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Quantum computers promise to execute complex tasks exponentially faster than any possible classical computer, and thus spur breakthroughs in quantum chemistry, material science and machine learning. However, quantum computers require fast and selective control of large numbers of individual qubits while maintaining coherence. Qubits based on hole spins in one-dimensional germanium/silicon nanostructures are predicted to experience an exceptionally strong yet electrically tunable spin–orbit interaction, which allows us to optimize qubit performance by switching between distinct modes of ultrafast manipulation, long coherence and individual addressability. Here we used millivolt gate voltage changes to tune the Rabi frequency of a hole spin qubit in a germanium/silicon nanowire from 31 to 219 MHz, its driven coherence time between 7 and 59 ns, and its Lande g-factor from 0.83 to 1.27. We thus demonstrated spin–orbit switch functionality, with on/off ratios of roughly seven, which could be further increased through improved gate design. Finally, we used this control to optimize our qubit further and approach the strong driving regime, with spin-flipping times as short as ~1 ns. Quantum computing requires fast and selective control of a large number of individual qubits while maintaining coherence, which is hard to achieve concomitantly. All-electrical operation of a hole spin qubit in a Ge/Si nanowire demonstrates the principle of switching from a mode of selective and fast control to idling with increased coherence.

Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies., To optimize operating conditions of large scale semiconductor quantum devices, a large parameter space has to be explored. Here, the authors report a machine learning algorithm to navigate the entire parameter space of gate-defined quantum dot devices, showing about 180 times faster than a pure random search.

Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimize this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.

Scalable quantum technologies such as quantum computers will require very large numbers of quantum devices to be characterised and tuned. As the number of devices on chip increases, this task becomes ever more time-consuming, and will be intractable on a large scale without efficient automation. We present measurements on a quantum dot device performed by a machine learning algorithm in real time. The algorithm selects the most informative measurements to perform next by combining information theory with a probabilistic deep-generative model that can generate full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different current map configurations that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead demonstrates the use of algorithms to automate measurements. This works lays the foundation for learning-based automated measurement of quantum devices.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

We theoretically investigate the orbital effects of an in-plane magnetic field on the spectrum of a quantum dot embedded in a two-dimensional electron gas (2DEG). We derive an effective two-dimensional Hamiltonian where these effects enter in proportion to the flux penetrating the 2DEG. We quantify the latter in detail for harmonic, triangular, and square potential of the heterostructure. We show how the orbital effects allow one to extract a wealth of information, for example, on the heterostructure interface, the quantum dot size and orientation, and the spin-orbit fields. We illustrate the formalism by extracting this information from recent measured data [L.~C.~Camenzind, et al., arXiv:1804.00162; Nat. Commun. 9, 3454 (2018)]., Comment: 14 pages, 9 figures; minor changes resulting from refereeing and proofs

We show that in-plane-magnetic-field assisted spectroscopy allows extraction of the in-plane orientation and full 3D shape of the quantum mechanical orbitals of a single electron GaAs lateral quantum dot with sub-nm precision. The method is based on measuring orbital energies in a magnetic field with various strengths and orientations in the plane of the 2D electron gas. As a result, we deduce the microscopic quantum dot confinement potential landscape, and quantify the degree by which it differs from a harmonic oscillator potential. The spectroscopy is used to validate shape manipulation with gate voltages, agreeing with expectations from the gate layout. Our measurements demonstrate a versatile tool for quantum dots with one dominant axis of strong confinement., Comment: 4 pages, 3 color figures, including supplementary on arXiv

Silicon quantum dot spin qubits have great potential for application in large-scale quantum circuits as they share many similarities with conventional transistors that represent the prototypical example for scalable electronic platforms. However, for quantum dot formation and control, additional gates are required, which add to device complexity and, thus, hinder upscaling. Here, we meet this challenge by demonstrating the scalable integration of a multilayer gate stack in silicon quantum dot devices using self-alignment, which allows for ultra-small gate lengths and intrinsically perfect layer-to-layer alignment. We explore the prospects of these devices as hosts for hole spin qubits that benefit from electrically driven spin control via spin–orbit interaction. Therefore, we study hole transport through a double quantum dot and observe current rectification due to the Pauli spin blockade. The application of a small magnetic field leads to lifting of the spin blockade and reveals the presence of spin–orbit interaction. From the magnitude of a singlet-triplet anticrossing at a high magnetic field, we estimate a spin–orbit energy of ∼ 37 μ eV, which corresponds to a spin–orbit length of ∼ 48 nm. This work paves the way for scalable spin-based quantum circuits with fast, all-electrical qubit control.

Fault-tolerant spin-based quantum computers will require fast and accurate qubit read out. This can be achieved using radiofrequency reflectometry given sufficient sensitivity to the change in quantum capacitance associated with the qubit states. Here, we demonstrate a 23-fold improvement in capacitance sensitivity by supplementing a cryogenic semiconductor amplifier with a SQUID preamplifier. The SQUID amplifier operates at a frequency near 200MHz and achieves a noise temperature below 600mK when integrated into a reflectometry circuit, which is within a factor 120 of the quantum limit. It enables a record sensitivity to capacitance of 0.07 mml:mspace width=".1em"mml:mspace aF / mml:msqrt Hzmml:msqrt. The setup is used to acquire charge stability diagrams of a gate-defined double quantum dot in a short time with a signal-to-noise ration of about 38 in 1 mml:mspace width=".1em"mml:mspace mu s of integration time.

We integrate ambipolar quantum dots in silicon fin field-effect transistors using exclusively standard complementary metal-oxide-semiconductor fabrication techniques. We realize ambipolarity by replacing conventional highly doped source and drain electrodes by a metallic nickel silicide with the Fermi level close to the silicon mid-gap position. Such devices operate in a dual mode, as either a classical field-effect or single-electron transistor. We implement a classical logic NOT gate at low temperature by tuning two interconnected transistors into opposite polarities. In the quantum regime, we demonstrate stable quantum dot operation in the few charge carrier Coulomb blockade regime for both electrons and holes.

We analyze orbital effects of an in-plane magnetic field on the spin structure of states of a gated quantum dot based in a two-dimensional electron gas. Starting with a $k \cdot p$ Hamiltonian, we perturbatively calculate these effects for the conduction band of GaAs, up to the third power of the magnetic field. We quantify several corrections to the g-tensor and reveal their relative importance. We find that for typical parameters, the Rashba spin-orbit term and the isotropic term, $H_{43} \propto {\bf P}^2 {\bf B} \cdot \boldsymbol{\sigma}$, give the largest contributions in magnitude. The in-plane anisotropy of the g-factor is, on the other hand, dominated by the Dresselhaus spin-orbit term. At zero magnetic field, the total correction to the g-factor is typically 5-10% of its bulk value. In strong in-plane magnetic fields, the corrections are modified appreciably., Comment: 24 pages, 8 figures; v2 is in content identical to the version published in PRB. Compared to v1, the minor changes adopted in v2 are reflecting the PRB referees' suggestions

Understanding and control of the spin relaxation time $T_1$ is among the key challenges for spin based qubits. A larger $T_1$ is generally favored, setting the fundamental upper limit to the qubit coherence and spin readout fidelity. In GaAs quantum dots at low temperatures and high in-plane magnetic fields $B$, the spin relaxation relies on phonon emission and spin-orbit coupling. The characteristic dependence $T_1 \propto B^{-5}$ and pronounced $B$-field anisotropy were already confirmed experimentally. However, it has also been predicted 15 years ago that at low enough fields, the spin-orbit interaction is replaced by the coupling to the nuclear spins, where the relaxation becomes isotropic, and the scaling changes to $T_1 \propto B^{-3}$. We establish these predictions experimentally, by measuring $T_1$ over an unprecedented range of magnetic fields -- made possible by lower temperature -- and report a maximum $T_1 = 57\pm15$ s at the lowest fields, setting a new record for the electron spin lifetime in a nanostructure., 7 pages, 4 (color) figures

Basement membranes (BMs) are thin sheets of extracellular matrix that outline epithelia, muscle fibers, blood vessels and peripheral nerves. The current view of BM structure and functions is based mainly on transmission electron microscopy imaging, in vitro protein binding assays, and phenotype analysis of human patients, mutant mice and invertebrata. Recently, MS-based protein analysis, biomechanical testing and cell adhesion assays with in vivo derived BMs have led to new and unexpected insights. Proteomic analysis combined with ultrastructural studies showed that many BMs undergo compositional and structural changes with advancing age. Atomic force microscopy measurements in combination with phenotype analysis have revealed an altered mechanical stiffness that correlates with specific BM pathologies in mutant mice and human patients. Atomic force microscopy-based height measurements strongly suggest that BMs are more than two-fold thicker than previously estimated, providing greater freedom for modelling the large protein polymers within BMs. In addition, data gathered using BMs extracted from mutant mice showed that laminin has a crucial role in BM stability. Finally, recent evidence demonstrate that BMs are bi-functionally organized, leading to the proposition that BM-sidedness contributes to the alternating epithelial and stromal tissue arrangements that are found in all metazoan species. We propose that BMs are ancient structures with tissue-organizing functions and were essential in the evolution of metazoan species.

PURPOSE Cataract surgery requires the removal of a circular segment of the anterior lens capsule (LC) by manual or femtosecond laser (FL) capsulotomy. Tears in the remaining anterior LC may compromise surgical outcome. We investigated whether biophysical differences in the rim properties of the LC remaining in the patient after manual or FL capsulotomy (FLC) lead to different risks with regard to anterior tear formation. METHODS Lens capsule samples obtained by either continuous curvilinear capsulorhexis (CCC) or FLC were investigated by light microscopy, laser scanning confocal microscopy, and scanning electron microscopy; atomic force microscopy (AFM) was used to test the biomechanical properties of the LC. The mechanical stability of the LC following either of the two capsulotomy techniques was simulated by using finite-element modeling. RESULTS Continuous curvilinear capsulorhexis produced wedge-shaped, uniform rims, while FLC resulted in nearly perpendicular, frayed rims with numerous notches. The LC is composed of two sublayers: a stiff epithelial layer that is abundant with laminin and a softer anterior chamber layer that is predominantly made from collagen IV. Computer models show that stress is uniformly distributed over the entire rim after CCC, while focal high stress concentrations are observed in the frayed profiles of LC after FLC, making the latter procedure more prone to anterior tear formation. CONCLUSIONS Finite-element modeling based on three-dimensional AFM maps indicated that CCC leads to a capsulotomy rim with higher stress resistance, leading to a lower propensity for anterior radial tears than FLC.

The current study investigates the structural and compositional changes of ocular basement membranes (BMs) during long-term diabetes. By comparing retinal vascular BMs and the inner limiting membrane (ILM) from diabetic and non-diabetic human eyes by light and transmission electron microscopy (TEM), a massive, diabetes-related increase in the thickness of these BMs was detected. The increase in ILM thickness was confirmed by atomic force microscopy (AFM) on native ILM flat-mount preparations. AFM also detected a diabetes-induced increase in ILM stiffness. The changes in BM morphology and biophysical properties were accompanied by partial changes in the biochemical composition as shown by immunocytochemistry and western blots: agrin, fibronectin and tenascin underwent relative increases in concentration in diabetic BMs as compared to non-diabetic BMs. Fibronectin and tenascin were particularly high in the BMs of outlining microvascular aneurisms. The present data showed that retinal vascular BMs and the ILM undergo morphological, biomechanical and compositional changes during long-term diabetes. The increase in BM thickness not only resulted from an up-regulation of the standard BM proteins, but also from the expression of diabetes-specific extracellular matrix proteins that are not normally found in retinal BMs.

We experimentally determine isotropic and anisotropic g-factor corrections in lateral GaAs single-electron quantum dots. We extract the Zeeman splitting by measuring the tunnel rates into the individual spin states of an empty quantum dot for an in-plane magnetic field with various strengths and directions. We quantify the Zeeman energy and find a linear dependence on the magnetic field strength which allows us to extract the g-factor. The measured g-factor is understood in terms of spin-orbit interaction induced isotropic and anisotropic corrections to the GaAs bulk g-factor. Because this implies a dependence of the spin splitting on the magnetic field direction, these findings are of significance for spin qubits in GaAs quantum dots., Comment: 6 pages, 3 figures