arXiv:2608. 12936v1 Announce Type: cross Abstract: As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algorithmic feasibility with system-level optimization across heterogeneous hardware and software stacks.
arXiv:2602. 05541v3 Announce Type: replace-cross Abstract: Matrix multiplication is a fundamental operation in compute-intensive tasks and a key component of modern quantum acceleration frameworks.
As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algorithmic feasibility with system-level optimization across heterogeneous hardware and software stacks. Quantum resource estimation (QRE) plays a central role in this transition, yet existing approaches remain largely compilation-heavy or domain-knowledge-guided symbolic annotations, and tightly coupled to long-term fault-tolerant assumptions, limiting their topical applicability.
arXiv:2607. 05814v1 Announce Type: cross Abstract: Real-time decoding is a major bottleneck in scaling quantum error correction (QEC) from noisy intermediate-scale quantum (NISQ) devices to fault-tolerant quantum computing.
arXiv:2607. 20225v1 Announce Type: cross Abstract: While combinatorial optimization problems are central to many scientific and engineering applications, their solution remains challenging due to exponentially large search spaces.
arXiv:2606. 11620v1 Announce Type: cross Abstract: Approximate tensor-network simulators enable classical simulation of quantum circuits beyond the reach of exact methods, but selecting optimal approximation parameters -- such as bond dimension thresholds -- remains a costly trial-and-error process.
arXiv:2605. 27410v2 Announce Type: replace-cross Abstract: Variational Quantum Algorithms (VQAs) are a leading approach to exploiting near-term quantum hardware, leveraging parameterized quantum circuits and classical optimization to achieve advantage.
Cross-Backend QIEO is a runtime core for quantum-inspired evolutionary optimization that unifies execution across OpenMP5, CUDA, HIP, and multiple high-level languages. It compiles a single C++ implementation per hardware target and dispatches to CPU, multi-core, NVIDIA, or AMD backends at runtime, adapting kernels to each device’s memory hierarchy. The framework is validated with real-world bindings: a Python neural‑network hyperparameter optimizer achieving 88.60 % MNIST accuracy, a MATLAB wind‑farm layout optimizer matching particle swarm results and outperforming genetic algorithms, and a Julia package that reduces mean SSE by 2.1× in Lotka–Volterra parameter estimation.
arXiv:2508. 10807v2 Announce Type: replace-cross Abstract: We present a native three-qubit entangling gate that exploits engineered interactions to realize control-control-target and control-target-target operations in a single coherent step.
The paper introduces MQSS-Selector, a reinforcement‑learning guided pass selection system for an MLIR compilation pipeline aimed at unified High Performance Computing‑Quantum Computing (HPCQC) infrastructures. It addresses the challenges of Noisy Intermediate‑Scale Quantum (NISQ) devices by integrating device selection, compiler‑pass optimization, and job queue scheduling into a single learning‑based framework. The selector can simultaneously optimize multiple objectives—fidelity, compilation time, and scheduling latency—while adapting to circuit characteristics and device conditions.
arXiv:2607. 28422v1 Announce Type: new Abstract: Fault-tolerant quantum computing (FTQC) relies on quantum error correction to suppress physical errors and preserve logical information at scale.
arXiv:2607. 18552v1 Announce Type: cross Abstract: Quantum reservoir computing (QRC) uses the dynamics of a fixed or weakly tuned quantum system to transform temporal and sequential inputs into measured features, while training is typically confined to a classical readout.