FAlCon: A unified framework for algorithmic control of quantum dot devices

Abstract

As spin-based quantum systems scale, their setup and control complexity increase sharply. In semiconductor quantum dot (QD) experiments, device-to-device variability, heterogeneous control-electronics stacks, and differing operational modalities make it difficult to reuse characterization, calibration, and control logic across laboratories. We present FAlCon, an open-source software ecosystem for portable, automated characterization and tuning measurement workflows. FAlCon provides (i) a lightweight domain-specific language for expressing state-based tuning logic in a hardware-agnostic form; (ii) specialized transmittable libraries of physics-informed QD data structures (``tuning vernacula''); and (iii) extensible libraries of shared measurement protocols enabling an interoperable lab-agnostic measurement stack. By separating algorithm intent from instrument realization, while preserving traceability and supporting typed scripting, FAlCon enables researchers and engineers to exchange, adapt, and deploy characterization and autotuning routines across heterogeneous QD setups. The framework supports all users, ranging from end users running prebuilt algorithms with custom initial conditions to developers extending instrumentation support and contributing new tuning strategies. Although the present release targets QD experiments, other qubit modalities and scientific experiments could reuse FAlCon's modular abstractions by providing new tuning data types and instrument control templates.

Figures (3)

• Figure 1: Examples of three gate-defined QD device architectures illustrating the diversity of layouts and the common functional roles of gates (reservoir, screening, plunger, and barrier). In principle, each of these systems can be tuned to regimes hosting QDs suitable for spin-qubit operation. (a) A SiGe QD device Kovach24-BATIS, hosting up to four qubit QDs in the bottom channel and two charge sensors in the top channel. (b) A Ge QD device Stehouwer24-ESG, hosting up to ten qubit QDs in the center region between the screening gates and four charge sensors on the outside of the array. (c) A GaAs QD device Zubchenko24-ABQ, hosting up to eight possible QDs organized into four double-QDs, and four charge sensors adjacent to each pair.
• Figure 2: High-level architecture of the FAlCon control and measurement stack. The Runtime Engine (right) executes FAlCon routines, maintains tuning state and global variables, and persists structured metadata in a PostgreSQL database. Measurement requests and results are exchanged with the laboratory-side measurement stack via message passing (e.g., over NATS). The Instrument Hub (left) receives measurement requests from the Runtime Engine and translates them into concrete actions executed by the Instrument Server. The Instrument Server manages instrument lifetimes and distributes requests across one or more connected instruments (Inst. 1, $\dots$, Inst. N), which directly interface with the experimental hardware. The Instrument Hub aggregates returned results and stores measured datasets as portable HDF5 artifacts HDF5. The bottom schematic illustrates a representative device-level wiring context for QD measurements, with a voltage source applied to a gate (V) and a current readout at an Ohmic contact (A).
• Figure 3: (a) A simplistic example of FAlCon DSL program visualized as a signal-flow graph. In this example, the program iterates through a list of connections, prints each valid connection, and terminates when the list is exhausted. A FAlCon implementation of this program is presented in Listing \ref{['lst:dsl']}. (b) Illustration of hierarchical composition in FAlCon. A parent (Master) autotuner nests multiple child autotuners (Atuner 1--3) as internal states, enabling reuse by composing existing autotuners into higher-level workflows.