and S. Montangero, Violation of Bell inequalities in
larger Hilbert spaces: robustness and challenges, New J.
Phys. 18, 013021 (2016).
[10] G. Benenti, Entanglement,
randomness
and chaos, Riv. Nuovo Cimento 32, 105 (2009)
[11] D. Rossini and G. Benenti, Robust
and efficient generator of almost maximal multipartite
entanglement,
Phys. Rev. Lett. 100,
060501
(2008).