Approximate Message Passing for Quantum State Tomography

• URL: http://arxiv.org/abs/2511.12857v1
• Date: Mon, 17 Nov 2025 01:05:11 GMT
• Title: Approximate Message Passing for Quantum State Tomography
• Authors: Noah Siekierski, Kausthubh Chandramouli, Christian Kümmerle, Bojko N. Bakalov, Dror Baron,
• Abstract summary: We show how approximate message passing (AMP) can be used to perform low-rank quantum tomography (QST)<n>AMP providesally optimal performance guarantees for large systems, which suggests its utility for QST.<n>Our work advances the state of low-rank QST and may be applicable to other quantum tomography protocols.
• Score: 17.166996204619156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum state tomography (QST) is an indispensable tool for characterizing many-body quantum systems. However, due to the exponential scaling cost of the protocol with system size, many approaches have been developed for quantum states with specific structure, such as low-rank states. In this paper, we show how approximate message passing (AMP), a compressed sensing technique, can be used to perform low-rank QST. AMP provides asymptotically optimal performance guarantees for large systems, which suggests its utility for QST. We discuss the design challenges that come with applying AMP to QST, and show that by properly designing the AMP algorithm, we can reduce the reconstruction infidelity by over an order of magnitude compared to existing approaches to low-rank QST. We also performed tomographic experiments on IBM Kingston and considered the effect of device noise on the reliability of the predicted fidelity of state preparation. Our work advances the state of low-rank QST and may be applicable to other quantum tomography protocols.

Related papers

• Quantum Machine Learning for State Tomography Using Classical Data [0.0]
  Reconstructing quantum states from measurement data represents a formidable challenge in quantum information science.<n>Recent studies have explored quantum machine learning (QML) for quantum state tomography (QST)<n>We present a QML-based tomography protocol that operates entirely on classical measurement data and is fully executable on noisy intermediate-scale quantum (NISQ) devices.
  arXiv  Detail & Related papers  (2025-07-01T23:42:19Z)
• Minimal Quantum Reservoirs with Hamiltonian Encoding [72.27323884094953]
  We investigate a minimal architecture for quantum reservoir computing based on Hamiltonian encoding.<n>This approach circumvents many of the experimental overheads typically associated with quantum machine learning.
  arXiv  Detail & Related papers  (2025-05-28T16:50:05Z)
• Bayesian Quantum Amplitude Estimation [46.03321798937855]
  We present BAE, a problem-tailored and noise-aware Bayesian algorithm for quantum amplitude estimation.<n>In a fault tolerant scenario, BAE is capable of saturating the Heisenberg limit; if device noise is present, BAE can dynamically characterize it and self-adapt.<n>We propose a benchmark for amplitude estimation algorithms and use it to test BAE against other approaches.
  arXiv  Detail & Related papers  (2024-12-05T18:09:41Z)
• Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
  We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC) This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions. Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
  arXiv  Detail & Related papers  (2024-11-13T12:03:39Z)
• Resource-Efficient Quantum Correlation Measurement: A Multicopy Neural Network Approach for Practical Applications [0.0]
  We propose an alternative strategy that reduces the required information by combining multicopy measurements with artificial neural networks.<n>We have successfully measured two-qubit quantum correlations of Bell states subjected to a depolarizing channel.<n>Our experiments, conducted with transmon qubits on IBMQ processors, quantified the violation of Bell's inequality and the negativity of two-qubit entangled states.
  arXiv  Detail & Related papers  (2024-11-08T18:07:23Z)
• Optimal Quantum Purity Amplification [2.05170973574812]
  We present the optimal QPA protocol for general quantum systems and global noise.<n>We provide an efficient implementation of the protocol based on generalized quantum phase estimation.<n> Numerical simulations demonstrate the effectiveness of our protocol applied to quantum simulation of Hamiltonian evolution.
  arXiv  Detail & Related papers  (2024-09-26T17:46:00Z)
• Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning [0.5181797490530444]
  We implement methods for the efficient preparation of quantum states representing encoded image data using variational, genetic and matrix product state based algorithms. Results show that these methods can approximately prepare states to a level suitable for QML using circuits two orders of magnitude shallower than a standard state preparation implementation.
  arXiv  Detail & Related papers  (2023-09-18T01:49:36Z)
• Quantum State Tomography using Quantum Machine Learning [0.0]
  We propose the integration of Quantum Machine Learning (QML) techniques to enhance the efficiency of Quantum State Tomography (QST) Our results show that our QML-based QST approach can achieve high fidelity (98%) with significantly fewer measurements than conventional methods.
  arXiv  Detail & Related papers  (2023-08-20T17:51:24Z)
• Robust and efficient verification of graph states in blind measurement-based quantum computation [52.70359447203418]
  Blind quantum computation (BQC) is a secure quantum computation method that protects the privacy of clients. It is crucial to verify whether the resource graph states are accurately prepared in the adversarial scenario. Here, we propose a robust and efficient protocol for verifying arbitrary graph states with any prime local dimension.
  arXiv  Detail & Related papers  (2023-05-18T06:24:45Z)
• Potential and limitations of quantum extreme learning machines [55.41644538483948]
  We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements. Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs.
  arXiv  Detail & Related papers  (2022-10-03T09:32:28Z)
• Dynamical learning of a photonics quantum-state engineering process [48.7576911714538]
  Experimentally engineering high-dimensional quantum states is a crucial task for several quantum information protocols. We implement an automated adaptive optimization protocol to engineer photonic Orbital Angular Momentum (OAM) states. This approach represents a powerful tool for automated optimizations of noisy experimental tasks for quantum information protocols and technologies.
  arXiv  Detail & Related papers  (2022-01-14T19:24:31Z)
• 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 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.