Quantum circuit simulation is usually evaluated through final numerical accuracy, while the dynamics of stochastic execution itself are less explicitly characterized. This work presents a precision-elastic persistent stochastic execution framework based on integral stochastic computing (ISC), where execution behavior is controlled by stream length N and ISC multiplicity m, where m represents the number of aggregated stochastic sub-streams per cycle (standard SC corresponds to m = 1). We focus on correlation-sensitive propagation under persistent reuse, and show that this regime produces circuit-dependent stochastic behavior and execution uncertainty patterns that are not captured by stage-wise re-encoded execution alone. To characterize this behavior, we use high-m tail descriptors, including the circuit-dependent coefficient γc, as compact indicators of persistent stochastic sensitivity. Across benchmark circuits, persistent execution exhibits reproducible tail regimes and structured cross-circuit variability, while deterministic reduced-precision baselines are used only as trend-consistency references. We further demonstrate adaptive stochastic precision scheduling, where circuit-dependent (N,m) settings satisfy a target fidelity with reduced stochastic workload. These results position persistent ISC as a configurable stochastic execution framework for analyzing execution-induced uncertainty propagation and correlation-sensitive behavior in quantum circuits.
Quantum circuit simulation is usually evaluated through final numerical accuracy, while the dynamics of stochastic execution itself are less explicitly characterized. This work presents a precision-elastic persistent stochastic execution framework based on integral stochastic computing (ISC), where execution behavior is controlled by stream length N and ISC multiplicity m, where m represents the number of aggregated stochastic sub-streams per cycle (standard SC corresponds to m = 1). We focus on correlation-sensitive propagation under persistent reuse, and show that this regime produces circuit-dependent stochastic behavior and execution uncertainty patterns that are not captured by stage-wise re-encoded execution alone. To characterize this behavior, we use high-m tail descriptors, including the circuit-dependent coefficient γc, as compact indicators of persistent stochastic sensitivity. Across benchmark circuits, persistent execution exhibits reproducible tail regimes and structured cross-circuit variability, while deterministic reduced-precision baselines are used only as trend-consistency references. We further demonstrate adaptive stochastic precision scheduling, where circuit-dependent (N,m) settings satisfy a target fidelity with reduced stochastic workload. These results position persistent ISC as a configurable stochastic execution framework for analyzing execution-induced uncertainty propagation and correlation-sensitive behavior in quantum circuits.
Quantum computing is considered a promising platform for next-generation large-scale computing. Although fully scalable quantum computers are still under development, quantum accelerators already offer a practical means to test, evaluate, and better understand quantum computation. Existing simulators provide valuable tools, but they typically rely on deterministic arithmetic and often incur significant computational overhead. To address these limitations, we developed a quantum computing simulator based on bipolar stochastic arithmetic, designed with two goals in mind. First, stochastic computation enables flexible, on-demand control of simulation precision and runtime. Second, stochastic circuits generally require significantly less hardware area than their deterministic counterparts. In our approach, quantum states and operations are encoded using bipolar stochastic bitstreams, allowing quantum circuits to be simulated using lightweight stochastic logic. We implement a representative set of quantum circuits and evaluate the simulator with respect to accuracy and required stream length. The results demonstrate that stochastic simulation yields satisfactory accuracy for small quantum circuits in software, and with improved error-correction techniques, it has the potential to serve as an efficient alternative to conventional quantum simulators.
Various emerging technologies promise advantages with respect to integration density, performance or power consumption, at the cost of approximate or probabilistic behavior. Approximate computing, where limited computational inaccuracies are tolerated at the system or application level is therefore of increasing interest. This paper investigates the use of stochastic computing (SC) as a tool for approximate simulation of probabilistic behavior. SC has the advantage of processing probabilities directly at very low hardware cost. It also allows accuracy to be traded for run-time in a natural way (progressive precision). AS a target technology to be simulated, we choose quantum computing circuits, whose behavior is inherently probabilistic and cannot be efficiently simulated by conventional (classical) means. We show how complex operations such as superposition and entanglement can be handled by SC. Finally, we report experimental results on software-based simulation of representative quantum circuits, both stand-alone and FPGA-supported. The results show that the SC implementations are orders of magnitude more compact than those based on classical circuits. Accurate results may require very long simulation runs, but run-times can be reduced by exploiting SC's progressive precision property.
Temporally correlated flux-induced phase noise can influence superconducting-quantum-circuit execution in ways that are not fully captured by uncorrelated, memoryless, or gate-averaged noise models. In this work, we develop an execution-oriented, circuit-level workflow for modeling and evaluating such effects. The workflow combines source-specific circuit-level noise components with a phenomenological segmented correlation-time construction for flux-induced phase noise, thereby enabling explicit control of a tunable correlation-time parameter τc within a composite circuit-level noise model. Using a single-qubit Carr-Purcell-Meiboom-Gill (CPMG) sequence and standard randomized benchmarking as the representative single-qubit circuit settings, we evaluate how circuit outputs respond to temporally correlated flux-induced phase noise under otherwise matched simulation conditions. The results show that temporally correlated flux-induced phase noise produces circuit-level behavior that differs qualitatively from uncorrelated or memoryless descriptions, and that its impact is governed jointly by the correlation-time parameter τc, the temporal structure of the circuit, and the way in which the circuit samples the noise. The proposed workflow provides a circuit-level framework for analyzing temporally correlated noise in superconducting quantum computing.
Dynamic quantum circuits with mid-circuit measurements (MCMs) and feed-forward operations play a crucial role in various applications, such as quantum error correction and quantum algorithms. With advancements in quantum hardware enabling the implementation of MCM and feed-forward loops, the use of dynamic circuits has become increasingly prevalent. There is a significant need for a benchmarking framework specially designed for dynamic circuits to capture their unique properties, as current benchmarking tools are designed primarily for unitary circuits and cannot be trivially extended to dynamic circuits. We propose dynamarq, a scalable and hardware-agnostic benchmarking framework for dynamic circuits. We collect a set of dynamic circuit benchmarks spanning various applications and propose a broad set of circuit features to characterize the structure of these dynamic circuits. We run them on two IBM quantum processors and the Quantinuum Helios-1E emulator, and propose scalable, application-dependent fidelity scores for each benchmark based on hardware execution results. We perform statistical modeling to identify correlations between circuit features and fidelity scores, and demonstrate highly accurate fidelity prediction using our model. Our model parameters are also transferable across hardware backends and calibration cycles. Our framework facilitates the understanding of dynamic circuit structures and provides insights for designing and optimizing dynamic circuits to achieve high execution fidelity on quantum hardware.
Quantum computers in the Noisy Intermediate-Scale Quantum (NISQ) era face significant challenges due to inherent noise and limited qubit coherence. Accurate fidelity evaluation of quantum states necessitates multiple repeated measurements to obtain statistical results. But determining the optimal number of measurements remains an open problem due to the dynamic, device-dependent nature of quantum noise. Existing methods either assume prior knowledge of the noise model or inherently employ a fixed measurement strategy, which limits their applicability in practical deployment scenarios. This paper presents AutoFid, an adaptive and noise-aware fidelity measurement framework that automatically determines the number of required tests based on circuit structure and hardware feedback. AutoFid models quantum circuits as Directed Acyclic Graphs and estimates structural complexity via random walks, enabling estimation of measurement effort. It further incorporates transpilation-aware features such as gate fidelity, depth inflation, and crosstalk to refine iteration budgets. During runtime, AutoFid dynamically samples fidelity results and employs an early stopping strategy based on confidence intervals to reduce redundant measurements while preserving accuracy guarantees. We evaluate AutoFid on 18 quantum benchmarks executed on real IBMQ hardware platforms. Experimental results show that AutoFid reduces measurement costs by more than 50% compared to both fixed-shot and learning-based baselines, while consistently maintaining fidelity bias below 0.01. Additional analysis using classical software testing metrics and ablation studies demonstrate its effectiveness, robustness, and adaptability across a wide range of quantum workloads.
Hybrid quantum--classical workflows often execute large ensembles of circuits that differ syntactically but implement identical operations, leading to substantial redundant computation. To address this, we introduce the Quantum Circuit Cache, a content-addressable system that detects semantic equivalence and reuses previously computed results across executions, backends, and workflow stages.
Our approach combines ZX-calculus reduction with isomorphism-invariant Weisfeiler--Leman graph hashing to generate deterministic circuit identifiers, enabling constant-time lookup in distributed caches supporting both lightweight LMDB and scalable Redis deployments. The system integrates transparently into hybrid HPC workflows and remains backend-agnostic across CPU, GPU, and QPU environments.
We evaluate the system on MareNostrum 5 with two representative workloads: distributed wire cutting and Differential Evolution-based QAOA optimization. For wire cutting, caching eliminates up to 91.98% of redundant subcircuit simulations, yielding speedups up to 7.0 times on a single node and maintaining advantages at scale, with Redis-based caching achieving up to 1.6 times speedups under high parallelism. Validation on a 35-qubit superconducting QPU confirms these benefits, achieving an 11.2 times speedup on real hardware. In distributed QAOA optimization, equivalence-aware caching avoids up to 27.6% of circuit evaluations and consistently reduces execution cost without altering the optimization algorithm. In both cases, reuse grows with concurrency and circuit structure, highlighting redundancy as a major systems bottleneck and demonstrating the effectiveness of our Quantum Circuit Cache.