arXiv subjects

Chaithanya Naik Mude

Publications and source records attributed to Chaithanya Naik Mude.

7 recordsLinked to original sources

arXiv2025

Enabling Fast and Accurate Neutral Atom Readout through Image Denoising

Neutral atom quantum computers hold promise for scaling up to hundreds of thousands or more qubits, but their progress is constrained by slow qubit readout. Parallel measurement of qubit arrays currently takes milliseconds, much longer than the underlying quantum gate operations-making readout the primary bottleneck in deploying quantum error correction. Because each round of QEC depends on measurement, long readout times increase cycle duration and slow down program execution. Reducing the readout duration speeds up cycles and reduces decoherence errors that accumulate while qubits idle, but it also lowers the number of collected photons, making measurements noisier and more error-prone. This tradeoff leaves neutral atom systems stuck between slow but accurate readout and fast but unreliable readout. We show that image denoising can resolve this tension. Our framework, GANDALF, uses explicit denoising using image translation to reconstruct clear signals from short, low-photon measurements, enabling reliable classification at up to 1.6x shorter readout times. Combined with lightweight classifiers and a pipelined readout design, our approach both reduces logical error rate by up to 35x and overall QEC cycle time up to 1.77x compared to state-of-the-art convolutional neural network (CNN)-based readout for Cesium (Cs) Neutral Atom arrays.

quant-ph↗

arXiv2025

Accurate Leakage Speculation for Quantum Error Correction

Quantum Error Correction (QEC) protects qubits against bit- and phase-flip errors in the |0> or |1> subspace, but physical qubits can also leak into higher energy levels (e.g., |2>). Leakage is especially harmful, as it corrupts all subsequent syndrome measurements and can spread to neighboring qubits. Detecting leakage on data qubits is particularly challenging, since they are never measured directly during QEC cycles. Prior work, such as eraser, addresses this by inferring leakage from syndrome patterns using a fixed heuristic. However, this approach often misclassifies benign syndromes, triggering excessive leakage-reduction circuits (LRCs). Because LRCs are themselves noisy and slow, these false triggers lengthen QEC cycles and inflate logical error rates. We propose gladiator, a general and adaptable leakage speculation framework that works across surface code, color code, and qLDPC codes. Offline, gladiator builds a code-aware error-propagation graph calibrated to device data. Online, it classifies each syndrome in a few nanoseconds and schedules LRC only when the observed pattern is provably leakage-dominated. This precise speculation eliminates up to 3x (and on average 2x) unnecessary LRCs, shortens QEC cycles, and suppresses false positives at their source. Evaluated on standard fault-tolerant benchmarks, gladiator delivers 1.7x-3.9x speedups and 16% reduction in logical error rate, advancing the efficiency of fault-tolerant quantum computing.

quant-ph↗

arXiv2025

Efficient measurement of neutral-atom qubits with matched filters

Quantum computers require high-fidelity measurement of many qubits to achieve a quantum advantage. Traditional approaches suffer from readout crosstalk for a neutral-atom quantum processor with a tightly spaced array. Although classical machine learning algorithms based on convolutional neural networks can improve fidelity, they are computationally expensive, making it difficult to scale them to large qubit counts. We present two simpler and scalable machine learning algorithms that realize matched filters for the readout problem. One is a local model that focuses on a single qubit, and the other uses information from neighboring qubits in the array to prevent crosstalk among the qubits. We demonstrate error reductions of up to 32% and 43% for the site and array models, respectively, compared to a conventional Gaussian threshold approach. Additionally, our array model uses two orders of magnitude fewer trainable parameters and four orders of magnitude fewer multiplications and nonlinear function evaluations than a recent convolutional neural network approach, with only a minor (3.5%) increase in error across different readout times. Another strength of our approach is its physical interpretability: the learned filter can be visualized to provide insights into experimental imperfections. We also show that a convolutional neural network model for improved can be pruned to have 70x and 4000x fewer parameters, respectively, while maintaining similar errors. Our work shows that simple machine learning approaches can achieve high-fidelity qubit measurements while remaining scalable to systems with larger qubit counts.

quant-ph↗

arXiv2025

Efficient and Scalable Architectures for Multi-Level Superconducting Qubit Readout

Realizing the full potential of quantum computing requires large-scale quantum computers capable of running quantum error correction (QEC) to mitigate hardware errors and maintain quantum data coherence. While quantum computers operate within a two-level computational subspace, many processor modalities are inherently multi-level systems. This leads to occasional leakage into energy levels outside the computational subspace, complicating error detection and undermining QEC protocols. The problem is particularly severe in engineered qubit devices like superconducting transmons, a leading technology for fault-tolerant quantum computing. Addressing this challenge requires effective multi-level quantum system readout to identify and mitigate leakage errors. We propose a scalable, high-fidelity three-level readout that reduces FPGA resource usage by $60\times$ compared to the baseline while reducing readout time by 20\%, enabling faster leakage detection. By employing matched filters to detect relaxation and excitation error patterns and integrating a modular lightweight neural network to correct crosstalk errors, the protocol significantly reduces hardware complexity, achieving a $100\times$ reduction in neural network size. Our design supports efficient, real-time implementation on off-the-shelf FPGAs, delivering a 6.6\% relative improvement in readout accuracy over the baseline. This innovation enables faster leakage mitigation, enhances QEC reliability, and accelerates the path toward fault-tolerant quantum computing.

quant-ph↗

arXiv2024

Crosstalk-induced Side Channel Threats in Multi-Tenant NISQ Computers

As quantum computing rapidly advances, its near-term applications are becoming increasingly evident. However, the high cost and under-utilization of quantum resources are prompting a shift from single-user to multi-user access models. In a multi-tenant environment, where multiple users share one quantum computer, protecting user confidentiality becomes crucial. The varied uses of quantum computers increase the risk that sensitive data encoded by one user could be compromised by others, rendering the protection of data integrity and confidentiality essential. In the evolving quantum computing landscape, it is imperative to study these security challenges within the scope of realistic threat model assumptions, wherein an adversarial user can mount practical attacks without relying on any heightened privileges afforded by physical access to a quantum computer or rogue cloud services. In this paper, we demonstrate the potential of crosstalk as an attack vector for the first time on a Noisy Intermediate Scale Quantum (NISQ) machine, that an adversarial user can exploit within a multi-tenant quantum computing model. The proposed side-channel attack is conducted with minimal and realistic adversarial privileges, with the overarching aim of uncovering the quantum algorithm being executed by a victim. Crosstalk signatures are used to estimate the presence of CNOT gates in the victim circuit, and subsequently, this information is encoded and classified by a graph-based learning model to identify the victim quantum algorithm. When evaluated on up to 336 benchmark circuits, our attack framework is found to be able to unveil the victim's quantum algorithm with up to 85.7\% accuracy.

cs.ET↗

arXiv2024

Understanding Side-Channel Vulnerabilities in Superconducting Qubit Readout Architectures

Frequency-multiplexing is an effective method to achieve resource-efficient superconducting qubit readout. Allowing multiple resonators to share a common feedline, the number of cables and passive components involved in the readout of a qubit can be drastically reduced. However, this improvement in scalability comes at the price of a crucial non-ideality -- an increased readout crosstalk. Prior works have targeted building better devices and discriminators to reduce its effects, as readout-crosstalk-induced qubit measurement errors are detrimental to the reliability of a quantum computer. However, in this work, we show that beyond the reliability of a system, readout crosstalk can introduce vulnerabilities in a system being shared among multiple users. These vulnerabilities are directly related to correlated errors due to readout crosstalk. These correlated errors can be exploited by nefarious attackers to predict the state of the victim qubits, resulting in information leakage.

quant-ph↗

arXiv2023

Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures

Reading a qubit is a fundamental operation in quantum computing. It translates quantum information into classical information enabling subsequent classification to assign the qubit states `0' or `1'. Unfortunately, qubit readout is one of the most error-prone and slowest operations on a superconducting quantum processor. On state-of-the-art superconducting quantum processors, readout errors can range from 1-10%. High readout accuracy is essential for enabling high fidelity for near-term noisy quantum computers and error-corrected quantum computers of the future. Prior works have used machine-learning-assisted single-shot qubit-state classification, where a deep neural network was used for more robust discrimination by compensating for crosstalk errors. However, the neural network size can limit the scalability of systems, especially if fast hardware discrimination is required. This state-of-the-art baseline design cannot be implemented on off-the-shelf FPGAs used for the control and readout of superconducting qubits in most systems, which increases the overall readout latency as discrimination has to be performed in software. In this work, we propose HERQULES, a scalable approach to improve qubit-state discrimination by using a hierarchy of matched filters in conjunction with a significantly smaller and scalable neural network for qubit-state discrimination. We achieve substantially higher readout accuracies (16.4% relative improvement) than the baseline with a scalable design that can be readily implemented on off-the-shelf FPGAs. We also show that HERQULES is more versatile and can support shorter readout durations than the baseline design without additional training overheads.

quant-ph↗