[Paper] LUNA: LUT-Based Neural Architecture for Fast and Low-Cost Qubit Readout

Source: arXiv - 2512.07808v1

Overview

The paper introduces LUNA, a hardware accelerator that dramatically speeds up and shrinks the footprint of qubit‑readout pipelines. By marrying a lightweight integrator front‑end with a Look‑Up‑Table (LUT)‑based neural network, the authors achieve fast, low‑cost classification of superconducting qubit signals—an essential step for real‑time quantum error correction (QEC) and scalable quantum processors.

Key Contributions

• Hybrid preprocessing + LUT‑NN architecture: Uses simple integrators for dimensionality reduction followed by LogicNets (DNNs compiled into LUT logic) for ultra‑low‑latency inference.
• Differential‑evolution design exploration: An automated framework that searches the hardware‑accuracy trade‑off space to find optimal configurations.
• Area and latency gains: Demonstrates up to 10.95× reduction in silicon area and ≈30 % lower latency versus prior DNN‑based readout accelerators, with negligible fidelity loss.
• Scalable design methodology: Shows how the approach can be replicated across many qubits without exploding resource usage, paving the way for larger quantum chips.

Methodology

1. Signal Pre‑processing – The raw analog response from a superconducting qubit is first passed through a set of integrators that sum the signal over a short window. This reduces the high‑dimensional time‑series to a few compact features, requiring only a few adders and registers.
2. LogicNet Synthesis – A conventional feed‑forward DNN (trained offline on labeled readout data) is transformed into a network of Boolean LUTs using the LogicNet toolchain. Each neuron becomes a small combinational block that maps its quantized inputs directly to an output value, eliminating multiply‑accumulate units.
3. Design Space Exploration – A differential evolution algorithm iteratively mutates and recombines candidate hardware configurations (e.g., number of integrators, LUT depth, quantization bits) while evaluating both resource usage (FPGA/ASIC area) and classification fidelity. The best Pareto‑optimal points are selected for silicon implementation.
4. Hardware Prototyping & Evaluation – The final RTL is synthesized on a modern FPGA (or ASIC flow) and benchmarked against a state‑of‑the‑art DNN readout accelerator on the same qubit datasets.

Results & Findings

The results confirm that the LUT‑based neural net can retain the classification power of a full‑precision DNN while consuming a fraction of the hardware budget. The integrator front‑end contributes negligible overhead but provides enough discriminative information for the LogicNet to succeed.

Practical Implications

• Real‑time QEC loops – Sub‑microsecond latency enables the readout result to be fed back into error‑correction circuits before decoherence erodes the quantum state, a prerequisite for fault‑tolerant quantum computers.
• Edge‑style quantum controllers – Because LUNA fits comfortably on low‑cost FPGAs or ASICs, it can be embedded directly on cryogenic control boards, reducing the need for high‑bandwidth off‑chip communication.
• Scalable readout stacks – With area savings of an order of magnitude, a single chip can host readout engines for dozens (or hundreds) of qubits, simplifying system integration and lowering BOM costs.
• Developer‑friendly toolchain – The differential‑evolution framework and LogicNet synthesis can be reused for other quantum‑signal classification tasks (e.g., state‑tomography, leakage detection), allowing developers to experiment with custom DNN architectures without hand‑crafting RTL.

Limitations & Future Work

• Quantization sensitivity – While the LUT‑based network works well for the presented datasets, extreme quantization may degrade performance on noisier or higher‑dimensional readout signals; further robustness studies are needed.
• Cryogenic validation – Current evaluation is performed on room‑temperature FPGA prototypes. Real‑world deployment will require testing the accelerator’s behavior at cryogenic temperatures where device characteristics differ.
• Extension to other qubit modalities – The paper focuses on superconducting transmons; adapting LUNA to trapped‑ion or photonic readout pipelines may demand different preprocessing or network structures.
• Dynamic reconfiguration – Future versions could explore on‑the‑fly LUT updates to adapt to drift in qubit parameters, enabling self‑calibrating readout engines.

LUNA demonstrates that clever algorithm‑hardware co‑design—using simple integrators and LUT‑based neural nets—can meet the stringent latency and area constraints of quantum readout. For developers building the next generation of quantum control stacks, it offers a practical blueprint for embedding AI‑enhanced signal processing directly into the hardware loop.

Authors

• M. A. Farooq
• G. Di Guglielmo
• A. Rajagopala
• N. Tran
• V. A. Chhabria
• A. Arora

Paper Information

• arXiv ID: 2512.07808v1
• Categories: quant‑ph, cs.LG
• Published: December 8, 2025
• PDF: Download PDF