Showing papers by "Mark A. Eriksson published in 2021"

Quantum Simulators: Architectures and Opportunities

Abstract: Quantum simulators are a promising technology on the spectrum of quantum devices from specialized quantum experiments to universal quantum computers. These quantum devices utilize entanglement and many-particle behaviors to explore and solve hard scientific, engineering, and computational problems. Rapid development over the last two decades has produced more than 300 quantum simulators in operation worldwide using a wide variety of experimental platforms. Recent advances in several physical architectures promise a golden age of quantum simulators ranging from highly optimized special purpose simulators to flexible programmable devices. These developments have enabled a convergence of ideas drawn from fundamental physics, computer science, and device engineering. They have strong potential to address problems of societal importance, ranging from understanding vital chemical processes, to enabling the design of new materials with enhanced performance, to solving complex computational problems. It is the position of the community, as represented by participants of the NSF workshop on "Programmable Quantum Simulators," that investment in a national quantum simulator program is a high priority in order to accelerate the progress in this field and to result in the first practical applications of quantum machines. Such a program should address two areas of emphasis: (1) support for creating quantum simulator prototypes usable by the broader scientific community, complementary to the present universal quantum computer effort in industry; and (2) support for fundamental research carried out by a blend of multi-investigator, multi-disciplinary collaborations with resources for quantum simulator software, hardware, and education.

Coherent Control and Spectroscopy of a Semiconductor Quantum Dot Wigner Molecule.

Abstract: Semiconductor quantum dots containing more than one electron have found wide application in qubits, where they enable readout and enhance polarizability. However, coherent control in such dots has typically been restricted to only the lowest two levels, and such control in the strongly interacting regime has not been realized. Here we report quantum control of eight different transitions in a silicon-based quantum dot. We use qubit readout to perform spectroscopy, revealing a dense set of energy levels with characteristic spacing far smaller than the single-particle energy. By comparing with full configuration interaction calculations, we argue that the dense set of levels arises from Wigner-molecule physics.

Valley splittings in Si/SiGe quantum dots with a germanium spike in the silicon well

Abstract: Silicon-germanium heterostructures have successfully hosted quantum dot qubits, but the intrinsic near-degeneracy of the two lowest valley states poses an obstacle to high-fidelity quantum computing. We present a modification to the Si/SiGe heterostructure by the inclusion of a spike in germanium concentration within the quantum well in order to increase the valley splitting. The heterostructure is grown by chemical vapor deposition and magnetospectroscopy is performed on gate-defined quantum dots to measure the excited state spectrum. We demonstrate a large and widely tunable valley splitting as a function of applied vertical electric field and lateral dot confinement. We further investigate the role of the germanium spike by means of tight-binding simulations in single-electron dots and show a robust doubling of the valley splitting when the spike is present, as compared to a standard (spike-free) heterostructure. This doubling effect is nearly independent of the electric field, germanium content of the spike, and spike location. This experimental evidence of a stable, tunable quantum dot, despite a drastic change to the heterostructure, provides a foundation for future heterostructure modifications.

3D integration and measurement of a semiconductor double quantum dot with a high-impedance TiN resonator

Abstract: One major challenge to scaling quantum dot qubits is the dense wiring requirements, making it difficult to envision fabricating large 2D arrays of nearest-neighbor-coupled qubits necessary for error correction. We describe a method to ameliorate this issue by spacing out the qubits using superconducting resonators facilitated by 3D integration. To prove the viability of this approach, we use integration to couple an off-chip high-impedance TiN resonator to a double quantum dot in a Si/SiGe heterostructure. Using the resonator as a dispersive gate sensor, we tune the device down to the single electron regime with an SNR = 5.36. Characterizing the individual systems shows 3D integration can be done while maintaining low-charge noise for the quantum dots and high-quality factors for the superconducting resonator (single photon QL = 2.14 × 104 with Qi ≈ 3 × 105), necessary for readout and high-fidelity two-qubit gates.

Magnetic-Gradient-Free Two-Axis Control of a Valley Spin Qubit in Si x Ge 1 − x

Abstract: Silicon-based spin qubits are of significant interest in quantum information processing, due to their small size and potential for scalability. Implementations have been hindered, though, by the presence of electronic valley degeneracy (which causes spin decoherence) and by silicon's weak spin-orbit coupling (which necessitates complicated micromagnet fabrication to create a magnetic field gradient). The authors measure the valley spectrum of a double-quantum-dot device, and establish two-axis control of a singlet-triplet qubit using the artificial magnetic field gradient generated by the valley subspace. This carries important ramifications for scaling up silicon-based spin qubits.

Radio-Frequency Reflectometry in Silicon-Based Quantum Dots

Abstract: Radio-frequency (rf) reflectometry offers a fast and sensitive method for charge sensing and spin readout in gated quantum dots. We focus in this work on the implementation of rf readout in accumulation-mode gate-defined quantum dots, where the large parasitic capacitance poses a challenge. We describe and test two methods for mitigating the effect of the parasitic capacitance, one by on-chip modifications and a second by off-chip changes. We demonstrate that on-chip modifications enable high-performance charge readout in Si/SixGe1-x quantum dots, achieving a fidelity of 99.9% for a measurement time of 1μs.

Ray-based framework for state identification in quantum dot devices

Abstract: Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexity of the control parameter space also grows. Traditional measurement techniques, relying on complete or near-complete exploration via two-parameter scans (images) of the device response, quickly become impractical with increasing numbers of gates. Here, we propose to circumvent this challenge by introducing a measurement technique relying on one-dimensional projections of the device response in the multi-dimensional parameter space. Dubbed as the ray-based classification (RBC) framework, we use this machine learning (ML) approach to implement a classifier for QD states, enabling automated recognition of qubit-relevant parameter regimes. We show that RBC surpasses the 82 % accuracy benchmark from the experimental implementation of image-based classification techniques from prior work while cutting down the number of measurement points needed by up to 70 %. The reduction in measurement cost is a significant gain for time-intensive QD measurements and is a step forward towards the scalability of these devices. We also discuss how the RBC-based optimizer, which tunes the device to a multi-qubit regime, performs when tuning in the two- and three-dimensional parameter spaces defined by plunger and barrier gates that control the dots. This work provides experimental validation of both efficient state identification and optimization with ML techniques for non-traditional measurements in quantum systems with high-dimensional parameter spaces and time-intensive measurements.

Ray-Based Framework for State Identification in Quantum Dot Devices

Abstract: A machine-learning classifier for quantum dot states is demonstrated using multi-dimensional `rays' rather than images, reducing the measurement cost by up to 70%.

High-Density Covalent Grafting of Spin-Active Molecular Moieties to Diamond Surfaces.

Abstract: Functionalization of diamond surfaces with TEMPO and other surface paramagnetic species represents one approach to the implementation of novel chemical detection schemes that make use of shallow quantum color defects such as silicon-vacancy (SiV) and nitrogen-vacancy (NV) centers. Yet, prior approaches to quantum-based chemical sensing have been hampered by the absence of high-quality surface functionalization schemes for linking radicals to diamond surfaces. Here, we demonstrate a highly controlled approach to the functionalization of diamond surfaces with carboxylic acid groups via all-carbon tethers of different lengths, followed by covalent chemistry to yield high-quality, TEMPO-modified surfaces. Our studies yield estimated surface densities of 4-amino-TEMPO of approximately 1.4 molecules nm-2 on nanodiamond (varying with molecular linker length) and 3.3 molecules nm-2 on planar diamond. These values are higher than those reported previously using other functionalization methods. The ζ-potential of nanodiamonds was used to track reaction progress and elucidate the regioselectivity of the reaction between ethenyl and carboxylate groups and surface radicals.

Long Phonon Mean Free Paths Observed in Cross-plane Thermal-Conductivity Measurements of Exfoliated Hexagonal Boron Nitride

Abstract: Sub-micron-thick layers of hexagonal boron nitride (hBN) exhibit high in-plane thermal conductivity, useful optical properties, and serve as dielectric encapsulation layers with low electrostatic inhomogeneity for graphene devices. Despite the promising applications of hBN as a heat spreader, the thickness dependence of the cross-plane thermal conductivity is not known, and the cross-plane phonon mean free paths in hBN have not been measured. We measure the cross-plane thermal conductivity of hBN flakes exfoliated from bulk crystals. We find that the thermal conductivity is extremely sensitive to film thickness. We measure a forty-fold increase in the cross-plane thermal conductivity between 7 nm and 585 nm flakes at 295 K. We attribute the large increase in thermal conductivity with increasing thickness to contributions from phonons with long mean free paths (MFPs), spanning many hundreds of nanometers in the thickest flakes. When planar twist interfaces are introduced into the crystal by mechanically stacking multiple thin flakes, the cross-plane thermal conductivity of the stack is found to be a factor of seven below that of individual flakes with similar total thickness, thus providing strong evidence that phonon scattering at twist boundaries limits the maximum phonon MFPs. These results have important implications for hBN integration in nanoelectronics and improve our understanding of thermal transport in two-dimensional materials.

A simple numerical method for evaluating heat dissipation from curved wires with periodic applied heating

Abstract: In many situations, the dual-purpose heater/thermometer wires used in the three-omega method—one of the most precise and sensitive techniques for measuring the thermal conductivity of thin films and interfaces—must include bends and curves to avoid obstructions on the surface of a sample. Although the three-omega analysis assumes that the heating wire is infinitely long and straight, recent experimental work has demonstrated that, in some cases, curved-wire geometries can be used without introducing detectable systematic error. We describe a general numerical method that can be used to calculate the temperature of three-omega heating wires with arbitrary wire geometries. This method provides experimentalists with a simple quantitative procedure for calculating how large the systematic error caused by a particular wire asymmetry will be. We show calculations of two useful cases: a straight wire with a single bend of arbitrary angle and a wire that forms a circle. We find that the amplitude of the in-phase temperature oscillations near a wire that forms a circle differs from the prediction using the analytic straight-line source solution by <12%, provided that the thermal penetration depth is less than ten times the radius of curvature of the wire path. The in-phase temperature amplitude 1.5 wire widths away from a 90° bend in a wire is within 11% of the straight-line source prediction for all penetration depths greater than the wire width. Our calculations indicate that the straight-line source solution breaks down significantly when the wire bend angle is less than 45°.

Magnetic gradient free two axis control of a valley spin qubit in SiGe

Abstract: Spins in SiGe quantum dots are promising candidates for quantum bits but are also challenging due to the valley degeneracy which could potentially cause spin decoherence and weak spin-orbital coupling. In this work we demonstrate that valley states can serve as an asset that enables two-axis control of a singlet-triplet qubit formed in a double quantum dot without the application of a magnetic field gradient. We measure the valley spectrum in each dot using magnetic field spectroscopy of Zeeman split triplet states. The interdot transition between ground states requires an electron to flip between valleys, which in turn provides a g-factor difference $\Delta g$ between two dots. This $\Delta g$ serves as an effective magnetic field gradient and allows for qubit rotations with a rate that increases linearly with an external magnetic field. We measured several interdot transitions and found that this valley introduced $\Delta g$ is universal and electrically tunable. This could potentially simplify scaling up quantum information processing in the SiGe platform by removing the requirement for magnetic field gradients which are difficult to engineer.

Toward Robust Autotuning of Noisy Quantum Dot Devices.

Abstract: The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a ``gatekeeper'' system, ensuring that only reliable data is processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.1(7) % when tested on experimental data. We then validate the functionality of the data quality control module by showing the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.