arXiv subjects

Sean Garner

Publications and source records attributed to Sean Garner.

9 recordsLinked to original sources

arXiv2026

Early Fault-Tolerant Magic State Synthesis and Spacetime Efficient Analog Rotations with Color Code on a Superconducting Quantum Processor

With recent improvements to syndrome extraction scheduling and decoding, and the availability of complete transversal Clifford operations and lattice surgery, color code is an especially promising platform for quantum processors with local connectivity. The key ingredient for universal computation is thus generating useful magic states with limited resources. Space-Time Efficient Analog Rotations (STAR) and Fault-tolerant magic state synthesis (MSS) are two such methods for magic state generation, targeted for near-term and long-term quantum computing, respectively. In this work, we perform the first implementations of both magic state paradigms on a superconducting quantum processor. We present a STAR-type injection protocol on the color code, with favorable error rate and spacetime costs versus prior protocols on both the color and surface codes. We then implement a MSS factory by performing multi-patch magic state cultivations, interleaved with transversal Clifford operations, and lattice-surgery magic state teleportation. Under post-selection, we achieve a kickback tomography correct sign probability of $99.9859\%$ on cultivated $T_L$ states, $96\%$ tomographic fidelity on logical analog rotation states, and $91\%$ fidelity on synthesized logical magic states consuming 3 cultivated $T_L$ states, 3 transversal Hadamard operations, and real-time feed-forward $S_L$ corrections during magic state teleportation. We compare the efficacy of post-selection, Most-Likely-Error (MLE) decoding, and concatenated-matching decoding throughout, highlighting the potential of end-to-end color code computation.

quant-ph↗

arXiv2026

Demonstration of Logical Error Suppression in a Surface Code Memory on a Cloud-Accessible IBM Superconducting Processor

Quantum error correction can suppress logical errors exponentially with code distance when physical noise is below threshold. Realizing this on hardware requires tight coordination among quantum gates, mid-circuit measurements, and qubit coherence. Device variation, noise asymmetry, and correlations across space and time complicate the translation of these capabilities into logical error suppression. Here, we study rotated surface-code memory on an IBM Nighthawk r2 processor. Performance varies across tested processor placements, which we attribute to differences in qubit quality. We optimize the code by combining short readout, fused measurement and reset, and an XX echo on data qubits during ancilla readout. Compared with standard readout without reset or echo, the optimized syndrome extraction circuit reduces the median per-round logical error rate by factors of 3.8 in the X basis and 1.4 in the Z basis. In memory experiments of up to 150 rounds, the distance advantage weakens, alongside long-lived correlations that may reflect a persistent error component. Under our optimization recipe, increasing the protected distance from 3 to 6 while holding the other distance fixed reduces the per-round logical error rate from 0.53% to 0.24% in Z memory and from 0.52% to 0.22% in X memory. The corresponding error-suppression factors under maximum-likelihood decoding are 1.68 and 1.85. These results connect circuit compilation and optimization to surface-code memory performance and limitations, and motivate QPU and QEC circuit codesign for fault-tolerant quantum computing primitives on current cloud hardware.

quant-ph↗

arXiv2026

Decoder Model Compatibility Provides Information beyond the Logical Gap under Drifting and Correlated Quantum Noise

Quantum error-correcting decoders provide both corrections and confidence estimates for the logical-error risk of individual measurement records. Such confidence can guide postselection, but it depends on the accuracy of the decoder noise model. When the physical device drifts away from that model, confidence measures such as the logical gap can become miscalibrated. We show that this mismatch can itself provide useful information. On IBM quantum hardware, syndrome statistics drift across consecutive acquisition windows, and the resulting detector histories differ substantially from those predicted by the stationary detector error model used for decoding. We observe a similar hardware-model mismatch in independent Google surface-code memory data. We exploit this effect by assigning additional rejection weight to detector histories that are unlikely under the assumed model. For minimum-weight perfect matching (MWPM), the minimum weight $W$ provides an inexpensive proxy for syndrome surprisal. On held-out IBM repetition-code memories, likelihood-aware postselection reduces the retained logical-error probability (LEP) by $10.6\%$ at $d=7$ and $28.8\%$ at $d=9$, both at $15\%$ rejection. On Google ten-round $d=5$ XZZX surface-code memory data, it reduces retained LEP by $75.4\%$ at $90\%$ rejection. Applied to Google's magic-state cultivation experiment with the Tesseract decoder, it reduces tomography infidelity by $34.7\%$ with $30\%$ additional grafting-stage rejection, or increases accepted yield by $30.4\%$ at a target fidelity of $97.0\%$. Burst-noise simulations show that the advantage persists as code distance and syndrome-extraction volume increase. The method reuses quantities already produced during logical-gap decoding and requires only an additional fitting step.

quant-ph↗

arXiv2026

FTPrimitiveBench: A Benchmark Suite For Logical Computation Under Hardware-Motivated and Biased Noise Models

Fault-tolerant quantum computing requires understanding how error-correcting codes perform on diverse physical hardware. This is typically assessed via noisy stabilizer simulation of logical circuits at HPC scale, combined with a noise model that yields a logical error rate for the relevant code distances and depths. The uniform depolarizing model is the standard baseline, but its homogeneous assumptions fail to capture the heterogeneity, asymmetries, and correlations of real devices, where Pauli, measurement, and spatio-temporal errors are not weakly coupled. Yet these same structured features create opportunities for joint code-hardware co-design, motivating noise models that more faithfully reflect target hardware while remaining tractable to simulate. We introduce FTPrimitiveBench, a systematic benchmarking approach for studying how logical primitives interact with hardware-motivated noise. It supports both custom specifications and representative structured noise families: Pauli bias, measurement bias, and spatial or spatio-temporal non-uniformity -- together with generators for core surface-code Clifford primitives: logical memory, lattice surgery, transversal logical Hadamard, and the logical phase gate via lattice surgery. We find that structured noise affects these primitives in qualitatively distinct ways, with outcomes shaped by the interplay between noise model, primitive, and decoder choice. These results extend memory benchmarks to active logical computation, where the interaction between noise structure and primitive implementation matters. By standardizing the link between noise-model specification and primitive construction, FTPrimitiveBench enables reproducible comparative studies of QEC protocols and decoders, supporting hardware-aware co-design of fault-tolerant architectures. Code: https://github.com/ShuwenKan/FTPrimitiveBench.

quant-ph↗

arXiv2026

Calibration-Conditioned FiLM Decoders for Low-Latency Decoding of Quantum Error Correction Evaluated on IBM Repetition-Code Experiments

Real-time decoding of quantum error correction (QEC) is essential for enabling fault-tolerant quantum computation. A practical decoder must operate with high accuracy at low latency, while remaining robust to spatial and temporal variations in hardware noise. We introduce a hardware-conditioned neural decoder framework designed to exploit the natural separation of timescales in superconducting processors, where calibration drifts occur over hours while error correction requires microsecond-scale responses. By processing calibration data through a graph-based encoder and conditioning a lightweight convolutional backbone via feature-wise linear modulation (FiLM), we decouple the heavy processing of device statistics from the low-latency syndrome decoding. We evaluate this approach using the 1D repetition code as a testbed on IBM Fez, Kingston, and Pittsburgh processors, collecting over 2.7 million experimental shots spanning distances up to d = 11. We demonstrate that a single trained model generalizes to unseen qubit chains and new calibration data acquired days later without retraining. On these unseen experiments, the FiLM-conditioned decoder achieves up to an 11.1x reduction in logical error rate relative to modified minimum-weight perfect matching. We observe that by employing a network architecture that exploits the highly asynchronous nature of system calibration and decoding, hardware-conditioned neural decoding demonstrates promising, adaptive performance with negligible latency overhead relative to unconditioned baselines.

quant-ph↗

arXiv2026

Implementation of Tensor Network Simulation TN-Sim under NWQ-Sim

Large-scale tensor network simulations are crucial for developing robust complexity-theoretic bounds on classical quantum simulation, enabling circuit cutting approaches, and optimizing circuit compilation, all of which aid efficient quantum computation on limited quantum resources. Modern exascale high-performance computing platforms offer significant potential for advancing tensor network quantum circuit simulation capabilities. We implement TN-Sim, a tensor network simulator backend within the NWQ-Sim software package that utilizes the Tensor Algebra for Many-body Methods (TAMM) framework to support both distributed HPC-scale computations and local simulations with ITensor. To optimize the scale up in computation across multiple nodes we implement a task based parallelization scheme to demonstrate parallelized gate contraction for wide quantum circuits with many gates per layer. Through the integration of the TAMM framework with Matrix Product State (MPS) tensor network approaches, we deliver a simulation environment that can scale from local systems to HPC clusters. We demonstrate an MPS tensor network simulator running on the state-of-the-art Perlmutter (NVIDIA) supercomputer and discuss the potential portability of this software to HPC clusters such as Frontier (AMD) and Aurora (Intel). We also discuss future improvements including support for different tensor network topologies and enhanced computational efficiency.

quant-ph↗

arXiv2025

Tableau-Based Framework for Efficient Logical Quantum Compilation

Quantum computing holds the promise of solving problems intractable for classical computers, but practical large-scale quantum computation requires error correction to protect against errors. Fault-tolerant quantum computing (FTQC) enables reliable execution of quantum algorithms, yet they often demand substantial physical qubit overhead. Resource-efficient FTQC architectures minimize the number of physical qubits required, saving more than half compared to other architectures, but impose constraints that introduce up to 4.7$\times$ higher runtime overhead. In this paper, we present TQC, a \underline{T}ableau-based \underline{Q}uantum \underline{C}ompiler framework that minimizes FTQC runtime overhead without requiring additional physical qubits. By leveraging operation reorderability and latency hiding through parallel execution, TQC reduces FTQC runtime overhead by \textbf{2.57$\times$} on average. Furthermore, FTQC circuits often contain millions of gates, leading to substantial compilation overhead. To address this, we optimize the core data structure, the tableau, used in stabilizer formalism. We provide two tailored versions of the Tableau data type, each designed for different usage scenarios. These optimizations yield an overall performance improvement of more than \textbf{1000$\times$} compared to state-of-the-art FTQC optimization tools.

quant-ph↗

arXiv2025

STABSim: A Parallelized Clifford Simulator with Features Beyond Direct Simulation

The quantum stabilizer formalism became foundational for understanding error correction soon after the realization of the first useful quantum error correction codes. Stabilizers provide a way to describe sets of quantum states which are valid codewords within a quantum error correction (QEC) scheme. Existing stabilizer simulators are single threaded applications used to sample larger codes than is possible with other methods. However, there is an outstanding gap in the scaling and accuracy of current simulators for QEC as quantum computing exceeds hundreds of qubits, along with an under-utilization of the capabilities of highly-efficient stabilizer simulation across other quantum domains. In this work, we present the first GPU-accelerated tableau stabilizer simulator to scale better than CPU methods in QEC workloads, by trivializing Clifford gates and exploiting the large parallelism of dedicated GPUs with CUDA warp-level primitives to quickly overcome costly measurement gates. We then implement a new error model that captures non-unitarity in T1/T2 error channels much faster and with exact accuracy for most physical qubits, demonstrate a chemistry use case, and present a new Clifford+T to Pauli-Based Computing (PBC) transpilation optimization through our simulator.

quant-ph↗

arXiv2024

A MultiWavelength Study of the Symbiotic Mira HM Sge with SOFIA and HST

We have targeted the dusty symbiotic mira system HM Sge with four instruments from the IR to the UV. We have used these observations along with archival observations to study how the system has been evolving after its 1975 nova-like outburst. We have detected ro-vibrational water emission in a symbiotic system for the first time using new EXES high spectral resolution infrared spectroscopy. The features, detected in emission, have velocities consistent with the systemic velocity but do not show any clear evidence of high velocity outflows. Mid-infrared photometry and grism spectroscopy show that the oxygen-rich Asymptotic Giant Branch (AGB) dust and dust output has shown little to no change over the past 39 years. In the optical/UV, we detect three main [NII] nebular features that were detected 22 years ago. Two of these features show a small amount of movement corresponding to average outflows speeds of 38 kms and 78 kms since they were previously observed; some previously detected [NII] features are no longer visible. New UV spectroscopy has shown that the nebular environment continues to steadily relax after the system's 1975 outburst. The data suggest however, that the hot component has increased in temperature from 200,000 K in 1989 to now greater than 250,000 K. Our new and archival observations suggest that the evolution of the system after its outburst is swift with little to no major changes after a period of a couple years.

astro-ph.SR↗