Analytical blueprint for 99.999% fidelity X-gates on present superconducting hardware under strong driving
- URL: http://arxiv.org/abs/2512.19919v1
- Date: Mon, 22 Dec 2025 22:47:43 GMT
- Title: Analytical blueprint for 99.999% fidelity X-gates on present superconducting hardware under strong driving
- Authors: José Diogo Da Costa Jesus, Boxi Li, Yuan Gao, Rami Barends, Francisco Andrés Cárdenas-López, Felix Motzoi,
- Abstract summary: We numerically demonstrate gate infidelities below $10-5$ for a 7ns $$-rotation when incorporating existing decoherence rates.<n>We also answer long-standing questions about the optimal values of the DRAG prefactor as well as constant detuning.
- Score: 4.406638884109584
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Achieving very fast gates that undercut the natural limits set by decoherence requires going into the strong driving limit. Realizing single-qubit control predicted beyond semi-classical, time-dependent modeling has yet to be experimentally realized on superconducting and most other computing platforms. In this regime, the common model of dynamics within a three-level manifold breaks down, and instead, we see new quantum error channels growing abruptly with decreasing time. To identify these error processes we systematically calculate the effect of multi-photon transitions that occur out of the computational space. We then derive analytical formulas to suppress these effects, as well as amplitude and phase errors on the qubit space; we term these R1D for suppressing the $|0\rangle-|2\rangle$ transition and R2D when also suppressing $|1\rangle-|3\rangle$ leakage. We also answer long-standing questions about the optimal values of the DRAG prefactor as well as constant detuning, when accounting for time-ordering, and also show how to calibrate other prefactors for further performance improvement. Upon correcting these varied sources of error, we numerically demonstrate gate infidelities below $10^{-5}$ for a 7ns $π$-rotation when incorporating existing decoherence rates.
Related papers
- Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol [69.11739400975445]
We introduce the first theoretical framework for analyzing error accumulation in Model Context Protocol (MCP) agents.<n>We show that cumulative distortion exhibits linear growth and high-probability deviations bounded by $O(sqrtT)$.<n>Key findings include: semantic weighting reduces distortion by 80%, and periodic re-grounding approximately every 9 steps suffices for error control.
arXiv Detail & Related papers (2026-02-10T21:08:53Z) - Ultrafast Single Qubit Gates through Multi-Photon Transition Removal [0.23191656838250044]
Single qubit gates with a total leakage error consistently below $2.0times10-5$, and obtain fidelities above $99.98%$ for pulse durations down to 6.8 ns for both $X$ and $X/2$ gates.<n>We find that at such short gate durations and strong driving strengths the main error source is from higher order transitions.<n>This is all shown in the widely-used superconducting transmon qubit, which has a weakly anharmonic level structure and suffers from higher order transitions significantly.
arXiv Detail & Related papers (2025-11-27T12:04:18Z) - Improved Convergence in Parameter-Agnostic Error Feedback through Momentum [49.163769734936295]
We study normalized error feedback algorithms that combine EF with normalized updates, various momentum variants, and parameter-agnostic, time-varying stepsizes.<n>Our results hold with decreasing stepsizes and small mini-batches.
arXiv Detail & Related papers (2025-11-18T13:47:08Z) - INC: An Indirect Neural Corrector for Auto-Regressive Hybrid PDE Solvers [61.84396402100827]
We propose the Indirect Neural Corrector ($mathrmINC$), which integrates learned corrections into the governing equations.<n>$mathrmINC$ reduces the error amplification on the order of $t-1 + L$, where $t$ is the timestep and $L$ the Lipschitz constant.<n>We test $mathrmINC$ in extensive benchmarks, covering numerous differentiable solvers, neural backbones, and test cases ranging from a 1D chaotic system to 3D turbulence.
arXiv Detail & Related papers (2025-11-16T20:14:28Z) - Enhancing Kerr-Cat Qubit Coherence with Controlled Dissipation [64.05054054401175]
Kerr-cat qubit (KCQ) is a bosonic quantum processor.<n>KCQs are experimentally compatible with on-chip architectures and high-fidelity operations.<n>We present direct evidence that the bit-flip time in a KCQ is limited by leakage out of the qubit manifold.
arXiv Detail & Related papers (2025-11-02T17:58:36Z) - High-performance conditional-driving gate for Kerr parametric oscillator qubits [0.0]
We show that an AC-Zeeman shift due to the flux pulse for the gate operation largely affects the gate performance.<n>We propose a method to cancel this undesirable effect.<n>We numerically demonstrate a conditional-driving gate with average fidelity exceeding 99.9$%$ twice faster than that without the proposed method.
arXiv Detail & Related papers (2024-10-01T09:58:52Z) - Long distance spin shuttling enabled by few-parameter velocity optimization [37.69303106863453]
Spin qubit shuttling via moving conveyor-mode quantum dots in Si/SiGe offers a promising route to scalable miniaturized quantum computing.
Recent modeling of dephasing via valley degrees of freedom and well disorder dictate a slow shutting speed which seems to limit errors to above correction thresholds if not mitigated.
We show that typical errors for 10 $mu$m shuttling at constant speed results in O(1) error, using fast, automatically differentiable numerics and including improved disorder modeling and potential noise ranges.
arXiv Detail & Related papers (2024-09-11T20:21:45Z) - Fast Flux-Activated Leakage Reduction for Superconducting Quantum
Circuits [84.60542868688235]
leakage out of the computational subspace arising from the multi-level structure of qubit implementations.
We present a resource-efficient universal leakage reduction unit for superconducting qubits using parametric flux modulation.
We demonstrate that using the leakage reduction unit in repeated weight-two stabilizer measurements reduces the total number of detected errors in a scalable fashion.
arXiv Detail & Related papers (2023-09-13T16:21:32Z) - Hardware optimized parity check gates for superconducting surface codes [0.0]
Error correcting codes use multi-qubit measurements to realize fault-tolerant quantum logic steps.
We analyze an unconventional surface code based on multi-body interactions between superconducting transmon qubits.
Despite the multi-body effects that underpin this approach, our estimates of logical faults suggest that this design can be at least as robust to realistic noise as conventional designs.
arXiv Detail & Related papers (2022-11-11T18:00:30Z) - Beyond Heisenberg Limit Quantum Metrology through Quantum Signal
Processing [0.0]
We propose a quantum-signal-processing framework to overcome noise-induced limitations in quantum metrology.
Our algorithm achieves an accuracy of $10-4$ radians in standard deviation for learning $theta$ in superconductingqubit experiments.
Our work is the first quantum-signal-processing algorithm that demonstrates practical application in laboratory quantum computers.
arXiv Detail & Related papers (2022-09-22T17:47:21Z) - Erasure qubits: Overcoming the $T_1$ limit in superconducting circuits [105.54048699217668]
amplitude damping time, $T_phi$, has long stood as the major factor limiting quantum fidelity in superconducting circuits.
We propose a scheme for overcoming the conventional $T_phi$ limit on fidelity by designing qubits in a way that amplitude damping errors can be detected and converted into erasure errors.
arXiv Detail & Related papers (2022-08-10T17:39:21Z) - Controlling Rayleigh-B\'enard convection via Reinforcement Learning [62.997667081978825]
The identification of effective control strategies to suppress or enhance the convective heat exchange under fixed external thermal gradients is an outstanding fundamental and technological issue.
In this work, we explore a novel approach, based on a state-of-the-art Reinforcement Learning (RL) algorithm.
We show that our RL-based control is able to stabilize the conductive regime and bring the onset of convection up to a Rayleigh number.
arXiv Detail & Related papers (2020-03-31T16:39:25Z)
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.