140 research outputs found
Measure of decoherence in quantum error correction for solid-state quantum computing
We considered the interaction of semiconductor quantum register with noisy
environment leading to various types of qubit errors. We analysed both phase
and amplitude decays during the process of electron-phonon interaction. The
performance of quantum error correction codes (QECC) which will be inevitably
used in full scale quantum information processors was studied in realistic
conditions in semiconductor nanostructures. As a hardware basis for quantum bit
we chose the quantum spatial states of single electron in semiconductor coupled
double quantum dot system. The modified 5- and 9-qubit quantum error correction
(QEC) algorithms by Shor and DiVincenzo without error syndrome extraction were
applied to quantum register. 5-qubit error correction procedures were
implemented for Si charge double dot qubits in the presence of acoustic phonon
environment. Chi-matrix, Choi-Jamiolkowski state and measure of decoherence
techniques were used to quantify qubit fault-tolerance. Our results showed that
the introduction of above quantum error correction techniques at small phonon
noise levels provided quadratic improvement of output error rates. The
efficiency of 5-qubits quantum error correction algorithm in semiconductor
quantum information processors was demonstrated
Benchmarking projective simulation in navigation problems
Projective simulation (PS) is a model for intelligent agents with a
deliberation capacity that is based on episodic memory. The model has been
shown to provide a flexible framework for constructing reinforcement-learning
agents, and it allows for quantum mechanical generalization, which leads to a
speed-up in deliberation time. PS agents have been applied successfully in the
context of complex skill learning in robotics, and in the design of
state-of-the-art quantum experiments. In this paper, we study the performance
of projective simulation in two benchmarking problems in navigation, namely the
grid world and the mountain car problem. The performance of PS is compared to
standard tabular reinforcement learning approaches, Q-learning and SARSA. Our
comparison demonstrates that the performance of PS and standard learning
approaches are qualitatively and quantitatively similar, while it is much
easier to choose optimal model parameters in case of projective simulation,
with a reduced computational effort of one to two orders of magnitude. Our
results show that the projective simulation model stands out for its simplicity
in terms of the number of model parameters, which makes it simple to set up the
learning agent in unknown task environments.Comment: 8 pages, 10 figure
Projective simulation with generalization
The ability to generalize is an important feature of any intelligent agent.
Not only because it may allow the agent to cope with large amounts of data, but
also because in some environments, an agent with no generalization capabilities
cannot learn. In this work we outline several criteria for generalization, and
present a dynamic and autonomous machinery that enables projective simulation
agents to meaningfully generalize. Projective simulation, a novel, physical
approach to artificial intelligence, was recently shown to perform well in
standard reinforcement learning problems, with applications in advanced
robotics as well as quantum experiments. Both the basic projective simulation
model and the presented generalization machinery are based on very simple
principles. This allows us to provide a full analytical analysis of the agent's
performance and to illustrate the benefit the agent gains by generalizing.
Specifically, we show that already in basic (but extreme) environments,
learning without generalization may be impossible, and demonstrate how the
presented generalization machinery enables the projective simulation agent to
learn.Comment: 14 pages, 9 figure
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