Active Learning with Variational Quantum Circuits for Quantum Process Tomography

• URL: http://arxiv.org/abs/2412.20925v1
• Date: Mon, 30 Dec 2024 13:12:56 GMT
• Title: Active Learning with Variational Quantum Circuits for Quantum Process Tomography
• Authors: Jiaqi Yang, Xiaohua Xu, Wei Xie,
• Abstract summary: We propose a framework for active learning (AL) to adaptively select a set of informative quantum states that improves the reconstruction most efficiently. We design and evaluate three types of AL algorithms: committee-based, uncertainty-based, and diversity-based. Results demonstrate that our algorithms achieve significantly improved reconstruction compared to the baseline method that selects a set of quantum states randomly.
• Score: 6.842224049271109
• License:
• Abstract: Quantum process tomography (QPT), used for reconstruction of an unknown quantum process from measurement data, is a fundamental tool for the diagnostic and full characterization of quantum systems. It relies on querying a set of quantum states as input to the quantum process. Previous works commonly use a straightforward strategy to select a set of quantum states randomly, overlooking differences in informativeness among quantum states. Since querying the quantum system requires multiple experiments that can be prohibitively costly, it is always the case that there are not enough quantum states for high-quality reconstruction. In this paper, we propose a general framework for active learning (AL) to adaptively select a set of informative quantum states that improves the reconstruction most efficiently. In particular, we introduce a learning framework that leverages the widely-used variational quantum circuits (VQCs) to perform the QPT task and integrate our AL algorithms into the query step. We design and evaluate three various types of AL algorithms: committee-based, uncertainty-based, and diversity-based, each exhibiting distinct advantages in terms of performance and computational cost. Additionally, we provide a guideline for selecting algorithms suitable for different scenarios. Numerical results demonstrate that our algorithms achieve significantly improved reconstruction compared to the baseline method that selects a set of quantum states randomly. Moreover, these results suggest that active learning based approaches are applicable to other complicated learning tasks in large-scale quantum information processing.

Related papers

• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Quantum algorithms: A survey of applications and end-to-end complexities [90.05272647148196]
  The anticipated applications of quantum computers span across science and industry. We present a survey of several potential application areas of quantum algorithms. We outline the challenges and opportunities in each area in an "end-to-end" fashion.
  arXiv  Detail & Related papers  (2023-10-04T17:53:55Z)
• Enhancing variational quantum state diagonalization using reinforcement learning techniques [1.583327010995414]
  We tackle the problem of designing a very shallow quantum circuit, required in the quantum state diagonalization task. We use a novel encoding method for the RL-state, a dense reward function, and an $epsilon$-greedy policy to achieve this. We demonstrate that the circuits proposed by the reinforcement learning methods are shallower than the standard variational quantum state diagonalization algorithm.
  arXiv  Detail & Related papers  (2023-06-19T17:59:04Z)
• QNEAT: Natural Evolution of Variational Quantum Circuit Architecture [95.29334926638462]
  We focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks. Although showing promising results, VQCs can be hard to train because of different issues, e.g., barren plateau, periodicity of the weights, or choice of architecture. We propose a gradient-free algorithm inspired by natural evolution to optimize both the weights and the architecture of the VQC.
  arXiv  Detail & Related papers  (2023-04-14T08:03:20Z)
• An introduction to variational quantum algorithms for combinatorial optimization problems [0.0]
  This tutorial provides a mathematical description of the class of Variational Quantum Algorithms. We introduce precisely the key aspects of these hybrid algorithms on the quantum side and the classical side. We devote a particular attention to QAOA, detailing the quantum circuits involved in that algorithm, as well as the properties satisfied by its possible guiding functions.
  arXiv  Detail & Related papers  (2022-12-22T14:27:52Z)
• Improved iterative quantum algorithm for ground-state preparation [4.921552273745794]
  We propose an improved iterative quantum algorithm to prepare the ground state of a Hamiltonian system. Our approach has advantages including the higher success probability at each iteration, the measurement precision-independent sampling complexity, the lower gate complexity, and only quantum resources are required when the ancillary state is well prepared.
  arXiv  Detail & Related papers  (2022-10-16T05:57:43Z)
• Adaptive Quantum State Tomography with Active Learning [0.0]
  We propose and implement an efficient scheme for quantum state tomography using active learning. We apply the scheme to reconstruct different multi-qubit states with varying degree of entanglement as well as to ground states of the XXZ model in 1D and a kinetically constrained spin chain. Our scheme is highly relevant to gain physical insights in quantum many-body systems as well as for benchmarking and characterizing quantum devices.
  arXiv  Detail & Related papers  (2022-03-29T16:23:10Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• Single-preparation unsupervised quantum machine learning: concepts and applications [1.7056768055368385]
  We first analyze the connections between all problems, in the classical and quantum frameworks. We then focus on their most challenging versions, involving quantum data and/or quantum processing means. We propose the quite general concept of SIngle-Preparation Quantum Information Processing.
  arXiv  Detail & Related papers  (2021-01-05T10:31:05Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

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.