Learning the physics of open quantum systems from experiments

• URL: http://arxiv.org/abs/2412.00078v1
• Date: Tue, 26 Nov 2024 19:23:02 GMT
• Title: Learning the physics of open quantum systems from experiments
• Authors: Alexandra Ramôa,
• Abstract summary: This thesis explores adaptive inference as a tool to characterize quantum systems using experimental data. I propose and test algorithms for learning Hamiltonian and Kraus operators using Bayesian experimental design and advanced Monte Carlo techniques.
• Score: 55.2480439325792
• License:
• Abstract: This thesis explores adaptive inference as a tool to characterize quantum systems using experimental data, with applications in sensing, calibration, control, and metrology. I propose and test algorithms for learning Hamiltonian and Kraus operators using Bayesian experimental design and advanced Monte Carlo techniques, including Sequential and Hamiltonian Monte Carlo. Application to the characterization of quantum devices from IBMQ shows a robust performance, surpassing the built-in characterization functions of Qiskit for the same number of measurements. Introductions to Bayesian statistics, experimental design, and numerical integration are provided, as well as an overview of existing literature.

Related papers

• Digital quantum simulation of bosonic systems and quantum complementarity [0.0]
  Many-particle bosonic systems and intricate optical experimental setups pose significant challenges for classical simulation methods. We digitally simulate interferometric variants of Afshar's experiment on IBM's quantum computers. We analyze these experiments within the framework of an updated quantum complementarity principle.
  arXiv  Detail & Related papers  (2025-02-03T21:04:34Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Wigner State and Process Tomography on Near-Term Quantum Devices [0.0]
  We present an experimental scanning-based tomography approach for near-term quantum devices. The approach is based on a Wigner-type representation of quantum states and operators. It can be directly implemented using the Python-based software package textttDROPStomo
  arXiv  Detail & Related papers  (2023-02-24T16:22:51Z)
• Mathematical approaches for characterization, control, calibration and validation of a quantum computing device [0.4499833362998487]
  This report walks through the entire procedure from the characterization and optimal control of a qudit device to the validation of the results. A goal of the report is to provide an introduction for students and researchers, especially computational mathematicians, who are interested in but new to quantum computing.
  arXiv  Detail & Related papers  (2023-01-25T17:23:29Z)
• Quantum Simulations in Effective Model Spaces (I): Hamiltonian Learning-VQE using Digital Quantum Computers and Application to the Lipkin-Meshkov-Glick Model [0.0]
  We introduce an iterative hybrid-classical-quantum algorithm, Hamiltonian learning variational quantum eigensolver (HL-VQE) HL-VQE is found to provide an exponential improvement in Lipkin-Meshkov-Glick model calculations. This work constitutes a step in the development of entanglement-driven quantum algorithms for the description of nuclear systems.
  arXiv  Detail & Related papers  (2023-01-14T21:10:02Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• Numerical and geometrical aspects of flow-based variational quantum Monte Carlo [4.2463407840464615]
  This article focuses on the example of bosons in the field amplitude (variablequadrature) basis. Some practical instructions are provided to guide the implementation of a PyTorch code. The review is intended to be accessible to researchers interested in machine learning and quantum information science.
  arXiv  Detail & Related papers  (2022-03-28T15:04:29Z)
• Quantum circuits for the preparation of spin eigenfunctions on quantum computers [63.52264764099532]
  Hamiltonian symmetries are an important instrument to classify relevant many-particle wavefunctions. This work presents quantum circuits for the exact and approximate preparation of total spin eigenfunctions on quantum computers.
  arXiv  Detail & Related papers  (2022-02-19T00:21:46Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• Preparing random states and benchmarking with many-body quantum chaos [48.044162981804526]
  We show how to predict and experimentally observe the emergence of random state ensembles naturally under time-independent Hamiltonian dynamics. The observed random ensembles emerge from projective measurements and are intimately linked to universal correlations built up between subsystems of a larger quantum system. Our work has implications for understanding randomness in quantum dynamics, and enables applications of this concept in a wider context.
  arXiv  Detail & Related papers  (2021-03-05T08:32:43Z)

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.