arXiv subjects

Emmanouil Giortamis

Publications and source records attributed to Emmanouil Giortamis.

12 recordsLinked to original sources

arXiv2026

Harvest: Resource-Aware Quantum Compilation for Magic State Protocols

Fault-tolerant quantum processors based on topological codes execute programs through lattice surgery, where operations must be mapped, routed, and supplied with magic states across a 2D grid of physical patches. Non-Clifford operations require these magic states, produced either by distillation factories or by cultivation, each trading footprint against preparation latency, and delivering a magic state to the data patches that consume it requires routing through the same shared layout as every other operation. Yet placement, routing, scheduling, and magic-state supply cannot be optimized in isolation: two operations with no circuit-level dependency can still contend for the same ports, routes, or magic-state terminals once placed, so a compiler that decouples instruction scheduling from magic-state generation, or hard-codes a single generation protocol, is forced to trade execution time against layout footprint instead of co-optimizing both across protocols. We present Harvest, a resource-aware compilation approach for lattice-surgery that co-optimizes magic-state consumption with circuit-aware placement and congestion-aware routing under a protocol-agnostic resource model, then reclaims unused layout footprint after scheduling. Across standard benchmark suites (QAOA, QFT, QASMBench), Harvest achieves an average speedup of $4.83\times$ (up to $17.8\times$) over sequential execution, improves schedule length by up to $1.35\times$ through circuit-aware placement, and reclaims up to $72.0\%$ of unused magic-state patches and $33.9\%$ of unused routing patches.

quant-ph↗

arXiv2026

Oraqle: An Empirical Analysis of Qubit Readout and Discriminators in Quantum Error Correction

Quantum error correction (QEC) is the most promising route toward fault-tolerant quantum computing and, thus, useful quantum computers. QEC operates as a continuous measure-decode-correct cycle: ancilla qubits are read out, a decoder infers errors from the resulting syndromes, and corrections are applied before the next round begins. Within this loop, readout occupies a uniquely critical role, as it is the sole source of ground truth available to the decoder. Yet readout is also the slowest and most error-prone operation in the stack, with characteristics that vary across qubits and drift over time; This complexity propagates directly to the classical control hardware, and in particular to the FPGA-hosted machine-learning (ML) discriminator that must classify each analog signal into a binary syndrome outcome. Despite this central role, QEC performance has not yet been studied in depth from the perspective of readout characteristics, readout length, and their co-design with an ML discriminator. We introduce Oraqle, an end-to-end benchmarking framework that evaluates qubit-state readout and its impact on QEC performance across real experimentally extracted qubit-state-readout datasets, state-of-the-art ML discriminators, multiple QEC codes, and hardware regimes spanning current to projected devices. Our study reveals three asymmetric findings: The measurement duration can be significantly reduced with nearly no penalty to the logical error rate; The discriminator complexity barely affects the QEC performance, as residual errors are written into device physics rather than the model; and the impact of qubit-state readout on the logical error rate is conditional on where the hardware sits in the QEC landscape, a window that widens as devices mature.

quant-ph↗

arXiv2026

Zero-G: A Pre-Decoder-Aware Decoder for Quantum Error Correction

Fault-tolerant quantum computing requires classical decoders that keep pace with the underlying hardware, translating syndrome measurements into corrections fast enough to avoid an exponential backlog. To meet this real-time constraint, pre-decoders have emerged as part of a hierarchical decoding approach to resolve simple, local errors before passing a sparser residual syndrome to a strong decoder. While pre-decoding should, in theory, speed up the strong decoder, in practice, the speedup is only marginal, since existing strong decoders are designed to decode dense syndromes and cannot exploit the sparsity provided by pre-decoders. To address this, we present Zero-G, a strong decoder designed for use alongside pre-decoders. As a stochastic approximate minimum-weight perfect matching (MWPM) decoder, Zero-G exploits sparse residual syndromes, dynamically trading latency for accuracy rather than relying on an all-or-nothing runtime-accuracy trade-off. By decoupling hardware control from the decoding core itself, we enable heterogeneous deployment across both FPGAs and CPUs without maintaining separate implementations. Zero-G achieves a $10\times$ latency improvement over existing strong decoders at matching accuracy, with worst-case sub-350ns decoding at code distances up to d=15, while scaling to 640 logical qubits on a single 128-core CPU and 32 logical qubits on a single AMD Versal V80 FPGA.

quant-ph↗

arXiv2026

Multi-Stage Mamba-Based Architecture for Fast and Scalable Superconducting Qubit Readout

Reliable qubit readout is a critical bottleneck toward fault-tolerant quantum computing (FTQC). In superconducting quantum processors, readout operations are both error-prone and high-latency. These challenges become more severe in frequency-multiplexed architectures, where signal crosstalk among neighboring qubits significantly degrades readout fidelity. Existing machine learning (ML)-based approaches rely on feed-forward neural networks (FNNs) that suffer from large parameter sizes and lack an end-to-end network that jointly addresses relaxation errors and discriminates qubit states. In this work, we present a multi-stage qubit state discriminator based on the Mamba model, which enables efficient sequence modeling with linear complexity. The first stage performs initial state discrimination, followed by a refinement stage that identifies and mitigates relaxation-induced errors. Our lightweight model achieves a geometric mean readout fidelity of 0.906, outperforming the best-reported state-of-the-art method while reducing parameter size by 49.6%; our optimal model further reaches 0.911. Both models remain robust across varying input trace lengths, maintaining a high fidelity of 0.893 at readout durations as short as 500 $ns$, achieving up to a 26% reduction in logical error rate over prior work in quantum error correction (QEC).

quant-ph↗

arXiv2026

MCMit: Hardware-Software Co-Design for Mid-Circuit Measurement Error Mitigation

Distributed Quantum Computing (DQC) and Quantum Error Correction (QEC) rely on dynamic circuits that include Mid-Circuit Measurements (MCMs) and classical feedback. These operations present a major bottleneck: MCMs suffer from high error rates that lead to real-time branching errors, while MCM and classical feedback latencies amplify decoherence errors. Current hardware controllers, qubit-state discriminators, and software error mitigation techniques fail to address these challenges holistically. We propose MCMit, a hardware-software co-design to mitigate branching and latency-induced errors. MCMit introduces a scalable, constant-latency multi-control branch instruction for faster classical feedback and two qubit-state discriminators, a transformer, and a CNN, with high accuracy even under short measurement durations. On the software side, static MCM elimination and stochastic branching complement the hardware by mitigating residual branching errors that persist despite hardware improvements. We implement MCMit on Qubic and evaluate it using experimentally extracted QPU readout traces. Our branch instruction reduces feedback latency by up to 70\%, improving circuit depths by up to $7\times$ over Qubic. Our CNN discriminator achieves up to 62\% higher accuracy for short measurement durations than the baselines, driving $1.2-9.4\times$ lower logical error rates in QEC. Last, our software mitigation improves fidelity by 18--30\% over baseline methods in DQC workloads.

quant-ph↗

arXiv2026

Chipmunq: A Fault-Tolerant Compiler for Chiplet Quantum Architectures

As quantum computing advances toward fault-tolerance through quantum error correction, modular chiplet architectures have emerged to provide the massive qubit counts required while overcoming fabrication limits of monolithic chips. However, this transition introduces a critical compilation gap: existing frameworks cannot handle the scale of fault-tolerant quantum circuits while managing the noisy, sparse interconnects of chiplet backends. We present Chipmunq, the first hardware-aware compiler for mapping and routing fault-tolerant circuits onto modular architectures. Chipmunq employs a quantum-error-correction-aware partitioning strategy that preserves the integrity of logical qubit patches, preventing prohibitive gate overheads common in general-purpose compilers. Our evaluation demonstrates that Chipmunq achieves a 13.5x speedup in compilation time compared to state-of-the-art tools. By incorporating chiplet constraints and defective qubits, it reduces circuit depth by 86.4% and SWAP gate counts by 91.4% across varying code distances. Crucially, Chipmunq overcomes heterogeneous inter-chiplet links, improving logical error rates by up to two orders of magnitude.

quant-ph↗

arXiv2025

Qonductor: A Cloud Orchestrator for Quantum Computing

We describe Qonductor, a cloud orchestrator for hybrid quantum-classical applications that run on heterogeneous hybrid resources. Qonductor abstracts away the complexity of hybrid programming and resource management by exposing the Qonductor API, a high-level and hardware-agnostic API. The resource estimator strategically balances quantum and classical resources to mitigate resource contention and the effects of hardware noise. The hybrid scheduler automates job scheduling on hybrid resources and balances the tradeoff between users' objectives of QoS and the cloud operator's objective of resource efficiency. We implement an open-source prototype and evaluate Qonductor using more than 7000 real quantum runs on the IBM quantum cloud to simulate real cloud workloads. Qonductor achieves up to 54% lower job completion times (JCTs) while sacrificing 3% execution quality, balances the load across QPU, which increases quantum resource utilization by up to 66%, and scales with growing system sizes and loads.

quant-ph↗

arXiv2025

ECCentric: An Empirical Analysis of Quantum Error Correction Codes

Quantum Error Correction (QEC) is essential for building scalable quantum computers, but a lack of systematic, end-to-end evaluation methods makes it difficult to assess how different QEC codes perform under realistic conditions. The vast diversity of codes, an expansive experimental search space, and the absence of a standardized framework prevent a thorough, holistic analysis. To address this, we introduce ECCentric, an end-to-end benchmarking framework designed to systematically evaluate QEC codes across the full quantum computing stack. ECCentric is designed to be modular, extensible, and general, allowing for a comprehensive analysis of QEC code families under varying hardware topologies, noise models, and compilation strategies. Using ECCentric, we conduct the first systematic benchmarking of major QEC code families against realistic, mid-term quantum device parameters. Our empirical analysis reveals that intra-QPU execution significantly outperforms distributed methods, that qubit connectivity is a far more critical factor for reducing logical errors than increasing code distance, and that compiler overhead remains a major source of error. Furthermore, our findings suggest that trapped-ion architectures with qubit shuttling are the most promising near-term platforms and that on noisy devices, a strategic and selective application of QEC is necessary to avoid introducing more errors than are corrected. This study provides crucial, actionable insights for both hardware designers and practitioners, guiding the development of fault-tolerant quantum systems.

quant-ph↗

arXiv2025

Weaver: A Retargetable Compiler Framework for FPQA Quantum Architectures

While the prominent quantum computing architectures are based on superconducting technology, new quantum hardware technologies are emerging, such as Trapped Ions, Neutral Atoms (or FPQAs), Silicon Spin Qubits, etc. This diverse set of technologies presents fundamental trade-offs in terms of scalability, performance, manufacturing, and operating expenses. To manage these diverse quantum technologies, there is a growing need for a retargetable compiler that can efficiently adapt existing code to these emerging hardware platforms. Such a retargetable compiler must be extensible to support new and rapidly evolving technologies, performant with fast compilation times and high-fidelity execution, and verifiable through rigorous equivalence checking to ensure the functional equivalence of the retargeted code. To this end, we present $Weaver$, the first extensible, performant, and verifiable retargetable quantum compiler framework with a focus on FPQAs due to their unique, promising features. $Weaver$ introduces WQASM, the first formal extension of the standard OpenQASM quantum assembly with FPQA-specific instructions to support their distinct capabilities. Next, $Weaver$ implements the WOptimizer, an extensible set of FPQA-specific optimization passes to improve execution quality. Last, the WChecker automatically checks for equivalence between the original and the retargeted code. Our evaluation shows that $Weaver$ improves compilation times by $10^3\times$, execution times by $4.4\times$, and execution fidelity by $10\%$, on average, compared to superconducting and state-of-the-art (non-retargetable) FPQA compilers.

quant-ph↗

arXiv2025

QOS: A Quantum Operating System

Quantum computers face challenges due to hardware constraints, noise errors, and heterogeneity, and face fundamental design tradeoffs between key performance metrics such as \textit{quantum fidelity} and system utilization. This substantially complicates managing quantum resources to scale the size and number of quantum algorithms that can be executed reliably in a given time. We introduce QOS, a cloud operating system for managing quantum resources while mitigating their inherent limitations and balancing the design tradeoffs of quantum computing. QOS exposes a hardware-agnostic API for transparent quantum job execution, mitigates hardware errors, and systematically multi-programs and schedules the jobs across space and time to achieve high quantum fidelity in a resource-efficient manner. To achieve this, it leverages two key insights: First, to maximize utilization and minimize fidelity loss, some jobs are more compatible than others for multi-programming on the same quantum computer. Second, sacrificing minimal fidelity can significantly reduce job waiting times. We evaluate QOS on real quantum devices hosted by IBM, using 7000 real quantum runs of more than 70.000 benchmark instances. We show that the QOS achieves 2.6--456.5$\times$ higher fidelity, increases resource utilization by up to 9.6$\times$, and reduces waiting times by up to 5$\times$ while sacrificing only 1--3\% fidelity, on average, compared to the baselines.

quant-ph↗

arXiv2025

Recipe: Hardware-Accelerated Replication Protocols

Replication protocols are essential for distributed systems, ensuring consistency, reliability, and fault tolerance. Traditional Crash Fault Tolerant (CFT) protocols, which assume a fail-stop model, are inadequate for untrusted cloud environments where adversaries or software bugs can cause Byzantine behavior. Byzantine Fault Tolerant (BFT) protocols address these threats but face significant performance, resource overheads, and scalability challenges. This paper introduces Recipe, a novel approach to transforming CFT protocols to operate securely in Byzantine settings without altering their core logic. Recipe rethinks CFT protocols in the context of modern cloud hardware, including many-core servers, RDMA-capable networks, and Trusted Execution Environments (TEEs). The approach leverages these advancements to enhance the security and performance of replication protocols in untrusted cloud environments. Recipe implements two practical security mechanisms, i.e., transferable authentication and non-equivocation, using TEEs and high-performance networking stacks (e.g., RDMA, DPDK). These mechanisms ensure that any CFT protocol can be transformed into a BFT protocol, guaranteeing authenticity and non-equivocation. The Recipe protocol consists of five key components: transferable authentication, initialization, normal operation, view change, and recovery phases. The protocol's correctness is formally verified using Tamarin, a symbolic model checker. Recipe is implemented as a library and applied to transform four widely used CFT protocols-Raft, Chain Replication, ABD, and AllConcur-into Byzantine settings. The results demonstrate up to 24x higher throughput compared to PBFT and 5.9x better performance than state-of-the-art BFT protocols. Additionally, Recipe requires fewer replicas and offers confidentiality, a feature absent in traditional BFT protocols.

cs.CR↗

arXiv2024

Scaling Quantum Computations via Gate Virtualization

We present the Quantum Virtual Machine (QVM), an end-to-end generic system for scalable execution of large quantum circuits with high fidelity on noisy and small quantum processors (QPUs) by leveraging gate virtualization. QVM exposes a virtual circuit intermediate representation (IR) that extends the notion of quantum circuits to incorporate gate virtualization. Based on the virtual circuit as our IR, we propose the QVM compiler - an extensible compiler infrastructure to transpile a virtual circuit through a series of modular optimization passes to produce a set of optimized circuit fragments. Lastly, these transpiled circuit fragments are executed on QPUs using our QVM runtime - a scalable and distributed infrastructure to virtualize and execute circuit fragments on a set of distributed QPUs. We evaluate QVM on IBM's 7- and 27-qubit QPUs. Our evaluation shows that using our system, we can scale the circuit sizes executable on QPUs up to double the size of the QPU while improving fidelity by 4.7$\times$ on average compared to larger QPUs and that we can effectively reduce circuit depths to only 40\% of the original circuit depths.

quant-ph↗