Direct Fidelity Estimation of Quantum States using Machine Learning

• URL: http://arxiv.org/abs/2102.02369v2
• Date: Mon, 27 Sep 2021 14:24:00 GMT
• Title: Direct Fidelity Estimation of Quantum States using Machine Learning
• Authors: Xiaoqian Zhang, Maolin Luo, Zhaodi Wen, Qin Feng, Shengshi Pang, Weiqi Luo, and Xiaoqi Zhou
• Abstract summary: In almost all quantum applications, one of the key steps is to verify that the fidelity of the prepared quantum state meets expectations. We propose a new approach solving this problem using machine-learning techniques.
• Score: 8.306287613158094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In almost all quantum applications, one of the key steps is to verify that the fidelity of the prepared quantum state meets expectations. In this Letter, we propose a new approach solving this problem using machine-learning techniques. Compared to other fidelity estimation methods, our method is applicable to arbitrary quantum states, the number of required measurement settings is small, and this number does not increase with the size of the system. For example, for a general five-qubit quantum state, only four measurement settings are required to predict its fidelity with $\pm1\%$ precision in a nonadversarial scenario. This machine-learning-based approach for estimating quantum state fidelity has the potential to be widely used in the field of quantum information.

Related papers

• On-Chip Verified Quantum Computation with an Ion-Trap Quantum Processing Unit [0.5497663232622965]
  We present and experimentally demonstrate a novel approach to verification and benchmarking of quantum computing. Unlike previous information-theoretically secure verification protocols, our approach is implemented entirely on-chip. Our results pave the way for more accessible and efficient verification and benchmarking strategies in near-term quantum devices.
  arXiv  Detail & Related papers  (2024-10-31T16:54:41Z)
• Non-unitary Coupled Cluster Enabled by Mid-circuit Measurements on Quantum Computers [37.69303106863453]
  We propose a state preparation method based on coupled cluster (CC) theory, which is a pillar of quantum chemistry on classical computers. Our approach leads to a reduction of the classical computation overhead, and the number of CNOT and T gates by 28% and 57% on average.
  arXiv  Detail & Related papers  (2024-06-17T14:10:10Z)
• A universal scheme to self-test any quantum state and extremal measurement [41.94295877935867]
  quantum network considered in this work is the simple star network, which is implementable using current technologies. For our purposes, we also construct a scheme that can be used to self-test the two-dimensional tomographically complete set of measurements with an arbitrary number of parties.
  arXiv  Detail & Related papers  (2023-12-07T16:20:28Z)
• Preparation of Entangled Many-Body States with Machine Learning [0.06768558752130309]
  Preparation of a target quantum many-body state on quantum simulators is one of the significant steps in quantum science and technology. With a small number of qubits, a few quantum states, such as the Greenberger-Horne-Zeilinger state, have been prepared, but fundamental difficulties in systems with many qubits remain. Here, we provide one algorithm with an implementation of a deep learning process and achieve to prepare the target ground states with many qubits.
  arXiv  Detail & Related papers  (2023-07-27T05:03:57Z)
• Estimating many properties of a quantum state via quantum reservoir processing [2.5432391525687748]
  We propose a general framework for constructing classical approximations of arbitrary quantum states with quantum reservoirs. A key advantage of our method is that only a single local measurement setting is required for estimating arbitrary properties. This estimation scheme is extendable to higher-dimensional systems and hybrid systems with non-identical local dimensions.
  arXiv  Detail & Related papers  (2023-05-11T15:21:21Z)
• Assessing requirements to scale to practical quantum advantage [56.22441723982983]
  We develop a framework for quantum resource estimation, abstracting the layers of the stack, to estimate resources required for large-scale quantum applications. We assess three scaled quantum applications and find that hundreds of thousands to millions of physical qubits are needed to achieve practical quantum advantage. A goal of our work is to accelerate progress towards practical quantum advantage by enabling the broader community to explore design choices across the stack.
  arXiv  Detail & Related papers  (2022-11-14T18:50:27Z)
• Anticipative measurements in hybrid quantum-classical computation [68.8204255655161]
  We present an approach where the quantum computation is supplemented by a classical result. Taking advantage of its anticipation also leads to a new type of quantum measurements, which we call anticipative. In an anticipative quantum measurement the combination of the results from classical and quantum computations happens only in the end.
  arXiv  Detail & Related papers  (2022-09-12T15:47:44Z)
• Efficient criteria of quantumness for a large system of qubits [58.720142291102135]
  We discuss the dimensionless combinations of basic parameters of large, partially quantum coherent systems. Based on analytical and numerical calculations, we suggest one such number for a system of qubits undergoing adiabatic evolution.
  arXiv  Detail & Related papers  (2021-08-30T23:50:05Z)
• 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)
• Quantum Fisher information from randomized measurements [0.0]
  The quantum Fisher information (QFI) is a fundamental quantity of interest in many areas. We use measurements of the density matrix to construct lower bounds that converge to the QFI. We present two examples of applications of the method in quantum systems made of coupled qubits and collective spins.
  arXiv  Detail & Related papers  (2021-05-27T14:16:14Z)
• Pure State Tomography with Fourier Transformation [3.469001874498102]
  Two adaptive protocols are proposed, with their respective quantum circuits. Experiments on the IBM 5-qubit quantum computer, as well as numerical investigations, demonstrate the feasibility of the proposed protocols.
  arXiv  Detail & Related papers  (2020-08-20T17:13:09Z)

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.