arXiv subjects

Swamit Tannu

Publications and source records attributed to Swamit Tannu.

At least 19 recordsLinked to original sources

arXiv2026

PropHunt: Automated Optimization of Quantum Syndrome Measurement Circuits

Fault-Tolerant Quantum Computing (FTQC) relies on Quantum Error Correction (QEC) codes to reach error rates necessary for large scale quantum applications. At a physical level, QEC codes perform parity checks on data qubits, producing syndrome information, through Syndrome Measurement (SM) circuits. These circuits define a code's logical error rate and must be run repeatedly throughout the entire program. The performance of SM circuits is therefore critical to the success of a FTQC system. While ultimately implemented as physical circuits, SM circuits have challenges that are not addressed by existing circuit optimization tools. Importantly, inside SM circuits themselves errors are expected to occur, and how errors propagate through SM circuits directly impacts which errors are detectable and correctable, defining the code's logical error rate. This is not modeled in NISQ-era tools, which instead optimize for targets such as gate depth or gate count to mitigate the chance that any error occurs. This gap leaves key questions unanswered about the expected real-world effectiveness of QEC codes. In this work we address this gap and present PropHunt, an automated tool for optimizing SM circuits for CSS codes. We evaluate PropHunt on a suite of relevant QEC codes and demonstrate PropHunt's ability to iteratively improve performance and recover existing hand-designed circuits automatically. We also propose a near-term QEC application, Hook-ZNE, which leverages PropHunt's fine-grained control over logical error rate to improve Zero-Noise Extrapolation (ZNE), a promising error mitigation strategy.

quant-ph↗

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

Generating Compilers for Qubit Mapping and Routing

To evaluate a quantum circuit on a quantum processor, one must find a mapping from circuit qubits to processor qubits and plan the instruction execution while satisfying the processor's constraints. This is known as the qubit mapping and routing (QMR) problem. High-quality QMR solutions are key to maximizing the utility of scarce quantum resources and minimizing the probability of logical errors affecting computation. The challenge is that the landscape of quantum processors is incredibly diverse and fast-evolving. Given this diversity, dozens of papers have addressed the QMR problem for different qubit hardware, connectivity constraints, and quantum error correction schemes by a developing a new algorithm for a particular context. We present an alternative approach: automatically generating qubit mapping and routing compilers for arbitrary quantum processors. Though each QMR problem is different, we identify a common core structure-device state machine-that we use to formulate an abstract QMR problem. Our formulation naturally leads to a compact domain-specific language for specifying QMR problems and a powerful parametric algorithm that can be instantiated for any QMR specification. Our thorough evaluation on case studies of important QMR problems shows that generated compilers are competitive with handwritten, specialized compilers in terms of runtime and solution quality.

cs.PL↗

arXiv2025

Synchronization for Fault-Tolerant Quantum Computers

Quantum Error Correction (QEC) codes store information reliably in logical qubits by encoding them in a larger number of less reliable qubits. The surface code, known for its high resilience to physical errors, is a leading candidate for fault-tolerant quantum computing (FTQC). Logical qubits encoded with the surface code can be in different phases of their syndrome generation cycle, thereby introducing desynchronization in the system. This can occur due to the production of non-Clifford states, dropouts due to fabrication defects, and the use of other QEC codes with the surface code to reduce resource requirements. Logical operations require the syndrome generation cycles of the logical qubits involved to be synchronized. This requires the leading qubit to pause or slow down its cycle, allowing more errors to accumulate before the next cycle, thereby increasing the risk of uncorrectable errors. To synchronize the syndrome generation cycles of logical qubits, we define three policies - Passive, Active, and Hybrid. The Passive policy is the baseline, and the simplest, wherein the leading logical qubits idle until they are synchronized with the remaining logical qubits. On the other hand, the Active policy aims to slow the leading logical qubits down gradually, by inserting short idle periods before multiple code cycles. This approach reduces the logical error rate (LER) by up to 2.4x compared to the Passive policy. The Hybrid policy further reduces the LER by up to 3.4x by reducing the synchronization slack and running a few additional rounds of error correction. Furthermore, the reduction in the logical error rate with the proposed synchronization policies enables a speedup in decoding latency of up to 2.2x with a circuit-level noise model.

quant-ph↗

arXiv2025

Iceberg Beyond the Tip: Co-Compilation of a Quantum Error Detection Code and a Quantum Algorithm

The rapid progress in quantum hardware is expected to make them viable tools for the study of quantum algorithms in the near term. The timeline to useful algorithmic experimentation can be accelerated by techniques that use many noisy shots to produce an accurate estimate of the observable of interest. One such technique is to encode the quantum circuit using an error detection code and discard the samples for which an error has been detected. An underexplored property of error-detecting codes is the flexibility in the circuit encoding and fault-tolerant gadgets, which enables their co-optimization with the algorthmic circuit. However, standard circuit optimization tools cannot be used to exploit this flexibility as optimization must preserve the fault-tolerance of the gadget. In this work, we focus on the $[[k+2, k, 2]]$ Iceberg quantum error detection code, which is tailored to trapped-ion quantum processors. We design new flexible fault-tolerant gadgets for the Iceberg code, which we then co-optimize with the algorithmic circuit for the quantum approximate optimization algorithm (QAOA) using tree search. By co-optimizing the QAOA circuit and the Iceberg gadgets, we achieve an improvement in QAOA success probability from $44\%$ to $65\%$ and an increase in post-selection rate from $4\%$ to $33\%$ at 22 algorithmic qubits, utilizing 330 algorithmic two-qubit gates and 744 physical two-qubit gates on the Quantinuum H2-1 quantum computer, compared to the previous state-of-the-art hardware demonstration. Furthermore, we demonstrate better-than-unencoded performance for up to 34 algorithmic qubits, employing 510 algorithmic two-qubit gates and 1140 physical two-qubit gates.

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

Reducing T Gates with Unitary Synthesis

Quantum error correction is essential for achieving practical quantum computing but has a significant computational overhead. Among fault-tolerant (FT) gate operations, non-Clifford gates, such as $T$, are particularly expensive due to their reliance on magic state distillation. These costly $T$ gates appear frequently in FT circuits as many quantum algorithms require arbitrary single-qubit rotations, such as $R_x$ and $R_z$ gates, which must be decomposed into a sequence of $T$ and Clifford gates. In many quantum circuits, $R_x$ and $R_z$ gates can be fused to form a single $U3$ unitary. However, existing synthesis methods, such as Gridsynth, rely on indirect decompositions, requiring separate $R_z$ decompositions that result in a threefold increase in $T$ count. This work presents TensoR-based Arbitrary unitary SYNthesis (trasyn), a novel FT synthesis algorithm that directly synthesizes arbitrary single-qubit unitaries, avoiding the overhead of separate $R_z$ decompositions. By leveraging tensor network-based search, our approach enables native $U3$ synthesis, reducing the $T$ count, Clifford gate count, and approximation error. Compared to Gridsynth-based circuit synthesis, for 187 representative benchmarks, our design reduces the T count by up to 3.5$\times$, and Clifford gates by 7$\times$, resulting in up to 4$\times$ improvement in overall circuit infidelity.

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

Optimizing Quantum Circuits, Fast and Slow

Optimizing quantum circuits is critical: the number of quantum operations needs to be minimized for a successful evaluation of a circuit on a quantum processor. In this paper we unify two disparate ideas for optimizing quantum circuits, rewrite rules, which are fast standard optimizer passes, and unitary synthesis, which is slow, requiring a search through the space of circuits. We present a clean, unifying framework for thinking of rewriting and resynthesis as abstract circuit transformations. We then present a radically simple algorithm, GUOQ, for optimizing quantum circuits that exploits the synergies of rewriting and resynthesis. Our extensive evaluation demonstrates the ability of GUOQ to strongly outperform existing optimizers on a wide range of benchmarks.

cs.PL↗

arXiv2024

Managing Classical Processing Requirements for Quantum Error Correction

Large-scale quantum computers promise transformative speedups, but their viability hinges on fast and reliable quantum error correction (QEC). At the center of QEC are decoders-classical algorithms running on hardware such as FPGAs, GPUs, or CPUs that process error syndromes to detect errors every microsecond to preserve fault-tolerance. Quantum processors, therefore, operate not in isolation, but as accelerators tightly coupled with powerful classical digital hardware. A key challenge is that decoder demand fluctuates unpredictably: bursts of activity can require orders of magnitude more decodes than idle periods. Provisioning hardware for the worst case wastes resources, while provisioning for the average case risks catastrophic slowdowns. We show that this mismatch is a systems problem of capacity planning and scheduling, and propose a two-level framework that treats decoders as shared accelerators managed by the quantum operating system. Our approach reduces decoder requirements by 10-40% across fault-tolerant benchmarks, demonstrating that efficient decoder scheduling is essential to making FTQC practical.

quant-ph↗

arXiv2024

Variational Quantum Algorithm Landscape Reconstruction by Low-Rank Tensor Completion

Variational quantum algorithms (VQAs) are a broad class of algorithms with many applications in science and industry. Applying a VQA to a problem involves optimizing a parameterized quantum circuit by maximizing or minimizing a cost function. A particular challenge associated with VQAs is understanding the properties of associated cost functions. Having the landscapes of VQA cost functions can greatly assist in developing and testing new variational quantum algorithms, but they are extremely expensive to compute. Reconstructing the landscape of a VQA using existing techniques requires a large number of cost function evaluations, especially when the dimension or the resolution of the landscape is high. To address this challenge, we propose a low-rank tensor-completion-based approach for local landscape reconstruction. By leveraging compact low-rank representations of tensors, our technique can overcome the curse of dimensionality and handle high-resolution landscapes. We demonstrate the power of landscapes in VQA development by showcasing practical applications of analyzing penalty terms for constrained optimization problems and examining the probability landscapes of certain basis states.

quant-ph↗

arXiv2024

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

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↗

arXiv2024

Exploiting many-body localization for scalable variational quantum simulation

Variational quantum algorithms (VQAs) represent a promising pathway toward achieving practical quantum advantage on near-term hardware. Despite this promise, for generic, expressive ans\"atze, their scalability is critically hindered by barren plateaus--regimes of exponentially vanishing gradients. We demonstrate that initializing a hardware-efficient, Floquet-structured ansatz within the many-body localized (MBL) phase mitigates barren plateaus and enhances algorithmic trainability. Through analysis of the inverse participation ratio, entanglement entropy, and a novel low-weight stabilizer R\'enyi entropy, we characterize a distinct MBL-thermalization transition. Below a critical kick strength, the circuit avoids forming a unitary 2-design, exhibits robust area-law entanglement, and maintains non-vanishing gradients. Leveraging this MBL regime facilitates the efficient variational preparation of ground states for several model Hamiltonians with significantly reduced computational resources. Crucially, experiments on a 127-qubit superconducting processor provide evidence for the preservation of trainable gradients in the MBL phase for a kicked Heisenberg chain, validating our approach on contemporary noisy hardware. Our findings position MBL-based initialization as a viable strategy for developing scalable VQAs and motivate broader integration of localization into quantum algorithm design.

quant-ph↗

arXiv2023

Dependency-Aware Compilation for Surface Code Quantum Architectures

Practical applications of quantum computing depend on fault-tolerant devices with error correction. Today, the most promising approach is a class of error-correcting codes called surface codes. We study the problem of compiling quantum circuits for quantum computers implementing surface codes. Optimal or near-optimal compilation is critical for both efficiency and correctness. The compilation problem requires (1) mapping circuit qubits to the device qubits and (2) routing execution paths between interacting qubits. We solve this problem efficiently and near-optimally with a novel algorithm that exploits the dependency structure of circuit operations to formulate discrete optimization problems that can be approximated via simulated annealing, a classic and simple algorithm. Our extensive evaluation shows that our approach is powerful and flexible for compiling realistic workloads.

quant-ph↗

arXiv2023

The Dirty Secret of SSDs: Embodied Carbon

Scalable Solid-State Drives (SSDs) have ushered in a transformative era in data storage and accessibility, spanning both data centers and portable devices. However, the strides made in scaling this technology can bear significant environmental consequences. On a global scale, a notable portion of semiconductor manufacturing relies on electricity derived from coal and natural gas sources. A striking example of this is the manufacturing process for a single Gigabyte of Flash memory, which emits approximately 0.16 Kg of CO2 - a considerable fraction of the total carbon emissions attributed to the system. Remarkably, the manufacturing of storage devices alone contributed to an estimated 20 million metric tonnes of CO2 emissions in the year 2021. In light of these environmental concerns, this paper delves into an analysis of the sustainability trade-offs inherent in Solid-State Drives (SSDs) when compared to traditional Hard Disk Drives (HDDs). Moreover, this study proposes methodologies to gauge the embodied carbon costs associated with storage systems effectively. The research encompasses four key strategies to enhance the sustainability of storage systems. In summation, this paper critically addresses the embodied carbon issues associated with SSDs, comparing them with HDDs, and proposes a comprehensive framework of strategies to enhance the sustainability of storage systems.

cs.AR↗

arXiv2023

Enabling High Performance Debugging for Variational Quantum Algorithms using Compressed Sensing

Variational quantum algorithms (VQAs) can potentially solve practical problems using contemporary Noisy Intermediate Scale Quantum (NISQ) computers. VQAs find near-optimal solutions in the presence of qubit errors by classically optimizing a loss function computed by parameterized quantum circuits. However, developing and testing VQAs is challenging due to the limited availability of quantum hardware, their high error rates, and the significant overhead of classical simulations. Furthermore, VQA researchers must pick the right initialization for circuit parameters, utilize suitable classical optimizer configurations, and deploy appropriate error mitigation methods. Unfortunately, these tasks are done in an ad-hoc manner today, as there are no software tools to configure and tune the VQA hyperparameters. In this paper, we present OSCAR (cOmpressed Sensing based Cost lAndscape Reconstruction) to help configure: 1) correct initialization, 2) noise mitigation techniques, and 3) classical optimizers to maximize the quality of the solution on NISQ hardware. OSCAR enables efficient debugging and performance tuning by providing users with the loss function landscape without running thousands of quantum circuits as required by the grid search. Using OSCAR, we can accurately reconstruct the complete cost landscape with up to 100X speedup. Furthermore, OSCAR can compute an optimizer function query in an instant by interpolating a computed landscape, thus enabling the trial run of a VQA configuration with considerably reduced overhead.

quant-ph↗