Modeling of an efficient singlet-triplet spin qubit to photon interface assisted by a photonic crystal cavity

Efficient interconnection between distant semiconductor spin qubits with the help of photonic qubits would offer exciting new prospects for future quantum communication applications. In this paper, we optimize the extraction efficiency of a novel interface between a singlet-triplet spin qubit and a photonic qubit. The interface is based on a 220 nm thick GaAs/AlGaAs heterostructure membrane and consists of a gate-defined double quantum dot (GDQD) supporting a singlet-triplet qubit, an optically active quantum dot (OAQD) consisting of a gate-defined exciton trap, a photonic crystal cavity providing in-plane optical confinement and efficient out-coupling to an ideal free space Gaussian beam while accommodating the gate wiring of the GDQD and OAQD, and a bottom gold reflector to recycle photons and increase the optical extraction efficiency. All essential components can be lithographically defined and deterministically fabricated on the GaAs/AlGaAs heterostructure membrane, which greatly increases the scalability of on-chip integration. According to our simulations, the interface provides an overall coupling efficiency of 28.7% into a free space Gaussian beam, assuming an SiO2 interlayer filling the space between the reflector and the membrane. The performance can be further increased by undercutting this SiO2 interlayer below the photonic crystal. In this case, the overall efficiency is calculated to be 48.5%

Plasma Profiling Time-of-Flight Mass Spectrometry for Fast Elemental Analysis of Semiconductor Structures with Depth Resolution in the Nanometer Range

Plasma profiling time of flight mass spectrometry (PP-TOFMS) has recently gained interest, as it enables the elemental profiling of semiconductor structures with high depth resolution in short acquisition times. As recently shown by Tempez et al., PP-TOFMS can be used to obtain the composition in the structures for modern field effect transistors [1]. There, the results were compared to conventional SIMS measurements. In the present study, we compare PP-TOFMS measurements of an Al-/In-/GaN quantum well multi stack to established micro- and nano-analysis techniques like cathodoluminescence (CL), scanning transmission electron microscopy (STEM), energy dispersive X-ray spectroscopy (EDX) and X-ray diffraction (XRD). We show that PP-TOFMS is able to resolve the layer structure of the sample even more than 500 nm deep into the sample and allows the determination of a relative elemental composition with an accuracy of about 10 rel. %. Therefore, it is an extremely rapid alternative method to obtain semiconductor elemental depth profiles without expensive and time consuming sample preparation as it is needed for TEM. Besides, PP-TOFMS offers better depth resolution and more elemental information than for example electrochemical capacitance-voltage (ECV), as the acquisition of all elements occurs in parallel and not only electrically (ECV) or optically (CL) active elements are observed

Semiconductor membranes for electrostatic exciton trapping in optically addressable quantum transport devices

Combining the capabilities of gate defined quantum transport devices in GaAs-based heterostructures and of optically addressed self-assembled quantum dots could open broad perspectives for new devices and functionalities. For example, interfacing stationary solid-state qubits with photonic quantum states would open a new pathway towards the realization of a quantum network with extended quantum processing capacity in each node. While gated devices allow very flexible confinement of electrons or holes, the confinement of excitons without some element of self-assembly is much harder. To address this limitation, we introduce a technique to realize exciton traps in quantum wells via local electric fields by thinning a heterostructure down to a 220 nm thick membrane. We show that mobilities over $1 \times 10^{6}$ cm$^{2}$V$^{-1}$s$^{-1}$ can be retained and that quantum point contacts and Coulomb oscillations can be observed on this structure, which implies that the thinning does not compromise the heterostructure quality. Furthermore, the local lowering of the exciton energy via the quantum-confined Stark effect is confirmed, thus forming exciton traps. These results lay the technological foundations for devices like single photon sources, spin photon interfaces and eventually quantum network nodes in GaAs quantum wells, realized entirely with a top-down fabrication process.Comment: v2: added missing acknowledgement. v3: fixed typos in acknolwedgemen

Filter Functions for Quantum Processes under Correlated Noise

Many qubit implementations are afflicted by correlated noise not captured by standard theoretical tools that are based on Markov approximations. While independent gate operations are a key concept for quantum computing, it is actually not possible to fully describe noisy gates locally in time if noise is correlated on times longer than their duration. To address this issue, we develop a method based on the filter function formalism to perturbatively compute quantum processes in the presence of correlated classical noise. We derive a composition rule for the filter function of a sequence of gates in terms of those of the individual gates. The joint filter function allows to efficiently compute the quantum process of the whole sequence. Moreover, we show that correlation terms arise which capture the effects of the concatenation and thus yield insight into the effect of noise correlations on gate sequences. Our generalization of the filter function formalism enables both qualitative and quantitative studies of algorithms and state-of-the-art tools widely used for the experimental verification of gate fidelities like randomized benchmarking, even in the presence of noise correlations.Comment: 6 pages, 1 figure. Letter accompanying arXiv:2103.02403. Open-source software available at https://github.com/qutech/filter_functions. v2: published versio

Filter-function formalism and software package to compute quantum processes of gate sequences for classical non-Markovian noise

Correlated, non-Markovian noise is present in many solid-state systems employed as hosts for quantum information technologies, significantly complicating the realistic theoretical description of these systems. In this regime, the effects of noise on sequences of quantum gates cannot be described by concatenating isolated quantum operations if the environmental correlation times are on the scale of the typical gate durations. The filter function formalism has been successful in characterizing the decay of coherence under the influence of such classical, non-Markovian environments and here we show it can be applied to describe unital evolution within the quantum operations formalism. We find exact results for the quantum process and a simple composition rule for a sequence of operations. This enables the detailed study of effects of noise correlations on algorithms and periodically driven systems. Moreover, we point out the method's suitability for numerical applications and present the open-source Python software package filter_functions. Amongst other things, it facilitates computing the noise-averaged transfer matrix representation of a unital quantum operation in the presence of universal classical noise for arbitrary control sequences. We apply the presented methods to selected examples.Comment: 35 pages, 8 figures. In-depth article accompanying arXiv:2103.02385. Open-source software available at https://github.com/qutech/filter_functions. v2: published versio

Minimising statistical errors in calibration of quantum-gate sets

Calibration of quantum gates is a necessary hurdle to overcome on the way to a reliable quantum computer. In a recent paper, a protocol called Gate Set Calibration protocol (GSC) has been introduced and used to learn coherent errors from multi-qubit quantum gates. Here, we extend this study in a number of ways: First, we perform a statistical analysis of the measurement uncertainties. Second, we find explicit measurement settings that minimize this uncertainty, while also requiring that the protocol involves only a small number of distinct gates, aiding physical realizability. We numerically demonstrate that, just by adding two more single-qubit gates to GSC, the statistical error produced in the calibration of a CNOT gate is divided by a factor of more than two.Comment: 22 pages, 8 figure

Analytic Filter-Function Derivatives for Quantum Optimal Control

Auto-correlated noise appears in many solid state qubit systems and hence needs to be taken into account when developing gate operations for quantum information processing. However, explicitly simulating this kind of noise is often less efficient than approximate methods. Here, we focus on the filter function formalism, which allows the computation of gate fidelities in the presence of auto-correlated classical noise. Hence, this formalism can be combined with optimal control algorithms to design control pulses, which optimally implement quantum gates. To enable the use of gradient-based algorithms with fast convergence, we present analytically derived filter function gradients with respect to control pulse amplitudes, and analyze the computational complexity of our results. When comparing pulse optimization using our derivatives to a gradient-free approach, we find that the gradient-based method is roughly two orders of magnitude faster for our test cases. We also provide a modular computational implementation compatible with quantum optimal control packages.Comment: Revised arguments in section 7, results unchanged. 13 pages, 7 figures. Open-source software available at https://github.com/qutech/filter_function

Noise Reduction Methods for Charge Stability Diagrams of Double Quantum Dots

Operating semiconductor quantum dots as quantum bits requires isolating single electrons by adjusting gate voltages. The transitions of electrons to and from the dots appear as a honeycomb-like pattern in recorded charge stability diagrams (CSDs). Thus, detecting the pattern is essential to tune a double dot, but manual tuning is seriously time-consuming. However, automation of this process is difficult because the transitions’ contrast is often low, and noise and background disorder potential shifts disturb the CSDs. Therefore, the signal-to-noise ratio needs to be increased to improve the detection of the line pattern. For this purpose, we evaluate a representative set of edge-preserving smoothing filters and compare them both quantitatively and qualitatively by suitable metrics and visual assessment. We generate artificial data to use full-reference metrics for the evaluation procedure and to optimize the filter parameters. Based on the results of this article, the methods attain a moderate to good amount of noise reduction and structure improvement dependent on the different CSD qualities. In conclusion, we suggest introducing the block-matching and three-dimensional transform-domain filter into the automated tuning processing pipeline. If the data are corrupted by significant amounts of random telegraph noise, the bilateral filter and the rolling guidance filter are also good choices

On Noise-Sensitive Automatic Tuning of Gate-Defined Sensor Dots

In gate-defined quantum dot systems, the conductance change of electrostatically coupled sensor dots allows the observation of the quantum dots' charge and spin states. Therefore, the sensor dots must be optimally sensitive to changes in its electrostatic environment. A series of conductance measurements varying the two sensor-dot-forming barrier gate voltages serve to tune the dot into a corresponding operating regime. In this paper, we analyze the noise characteristics of the measured data and define a criterion to identify continuous regions with a sufficient signal-gradient-to-noise ratio. Hence, accurate noise estimation is required when identifying the optimal operating regime. Therefore, we evaluate several existing noise estimators, modify them for 1D data, optimize their parameters, and analyze their quality based on simulated data. The estimator of Chen et al. [1] turns out to be best suited for our application concerning minimally scattering results. Furthermore, using this estimator in an algorithm for flank-of-interest classification in measured data shows the relevance and applicability of our approach