paper-with-me

홈 › Papers

Parity Cross-Resonance: A Multiqubit Gate

2025-08-14 · Xuexin Xu, Siyu Wang, Radhika Joshi, Rihan Hai, Mohammad H. Ansari arxiv

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. Unlike conventional decompositions into multiple two-qubit gates, our hybrid optimization approach selectively amplifies desired interactions while suppressing unwanted couplings, yielding robust performance across the computational subspace and beyond. The new gate can be classified as a cross-resonance gate. We show it can be utilized in several ways, for example, in GHZ triplet state preparation, Toffoli-class logic demonstrations with many-body interactions, and in implementing a controlled-ZZ gate. The latter maps the parity of two data qubits directly onto a measurement qubit, enabling faster and higher-fidelity stabilizer measurements in surface-code quantum error correction. In all these examples, we show that the three-qubit gate performance remains robust across Hilbert space sizes, as confirmed by testing under increasing total excitation numbers. This work lays the foundation for co-designing circuit architectures and control protocols that leverage native multiqubit interactions as core elements of next-generation superconducting quantum processors.

📄 PDF Abstract BibTeX arXiv:2508.10807

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Reinforcement Learning to Disentangle Multiqubit Quantum States from Partial Observations

2024-06-12 · Pavel Tashev, Stefan Petrov, Friederike Metz, Marin Bukov

Using partial knowledge of a quantum state to control multiqubit entanglement is a largely unexplored paradigm in the emerging field of quantum interactive dynamics with the potential to address outstanding challenges in…

BenchmarkingDeep Reinforcement Learningreinforcement-learningReinforcement Learning+1

Multiqubit and multilevel quantum reinforcement learning with quantum technologies

2017-09-22 · F. A. Cárdenas-López, L. Lamata, J. C. Retamal, E. Solano

We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent…

BIG-bench Machine Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)

HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems

2025-10-25 · Jialin Song, Yingheng Tang, Pu Ren, Shintaro Takayoshi 외 arxiv

Simulating hybrid magnonic quantum systems remains a challenge due to the large disparity between the timescales of the two systems. We present a massively parallel GPU-based simulation framework that enables fully coupl…

Bias in Machine Learning Models Can Be Significantly Mitigated by Careful Training: Evidence from Neuroimaging Studies

2022-05-26 · Rongguang Wang, Pratik Chaudhari, Christos Davatzikos

Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distributions, races and ethnicities,…

BIG-bench Machine LearningFairness

Ray-based framework for state identification in quantum dot devices

2021-02-23 · Justyna P. Zwolak, Thomas McJunkin, Sandesh S. Kalantre, Samuel F. Neyens 외

Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexity of the control parameter space also g…