arXiv subjects

Arshpreet Singh Maan

Publications and source records attributed to Arshpreet Singh Maan.

5 recordsLinked to original sources

arXiv2026

A Scalable FPGA Architecture for Real-Time Decoding of Quantum LDPC Codes Using GARI

In this work, we introduce a new hardware architecture for decoding correlated errors in quantum LDPC codes. The decoder is based on message passing and exploits the structure of the detector error model obtained through the recently introduced Graph Augmentation and Rewiring for Inference (GARI) method. The proposed architecture enables flexible scaling and can, in principle, adapt to any quantum LDPC codes using the GARI framework. It leverages resource reuse while maintaining a modest degree of parallelism, thereby reducing power consumption and area requirements, while preserving low decoding latency. As a case study, the architecture was implemented on a VCU19P FPGA as an ensemble of three decoder cores targeting the [[144,12,12]] bivariate bicycle code, achieving an average latency of 596 ns per decoding round. This implementation consumes six times fewer resources than the previous GARI-based proposal, being the first reported implementation of multiple decoder cores for correlated errors on a single FPGA device. This enables better energy-conscious scaling of the quantum error correction layer on the classical side, reducing overall power consumption while meeting real-time constraints without compromising decoding accuracy under correlated errors.

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arXiv2026

Superconducting qubits in the millions: the potential and limitations of modularity

The development of fault-tolerant quantum computers (FTQCs) is receiving increasing attention within the quantum computing community. Like conventional digital computers, FTQCs, which utilize error correction and millions of physical qubits, have the potential to address some of humanity's grand challenges. However, accurate estimates of the tangible scale of future FTQCs, based on transparent assumptions, are uncommon. How many physical qubits are necessary to solve a practical problem intractable for classical hardware? What costs arise from distributing quantum computation across multiple machines? This paper presents an architectural model of a potential FTQC based on superconducting qubits, divided into discrete modules and interconnected via coherent links. We employ a resource estimation framework and software tool to assess the physical resources required to execute specific quantum algorithms compiled into their graph-state form and arranged onto a modular superconducting hardware architecture. Our tool can predict the size, power consumption, and execution time of these algorithms based on explicit assumptions about the system's physical layout, thermal load, and modular connectivity. We assess the resources needed for quantum computation examples that serve as building blocks of proposed applications, quantifying the architectural bottlenecks and trade-offs that remain to be addressed to deliver utility.

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arXiv2026

Decoding Correlated Errors in Quantum LDPC Codes

We introduce a decoding framework for correlated errors in quantum LDPC codes under circuit-level noise. The core of our approach is a graph augmentation and rewiring for interference (GARI) method, which modifies the correlated detector error model by eliminating 4-cycles involving Y-type errors, while preserving the equivalence of the decoding problem. We test our approach on the bivariate bicycle codes of distances 6, 10, and 12. A normalized min-sum decoder with a hybrid serial-layered schedule is applied on the transformed graph, achieving high accuracy with low latency. Performance is further enhanced through ensemble decoding, where 24 randomized normalized min-sum decoders run in parallel on the transformed graph, yielding the highest reported accuracy (on par with XYZ-Relay-BP) with unprecedented speed for the tested codes under uniform depolarizing circuit level noise. For the distance 12 (gross) code, our approach yields a logical error rate of $(6.70 \pm 1.93) \times 10^{-9}$ at a practical physical error rate of $10^{-3}$. Furthermore, preliminary FPGA implementation results show that such high accuracy can be achieved in real time, with a per-round average decoding latency of 273 ns and sub-microsecond latency in 99.99% of the decoding instances.

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arXiv2024

Machine Learning Message-Passing for the Scalable Decoding of QLDPC Codes

We present Astra, a novel and scalable decoder using graph neural networks. Our decoder works similarly to solving a Sudoku puzzle of constraints represented by the Tanner graph. In general, Quantum Low Density Parity Check (QLDPC) decoding is based on Belief Propagation (BP, a variant of message-passing) and requires time intensive post-processing methods such as Ordered Statistics Decoding (OSD). Without using any post-processing, Astra achieves higher thresholds and better logical error rates when compared to BP+OSD, both for surface codes trained up to distance 11 and Bivariate Bicycle (BB) codes trained up to distance 18. Moreover, we can successfully extrapolate the decoding functionality: we decode high distances (surface code up to distance 25 and BB code up to distance 34) by using decoders trained on lower distances. Astra+OSD is faster than BP+OSD. We show that with decreasing physical error rates, Astra+OSD makes progressively fewer calls to OSD when compared to BP+OSD, even in the context of extrapolated decoding. Astra(+OSD) achieves orders of magnitude lower logical error rates for BB codes compared to BP(+OSD). The source code is open-sourced at \url{https://github.com/arshpreetmaan/astra}.

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arXiv2023

Testing the Accuracy of Surface Code Decoders

Large-scale, fault-tolerant quantum computations will be enabled by quantum error-correcting codes (QECC). This work presents the first systematic technique to test the accuracy and effectiveness of different QECC decoding schemes by comparing a look-up table decoder to solutions generated using algorithmic decoders. Specifically, we examine the results of minimum-weight-perfect-matching and belief-propagation decoders against exhaustive look-up tables for surface codes up to distance seven and categorise where errors are accurately corrected in both decoding schemes. While our results are preliminary, we show that significant quantitative results can be generated, comparing how actual error channels are successfully or unsuccessfully decoded. We show that different decoding schemes perform very differently under the same QECC scheme and error model, and detail how decoders can be tested and classified with respect to errors that are successfully decodable. This work paves the way to the data driven tuning of decoder ensembles and will enable tailored design of hybrid decoding schemes that allow for real-time decoding, while maintaining the high theoretical thresholds allowed by specific quantum error correction codes.

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