SesQ: A Surface Electrostatic Simulator for Precise Energy Participation Ratio Simulation in Superconducting Qubits
- URL: http://arxiv.org/abs/2603.28524v1
- Date: Mon, 30 Mar 2026 14:51:55 GMT
- Title: SesQ: A Surface Electrostatic Simulator for Precise Energy Participation Ratio Simulation in Superconducting Qubits
- Abstract summary: SesQ is a surface integral equation simulator tailored for the precise simulation of the EPR.<n>SesQ is a powerful tool for the automated design of low-loss superconducting quantum circuits.
- Score: 23.808940942742137
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: An accurate and efficient numerical electromagnetic model for superconducting qubits is essential for characterizing and minimizing design-dependent dielectric losses. The energy participation ratio (EPR) is the commonly adopted metric used to evaluate these losses, but its calculation presents a severe multiscale computational challenge. Conventional finite element method (FEM) requires 3D volumetric meshing, leading to prohibitive computational costs and memory requirements when attempting to capture singular electric fields at nanometer-thin material interfaces. To address this bottleneck, we propose SesQ, a surface integral equation simulator tailored for the precise simulation of the EPR. By applying discretization on 2D surfaces, deriving a semi-analytical multilayer Green's function, and employing a dedicated non-conformal boundary mesh refinement scheme, SesQ accurately resolves singular edge fields without an explosive growth in the number of unknowns. Validations with analytically solvable models demonstrate that SesQ accelerates capacitance extraction by roughly two orders of magnitude compared to commercial FEM tools. While achieving comparable accuracy for capacitance extraction, SesQ delivers superior precision for EPR calculation. Simulations of practical transmon qubits further reveal that FEM approaches tend to significantly underestimate the EPR. Finally, the high efficiency of SesQ enables rapid iteration in the layout optimization, as demonstrated by minimizing the EPR of the qubit pattern, establishing the simulator as a powerful tool for the automated design of low-loss superconducting quantum circuits.
Related papers
- AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries [47.27974914502511]
We introduce a deep learning surrogate pipeline based on the Swin3D Transformer to predict discharge dynamics directly from data.<n>The proposed method reduces the computational overhead by orders of magnitude, providing a scalable and efficient framework for high- throughput battery design and optimization.
arXiv Detail & Related papers (2026-07-22T09:10:48Z) - Dynamic Symmetric Point Tracking: Tackling Non-ideal Reference in Analog In-memory Training [46.75046795995564]
We present the first theoretical characterization of the pulse complexity of SP calibration and the resulting estimation error.<n>We propose a dynamic SP estimation method that tracks the SP during model training, and establishes its convergence guarantees.
arXiv Detail & Related papers (2026-02-24T19:41:34Z) - Open-Source Highly Parallel Electromagnetic Simulations for Superconducting Circuits [0.0]
We introduce SQDMetal, a Python-based API that integrates Qiskit Metal (IBM), Gmsh, Palace (AWS), and Paraview (Kitware) into an open-source, highly parallel simulation workflow for superconducting quantum circuits.<n> SQDMetal enables accurate, efficient, and scalable simulations while remaining community-driven and free from commercial constraints.
arXiv Detail & Related papers (2025-11-03T04:39:33Z) - Practical Application of the Quantum Carleman Lattice Boltzmann Method in Industrial CFD Simulations [44.99833362998488]
This work presents a practical numerical assessment of a hybrid quantum-classical approach to CFD based on the Lattice Boltzmann Method (LBM)<n>We evaluate this method on three benchmark cases featuring different boundary conditions, periodic, bounceback, and moving wall.<n>Our results confirm the validity of the approach, achieving median error fidelities on the order of $10-3$ and success probabilities sufficient for practical quantum state sampling.
arXiv Detail & Related papers (2025-04-17T15:41:48Z) - Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo [4.437335677401287]
We introduce a novel method for the geometrically transferable optimization of neural network wave functions.<n>Our method enables the efficient prediction of ground and excited-state PESs and their intersections at the highest accuracy.<n>We validate our approach on three challenging excited-state PESs, including ethylene, the carbon dimer, and the methylenimmonium cation.
arXiv Detail & Related papers (2025-03-25T17:12:29Z) - QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edge [55.75103034526652]
We propose QuartDepth which adopts post-training quantization to quantize MDE models with hardware accelerations for ASICs.<n>Our approach involves quantizing both weights and activations to 4-bit precision, reducing the model size and computation cost.<n>We design a flexible and programmable hardware accelerator by supporting kernel fusion and customized instruction programmability.
arXiv Detail & Related papers (2025-03-20T21:03:10Z) - TANQ-Sim: Tensorcore Accelerated Noisy Quantum System Simulation via QIR on Perlmutter HPC [16.27167995786167]
TANQ-Sim is a full-scale density matrix based simulator designed to simulate practical deep circuits with both coherent and non-coherent noise.
To address the significant computational cost associated with such simulations, we propose a new density-matrix simulation approach.
To optimize performance, we also propose specific gate fusion techniques for density matrix simulation.
arXiv Detail & Related papers (2024-04-19T21:16:29Z) - Optimized noise-assisted simulation of the Lindblad equation with
time-dependent coefficients on a noisy quantum processor [0.6990493129893112]
Noise can be an asset in digital quantum simulations of open systems on Noisy Intermediate-Scale Quantum (NISQ) devices.
We introduce an optimized decoherence rate control scheme that can significantly reduce computational requirements by multiple orders of magnitude.
arXiv Detail & Related papers (2024-02-12T12:48:03Z) - On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks [52.97107229149988]
We propose an On-Chip Hardware-Aware Quantization framework, performing hardware-aware mixed-precision quantization on deployed edge devices.
For efficiency metrics, we built an On-Chip Quantization Aware pipeline, which allows the quantization process to perceive the actual hardware efficiency of the quantization operator.
For accuracy metrics, we propose Mask-Guided Quantization Estimation technology to effectively estimate the accuracy impact of operators in the on-chip scenario.
arXiv Detail & Related papers (2023-09-05T04:39:34Z) - Green, Quantized Federated Learning over Wireless Networks: An
Energy-Efficient Design [68.86220939532373]
The finite precision level is captured through the use of quantized neural networks (QNNs) that quantize weights and activations in fixed-precision format.
The proposed FL framework can reduce energy consumption until convergence by up to 70% compared to a baseline FL algorithm.
arXiv Detail & Related papers (2022-07-19T16:37:24Z) - On the Tradeoff between Energy, Precision, and Accuracy in Federated
Quantized Neural Networks [68.52621234990728]
Federated learning (FL) over wireless networks requires balancing between accuracy, energy efficiency, and precision.
We propose a quantized FL framework that represents data with a finite level of precision in both local training and uplink transmission.
Our framework can reduce energy consumption by up to 53% compared to a standard FL model.
arXiv Detail & Related papers (2021-11-15T17:00:03Z)
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.