Colloquium: Advances in automation of quantum dot devices control

Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning

The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions and each device realisation requires a different tuning protocol. We demonstrate that it is possible to automate the tuning of a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate Ge/SiGe heterostructure double quantum dot device from scratch with the same algorithm. We achieve tuning times of 30, 10, and 92 min, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices, allowing for the characterization of the regions where double quantum dot regimes are found. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.

InGaAs quantum dot chains grown by twofold selective area molecular beam epitaxy

Increasing quantum confinement in semiconductor quantum dot (QD) systems is essential to perform robust simulations of many-body physics. By combining molecular beam epitaxy and lithographic techniques, we developed an approach consisting of a twofold selective area growth to build QD chains. Starting from 15 nm-thick and 65 nm-wide in-plane In0.53Ga0.47As nanowires on InP substrates, linear arrays of In0.53Ga0.47As QDs were grown on top, with tunable lengths and separations. Kelvin probe force microscopy performed at room temperature revealed a change of quantum confinement in chains with decreasing QD sizes, which was further emphasized by the spectral shift of quantum levels resolved in the conduction band with low temperature scanning tunneling spectroscopy. This approach, which allows the controlled formation of 25 nm-thick QDs with a minimum length and separation of 30 nm and 22 nm respectively, is suitable for the construction of scalable fermionic quantum lattices.

Functional fibrillar interfaces: Biological hair as inspiration across scales

Hair, or hair-like fibrillar structures, are ubiquitous in biology, from fur on the bodies of mammals, over trichomes of plants, to the mastigonemes on the flagella of single-celled organisms. While these long and slender protuberances are passive, they are multifunctional and help to mediate interactions with the environment. They provide thermal insulation, sensory information, reversible adhesion, and surface modulation (e.g., superhydrophobicity). This review will present various functions that biological hairs have been discovered to carry out, with the hairs spanning across six orders of magnitude in size, from the millimeter-thick fur of mammals down to the nanometer-thick fibrillar ultrastructures on bateriophages. The hairs are categorized according to their functions, including protection (e.g., thermal regulation and defense), locomotion, feeding, and sensing. By understanding the versatile functions of biological hairs, bio-inspired solutions may be developed across length scales.

Optimizing quantum gates towards the scale of logical qubits

A foundational assumption of quantum error correction theory is that quantum gates can be scaled to large processors without exceeding the error-threshold for fault tolerance. Two major challenges that could become fundamental roadblocks are manufacturing high-performance quantum hardware and engineering a control system that can reach its performance limits. The control challenge of scaling quantum gates from small to large processors without degrading performance often maps to non-convex, high-constraint, and time-dynamic control optimization over an exponentially expanding configuration space. Here we report on a control optimization strategy that can scalably overcome the complexity of such problems. We demonstrate it by choreographing the frequency trajectories of 68 frequency-tunable superconducting qubits to execute single- and two-qubit gates while mitigating computational errors. When combined with a comprehensive model of physical errors across our processor, the strategy suppresses physical error rates by ~3.7× compared with the case of no optimization. Furthermore, it is projected to achieve a similar performance advantage on a distance-23 surface code logical qubit with 1057 physical qubits. Our control optimization strategy solves a generic scaling challenge in a way that can be adapted to a variety of quantum operations, algorithms, and computing architectures.

Language models for quantum simulation

A key challenge in the effort to simulate today's quantum computing devices is the ability to learn and encode the complex correlations that occur between qubits. Emerging technologies based on language models adopted from machine learning have shown unique abilities to learn quantum states. We highlight the contributions that language models are making in the effort to build quantum computers and discuss their future role in the race to quantum advantage.

Voltage fluctuations and probe frequency jitter in electric force microscopy of a conductor

Electric force microscopy probes the statistics of electric field fluctuations from a sample surface, both through measurement of the noncontact friction exerted on the oscillating charged probe and by determination of the power spectrum of stochastic probe frequency fluctuations, referred to as "jitter." Here we calculate the frequency jitter power spectrum determined over a conducting sample of finite thickness, whose response is characterized by a dielectric function that is wavevector-dependent. These calculations complement previous predictions of the coefficient of noncontact friction in an electric force microscopy measurement for the same model, and also previous predictions of the jitter power spectrum for a dielectric continuum. The inclusion both of a finite sample thickness and a wavevector-dependent dielectric response can significantly enhance the magnitude of the predicted jitter spectrum for a conductor, relative to a simpler model of an infinitely thick dielectric continuum. These calculations provide a baseline prediction of the jitter power spectrum generated by the dynamics of conduction electrons in a metal sample.