673 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
NQ: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning
Value decomposition is widely used in cooperative multi-agent reinforcement
learning, however, its implicit credit assignment mechanism is not yet fully
understood due to black-box networks. In this work, we study an interpretable
value decomposition framework via the family of generalized additive models. We
present a novel method, named Neural Attention Additive
Q-learning~(NQ), providing inherent intelligibility of
collaboration behavior. NQ can explicitly factorize the
optimal joint policy induced by enriching shape functions to model all possible
coalitions of agents into individual policies. Moreover, we construct identity
semantics to promote estimating credits together with the global state and
individual value functions, where local semantic masks help us diagnose whether
each agent captures relevant-task information. Extensive experiments show that
NQ consistently achieves superior performance compared to
different state-of-the-art methods on all challenging tasks, while yielding
human-like interpretability
Sparse Spatial Transformers for Few-Shot Learning
Learning from limited data is a challenging task since the scarcity of data
leads to a poor generalization of the trained model. The classical global
pooled representation is likely to lose useful local information. Recently,
many few shot learning methods address this challenge by using deep descriptors
and learning a pixel-level metric. However, using deep descriptors as feature
representations may lose the contextual information of the image. And most of
these methods deal with each class in the support set independently, which
cannot sufficiently utilize discriminative information and task-specific
embeddings. In this paper, we propose a novel Transformer based neural network
architecture called Sparse Spatial Transformers (SSFormers), which can find
task-relevant features and suppress task-irrelevant features. Specifically, we
first divide each input image into several image patches of different sizes to
obtain dense local features. These features retain contextual information while
expressing local information. Then, a sparse spatial transformer layer is
proposed to find spatial correspondence between the query image and the entire
support set to select task-relevant image patches and suppress task-irrelevant
image patches. Finally, we propose to use an image patch matching module for
calculating the distance between dense local representations, thus to determine
which category the query image belongs to in the support set. Extensive
experiments on popular few-shot learning benchmarks show that our method
achieves the state-of-the-art performance
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