Quantum processors exhibit time-varying hardwaredrift, environmental fluctuations, and correlated control errorsthat challenge reliable execution and increase the physical errorburden presented to higher-level error-correction procedures.Existing approaches address important aspects of quantumcalibration, feedback control, scheduling, and error correction,but these functions are typically studied and implemented asdistinct components. We introduce QID-OS (Quantum Intelligent Dynamic Operating System), an operating-system-levelcontrol-plane architecture for quantum execution that mediatesbetween physical hardware telemetry and higher-level compilation, runtime, and quantum error-correction services. QID-OSintegrates causal recursive least-squares (RLS) online parameterestimation, predictive pulse-level compensation, and adaptiveexecution scheduling within a closed-loop runtime architecture.The proposed framework is evaluated using a distance-3 surfacecode simulation under a specified non-Markovian noise model, afull-factorial 23 module ablation, and experiments on a 5-qubitconnected subset of an IBM Quantum superconducting processor.In simulation, QID-OS improves logical success probability overthe evaluated noise conditions, and the full configuration achievesthe highest success probability among the evaluated modulecombinations. On superconducting hardware, the experimentallyvalidated online RLS estimation and active pulse-compensationcomponents increase computational-basis success probabilityfrom 68.5% ± 0.4% to 88.4% ± 0.3% across three independent8-hour calibration windows, comprising 300 randomized testcircuits and 2,457,600 total measurement shots. These resultsdemonstrate the feasibility of using a predictive control-planearchitecture to mitigate hardware-level errors prior to higherlevel quantum error-correction processing.
Quantum processors exhibit time-varying hardwaredrift, environmental fluctuations, and correlated control errorsthat challenge reliable execution and increase the physical errorburden presented to higher-level error-correction procedures.Existing approaches address important aspects of quantumcalibration, feedback control, scheduling, and error correction,but these functions are typically studied and implemented asdistinct components. We introduce QID-OS (Quantum Intelligent Dynamic Operating System), an operating-system-levelcontrol-plane architecture for quantum execution that mediatesbetween physical hardware telemetry and higher-level compilation, runtime, and quantum error-correction services. QID-OSintegrates causal recursive least-squares (RLS) online parameterestimation, predictive pulse-level compensation, and adaptiveexecution scheduling within a closed-loop runtime architecture.The proposed framework is evaluated using a distance-3 surfacecode simulation under a specified non-Markovian noise model, afull-factorial 23 module ablation, and experiments on a 5-qubitconnected subset of an IBM Quantum superconducting processor.In simulation, QID-OS improves logical success probability overthe evaluated noise conditions, and the full configuration achievesthe highest success probability among the evaluated modulecombinations. On superconducting hardware, the experimentallyvalidated online RLS estimation and active pulse-compensationcomponents increase computational-basis success probabilityfrom 68.5% ± 0.4% to 88.4% ± 0.3% across three independent8-hour calibration windows, comprising 300 randomized testcircuits and 2,457,600 total measurement shots. These resultsdemonstrate the feasibility of using a predictive control-planearchitecture to mitigate hardware-level errors prior to higherlevel quantum error-correction processing.
Abstract Realizing scalable fault-tolerant quantum computing (FTQC) has long been constrained by environmental decoherence, forcing conventional quantum error correction (QEC) into massive spatial and temporal overheads. In this work, we introduce QID-OS (Quantum Integrated Dynamic Operating System), a proactive control framework that integrates Recursive Least Squares (RLS) estimation with adaptive scheduling and pulse-level compensation. Rather than reacting to errors post-facto, QID-OS treats environmental noise as a dynamically observable control variable, anticipating fluctuations in real time and applying active compensation prior to error accumulation. QID-OS does not replace conventional fault-tolerant QEC, but rather complements it as a proactive control layer that reduces the effective error burden presented to the downstream QEC stack. Through large-scale numerical simulations under defined phenomenological noise models, QID-OS maintains a logical fidelity of 0.999995 (evaluated via Monte Carlo state-overlap estimators over 105 independent Monte Carlo trajectories) where the conventional baseline collapses. Furthermore, completed hardware validation on IBM Quantum processors demonstrates that the predictive RLS and pulse-compensation components of QID-OS successfully execute a 5-qubit Greenberger-Horne-Zeilinger (GHZ-5) state, improving fidelity from a standard baseline of 68.5% to 88.4% (a +19.9 percentage-point improvement). These results establish QID-OS as a powerful proactive control architecture for noisy quantum execution.
Scalable quantum processors require control, readout, feedback, calibration, and error correction to coexist under bounded latency and shared-resource constraints, whereas existing platforms typically optimize only a subset of these capabilities. This article presents QCORE (Quantum-Control-Oriented Real-Time Execution), a QPU-side digital control reference architecture positioned between the Host and a platform-specific analog/mixed-signal front end. QCORE separates task management, shared resources, hard-real-time execution, and long-timescale services into four hardware partitions. A fast-result sideband closes same-round feedback, a Measurement Packet provides a traceable measurement and service interface, and a common service-control skeleton, Tile-local QEC, and versioned safe-point commit organize calibration, error correction, and long-term state updates. Transaction-level, event-driven, and quantum-behavioral models are used for evaluation. At a background load of 0.8, the P99 latency of the shared Measurement Packet/Event feedback path is (1.984±0.004)Lmax. Closed-loop operation reduces the mean frequency error by 83.2%±0.8% and lowers the state-assignment error at maximum readout drift from 10.39%±0.54% to 5.37%±0.29%. No unsafe acceptance or mixed-version observation is observed in 100,000 configuration transactions, and Tile-local QEC reduces modeled global-boundary demand and yields a 2.08× capacity-normalized scaling estimate.
We experimentally study error mitigation and finite-shot sampling on superconducting quantum hardware under a constrained execution budget. The study combines calibration-aware qubit selection, circuit-depth scaling, zero-noise extrapolation, dynamical decoupling, readout-error mitigation, and repeated-shot estimation on an IBM Quantum processor. Experiments are organized across ideal simulation, noise-model simulation, and physical-device execution to separate sampling uncertainty from device-induced error. We investigate how mitigation performance changes with circuit depth, effective noise scale, qubit connectivity, and measurement budget, and quantify accuracy using expectation-value error, mean-squared error, statistical uncertainty, and mitigation gain. A fixed hardware-execution budget is used to evaluate shot allocation strategies and repeated measurements without relying on unlimited sampling. The resulting analysis provides a hardware-aware characterization of when mitigation improves expectation-value estimation and when finite-shot fluctuations offset the benefit of additional mitigation overhead. The implementation uses contemporary Qiskit and IBM Quantum Runtime workflows and is designed to provide reproducible experimental evidence for error-mitigation studies on current quantum processors.
Scalable quantum computation is expected to rely on fault-tolerant quantum computation (FTQC), in which quantum error correction (QEC) suppresses physical errors sufficiently to support reliable logical operations. This requires quantum compilation to move beyond general-purpose circuit optimization toward encoding-aware and protocol-structured compilation across the full stack of fault-tolerant quantum computers. Beyond circuit synthesis and hardware mapping, an FTQC compiler must lower algorithm-level operations into the logical gate set supported by the chosen code, coordinate encoded data and ancilla resources, realize logical operations together with repeated syndrome extraction under hardware constraints, and provide the resulting measurement stream to real-time decoding.
This survey presents a full-stack view of compiler design for QEC-protected quantum computation. We organize existing work into three interacting layers: logical-level QEC compilation, physical-level QEC realization, and decoder runtime integration. At the logical level, we review surface-code lattice-surgery compilers, beyond-surface-code code-surgery frameworks including emerging qLDPC approaches, and compilation support for non-Clifford operations such as magic-state distillation and code switching. At the physical level, we survey hardware-aware QEC realization on superconducting, trapped-ion, and neutral-atom platforms. We further examine decoder models, real-time decoding systems, and frame-management mechanisms that close the feedback loop during fault-tolerant execution. Finally, we identify open challenges in cross-layer optimization, qLDPC compilation, compiler-decoder co-design, runtime adaptivity, and the development of integrated and benchmarkable FTQC compilation stacks. An actively maintained paper list is available at: github.com/chenghongz/QEC-compiler-design.
Abstract Quantum computing has recently emerged as a novel paradigm for solving problems otherwise intractable on classical computers, yet the practical progress of quantum computers is severely impacted by noise, decoherence, and imperfect gate operations in Noisy Intermediate-Scale Quantum (NISQ) devices. Specific to IBM Quantum hardware, imperfections due to readout error, gate errors, and device drift, which depends on time, resolves limits for the accuracy of outputs from our algorithms. The mitigation strategies currently used, for example, Measurement Error Mitigation (MEM), Zero Noise Extrapolation (ZNE), and Clifford Data Regression (CDR), offer improvement over raw, unmitigated outputs, but they are static and not dynamic; the reduction of noise in within an NISQ device is temporal and can vary in patterns that are non-zero. In addition to this, using the conventional methods provides inconsistent and suboptimal results.This study will provide enhanced reliability with respect to performing quantum computation on IBM Quantum hardware with a unique mitigation framework that is adaptive and computationally efficient for a high-fidelity output. The problem taken on here is the failure of existing error mitigation strategies to provide adaptive noise compensation, as they assume a statically-distributed error, despite the performance-related characteristics already being documented in regards to calibration drift and variation over time.To achieve this, we propose a Hybrid Adaptive Error Mitigation (HAEM) approach that includes baseline measurement error mitigation and an additional correction layer that utilizes realtime calibration information, along with lightweight machine learning models. The approach can be broken down into three steps: (i) perform standard measurement error mitigation to reduce classical readout noise, (ii) execute compact calibration circuits, such as Bell states, GHZ states and Clifford benchmarks to capture then current error profile of the device, and (iii) use a lightweight machine learning model (having been trained on historical and newest calibration data) to dynamically change the mitigation weights applied for the target quantum algorithm. All implementation is done using Qiskit Runtime to ensure low-latency execution, and we completed experiments on both Qiskit Aer noisy simulators and IBM Quantum devices.Initial results suggest HAEM improved the outputs of quantum algorithms' fidelity compared to the unmitigated runs and the baseline measurement error mitigation (MEM) runs. On the noisy simulators, HAEM increased fidelity from a raw performance of 0.65 to 0.87, nearly a 34% improvement. In fairer hardware-like scenarios, fidelity-on-average was persistently 12% better than MEM, with nearly equal time duration for the additional adaptive measurements. The adaptive aspect of HAEM consistently supported subsequent calibration cycles of the device and was effective to continue providing fidelity improvements despite the static mitigation performance degrading.To sum up, the proposed HAEM framework is a modern and viable alternative for error mitigation which brings together the benefits of baseline approaches with corrections that are driven by fixed-function error-corrective, adaptive learning. As both learning-based and standard baseline approaches can only achieve fixed levels of performance, HAEM, through its use of noise mitigation both at execution time and through correlating it with standard data, provides a route towards reliable quantum computations on current IBM hardware. HAEM also presents a contribution to the NISQ error-resilient landscape, and however it is intended that it will continue to improve future applications of hybrid workflows of quantum and classical computation where error mitigation and resilience is important.
This work demonstrates real-time predictive classical-quantum feedback control on the IBM Kingston superconducting quantum processor, characterizing the hardware latency bottleneck of dynamic Hamiltonian shaping. Decoherence in quantum systems is traditionally viewed as a technical obstacle arising from environmental noise; this paper interprets decoherence as the coupling of a localized system to a dissipative, high-entropy environmental field. An ancilla-based modular sensor architecture is introduced to facilitate real-time error syndrome extraction without collapsing the target state. A software-defined controller, implemented via a quantized Variational Autoencoder-Feed-Forward Network (VAE-FFN) pipeline and optimized via empirical threshold calibration under strict 4-bit latency constraints, provides predictive Hamiltonian control to stabilize the system boundary. The integration of a 3D volumetric topological complex outlines a geometric architecture designed to mitigate spatial error cascades. Through temporal delay sweeps, empirical telemetry demonstrates that the volumetric lattice achieves localized containment of non-commuting phase defects, dynamically exploiting coherent Larmor precession to constrain spatial error propagation. Furthermore, rigorous statistical repetition mapping reveals a critical Noisy Intermediate-Scale Quantum (NISQ) latency bottleneck. The empirical data establishes that the temporal cost of classical-quantum feed-forward routing incurs a marginal macroscopic fidelity reduction (-1.01%). Crucially, this study reframes this differential not as a systemic regression, but as an explicit architectural trade-off: prioritizing rigorous variance suppression and operational stability over unmitigated peak fidelity. By establishing a highly predictable, predictively controlled baseline, this framework explores practical control strategies consistent with Approximate Quantum Error Correction (AQEC) approaches, bounding the operational consequences of the Eastin-Knill constraint within an addressable temporal control protocol.