Tuning arrays with rays: Physics-informed tuning of quantum dot charge states

Quantum computers based on gate-defined quantum dots (QDs) are expected to scale. However, as the number of qubits increases, the burden of manually calibrating these systems becomes unreasonable and autonomous tuning must be used. There has been a range of recent demonstrations of automated tuning of various QD parameters such as coarse gate ranges, global state topology (e.g. single QD, double QD), charge, and tunnel coupling with a variety of methods. Here, we demonstrate an intuitive, reliable, and data-efficient set of tools for an automated global state and charge tuning in a framework deemed physics-informed tuning (PIT). The first module of PIT is an action-based algorithm that combines a machine learning classifier with physics knowledge to navigate to a target global state. The second module uses a series of one-dimensional measurements to tune to a target charge state by first emptying the QDs of charge, followed by calibrating capacitive couplings and navigating to the target charge state. The success rate for the action-based tuning consistently surpasses 95 % on both simulated and experimental data suitable for off-line testing. The success rate for charge setting is comparable when testing with simulated data, at 95.5(5.4) %, and only slightly worse for off-line experimental tests, with an average of 89.7(17.4) % (median 97.5 %). It is noteworthy that the high performance is demonstrated both on data from samples fabricated in an academic cleanroom as well as on an industrial 300 mm} process line, further underlining the device agnosticism of PIT. Together, these tests on a range of simulated and experimental devices demonstrate the effectiveness and robustness of PIT.Comment: 14 pages, 7 figure

Automated extraction of capacitive coupling for quantum dot systems

Gate-defined quantum dots (QDs) have appealing attributes as a quantum computing platform, however, near-term devices possess a range of possible imperfections that need to be accounted for during the tuning and operation of QD devices. One such problem is the capacitive cross-talk between the metallic gates that define and control QD qubits. A way to compensate for the capacitive cross-talk and enable targeted control of specific QDs independent of coupling is by the use of virtual gates. Here, we demonstrate a reliable automated capacitive coupling identification method that combines machine learning with traditional fitting to take advantage of the desirable properties of each. We also show how the cross-capacitance measurement may be used for the identification of spurious QDs sometimes formed during tuning experimental devices. Our systems can autonomously flag devices with spurious dots near the operating regime which is crucial information for reliable tuning to a regime suitable for qubit operations.Comment: 8 pages, 5 figure

Probing single electrons across 300 mm spin qubit wafers

Building a fault-tolerant quantum computer will require vast numbers of physical qubits. For qubit technologies based on solid state electronic devices, integrating millions of qubits in a single processor will require device fabrication to reach a scale comparable to that of the modern CMOS industry. Equally importantly, the scale of cryogenic device testing must keep pace to enable efficient device screening and to improve statistical metrics like qubit yield and process variation. Spin qubits have shown impressive control fidelities but have historically been challenged by yield and process variation. In this work, we present a testing process using a cryogenic 300 mm wafer prober to collect high-volume data on the performance of industry-manufactured spin qubit devices at 1.6 K. This testing method provides fast feedback to enable optimization of the CMOS-compatible fabrication process, leading to high yield and low process variation. Using this system, we automate measurements of the operating point of spin qubits and probe the transitions of single electrons across full wafers. We analyze the random variation in single-electron operating voltages and find that this fabrication process leads to low levels of disorder at the 300 mm scale. Together these results demonstrate the advances that can be achieved through the application of CMOS industry techniques to the fabrication and measurement of spin qubits.Comment: 15 pages, 4 figures, 7 extended data figure

A Classification of Clay-Rich Subaqueous Density Flow Structures

This study presents a classification for subaqueous clay-laden sediment gravity flows. A series of laboratory flume experiments were performed using 9%, 15%, and 21% sediment mixture concentrations composed of sand, silt, clay, and tap water, on varying bed slopes of 6°, 8°, and 9.5°, and with discharge rates of 10 and 15 m3/hr. In addition to the characteristics of the boundary and plug layers, which have been previously used for the classification of open-channel clay-laden flows, the newly presented classification also incorporates the treatment of the free shear layer. The flow states within the boundary and free shear layers were established using calculation of the inner variable, self-similarity considerations, and the magnitude of the apparent viscosity. Based on the experimental observations four flow types were recognized: (1) a clay-rich plug flow with a laminar free shear layer, a plug layer, and a laminar boundary layer, (2) a top transitional plug flow containing a turbulent free shear layer, a plug layer, and a laminar boundary layer, (3) a transitional turbidity current with a turbulent free shear layer, no plug layer, and a laminar boundary layer, and (4) a fully turbulent turbidity current. A connection between the emplaced deposits and the relevant flow types is drawn and it is shown that a Froude number, two Reynolds numbers, and a dimensionless yield stress parameter are sufficient to associate an experimental flow type with a natural large-scale density flow

A Classification of Clay-Rich Subaqueous Density Flow Structures

This study presents a classification for subaqueous clay-laden sediment gravity flows. A series of laboratory flume experiments were performed using 9%, 15%, and 21% sediment mixture concentrations composed of sand, silt, clay, and tap water, on varying bed slopes of 6°, 8°, and 9.5°, and with discharge rates of 10 and 15 m3/hr. In addition to the characteristics of the boundary and plug layers, which have been previously used for the classification of open-channel clay-laden flows, the newly presented classification also incorporates the treatment of the free shear layer. The flow states within the boundary and free shear layers were established using calculation of the inner variable, self-similarity considerations, and the magnitude of the apparent viscosity. Based on the experimental observations four flow types were recognized: (1) a clay-rich plug flow with a laminar free shear layer, a plug layer, and a laminar boundary layer, (2) a top transitional plug flow containing a turbulent free shear layer, a plug layer, and a laminar boundary layer, (3) a transitional turbidity current with a turbulent free shear layer, no plug layer, and a laminar boundary layer, and (4) a fully turbulent turbidity current. A connection between the emplaced deposits and the relevant flow types is drawn and it is shown that a Froude number, two Reynolds numbers, and a dimensionless yield stress parameter are sufficient to associate an experimental flow type with a natural large-scale density flow

A Scalable Microarchitecture for Efficient Instruction-Driven Signal Synthesis and Coherent Qubit Control

Execution of quantum algorithms requires a quantum computer architecture with a dedicated quantum instruction set that is capable of supporting translation of workloads into actual quantum operations acting on the qubits. State-of-the-art qubit control setups typically utilize general purpose test instruments such as arbitrary waveform generators (AWGs) to generate a limited set of waveforms or pulses. These waveforms are precomputed and stored prior to execution, and then used to produce control pulses during execution. Besides their prohibitive cost and limited scalability, such instruments suffer from poor programmability due to the absence of an instruction set architecture (ISA). Limited memory for pulse storage ultimately determines the total number of supported quantum operations. In this work, we present a scalable qubit control system that enables efficient qubit control using a flexible ISA to drive a direct digital synthesis (DDS) pipeline producing nanosecond-accurate qubit control signals dynamically. The designed qubit controller provides a higher density of control channels, a scalable design, better programmability, and lower cost compared to state-of-the-art systems. In this work, we discuss the new qubit controller's capabilities, its architecture and instruction set, and present experimental results for coherent qubit control.Comment: 10 page