Running quantum programs is fraught with challenges on on today's noisy intermediate scale quantum (NISQ) devices. Many of these challenges originate from the error characteristics that stem from rapid decoherence and noise during measurement, qubit connections, crosstalk, the qubits themselves, and transformations of qubit state via gates. Not only are qubits not "created equal", but their noise level also changes over time. IBM is said to calibrate their quantum systems once per day and reports noise levels (errors) at the time of such calibration. This information is subsequently used to map circuits to higher quality qubits and connections up to the next calibration point.
This work provides evidence that there is room for improvement over this daily calibration cycle. It contributes a technique to measure noise levels (errors) related to qubits immediately before executing one or more sensitive circuits and shows that just-in-time noise measurements benefit late physical qubit mappings. With this just-in-time recalibrated transpilation, the fidelity of results is improved over IBM's default mappings, which only uses their daily calibrations. The framework assess two major sources of noise, namely readout errors (measurement errors) and two-qubit gate/connection errors. Experiments indicate that the accuracy of circuit results improves by 3-304% on average and up to 400% with on-the-fly circuit mappings based on error measurements just prior to application execution.
Optimizing QAOA circuit transpilation with parity twine and SWAP network encodings
Jülich Supercomputing Centre, Institute for Advanced Simulation, Forschungszentrum Jülich（于利希研究中心先进模拟研究所超级计算中心）；Institute for Quantum Computing Analytics (PGI-12), Forschungszentrum Jülich（于利希研究中心量子计算分析研究所）；Department of Mechanical Engineering, Virginia Tech（弗吉尼亚理工大学机械工程系）；Davidson School of Chemical Engineering, Purdue University（普渡大学戴维森化学工程学院）；AIDAS；RWTH Aachen University（亚琛工业大学）
Beyond Logical Circuits: Hardware-Aware Analysis of Expressibility and Trainability in Variational Quantum Algorithms
Division of Engineering, New York University Abu Dhabi (NYUAD)（纽约大学阿布扎比分校工程学院）；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD（纽约大学阿布扎比分校研究院量子与拓扑系统中心）
The SpinPulse library for transpilation and noise-accurate simulation of spin qubit quantum computers
Quobly；Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS, LPMMC（法国国家科学研究中心，微观与纳米物理实验室）；CEA-Léti（法国原子能和替代能源委员会-莱蒂研究所）；Center for Quantum Science and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院量子科学与工程中心）
Late Breaking Results: Hardware-Aware Compilation Reshapes Trainability in Variational Quantum Circuits
eBRAIN Lab, Division of Engineering, New York University (NYU) Abu Dhabi（纽约大学阿布扎比分校工程学院）；Center for Quantum and Topological Systems, NYUAD Research Institute, NYU Abu Dhabi（纽约大学阿布扎比分校研究学院量子与拓扑系统中心）
QASMTrans: An End-to-End QASM Compilation Framework with Pulse Generation for Near-Term Quantum Devices
University of Washington（华盛顿大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；University of California San Diego（加州大学圣地亚哥分校）；Oak Ridge National Laboratory（橡树岭国家实验室）
Treewidth-Aware Gate Cut Selection for Reducing Transpilation Overhead on Superconducting Quantum Devices
Graduate School of Science and Technology, Keio University（庆应义塾大学科学技术研究科）；Faculty of Science and Technology, Keio University（庆应义塾大学理工学部）；University College London（伦敦大学学院）
Q-fid: Quantum Circuit Fidelity Improvement with LSTM Networks
Graduate School of Science and Technology, Keio University（Keio大学科学与技术研究生院）；Graduate School of Information Science and Electrical Engineering, Kyushu University（九州大学信息科学与电气工程研究生院）；RIKEN Center for Computational Science（理化学研究所计算科学中心）
Circuit Depth Reduction for Executable Hamiltonian Dynamics of Covalent Inhibitor Reactivity on Quantum Hardware
Capgemini Quantum Lab（凯捷量子实验室）；IBM T. J. Watson Research Center（IBM T.J.沃森研究中心）；Haiqu Inc.（海奇公司）；IBM Quantum IBM France Lab（IBM量子法国实验室）；GSK Medicines Research Centre（葛兰素史克药物研究中心）
Pauli Network Circuit Synthesis with Reinforcement Learning
IBM Quantum, IBM T.J. Watson Research Center（IBM量子、IBM T.J.沃森研究中心）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；IBM Quantum, IBM France Lab（IBM量子、IBM法国实验室）
提出基于强化学习的 Pauli 网络量子电路重新综合方法，通过紧凑子块表示与学习启发式逐步综合，在 Benchpress 等测试中显著减少双量子比特门数量和电路深度。
Utility-scale quantum experiments using dynamic circuits to address collective dissipation in interacting qubits
Centro de Física de Materiales (CFM-MPC), CSIC-EHU（材料物理中心 (CFM-MPC)，CSIC-EHU）；University of the Basque Country（巴斯克大学）；IBM Quantum, IBM Thomas J. Watson Research Center（IBM量子，IBM托马斯·J·沃森研究中心）；Universidad Autónoma de Madrid（马德里自治大学）；Donostia International Physics Center DIPC（圣塞巴斯蒂安国际物理中心 DIPC）；IKERBASQUE, Basque Foundation for Science（IKERBASQUE，巴斯克科学基金会）
