open quantum systems

This work addresses the lack of systematic experimental tracking in current quantum software development, which hinders effective monitoring of hardware noise, software evolution, and error sources. It introduces, for the first time, a holistic experimental tracking methodology tailored to quantum characteristics, proposing an end-to-end tracking framework that integrates error mitigation techniques with quantum reservoir computing. Validated through a chaotic time series prediction case study, the framework enables fully reproducible tracking of quantum experiments, accurately identifies critical error sources, and aggregates marginal gains across the workflow. The approach offers a generalizable methodological foundation for advancing quantum software engineering practices.

This work addresses the accuracy–resource trade-off arising from high-dimensional encoding and bosonic mode truncation in fermion–boson coupled systems—such as those involving photons or phonons—for quantum simulation. We propose a compact, error-bound-driven adaptive truncation scheme for bosonic modes. Furthermore, we develop a hardware-aware and computationally efficient fermion–boson-to-qubit mapping method, integrating variants of the Jordan–Wigner and Bravyi–Kitaev encodings, combined with Hamiltonian downfolding and Trotter step optimization. Our approach enables high-fidelity approximation of both static observables and time-resolved dynamics, achieving significant reductions in qubit count and gate complexity while guaranteeing simulation accuracy within a rigorously bounded error tolerance. This provides a scalable theoretical framework and practical toolkit for quantum simulation of realistic physical systems with bosonic degrees of freedom.

This study addresses the challenge of generating structurally coherent yet perceptually novel music through systematic integration of quantum computing principles into algorithmic composition. We propose a quantum-inspired compositional paradigm that operates across four interdependent musical dimensions—rhythm, timbre, harmony, and spatialization. Specifically, quantum superposition models compositional decision-making; particle trajectory simulation synthesizes stochastic timbres; harmonic progression and granular synthesis are governed by basis-state rotations in complex vector spaces; and quantum measurement uncertainty introduces controlled randomness in spatial panning. For the first time, quantum simulation, state rotation, and measurement noise are coherently mapped to interpretable, parametrically controllable musical attributes. Implemented within a real-time audio synthesis framework, our open-source toolchain produces demonstrable compositions. Empirical evaluation confirms significant improvements in structural integrity, unpredictability, and auditory novelty compared to conventional generative approaches.

Classical simulation of the non-resonant, inhomogeneous open Tavis–Cummings model—including cavity dissipation and up to three excitations—suffers from exponential computational cost as the number of atoms (N) increases. Method: We propose two digital quantum simulation algorithms. First, we design a system-agnostic, fixed-interaction protocol that resolves the critical open-system challenge of sampling the Lindbladized fundamental matrix. Second, we construct quantum circuits for digital simulation of the Lindblad master equation, perform gate decomposition, and conduct error benchmarking under realistic noise models. Contribution/Results: The algorithms achieve gate complexities of (O(N^2)) and (O(N^3)), respectively, enabling polynomial scalability. Numerical validation confirms their capability to simulate large-(N) systems under noise, with accuracy verified against classical differential-equation solvers—demonstrating both fidelity and potential quantum advantage.

This work addresses decoherence in general qubit systems induced by mixed longitudinal and transverse noise, particularly anisotropic noise, by proposing a suppression strategy based on continuous dynamical decoupling (CDD). Through the introduction of a tailored unitary transformation, the noise is mapped onto an effective stochastic term dependent on driving parameters. By integrating this framework with a noisy Landau–Zener model, the impact of linearly ramped control fields on dressed states is analyzed. The study systematically uncovers, for the first time, the robustness mechanism of CDD under mixed and anisotropic noise environments and elucidates how control parameters modulate the effective noise spectrum. Proper optimization of these parameters significantly enhances decoherence suppression, demonstrating the strong adaptability and efficacy of the proposed approach in realistic quantum systems.

Achieving high-fidelity quantum control under decoherence noise remains a formidable challenge. This work proposes a path-space regularization framework grounded in Girsanov’s theorem, which leverages continuous measurement records to construct a differentiable estimator of the Kullback–Leibler (KL) divergence, directly penalizing the observable effects of control on decoherence channels. The approach innovatively introduces two types of regularizers—Wiener KL and drift-variance—that shift the optimization objective from minimizing control amplitude to explicitly mitigating decoherence impact, rendering it applicable to general noise models. Experimental results demonstrate that the method improves final-state fidelity by up to 50% on both single- and multi-qubit systems, including IBM Kingston hardware, significantly enhances robustness under noise model mismatch, and effectively suppresses population leakage into forbidden states.

As quantum software grows increasingly complex, traditional quality assurance approaches relying on classical simulation have become infeasible to scale. This work addresses this challenge by systematically introducing classical software testing principles into the quantum computing domain from a software engineering perspective, thereby breaking dependence on simulation and establishing a testing paradigm tailored for real quantum hardware. By integrating quantum program analysis, error model identification, and hardware-aware modeling, the study proposes a deployable testing strategy and quality assurance framework suitable for actual quantum devices. It clearly articulates the core challenges of large-scale quantum software testing and offers a practical engineering pathway toward developing highly reliable quantum software.

This work addresses the challenge of error propagation and unreliable outcomes in noisy quantum computing, arising from both hardware noise and intrinsic stochasticity. It introduces, for the first time, a systematic uncertainty quantification (UQ) framework into quantum computation by formulating the problem as a statistical inference task. By integrating tools from probabilistic modeling, Bayesian inference, stochastic analysis, and sensitivity analysis, the study establishes a novel paradigm for error characterization and algorithm design tailored to noisy intermediate-scale quantum (NISQ) devices. The proposed uncertainty-aware framework is not only scalable but also provides a unified and mathematically rigorous foundation for error verification, characterization, and mitigation strategies.

Emerging quantum sensors are increasingly envisioned as components of hybrid quantum-classical high-performance computing, enabling new capabilities in scientific, cyber-physical, and machine-learning pipelines. However, their practical utility is limited by environmental decoherence, which degrades sensing reliability. While dynamical decoupling (DD) pulse sequences can mitigate this, standard methods are often suboptimal in the presence of realistic noise. We present SpinTune, a reinforcement learning software approach that autonomously discovers adaptive, piecewise DD sequences tailored to specific environments. Using a simulation model of a Carbon-13 spin bath, we show that SpinTune significantly outperforms standard DD sequences in preserving coherence.