Multiobjective Optimization for Robust Holonomic Quantum Gates

• URL: http://arxiv.org/abs/2504.17259v1
• Date: Thu, 24 Apr 2025 05:30:22 GMT
• Title: Multiobjective Optimization for Robust Holonomic Quantum Gates
• Authors: Min-Hua Zhang, Jing Qian,
• Abstract summary: We propose a multiobjective optimization algorithm to achieve nonadiabatic holonomic quantum gates with enhanced robustness.<n>We show that by considering the amplitude error, the detuning error and the decoherence of the Rydberg state as three individual objectives, this algorithm can effectively balance multiple competing objectives.
• Score: 10.739722391579763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust pulses have been widely used to reduce the sensitivity of quantum gate operations against various systematic errors due to the imperfections in practical quantum control. Yet, the typical optimization focuses on minimizing one type of errors serving as the one-objective algorithm, which arises a more susceptible sensitivity to other error sources. Optimizing multiple conflicting objectives of errors simultaneously remains a big challenge in quantum computing. Here, we propose a multiobjective optimization algorithm to achieve nonadiabatic holonomic quantum gates with enhanced robustness. We show that by considering the amplitude error, the detuning error and the decoherence of the Rydberg state as three individual objectives to be minimized, this algorithm can effectively balance multiple competing objectives, giving rise to a set of Pareto optimal solutions. We apply the Entropy Weight method to select the best solution that implements the robust holonomic gates, outperforming existing optimal gates with one-objective by having both higher gate fidelity and stronger robustness. This numerical approach of optimizing gates with multiple objectives can be readily applied to other gate protocols featuring a promising advance in fault-tolerant quantum computing with Rydberg atoms.

Related papers

• Direct entanglement ansatz learning (DEAL) with ZNE on error-prone superconducting qubits [0.05999777817331316]
  We introduce Directanglement Ansatz Learning ( DEAL), which exploits a quantum entanglement-based ansatz to explore intricate latent spaces and zero noise extrapolation (ZNE)<n>Deal increases the success rate by up to 14% compared to the classic quantum approximation optimization algorithm.<n>In addition, we demonstrate the capability of DEAL to provide near optimum ground energy solutions for travelling salesman, knapsack, and maxcut problems.
  arXiv  Detail & Related papers  (2025-04-05T02:05:45Z)
• Error-mitigated Geometric Quantum Control over an Oscillator [2.7382619198694886]
  Quantum information is fragile to environmentally and operationally induced imperfections.<n>We propose a robust scheme based on quantum optimal control via functional theory.<n>Our scheme provides a promising alternative for fault-tolerant quantum computation.
  arXiv  Detail & Related papers  (2025-01-24T09:13:24Z)
• Q-VLM: Post-training Quantization for Large Vision-Language Models [73.19871905102545]
  We propose a post-training quantization framework of large vision-language models (LVLMs) for efficient multi-modal inference.<n>We mine the cross-layer dependency that significantly influences discretization errors of the entire vision-language model, and embed this dependency into optimal quantization strategy.<n> Experimental results demonstrate that our method compresses the memory by 2.78x and increase generate speed by 1.44x about 13B LLaVA model without performance degradation.
  arXiv  Detail & Related papers  (2024-10-10T17:02:48Z)
• Performant near-term quantum combinatorial optimization [1.1999555634662633]
  We present a variational quantum algorithm for solving optimization problems with linear-depth circuits. Our algorithm uses an ansatz composed of Hamiltonian generators designed to control each term in the target quantum function. We conclude our performant and resource-minimal approach is a promising candidate for potential quantum computational advantages.
  arXiv  Detail & Related papers  (2024-04-24T18:49:07Z)
• Variational Quantum Multi-Objective Optimization [5.381539115778766]
  We present a variational quantum optimization algorithm to solve discrete multi-objective optimization problems on quantum computers. We show the effectiveness of the proposed algorithm on several benchmark problems with up to five objectives.
  arXiv  Detail & Related papers  (2023-12-21T18:59:21Z)
• Two-qubit quantum gates with minimal pulse sequences [0.0]
  We show that one can implement entangling gates based on non-independent qubits using a single pulse per qubit, or a single structured pulse. The optimal parameters depend on approximate solutions of Diophantine equations, causing the fidelity to never be exactly perfect, even under ideal conditions. We fully characterize the mechanism by which the gates operate, and show that the main source of error in realistic implementations comes from fluctuations in the peak intensity.
  arXiv  Detail & Related papers  (2023-09-21T18:58:25Z)
• Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits [77.04894470683776]
  We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production. We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-09-11T18:00:00Z)
• Optimizing quantum gates towards the scale of logical qubits [78.55133994211627]
  A foundational assumption of quantum gates theory is that quantum gates can be scaled to large processors without exceeding the error-threshold for fault tolerance. Here we report on a strategy that can overcome such problems. We demonstrate it by choreographing the frequency trajectories of 68 frequency-tunablebits to execute single qubit while superconducting errors.
  arXiv  Detail & Related papers  (2023-08-04T13:39:46Z)
• Constrained Optimization via Quantum Zeno Dynamics [23.391640416533455]
  We introduce a technique that uses quantum Zeno dynamics to solve optimization problems with multiple arbitrary constraints, including inequalities. We show that the dynamics of quantum optimization can be efficiently restricted to the in-constraint subspace on a fault-tolerant quantum computer.
  arXiv  Detail & Related papers  (2022-09-29T18:00:40Z)
• Software mitigation of coherent two-qubit gate errors [55.878249096379804]
  Two-qubit gates are important components of quantum computing. But unwanted interactions between qubits (so-called parasitic gates) can degrade the performance of quantum applications. We present two software methods to mitigate parasitic two-qubit gate errors.
  arXiv  Detail & Related papers  (2021-11-08T17:37:27Z)
• Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
  We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems. By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
  arXiv  Detail & Related papers  (2021-08-03T17:49:09Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• Low depth mechanisms for quantum optimization [0.25295633594332334]
  We focus on developing a language and tools connected with kinetic energy on a graph for understanding the physical mechanisms of success and failure to guide algorithmic improvement. This is connected to effects from wavefunction confinement, phase randomization, and shadow defects lurking in the objective far away from the ideal solution.
  arXiv  Detail & Related papers  (2020-08-19T18:16:26Z)

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.