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IEEE International Conference on Quantum Software (QSW)

An Efficient Compilation Architecture for Quantum Experimentation, Calibration and Control Tasks

Lukas Scheller, Cedric Becker, Marvin Fuchs, Timo Muscheid, R. Gartmann, Oliver Sander, Luis Eduardo Ardila-Perez

Published Jul 1, 2026• 0 citations
Publisher
DOI
10.1109/qsw72780.2026.00011

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The development of energy-efficient and fast machine learning methods plays an increasingly important role in experimental physics, where data analysis and control tasks often need to operate under strict resource constraints. In these contexts, machine learning models can automate complex calibration and analysis tasks while enabling on-device data processing close to the experimental sensors.One representative application presented on this poster concerns the automated calibration of semiconductor spin qubits, while the outlook highlights extensions toward edge-computing approaches in detector systems.The automated calibration of quantum dots is a key prerequisite for realizing scalable quantum computers. In particular, the analysis of charge stability diagrams, used to detect charge transitions in quantum dots, represents a complex and time-consuming task. Neural networks, especially U-Net architectures, offer the potential to automate this process by reliably recognizing relevant patterns in simulated and experimental measurement data. State-of-the-art networks have already been successfully trained for this purpose.However, there remains significant potential for optimization to enable space- and energy-efficient integration close to the quantum bits within the cryostat.We have investigated the use of quantized neural networks for energy-efficient quantum dot calibration. The goal is to analyze the impact of post-training quantization and quantization-aware training on detection quality, as well as the general effects of quantization on memory requirements and inference speed. Three U-Nets with different architectures, parameter counts, and input dimensions serve as model bases, applied to simulated charge stability diagrams. The results show that appropriate quantization strategies can reduce memory usage without significantly affecting detection quality.The findings of this work contribute to the integration of energy-efficient machine learning methods into experimental quantum computing environments, thereby supporting overall scalability.Building on these results, the approach is extended toward the use of binarized neural networks (BNNs) to push energy efficiency and faster inference even further. Within edge computing applications, current efforts focus on implementing and demonstrating such networks on FPGA hardware, aiming to exploit binary-weight computation and hardware-level parallelism for minimal latency and power consumption. Beyond quantum dot calibration, these methods are also being investigated for other scientific applications, such as the autonomous self-triggering radio detection of extensive air showers, highlighting the broader potential of hardware-embedded AI for resource-constrained experimental environments.

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Quantum compilation is the problem of translating an input quantum circuit into the most efficient equivalent of itself, taking into account the characteristics of the device that will execute the computation. Compilation strategies are composed of sequential passes that perform placement, routing and optimization tasks. Noise-adaptive compilers do take the noise statistics of the device into account, for some or all passes. The noise statistics can be obtained from calibration data, and updated after each device calibration.
Efficient calibration and control of programmable photonic integrated circuits are fundamental for scaling quantum and classical optical computing processors. While neural network-based models offer an architecture-agnostic solution, existing approaches suffer from limited learning and generalization capabilities due to the many-to-one mapping problem between sets of control signals and optical responses, and biased training datasets derived from uniform current sampling. In this work, we propose a universal calibration and control framework employing tandem neural networks combined with two novel data generation strategies: architecture-aware sampling based on Haar measure principles, and optimized sampling, a physics-agnostic approach utilizing differential evolution. We experimentally validate these methods on 3x3 and 4x4 coherent MZI meshes, demonstrating that our approach addresses the sampling bias inherent in previous works. When evaluated using random unitary matrices, our solution outperforms standard uniform sampling baselines by ~2 bits of precision. Furthermore, we experimentally extend the application of this framework to coherent detection, achieving precise control over both amplitude and phase, and validate its impact on photonic neural network tasks.
The development of energy-efficient and fast machine learning methods plays an increasingly important role in experimental physics, where data analysis and control tasks often need to operate under strict resource constraints. In these contexts, machine learning models can automate complex calibration and analysis tasks while enabling on-device data processing close to the experimental sensors.One representative application presented on this poster concerns the automated calibration of semiconductor spin qubits, while the outlook highlights extensions toward edge-computing approaches in detector systems.The automated calibration of quantum dots is a key prerequisite for realizing scalable quantum computers. In particular, the analysis of charge stability diagrams, used to detect charge transitions in quantum dots, represents a complex and time-consuming task. Neural networks, especially U-Net architectures, offer the potential to automate this process by reliably recognizing relevant patterns in simulated and experimental measurement data. State-of-the-art networks have already been successfully trained for this purpose.However, there remains significant potential for optimization to enable space- and energy-efficient integration close to the quantum bits within the cryostat.We have investigated the use of quantized neural networks for energy-efficient quantum dot calibration. The goal is to analyze the impact of post-training quantization and quantization-aware training on detection quality, as well as the general effects of quantization on memory requirements and inference speed. Three U-Nets with different architectures, parameter counts, and input dimensions serve as model bases, applied to simulated charge stability diagrams. The results show that appropriate quantization strategies can reduce memory usage without significantly affecting detection quality.The findings of this work contribute to the integration of energy-efficient machine learning methods into experimental quantum computing environments, thereby supporting overall scalability.Building on these results, the approach is extended toward the use of binarized neural networks (BNNs) to push energy efficiency and faster inference even further. Within edge computing applications, current efforts focus on implementing and demonstrating such networks on FPGA hardware, aiming to exploit binary-weight computation and hardware-level parallelism for minimal latency and power consumption. Beyond quantum dot calibration, these methods are also being investigated for other scientific applications, such as the autonomous self-triggering radio detection of extensive air showers, highlighting the broader potential of hardware-embedded AI for resource-constrained experimental environments.
2-local qubit Hamiltonian simulation, a fundamental task in quantum computing, is widely applied in various applications. This paper presents QBX, the first quantum compiler designed for 2-local qubit Hamiltonian simulation on quantum chiplet architectures. Existing general-purpose quantum compilers for chiplet architectures target quantum programs at the gate level and miss optimizations requiring high-level semantics of Hamiltonian simulation. Conversely, existing domain-specific compilers for Hamiltonian simulation are designed for monolithic architectures, and their limited scalability and inability to adapt to the heterogeneity of chiplet architectures lead to sub-optimal circuit outputs. QBX implements a scalable hierarchical approach to reduce both the amount and distance of cross-chiplet communications. It integrates the highway mechanism, a chiplet-oriented solution for efficient long-range qubit communication, to diminish cross-chiplet interaction costs. Furthermore, QBX efficiently aggregates 2-local Pauli strings into multi-target controlled gates, harnessing highways for deep optimization. Our evaluations show that QBX markedly outperforms both general-purpose and domain-specific compilers in circuit depth and operation count, while scaling to much larger 2-local Hamiltonian simulations.