paper-with-me

Papers

Quantum process tomography with unknown single-preparation input states

2019-09-18 · Yannick Deville, Alain Deville

Quantum Process Tomography (QPT) methods aim at identifying, i.e. estimating, a given quantum process. QPT is a major quantum information processing tool, since it especially allows one to characterize the actual behavior of quantum gates, which are the building blocks of quantum computers. However, usual QPT procedures are complicated, since they set several constraints on the quantum states used as inputs of the process to be characterized. In this paper, we extend QPT so as to avoid two such constraints. On the one hand, usual QPT methods requires one to know, hence to precisely control (i.e. prepare), the specific quantum states used as inputs of the considered quantum process, which is cumbersome. We therefore propose a Blind, or unsupervised, extension of QPT (i.e. BQPT), which means that this approach uses input quantum states whose values are unknown and arbitrary, except that they are requested to meet some general known properties (and this approach exploits the output states of the considered quantum process). On the other hand, usual QPT methods require one to be able to prepare many copies of the same (known) input state, which is constraining. On the contrary, we propose "single-preparation methods", i.e. methods which can operate with only one instance of each considered input state. These two new concepts are here illustrated with practical BQPT methods which are numerically validated, in the case when: i) random pure states are used as inputs and their required properties are especially related to the statistical independence of the random variables that define them, ii) the considered quantum process is based on cylindrical-symmetry Heisenberg spin coupling. These concepts may be extended to a much wider class of processes and to BQPT methods based on other input quantum state properties.

📄 PDF Abstract BibTeX arXiv:1909.08401

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Single-preparation unsupervised quantum machine learning: concepts and applications

2021-01-05 · Yannick Deville, Alain Deville

The term "machine learning" especially refers to algorithms that derive mappings, i.e. intput/output transforms, by using numerical data that provide information about considered transforms. These transforms appear in ma…

BIG-bench Machine Learningparameter estimationQuantum Machine LearningState Estimation

Experimental neural network enhanced quantum tomography

2019-04-11 · Adriano Macarone Palmieri, Egor Kovlakov, Federico Bianchi, Dmitry Yudin 외

Quantum tomography is currently ubiquitous for testing any implementation of a quantum information processing device. Various sophisticated procedures for state and process reconstruction from measured data are well deve…

Agnostic Process Tomography

2024-10-15 · Chirag Wadhwa, Laura Lewis, Elham Kashefi, Mina Doosti

Characterizing a quantum system by learning its state or evolution is a fundamental problem in quantum physics and learning theory with a myriad of applications. Recently, as a new approach to this problem, the task of a…

Learning TheoryQuantum Machine Learning

Variational Quantum Circuits for Quantum State Tomography

2019-12-16 · Yong Liu, Dongyang Wang, Shichuan Xue, Anqi Huang 외

Quantum state tomography is a key process in most quantum experiments. In this work, we employ quantum machine learning for state tomography. Given an unknown quantum state, it can be learned by maximizing the fidelity b…

Quantum Machine LearningQuantum State Tomography

Foundations for learning from noisy quantum experiments

2022-04-28 · Hsin-Yuan Huang, Steven T. Flammia, John Preskill

Understanding what can be learned from experiments is central to scientific progress. In this work, we use a learning-theoretic perspective to study the task of learning physical operations in a quantum machine when all …

Benchmarking