20 research outputs found
Control-enhanced quantum metrology under Markovian noise
Quantum metrology is supposed to significantly improve the precision of
parameter estimation by utilizing suitable quantum resources. However, the
predicted precision can be severely distorted by realistic noises. Here, we
propose a control-enhanced quantum metrology scheme to defend against these
noises for improving the metrology performance. Our scheme can automatically
alter the parameter encoding dynamics with adjustable controls, thus leading to
optimal resultant states that are less sensitive to the noises under
consideration. As a demonstration, we numerically apply it to the problem of
frequency estimation under several typical Markovian noise channels. Through
comparing our control-enhanced scheme with the standard scheme and the
ancilla-assisted scheme, we show that our scheme performs better and can
improve the estimation precision up to around one order of magnitude.
Furthermore, we conduct a proof-of-principle experiment in nuclear magnetic
resonance system to verify the effectiveness of the proposed scheme. The
research here is helpful for current quantum platforms to harness the power of
quantum metrology in realistic noise environments.Comment: 9 pages, 5 figure
Practical Quantum Simulation of Non-Hermitian Dynamics
Non-Hermitian quantum systems have recently attracted considerable attentions
due to their exotic properties. Though many experimental realizations of
non-Hermitian systems have been reported, the non-Hermiticity usually resorts
to the hard-to-control environments. An alternative approach is to use quantum
simulation with the closed system, whereas how to simulate general
non-Hermitian Hamiltonian dynamics remains a great challenge. To tackle this
problem, we propose a protocol by combining a dilation method with the
variational quantum algorithm. The dilation method is used to transform a
non-Hermitian Hamiltonian into a Hermitian one through an exquisite quantum
circuit, while the variational quantum algorithm is for efficiently
approximating the complex entangled gates in this circuit. As a demonstration,
we apply our protocol to simulate the dynamics of an Ising chain with nonlocal
non-Hermitian perturbations, which is an important model to study quantum phase
transition at nonzero temperatures. The numerical simulation results are highly
consistent with the theoretical predictions, revealing the effectiveness of our
protocol. The presented protocol paves the way for practically simulating
general non-Hermitian dynamics in the multi-qubit case.Comment: 9 pages, 5 figure
Measuring Quantum Entanglement from Local Information by Machine Learning
Entanglement is a key property in the development of quantum technologies and
in the study of quantum many-body simulations. However, entanglement
measurement typically requires quantum full-state tomography (FST). Here we
present a neural network-assisted protocol for measuring entanglement in
equilibrium and non-equilibrium states of local Hamiltonians. Instead of FST,
it can learn comprehensive entanglement quantities from single-qubit or
two-qubit Pauli measurements, such as R\'enyi entropy, partially-transposed
(PT) moments, and coherence. It is also exciting that our neural network is
able to learn the future entanglement dynamics using only single-qubit traces
from the previous time. In addition, we perform experiments using a nuclear
spin quantum processor and train an adoptive neural network to study
entanglement in the ground and dynamical states of a one-dimensional spin
chain. Quantum phase transitions (QPT) are revealed by measuring static
entanglement in ground states, and the entanglement dynamics beyond measurement
time is accurately estimated in dynamical states. These precise results
validate our neural network. Our work will have a wide range of applications in
quantum many-body systems, from quantum phase transitions to intriguing
non-equilibrium phenomena such as quantum thermalization.Comment: 5 pages, 4 figures. All comments are welcom