186 research outputs found
Real-time Information, Uncertainty and Quantum Feedback Control
Feedback is the core concept in cybernetics and its effective use has made
great success in but not limited to the fields of engineering, biology, and
computer science. When feedback is used to quantum systems, two major types of
feedback control protocols including coherent feedback control (CFC) and
measurement-based feedback control (MFC) have been developed. In this paper, we
compare the two types of quantum feedback control protocols by focusing on the
real-time information used in the feedback loop and the capability in dealing
with parameter uncertainty. An equivalent relationship is established between
quantum CFC and non-selective quantum MFC in the form of operator-sum
representation. Using several examples of quantum feedback control, we show
that quantum MFC can theoretically achieve better performance than quantum CFC
in stabilizing a quantum state and dealing with Hamiltonian parameter
uncertainty. The results enrich understanding of the relative advantages
between quantum MFC and quantum CFC, and can provide useful information in
choosing suitable feedback protocols for quantum systems.Comment: 24 page
Robust manipulation of superconducting qubits in the presence of fluctuations
Superconducting quantum systems are promising candidates for quantum
information processing due to their scalability and design flexibility.
However, the existence of defects, fluctuations, and inaccuracies is
unavoidable for practical superconducting quantum circuits. In this paper, a
sampling-based learning control (SLC) method is used to guide the design of
control fields for manipulating superconducting quantum systems. Numerical
results for one-qubit systems and coupled two-qubit systems show that the
"smart" fields learned using the SLC method can achieve robust manipulation of
superconducting qubits, even in the presence of large fluctuations and
inaccuracies.Comment: 10 pages, 6 figure
Sampling-based learning control of inhomogeneous quantum ensembles
Compensation for parameter dispersion is a significant challenge for control
of inhomogeneous quantum ensembles. In this paper, we present a systematic
methodology of sampling-based learning control (SLC) for simultaneously
steering the members of inhomogeneous quantum ensembles to the same desired
state. The SLC method is employed for optimal control of the state-to-state
transition probability for inhomogeneous quantum ensembles of spins as well as
type atomic systems. The procedure involves the steps of (i) training
and (ii) testing. In the training step, a generalized system is constructed by
sampling members according to the distribution of inhomogeneous parameters
drawn from the ensemble. A gradient flow based learning and optimization
algorithm is adopted to find the control for the generalized system. In the
process of testing, a number of additional ensemble members are randomly
selected to evaluate the control performance. Numerical results are presented
showing the success of the SLC method.Comment: 8 pages, 9 figure
Sampling-based Learning Control for Quantum Systems with Uncertainties
Robust control design for quantum systems has been recognized as a key task
in the development of practical quantum technology. In this paper, we present a
systematic numerical methodology of sampling-based learning control (SLC) for
control design of quantum systems with uncertainties. The SLC method includes
two steps of "training" and "testing". In the training step, an augmented
system is constructed using artificial samples generated by sampling
uncertainty parameters according to a given distribution. A gradient flow based
learning algorithm is developed to find the control for the augmented system.
In the process of testing, a number of additional samples are tested to
evaluate the control performance where these samples are obtained through
sampling the uncertainty parameters according to a possible distribution. The
SLC method is applied to three significant examples of quantum robust control
including state preparation in a three-level quantum system, robust
entanglement generation in a two-qubit superconducting circuit and quantum
entanglement control in a two-atom system interacting with a quantized field in
a cavity. Numerical results demonstrate the effectiveness of the SLC approach
even when uncertainties are quite large, and show its potential for robust
control design of quantum systems.Comment: 11 pages, 9 figures, in press, IEEE Transactions on Control Systems
Technology, 201
Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning
Bayesian policy reuse (BPR) is a general policy transfer framework for
selecting a source policy from an offline library by inferring the task belief
based on some observation signals and a trained observation model. In this
paper, we propose an improved BPR method to achieve more efficient policy
transfer in deep reinforcement learning (DRL). First, most BPR algorithms use
the episodic return as the observation signal that contains limited information
and cannot be obtained until the end of an episode. Instead, we employ the
state transition sample, which is informative and instantaneous, as the
observation signal for faster and more accurate task inference. Second, BPR
algorithms usually require numerous samples to estimate the probability
distribution of the tabular-based observation model, which may be expensive and
even infeasible to learn and maintain, especially when using the state
transition sample as the signal. Hence, we propose a scalable observation model
based on fitting state transition functions of source tasks from only a small
number of samples, which can generalize to any signals observed in the target
task. Moreover, we extend the offline-mode BPR to the continual learning
setting by expanding the scalable observation model in a plug-and-play fashion,
which can avoid negative transfer when faced with new unknown tasks.
Experimental results show that our method can consistently facilitate faster
and more efficient policy transfer.Comment: 16 pages, 6 figures, under revie
Preliminary study of beta-blocker therapy on modulation of interleukin-33/ST2 signaling during ventricular remodeling after acute myocardial infarction
Background: This study aimed to evaluate the role of b-blocker therapy on modulating interleukin (IL)-33/ST2 (interleukin-1 receptor-like 1) signaling during ventricular remodeling related to heart failure (HF) after acute myocardial infarction (AMI).
Methods: Sprague-Dawley rats that survived surgery to induce AMI were randomly divided into the placebo group and the b-blocker treatment group. A sham group was used as a control. Left ventricular (LV) function variables, the myocardial infarct size, fibrosis and IL-33/ST2 protein expression was measured.
Results: Compared with the placebo group, b-blocker treatment significantly improved LV function and reduced infarct size (p < 0.05). There was higher protein expression of IL-33 (p < 0.05) and sST2 (p < 0.05), as well as higher expression of fibrosis (p < 0.05), compared to the sham group. Notably, the high expression of cardioprotective IL-33 was not affected by b-blocker treatment (p > 0.05), however, treatment with b-blocker enhanced IL-33/ST2 signaling, with lower expression of sST2 (p < 0.05) and significantly attenuated fibrosis (p < 0.05).
Conclusions: Our study suggested that b-blocker therapy might play a beneficial role in the modulation of IL-33/ST2 signaling during ventricular remodeling. These results may be helpful in identifying IL-33/ST2 systems as putative b-blocker targets at an early stage after AMI. (Cardiol J 2017; 24, 2: 188–194
On compression rate of quantum autoencoders: Control design, numerical and experimental realization
Quantum autoencoders which aim at compressing quantum information in a
low-dimensional latent space lie in the heart of automatic data compression in
the field of quantum information. In this paper, we establish an upper bound of
the compression rate for a given quantum autoencoder and present a learning
control approach for training the autoencoder to achieve the maximal
compression rate. The upper bound of the compression rate is theoretically
proven using eigen-decomposition and matrix differentiation, which is
determined by the eigenvalues of the density matrix representation of the input
states. Numerical results on 2-qubit and 3-qubit systems are presented to
demonstrate how to train the quantum autoencoder to achieve the theoretically
maximal compression, and the training performance using different machine
learning algorithms is compared. Experimental results of a quantum autoencoder
using quantum optical systems are illustrated for compressing two 2-qubit
states into two 1-qubit states
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