Daniel Brunner

Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing

Many information processing challenges are difficult to solve with traditional Turing or von Neumann approaches. Implementing unconventional computational methods is therefore essential and optics provides promising opportunities. Here we experimentally demonstrate optical information processing using a nonlinear optoelectronic oscillator subject to delayed feedback. We implement a neuro-inspired concept, called Reservoir Computing, proven to possess universal computational capabilities. We particularly exploit the transient response of a complex dynamical system to an input data stream. We employ spoken digit recognition and time series prediction tasks as benchmarks, achieving competitive processing figures of merit.

Parallel photonic information processing at gigabyte per second data rates using transient states

The increasing demands on information processing require novel computational concepts and true parallelism. Nevertheless, hardware realizations of unconventional computing approaches never exceeded a marginal existence. While the application of optics in super-computing receives reawakened interest, new concepts, partly neuro-inspired, are being considered and developed. Here we experimentally demonstrate the potential of a simple photonic architecture to process information at unprecedented data rates, implementing a learning-based approach. A semiconductor laser subject to delayed self-feedback and optical data injection is employed to solve computationally hard tasks. We demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond 1 Gbyte/s. We identify all digits with very low classification errors and perform chaotic time-series prediction with 10% error. Our approach bridges the areas of photonic information processing, cognitive and information science.

Optical pumping of a single hole spin in a quantum dot

The spin of an electron is a natural two-level system for realizing a quantum bit in the solid state. For an electron trapped in a semiconductor quantum dot, strong quantum confinement highly suppresses the detrimental effect of phonon-related spin relaxation. However, this advantage is offset by the hyperfine interaction between the electron spin and the 104 to 106 spins of the host nuclei in the quantum dot. Random fluctuations in the nuclear spin ensemble lead to fast spin decoherence in about ten nanoseconds. Spin-echo techniques have been used to mitigate the hyperfine interaction, but completely cancelling the effect is more attractive. In principle, polarizing all the nuclear spins can achieve this but is very difficult to realize in practice. Exploring materials with zero-spin nuclei is another option, and carbon nanotubes, graphene quantum dots and silicon have been proposed. An alternative is to use a semiconductor hole. Unlike an electron, a valence hole in a quantum dot has an atomic p orbital which conveniently goes to zero at the location of all the nuclei, massively suppressing the interaction with the nuclear spins. Furthermore, in a quantum dot with strong strain and strong quantization, the heavy hole with spin-3/2 behaves as a spin-1/2 system and spin decoherence mechanisms are weak. We demonstrate here high fidelity (about 99 per cent) initialization of a single hole spin confined to a self-assembled quantum dot by optical pumping. Our scheme works even at zero magnetic field, demonstrating a negligible hole spin hyperfine interaction. We determine a hole spin relaxation time at low field of about one millisecond. These results suggest a route to the realization of solid-state quantum networks that can intra-convert the spin state with the polarization of a photon.

A Coherent Single-Hole Spin in a Semiconductor

A Hole New Approach Quantum dots can behave as artificial atoms, exhibiting a ladder of quantized energy levels with the number of electrons added to the dot being controllable. They are thus being extensively studied for application in the likes of quantum information processing strategies. However, the electrons interact with their environment and quickly lose their coherence properties. Brunner et al. (p. 70; see the Perspective by Kolodrubetz and Petta) now show that if the charge of the dot is manipulated so that it is positive; that is, populated with a single hole, then the coherence properties of the dot can be extended. The strategy of using holes instead of electrons may provide a solution to the decoherence problem. Manipulating holes instead of electrons results in the enhancement of the coherence properties of quantum dots. Semiconductors have uniquely attractive properties for electronics and photonics. However, it has been difficult to find a highly coherent quantum state in a semiconductor for applications in quantum sensing and quantum information processing. We report coherent population trapping, an optical quantum interference effect, on a single hole. The results demonstrate that a hole spin in a quantum dot is highly coherent.

Optoelectronic reservoir computing: tackling noise-induced performance degradation

We present improved strategies to perform photonic information processing using an optoelectronic oscillator with delayed feedback. In particular, we study, via numerical simulations and experiments, the influence of a finite signal-to-noise ratio on the computing performance. We illustrate that the performance degradation induced by noise can be compensated for via multi-level pre-processing masks.

A dark-field microscope for background-free detection of resonance fluorescence from single semiconductor quantum dots operating in a set-and-forget mode

Optically active quantum dots, for instance self-assembled InGaAs quantum dots, are potentially excellent single photon sources. The fidelity of the single photons is much improved using resonant rather than non-resonant excitation. With resonant excitation, the challenge is to distinguish between resonance fluorescence and scattered laser light. We have met this challenge by creating a polarization-based dark-field microscope to measure the resonance fluorescence from a single quantum dot at low temperature. We achieve a suppression of the scattered laser exceeding a factor of 10(7) and background-free detection of resonance fluorescence. The same optical setup operates over the entire quantum dot emission range (920-980 nm) and also in high magnetic fields. The major development is the outstanding long-term stability: once the dark-field point has been established, the microscope operates for days without alignment. The mechanical and optical designs of the microscope are presented, as well as exemplary resonance fluorescence spectroscopy results on individual quantum dots to underline the microscopes excellent performance.

Information Processing Using Transient Dynamics of Semiconductor Lasers Subject to Delayed Feedback

The increasing amount of data being generated in different areas of science and technology require novel and efficient techniques of processing, going beyond traditional concepts. In this paper, we numerically study the information processing capabilities of semiconductor lasers subject to delayed optical feedback. Based on the recent concept of reservoir computing, we show that certain tasks, which are inherently hard for traditional computers, can be efficiently tackled by such systems. Major advantages of this approach comprise the possibility of simple and low-cost hardware implementation of the reservoir and ultrafast processing speed. Experimental results corroborate the numerical predictions.

A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron.

In this paper we present a unified framework for extreme learning machines and reservoir computing (echo state networks), which can be physically implemented using a single nonlinear neuron subject to delayed feedback. The reservoir is built within the delay-line, employing a number of “virtual” neurons. These virtual neurons receive random projections from the input layer containing the information to be processed. One key advantage of this approach is that it can be implemented efficiently in hardware. We show that the reservoir computing implementation, in this case optoelectronic, is also capable to realize extreme learning machines, demonstrating the unified framework for both schemes in software as well as in hardware.

Minimal approach to neuro-inspired information processing.

To learn and mimic how the brain processes information has been a major research challenge for decades. Despite the efforts, little is known on how we encode, maintain and retrieve information. One of the hypothesis assumes that transient states are generated in our intricate network of neurons when the brain is stimulated by a sensory input. Based on this idea, powerful computational schemes have been developed. These schemes, known as machine-learning techniques, include artificial neural networks, support vector machine and reservoir computing, among others. In this paper, we concentrate on the reservoir computing (RC) technique using delay-coupled systems. Unlike traditional RC, where the information is processed in large recurrent networks of interconnected artificial neurons, we choose a minimal design, implemented via a simple nonlinear dynamical system subject to a self-feedback loop with delay. This design is not intended to represent an actual brain circuit, but aims at finding the minimum ingredients that allow developing an efficient information processor. This simple scheme not only allows us to address fundamental questions but also permits simple hardware implementations. By reducing the neuro-inspired reservoir computing approach to its bare essentials, we find that nonlinear transient responses of the simple dynamical system enable the processing of information with excellent performance and at unprecedented speed. We specifically explore different hardware implementations and, by that, we learn about the role of nonlinearity, noise, system responses, connectivity structure, and the quality of projection onto the required high-dimensional state space. Besides the relevance for the understanding of basic mechanisms, this scheme opens direct technological opportunities that could not be addressed with previous approaches.

Real-time frequency dynamics and high-resolution spectra of a semiconductor laser with delayed feedback

The unstable emission of semiconductor lasers due to delayed optical feedback is characterized by combined intensity and frequency dynamics. Nevertheless, real-time experimental investigations have so far been restricted to measurements of intensity dynamics only. Detailed analysis and comparison with numerical models, therefore, have suffered from limited experimental information. Here, we report the simultaneous determination of the lasers optical emission intensity and emission frequency with high temporal resolution. The frequency dynamics is made accessible using a heterodyne detection scheme, in which a beat signal between the delayed feedback laser and a reference laser is generated. Our experiment provides insight into the overall spectral drift on nanosecond timescales, the spectral distribution of the unstable pulsations and the role of the individual external cavity modes. This opens new perspectives for the analysis, understanding and functional utilization of delayed feedback semiconductor lasers.