18 research outputs found
Unraveling the mesoscopic character of quantum dots in nanophotonics
We provide a microscopic theory for semiconductor quantum dots that explains
the pronounced deviations from the prevalent point-dipole description that were
recently observed in spectroscopic experiments on quantum dots in photonic
nanostructures. At the microscopic level the deviations originate from
structural inhomogeneities generating a large circular quantum current density
that flows inside the quantum dot over mesoscopic length scales. The model is
supported by the experimental data, where a strong variation of the multipolar
moments across the emission spectrum of quantum dots is observed. Our work
enriches the physical understanding of quantum dots and is of significance for
the fields of nanophotonics, quantum photonics, and quantum-information
science, where quantum dots are actively employed.Comment: 6 pages, 5 figure
Probing electric and magnetic vacuum fluctuations with quantum dots
The electromagnetic-vacuum-field fluctuations are intimately linked to the
process of spontaneous emission of light. Atomic emitters cannot probe
electric- and magnetic-field fluctuations simultaneously because electric and
magnetic transitions correspond to different selection rules. In this paper we
show that semiconductor quantum dots are fundamentally different and are
capable of mediating electric-dipole, magnetic-dipole, and electric-quadrupole
transitions on a single electronic resonance. As a consequence, quantum dots
can probe electric and magnetic fields simultaneously and can thus be applied
for sensing the electromagnetic environment of complex photonic nanostructures.
Our study opens the prospect of interfacing quantum dots with optical
metamaterials for tailoring the electric and magnetic light-matter interaction
at the single-emitter level.Comment: 6 pages, 4 figure
Reinforcement Learning with Neural Networks for Quantum Feedback
Machine learning with artificial neural networks is revolutionizing science.
The most advanced challenges require discovering answers autonomously. This is
the domain of reinforcement learning, where control strategies are improved
according to a reward function. The power of neural-network-based reinforcement
learning has been highlighted by spectacular recent successes, such as playing
Go, but its benefits for physics are yet to be demonstrated. Here, we show how
a network-based "agent" can discover complete quantum-error-correction
strategies, protecting a collection of qubits against noise. These strategies
require feedback adapted to measurement outcomes. Finding them from scratch,
without human guidance, tailored to different hardware resources, is a
formidable challenge due to the combinatorially large search space. To solve
this, we develop two ideas: two-stage learning with teacher/student networks
and a reward quantifying the capability to recover the quantum information
stored in a multi-qubit system. Beyond its immediate impact on quantum
computation, our work more generally demonstrates the promise of
neural-network-based reinforcement learning in physics.Comment: 7 pages maintext + methods + supplementary, 6 maintext figures; for
related lectures, see: http://machine-learning-for-physicists.or
Decay dynamics and exciton localization in large GaAs quantum dots grown by droplet epitaxy
We investigate the optical emission and decay dynamics of excitons confined
in large strain-free GaAs quantum dots grown by droplet epitaxy. From
time-resolved measurements combined with a theoretical model we show that
droplet-epitaxy quantum dots have a quantum efficiency of about 75% and an
oscillator strength between 8 and 10. The quantum dots are found to be fully
described by a model for strongly-confined excitons, in contrast to the
theoretical prediction that excitons in large quantum dots exhibit the
so-called giant oscillator strength. We attribute these findings to localized
ground-state excitons in potential minima created by material intermixing
during growth. We provide further evidence for the strong-confinement regime of
excitons by extracting the size of electron and hole wavefunctions from the
phonon-broadened photoluminescence spectra. Furthermore, we explore the
temperature dependence of the decay dynamics and, for some quantum dots,
observe a pronounced reduction in the effective transition strength with
temperature. We quantify and explain these effects as being an intrinsic
property of large quantum dots owing to thermal excitation of the ground-state
exciton. Our results provide a detailed understanding of the optical properties
of large quantum dots in general, and of quantum dots grown by droplet epitaxy
in particular.Comment: 13 pages, 7 figure
Scalable Meta-Learning with Gaussian Processes
Meta-learning is a powerful approach that exploits historical data to quickly
solve new tasks from the same distribution. In the low-data regime, methods
based on the closed-form posterior of Gaussian processes (GP) together with
Bayesian optimization have achieved high performance. However, these methods
are either computationally expensive or introduce assumptions that hinder a
principled propagation of uncertainty between task models. This may disrupt the
balance between exploration and exploitation during optimization. In this
paper, we develop ScaML-GP, a modular GP model for meta-learning that is
scalable in the number of tasks. Our core contribution is a carefully designed
multi-task kernel that enables hierarchical training and task scalability.
Conditioning ScaML-GP on the meta-data exposes its modular nature yielding a
test-task prior that combines the posteriors of meta-task GPs. In synthetic and
real-world meta-learning experiments, we demonstrate that ScaML-GP can learn
efficiently both with few and many meta-tasks
Single-Photon Superradiance from a Quantum Dot.
We report on the observation of single-photon superradiance from an exciton in a semiconductor quantum dot. The confinement by the quantum dot is strong enough for it to mimic a two-level atom, yet sufficiently weak to ensure superradiance. The electrostatic interaction between the electron and the hole comprising the exciton gives rise to an anharmonic spectrum, which we exploit to prepare the superradiant quantum state deterministically with a laser pulse. We observe a fivefold enhancement of the oscillator strength compared to conventional quantum dots. The enhancement is limited by the base temperature of our cryostat and may lead to oscillator strengths above 1000 from a single quantum emitter at optical frequencies
Indistinguishable and efficient single photons from a quantum dot in a planar nanobeam waveguide
We demonstrate a high-purity source of indistinguishable single photons using a quantum dot embedded in a nanophotonic waveguide. The source features a near-unity internal coupling efficiency and the collected photons are efficiently coupled off chip by implementing a taper that adiabatically couples the photons to an optical fiber. By quasiresonant excitation of the quantum dot, we measure a single-photon purity larger than 99.4% and a photon indistinguishability of up to 94±1% by using p-shell excitation combined with spectral filtering to reduce photon jitter. A temperature-dependent study allows pinpointing the residual decoherence processes, notably the effect of phonon broadening. Strict resonant excitation is implemented as well as another means of suppressing photon jitter, and the additional complexity of suppressing the excitation laser source is addressed. The paper opens a clear pathway towards the long-standing goal of a fully deterministic source of indistinguishable photons, which is integrated on a planar photonic chip