Fast Machine Learning for Quantum Control of Microwave Qudits on Edge Hardware

• URL: http://arxiv.org/abs/2506.03323v1
• Date: Tue, 03 Jun 2025 19:14:18 GMT
• Title: Fast Machine Learning for Quantum Control of Microwave Qudits on Edge Hardware
• Authors: Flor Sanders, Gaurav Agarwal, Luca Carloni, Giuseppe Di Guglielmo, Andy C. Y. Li, Gabriel N. Perdue,
• Abstract summary: It is paramount to have systems with extremely low delays to quickly adjust quantum hardware settings, where fidelity is defined as overlap with a target quantum state.<n>Here, we utilize machine learning (ML) models to determine control-pulse parameters for preparing Selective Number-dependent Arbitrary Phase (SNAP) gates in microwave cavity qudits.<n>Our results demonstrate the efficacy of the proposed approach, with optimized models achieving low gate trace infidelity near $10-3$ and efficient utilization of programmable logic resources.
• Score: 1.4029771773420519
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum optimal control is a promising approach to improve the accuracy of quantum gates, but it relies on complex algorithms to determine the best control settings. CPU or GPU-based approaches often have delays that are too long to be applied in practice. It is paramount to have systems with extremely low delays to quickly and with high fidelity adjust quantum hardware settings, where fidelity is defined as overlap with a target quantum state. Here, we utilize machine learning (ML) models to determine control-pulse parameters for preparing Selective Number-dependent Arbitrary Phase (SNAP) gates in microwave cavity qudits, which are multi-level quantum systems that serve as elementary computation units for quantum computing. The methodology involves data generation using classical optimization techniques, ML model development, design space exploration, and quantization for hardware implementation. Our results demonstrate the efficacy of the proposed approach, with optimized models achieving low gate trace infidelity near $10^{-3}$ and efficient utilization of programmable logic resources.

Related papers

• Quantum Circuit-Based Adaptation for Credit Risk Analysis [27.308408027453012]
  Noisy and Intermediate-Scale Quantum, or NISQ, processors are sensitive to noise, prone to quantum decoherence, and are not yet capable of continuous quantum error correction for fault-tolerant quantum computation.<n>We experimentally study how hardware-aware variational quantum circuits on a superconducting quantum processing unit can model distributions relevant to specific use-case applications for Credit Risk Analysis.
  arXiv  Detail & Related papers  (2026-01-11T11:17:37Z)
• Neural Architecture Search for Quantum Autoencoders [30.784540105806784]
  We propose a neural architecture search (NAS) framework that automates the design of quantum autoencoders.<n>By systematically evolving variational quantum circuit (VQC) configurations, our method seeks to identify high-performing hybrid quantum-classical autoencoders.
  arXiv  Detail & Related papers  (2025-11-24T15:55:44Z)
• Achieving fast and robust perfect entangling gates via reinforcement learning [0.08030359871216612]
  We leverage reinforcement learning techniques to discover near-optimal pulse shapes that yield PE gates.<n>A collection of RL agents is trained within robust simulation environments, enabling the identification of effective control strategies.<n>The RL approach is hardware agnostic with the potential for broad applicability across various quantum computing platforms.
  arXiv  Detail & Related papers  (2025-11-10T13:07:19Z)
• Reinforcement Learning for Quantum Network Control with Application-Driven Objectives [53.03367590211247]
  Dynamic programming and reinforcement learning offer promising tools for optimizing control strategies.<n>We propose a novel RL framework that directly optimize non-linear, differentiable objective functions.<n>Our work comprises the first step towards non-linear objective function optimization in quantum networks with RL, opening a path towards more advanced use cases.
  arXiv  Detail & Related papers  (2025-09-12T18:41:10Z)
• Q-Fusion: Diffusing Quantum Circuits [2.348041867134616]
  We propose a diffusion-based algorithm leveraging the LayerDAG framework to generate new quantum circuits.<n>Our results demonstrate that the proposed model consistently generates 100% valid quantum circuit outputs.
  arXiv  Detail & Related papers  (2025-04-29T14:10:10Z)
• An Accurate and Efficient Analytic Model of Fidelity Under Depolarizing Noise Oriented to Large Scale Quantum System Design [1.80755313284025]
  We present a comprehensive theoretical framework to predict the fidelity of quantum circuits under depolarizing noise.<n>We propose an efficient fidelity estimation algorithm based on device calibration data.<n>The proposed approach provides a scalable and practical tool for benchmarking quantum hardware.
  arXiv  Detail & Related papers  (2025-03-09T16:59:24Z)
• Differentiable Quantum Computing for Large-scale Linear Control [26.118874431217165]
  We introduce an end-to-end quantum algorithm for linear-quadratic control with provable speedups. Our algorithm, based on a policy gradient method, incorporates a novel quantum subroutine for solving the matrix Lyapunov equation.
  arXiv  Detail & Related papers  (2024-11-03T00:54:33Z)
• QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design [63.02824918725805]
  Quantum computing is recognized for the significant speedup it offers over classical computing through quantum algorithms.<n>QCircuitBench is the first benchmark dataset designed to evaluate AI's capability in designing and implementing quantum algorithms.
  arXiv  Detail & Related papers  (2024-10-10T14:24:30Z)
• Review: Quantum Architecture Search with Unsupervised Representation Learning [24.698519892763283]
  Unsupervised representation learning presents new opportunities for advancing Quantum Architecture Search (QAS)<n>QAS is designed to optimize quantum circuits for Variational Quantum Algorithms (VQAs)
  arXiv  Detail & Related papers  (2024-01-21T19:53:17Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Machine-learning-inspired quantum optimal control of nonadiabatic geometric quantum computation via reverse engineering [3.3216171033358077]
  We propose a promising average-fidelity-based machine-learning-inspired method to optimize the control parameters. We implement a single-qubit gate by cat-state nonadiabatic geometric quantum computation via reverse engineering. We demonstrate that the neural network possesses the ability to expand the model space.
  arXiv  Detail & Related papers  (2023-09-28T14:36:26Z)
• Quantum Gate Optimization for Rydberg Architectures in the Weak-Coupling Limit [55.05109484230879]
  We demonstrate machine learning assisted design of a two-qubit gate in a Rydberg tweezer system. We generate optimal pulse sequences that implement a CNOT gate with high fidelity. We show that local control of single qubit operations is sufficient for performing quantum computation on a large array of atoms.
  arXiv  Detail & Related papers  (2023-06-14T18:24:51Z)
• Direct pulse-level compilation of arbitrary quantum logic gates on superconducting qutrits [36.30869856057226]
  We demonstrate any arbitrary qubit and qutrit gate can be realized with high-fidelity, which can significantly reduce the length of a gate sequence. We show that optimal control gates are robust to drift for at least three hours and that the same calibration parameters can be used for all implemented gates.
  arXiv  Detail & Related papers  (2023-03-07T22:15:43Z)
• Hybrid Gate-Pulse Model for Variational Quantum Algorithms [33.73469431747376]
  Current quantum programs are mostly compiled on the gate-level, where quantum circuits are composed of quantum gates. pulse-level optimization has gained more attention from researchers due to their advantages in terms of circuit duration. We present a hybrid gate-pulse model that can mitigate these problems.
  arXiv  Detail & Related papers  (2022-12-01T17:06:35Z)
• Neural network accelerator for quantum control [3.9756120456577007]
  In this study, we demonstrate a machine learning algorithm for predicting optimal pulse parameters. This algorithm is lightweight enough to fit on a low-resource FPGA and perform inference with a latency of 175 ns. In the long term, such an accelerator could be used near quantum computing hardware where traditional computers cannot operate.
  arXiv  Detail & Related papers  (2022-08-04T13:23:53Z)
• Efficient exploration of Hamiltonian parameter space for optimal control of non-Markovian open quantum systems [0.0]
  We present a general method to efficiently design optimal control sequences for non-Markovian open quantum systems. We illustrate it by optimizing the shape of a laser pulse to prepare a quantum dot in a specific state.
  arXiv  Detail & Related papers  (2021-01-08T16:02:31Z)
• Electronic structure with direct diagonalization on a D-Wave quantum annealer [62.997667081978825]
  This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer. We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
  arXiv  Detail & Related papers  (2020-09-02T22:46:47Z)

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.