Characterizing and Benchmarking Dynamic Quantum Circuits

• URL: http://arxiv.org/abs/2604.03360v1
• Date: Fri, 03 Apr 2026 17:31:12 GMT
• Title: Characterizing and Benchmarking Dynamic Quantum Circuits
• Abstract summary: dynamarq is a scalable and hardware-agnostic benchmarking framework for dynamic circuits.<n>We propose a broad set of circuit features to characterize the structure of these dynamic circuits.<n>Our framework provides insights for designing and optimizing dynamic circuits to achieve high execution fidelity on quantum hardware.
• Score: 1.341649735289308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Branch-Aware Quantum Constant Propagation for Dynamic Quantum Circuits [0.30313876226441433]
  Branch-Aware Quantum Constant Propagation (BQCP) is a compile-time analysis for dynamic circuits.<n>BQCP tracks the classical information produced by mid-circuit measurements together with the corresponding post-measurement quantum states.<n>Our method consistently achieves larger reductions than other existing passes including QCP.
  arXiv  Detail & Related papers  (2026-06-01T10:08:05Z)
• Compile-Time Simplification of Classically Controlled Operations in Dynamic Circuits [0.24689994775563415]
  We propose a compile-time optimization framework that reduces the use of classical controls in dynamic circuits.<n>At its core, the framework uses a static analysis that symbolically executes the circuit by propagating classical information alongside the quantum state.<n>Compared to existing compile-time optimizations that target only mid-circuit measurements, our method applies to a broader class of dynamic circuits expressible in modern quantum programming languages.
  arXiv  Detail & Related papers  (2026-05-27T13:05:41Z)
• Probabilistic Design of Parametrized Quantum Circuits through Local Gate Modifications [40.28072745340568]
  We propose an evolution-inspired quantum architecture search algorithm, which we refer to as the local quantum architecture search.<n>The goal of the local quantum architecture search algorithm is to optimize parametrized quantum circuit architectures.<n>We evaluate the local quantum architecture search algorithm on two synthetic function-fitting regression tasks and two quantum chemistry regression datasets.
  arXiv  Detail & Related papers  (2026-02-12T22:47:03Z)
• Regression of Functions by Quantum Neural Networks Circuits [41.09105068326236]
  This work investigates automated quantum-circuit construction for regression tasks.<n>It introduces a genetic-algorithm framework that discovers Reduced Regressor QNN architectures.
  arXiv  Detail & Related papers  (2025-12-23T01:58:03Z)
• Platform-Agnostic Modular Architecture for Quantum Benchmarking [1.0654458441169534]
  We present a platform-agnostic modular architecture that addresses the increasingly fragmented landscape of quantum computing benchmarking.<n>We support over 20 benchmark variants ranging from simple tests like Bernstein-Vazirani to complex Hamiltonian simulation with observable calculations.<n>This architecture has been developed as a key enhancement to the continually evolving QED-C Application-Oriented Performance Benchmarks for Quantum Computing suite.
  arXiv  Detail & Related papers  (2025-10-09T17:09:56Z)
• ConQuER: Modular Architectures for Control and Bias Mitigation in IQP Quantum Generative Models [40.972673943861075]
  Quantum generative models based on instantaneous quantum (IQP) circuits show great promise in learning complex distributions.<n>Current implementations suffer from lack of controllability over generated outputs and severe generation bias towards certain expected patterns.<n>We present a Controllable Quantum Generative Framework, ConQuER, which addresses both challenges through a modular circuit architecture.
  arXiv  Detail & Related papers  (2025-09-26T16:32:41Z)
• Architect of the Bits World: Masked Autoregressive Modeling for Circuit Generation Guided by Truth Table [5.300504429005315]
  We propose a novel approach integrating conditional generative models with differentiable architecture search (DAS) for circuit generation.<n>Our approach first introduces CircuitVQ, a circuit tokenizer trained based on our Circuit AutoEncoder.<n>We then develop CircuitAR, a masked autoregressive model leveraging CircuitVQ as the tokenizer.
  arXiv  Detail & Related papers  (2025-02-18T11:13:03Z)
• RH: An Architecture for Redesigning Quantum Circuits on Quantum Hardware Devices [6.959884576408311]
  We present an architecture that enables the redesign of large-scale quantum circuits on quantum hardware.<n>By prepending a random quantum circuit module to the standard EQ-GAN framework, we extend its capability from quantum state learning to unitary transformation learning.
  arXiv  Detail & Related papers  (2024-12-30T12:05:09Z)
• AC/DC: Automated Compilation for Dynamic Circuits [0.41356970190072423]
  We present a novel framework for generating dynamic quantum circuits that automatically prepare any state or unitary operator.<n>We demonstrate the generation of dynamic circuits for state preparation, long-range entangling gates, circuit optimization, and the application of dynamic circuits to lattice simulations.
  arXiv  Detail & Related papers  (2024-12-10T23:14:42Z)
• Chip Placement with Diffusion Models [40.503264193497124]
  We train a diffusion model capable of placing new circuits zero-shot, using guided sampling in lieu of reinforcement learning.<n>We empirically study the design decisions of our dataset generation algorithm, and identify several key factors enabling generalization.<n>Our models generate high-quality placements on unseen, realistic circuits, achieving competitive performance on placement benchmarks compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-17T03:02:24Z)
• CktGNN: Circuit Graph Neural Network for Electronic Design Automation [67.29634073660239]
  This paper presents a Circuit Graph Neural Network (CktGNN) that simultaneously automates the circuit topology generation and device sizing. We introduce Open Circuit Benchmark (OCB), an open-sourced dataset that contains $10$K distinct operational amplifiers. Our work paves the way toward a learning-based open-sourced design automation for analog circuits.
  arXiv  Detail & Related papers  (2023-08-31T02:20:25Z)
• Pulse-level noisy quantum circuits with QuTiP [53.356579534933765]
  We introduce new tools in qutip-qip, QuTiP's quantum information processing package. These tools simulate quantum circuits at the pulse level, leveraging QuTiP's quantum dynamics solvers and control optimization features. We show how quantum circuits can be compiled on simulated processors, with control pulses acting on a target Hamiltonian.
  arXiv  Detail & Related papers  (2021-05-20T17:06:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.