arXiv subjects

Suhas Vittal

Publications and source records attributed to Suhas Vittal.

7 recordsLinked to original sources

arXiv2025

BARD: Reducing Write Latency of DDR5 Memory by Exploiting Bank-Parallelism

This paper studies the impact of DRAM writes on DDR5-based system. To efficiently perform DRAM writes, modern systems buffer write requests and try to complete multiple write operations whenever the DRAM mode is switched from read to write. When the DRAM system is performing writes, it is not available to service read requests, thus increasing read latency and reducing performance. We observe that, given the presence of on-die ECC in DDR5 devices, the time to perform a write operation varies significantly: from 1x (for writes to banks of different bankgroups) to 6x (for writes to banks within the same bankgroup) to 24x (for conflicting requests to the same bank). If we can orchestrate the write stream to favor write requests that incur lower latency, then we can reduce the stall time from DRAM writes and improve performance. However, for current systems, the write stream is dictated by the cache replacement policy, which makes eviction decisions without being aware of the variable latency of DRAM writes. The key insight of our work is to improve performance by modifying the cache replacement policy to increase bank-parallelism of DRAM writes. Our paper proposes {\em BARD (Bank-Aware Replacement Decisions)}, which modifies the cache replacement policy to favor dirty lines that belong to banks without pending writes. We analyze two variants of BARD: BARD-E (Eviction-based), which changes the eviction policy to evict low-cost dirty lines, and BARD-C (Cleansing-Based), which proactively cleans low-cost dirty lines without modifying the eviction decisions. We develop a hybrid policy (BARD-H), which uses a selective combination of both eviction and writeback. Our evaluations across workloads from SPEC2017, LIGRA, STREAM, and Google server traces show that BARD-H improves performance by 4.3\% on average and up-to 8.5\%. BARD requires only 8 bytes of SRAM per LLC slice.

cs.AR↗

arXiv2025

Moveless: Minimizing Overhead on QCCDs via Versatile Execution and Low Excess Shuttling

One of the most promising paths towards large scale fault tolerant quantum computation is the use of quantum error correcting stabilizer codes. Just like every other quantum circuit, these codes must be compiled to hardware in a way to minimize the total physical error introduced into the system, for example either due to high latency execution or excessive gates to meet connectivity limitations of the target hardware. However, unlike arbitrary quantum circuits, all syndrome extraction circuits have several common properties, for example they have a bipartite connectivity graph, consist only of commuting subcircuits, among other properties. For the most part, compilation methods have aimed at being generic, able to map any input circuit into executables on the hardware, and therefore cannot appropriately exploit these properties and result in executables which have higher physical error. In the case of modular trapped ion systems, specifically QCCDs, this corresponds to the insertion of excessive shuttling operations necessary to realize arbitrary qubit interactions. We propose a compilation scheme explicitly tailored for the structural regularity of QEC circuits based on several key observations: 1. only ancilla or data (but not both) should be shuttled, 2. stabilizers can be executed in any order meaning we can dynamically modify circuit execution on a per-cycle basis 3. ancilla are indistinguishable meaning any can be selected to begin a stabilizer measurement and retain a fixed-point mapping between cycles, and 4. QCCD hardware limits the number of parallel operations equal to the number traps in the system, meaning fewer ancilla are necessary and can be reused. Our resulting compiler, leads to QEC circuits which are on average 3.38x faster to execute, and lead to up to two orders of magnitude of improvement in logical error rates with realistic physical error rates.

quant-ph↗

arXiv2025

Variational Quantum Algorithms in the era of Early Fault Tolerance

Quantum computing roadmaps predict the availability of 10,000 qubit devices within the next 3-5 years. With projected two-qubit error rates of 0.1%, these systems will enable certain operations under quantum error correction (QEC) using lightweight codes, offering significantly improved fidelities compared to the NISQ era. However, the high qubit cost of QEC codes like the surface code (especially at near-threshold physical error rates) limits the error correction capabilities of these devices. In this emerging era of Early Fault Tolerance (EFT), it will be essential to use QEC resources efficiently and focus on applications that derive the greatest benefit. In this work, we investigate the implementation of Variational Quantum Algorithms in the EFT regime (EFT-VQA). We introduce partial error correction (pQEC), a strategy that error-corrects Clifford operations while performing Rz rotations via magic state injection instead of the more expensive T-state distillation. Our results show that pQEC can improve VQA fidelities by 9.27x over standard approaches. Furthermore, we propose architectural optimizations that reduce circuit latency by ~2x, and achieve qubit packing efficiency of 66% in the EFT regime.

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arXiv2024

Flag Proxy Networks: Tackling the Architectural, Scheduling, and Decoding Obstacles of Quantum LDPC codes

Quantum error correction is necessary for achieving exponential speedups on important applications. The planar surface code has remained the most studied error-correcting code for the last two decades because of its relative simplicity. However, encoding a singular logical qubit with the planar surface code requires physical qubits quadratic in the code distance~($d$), making it space-inefficient for the large-distance codes necessary for promising applications. Thus, {\em Quantum Low-Density Parity-Check (QLDPC)} have emerged as an alternative to the planar surface code but require a higher degree of connectivity. Furthermore, the problems of fault-tolerant syndrome extraction and decoding are understudied for these codes and also remain obstacles to their usage. In this paper, we consider two under-studied families of QLDPC codes: hyperbolic surface codes and hyperbolic color codes. We tackle the three challenges mentioned above as follows. {\em First}, we propose {\em Flag-Proxy Networks (FPNs)}, a generalizable architecture for quantum codes that achieves low connectivity through flag and proxy qubits. {\em Second}, we propose a {\em greedy syndrome extraction scheduling} algorithm for general quantum codes and further use this algorithm for fault-tolerant syndrome extraction on FPNs. {\em Third}, we present two decoders that leverage flag measurements to decode the hyperbolic codes accurately. Our work finds that degree-4 FPNs of the hyperbolic surface and color codes are respectively $2.9\times$ and $5.5\times$ more space-efficient than the $d = 5$ planar surface code, and become even more space-efficient when considering higher distances. The hyperbolic codes also have error rates comparable to their planar counterparts.

quant-ph↗

arXiv2024

Promatch: Extending the Reach of Real-Time Quantum Error Correction with Adaptive Predecoding

Fault-tolerant quantum computing relies on Quantum Error Correction, which encodes logical qubits into data and parity qubits. Error decoding is the process of translating the measured parity bits into types and locations of errors. To prevent a backlog of errors, error decoding must be performed in real-time. Minimum Weight Perfect Matching (MWPM) is an accurate decoding algorithm for surface code, and recent research has demonstrated real-time implementations of MWPM (RT-MWPM) for a distance of up to 9. Unfortunately, beyond d=9, the number of flipped parity bits in the syndrome, referred to as the Hamming weight of the syndrome, exceeds the capabilities of existing RT-MWPM decoders. In this work, our goal is to enable larger distance RT-MWPM decoders by using adaptive predecoding that converts high Hamming weight syndromes into low Hamming weight syndromes, which are accurately decoded by the RT-MWPM decoder. An effective predecoder must balance both accuracy and coverage. In this paper, we propose Promatch, a real-time adaptive predecoder that predecodes both simple and complex patterns using a locality-aware, greedy approach. Our approach ensures two crucial factors: 1) high accuracy in prematching flipped bits, ensuring that the decoding accuracy is not hampered by the predecoder, and 2) enough coverage adjusted based on the main decoder's capability given the time constraints. Promatch represents the first real-time decoding framework capable of decoding surface codes of distances 11 and 13, achieving an LER of $2.6\times 10^{-14}$ for distance 13. Moreover, we demonstrate that running Promatch concurrently with the recently proposed Astrea-G achieves LER equivalent to MWPM LER, $3.4\times10^{-15}$, for distance 13, representing the first real-time accurate decoder for up-to a distance of 13.

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arXiv2023

ERASER: Towards Adaptive Leakage Suppression for Fault-Tolerant Quantum Computing

Quantum error correction (QEC) codes can tolerate hardware errors by encoding fault-tolerant logical qubits using redundant physical qubits and detecting errors using parity checks. Leakage errors occur in quantum systems when a qubit leaves its computational basis and enters higher energy states. These errors severely limit the performance of QEC due to two reasons. First, they lead to erroneous parity checks that obfuscate the accurate detection of errors. Second, the leakage spreads to other qubits and creates a pathway for more errors over time. Prior works tolerate leakage errors by using leakage reduction circuits (LRCs) that modify the parity check circuitry of QEC codes. Unfortunately, naively using LRCs always throughout a program is sub-optimal because LRCs incur additional two-qubit operations that (1) facilitate leakage transport, and (2) serve as new sources of errors. Ideally, LRCs should only be used if leakage occurs, so that errors from both leakage as well as additional LRC operations are simultaneously minimized. However, identifying leakage errors in real-time is challenging. To enable the robust and efficient usage of LRCs, we propose ERASER that speculates the subset of qubits that may have leaked and only uses LRCs for those qubits. Our studies show that the majority of leakage errors typically impact the parity checks. We leverage this insight to identify the leaked qubits by analyzing the patterns in the failed parity checks. We propose ERASER+M that enhances ERASER by detecting leakage more accurately using qubit measurement protocols that can classify qubits into $|0\rangle, |1\rangle$ and $|L\rangle$ states. ERASER and ERASER+M improve the logical error rate by up to $4.3\times$ and $23\times$ respectively compared to always using LRC.

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arXiv2022

Exploiting Qubit Reuse through Mid-circuit Measurement and Reset

Quantum measurement is important to quantum computing as it extracts the outcome of the circuit at the end of the computation. Previously, all measurements have to be done at the end of the circuit. Otherwise, it will incur significant errors. But it is not the case now. Recently IBM started supporting dynamic circuits through hardware (instead of software by simulator). With mid-circuit hardware measurement, we can improve circuit efficacy and fidelity from three aspects: (a) reduced qubit usage, (b) reduced swap insertion, and (c) improved fidelity. We demonstrate this using real-world applications Bernstein Verizani on real hardware and show that circuit resource usage can be improved by 60\%, and circuit fidelity can be improved by 15\%. We design a compiler-assisted tool that can find and exploit the tradeoff between qubit reuse, fidelity, gate count, and circuit duration. We also developed a method for identifying whether qubit reuse will be beneficial for a given application. We evaluated our method on a representative set of essential applications. We can reduce resource usage by up to 80\% and circuit fidelity by up to 20\%.

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