6,179 research outputs found
Combining Hebbian and reinforcement learning in a minibrain model
A toy model of a neural network in which both Hebbian learning and
reinforcement learning occur is studied. The problem of `path interference',
which makes that the neural net quickly forgets previously learned input-output
relations is tackled by adding a Hebbian term (proportional to the learning
rate ) to the reinforcement term (proportional to ) in the learning
rule. It is shown that the number of learning steps is reduced considerably if
, i.e., if the Hebbian term is neither too small nor too
large compared to the reinforcement term
A split-cavity design for the incorporation of a DC bias in a 3D microwave cavity
We report on a technique for applying a DC bias in a 3D microwave cavity. We
achieve this by isolating the two halves of the cavity with a dielectric and
directly using them as DC electrodes. As a proof of concept, we embed a
variable capacitance diode in the cavity and tune the resonant frequency with a
DC voltage, demonstrating the incorporation of a DC bias into the 3D cavity
with no measurable change in its quality factor at room temperature. We also
characterize the architecture at millikelvin temperatures and show that the
split cavity design maintains a quality factor , making it promising for future quantum applications
Reionization history constraints from neural network based predictions of high-redshift quasar continua
Observations of the early Universe suggest that reionization was complete by
, however, the exact history of this process is still unknown. One
method for measuring the evolution of the neutral fraction throughout this
epoch is via observing the Ly damping wings of high-redshift quasars.
In order to constrain the neutral fraction from quasar observations, one needs
an accurate model of the quasar spectrum around Ly, after the spectrum
has been processed by its host galaxy but before it is altered by absorption
and damping in the intervening IGM. In this paper, we present a novel machine
learning approach, using artificial neural networks, to reconstruct quasar
continua around Ly. Our QSANNdRA algorithm improves the error in this
reconstruction compared to the state-of-the-art PCA-based model in the
literature by 14.2% on average, and provides an improvement of 6.1% on average
when compared to an extension thereof. In comparison with the extended PCA
model, QSANNdRA further achieves an improvement of 22.1% and 16.8% when
evaluated on low-redshift quasars most similar to the two high-redshift quasars
under consideration, ULAS J1120+0641 at and ULAS J1342+0928 at
, respectively. Using our more accurate reconstructions of these two
quasars, we estimate the neutral fraction of the IGM using a homogeneous
reionization model and find at
and at . Our
results are consistent with the literature and favour a rapid end to
reionization
Multi-mode ultra-strong coupling in circuit quantum electrodynamics
With the introduction of superconducting circuits into the field of quantum
optics, many novel experimental demonstrations of the quantum physics of an
artificial atom coupled to a single-mode light field have been realized.
Engineering such quantum systems offers the opportunity to explore extreme
regimes of light-matter interaction that are inaccessible with natural systems.
For instance the coupling strength can be increased until it is comparable
with the atomic or mode frequency and the atom can be coupled to
multiple modes which has always challenged our understanding of light-matter
interaction. Here, we experimentally realize the first Transmon qubit in the
ultra-strong coupling regime, reaching coupling ratios of
and we measure multi-mode interactions through a hybridization of the qubit up
to the fifth mode of the resonator. This is enabled by a qubit with 88% of its
capacitance formed by a vacuum-gap capacitance with the center conductor of a
coplanar waveguide resonator. In addition to potential applications in quantum
information technologies due to its small size and localization of electric
fields in vacuum, this new architecture offers the potential to further explore
the novel regime of multi-mode ultra-strong coupling.Comment: 15 pages, 9 figure
Approaching ultra-strong coupling in Transmon circuit-QED using a high-impedance resonator
In this experiment, we couple a superconducting Transmon qubit to a
high-impedance microwave resonator. Doing so leads to a large
qubit-resonator coupling rate , measured through a large vacuum Rabi
splitting of MHz. The coupling is a significant fraction of the
qubit and resonator oscillation frequencies , placing our system close
to the ultra-strong coupling regime ( on resonance).
Combining this setup with a vacuum-gap Transmon architecture shows the
potential of reaching deep into the ultra-strong coupling
with Transmon qubits
A Heterosynaptic Learning Rule for Neural Networks
In this article we intoduce a novel stochastic Hebb-like learning rule for
neural networks that is neurobiologically motivated. This learning rule
combines features of unsupervised (Hebbian) and supervised (reinforcement)
learning and is stochastic with respect to the selection of the time points
when a synapse is modified. Moreover, the learning rule does not only affect
the synapse between pre- and postsynaptic neuron, which is called homosynaptic
plasticity, but effects also further remote synapses of the pre- and
postsynaptic neuron. This more complex form of synaptic plasticity has recently
come under investigations in neurobiology and is called heterosynaptic
plasticity. We demonstrate that this learning rule is useful in training neural
networks by learning parity functions including the exclusive-or (XOR) mapping
in a multilayer feed-forward network. We find, that our stochastic learning
rule works well, even in the presence of noise. Importantly, the mean learning
time increases with the number of patterns to be learned polynomially,
indicating efficient learning.Comment: 19 page
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