QubitLens: An Interactive Learning Tool for Quantum State Tomography

• URL: http://arxiv.org/abs/2505.08056v2
• Date: Thu, 10 Jul 2025 23:06:47 GMT
• Title: QubitLens: An Interactive Learning Tool for Quantum State Tomography
• Authors: Mohammad Aamir Sohail, Ranga Sudharshan, S. Sandeep Pradhan, Arvind Rao,
• Abstract summary: Quantum state tomography is a fundamental task in quantum computing, involving the reconstruction of an unknown quantum state from measurement outcomes.<n>We introduce QubitLens, an interactive visualization tool designed to make quantum state tomography more accessible and intuitive.
• Score: 5.823436226004177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum state tomography is a fundamental task in quantum computing, involving the reconstruction of an unknown quantum state from measurement outcomes. Although essential, it is typically introduced at the graduate level due to its reliance on advanced concepts such as the density matrix formalism, tensor product structures, and partial trace operations. This complexity often creates a barrier for students and early learners. In this work, we introduce QubitLens, an interactive visualization tool designed to make quantum state tomography more accessible and intuitive. QubitLens leverages maximum likelihood estimation (MLE), a classical statistical method, to estimate pure quantum states from projective measurement outcomes in the X, Y, and Z bases. The tool emphasizes conceptual clarity through visual representations, including Bloch sphere plots of true and reconstructed qubit states, bar charts comparing parameter estimates, and fidelity gauges that quantify reconstruction accuracy. QubitLens offers a hands-on approach to learning quantum tomography without requiring deep prior knowledge of density matrices or optimization theory. The tool supports both single- and multi-qubit systems and is intended to bridge the gap between theory and practice in quantum computing education.

Related papers

• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
  Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Hamiltonian Dynamics Learning: A Scalable Approach to Quantum Process Characterization [6.741097425426473]
  We introduce an efficient quantum process learning method specifically designed for short-time Hamiltonian dynamics.<n>We demonstrate applications in quantum machine learning, where our protocol enables efficient training of variational quantum neural networks by directly learning unitary transformations.<n>This work establishes a new theoretical foundation for practical quantum dynamics learning, paving the way for scalable quantum process characterization in both near-term and fault-tolerant quantum computing.
  arXiv  Detail & Related papers  (2025-03-31T14:50:00Z)
• Experimental demonstration of enhanced quantum tomography via quantum reservoir processing [0.8672788660913944]
  We experimentally demonstrate a quantum reservoir processing approach for continuous-variable state reconstruction on a bosonic circuit quantum electrodynamics platform.<n>We show that the map learnt this way achieves high reconstruction fidelity for several test states, offering significantly enhanced performance over using a map calculated based on an idealised model of the system.
  arXiv  Detail & Related papers  (2024-12-15T02:02:43Z)
• 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 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)
• Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning [47.991114317813555]
  Quantum models implement implicit probabilistic predictors that produce multiple random decisions for each input through measurement shots. This paper proposes to leverage such randomness to define prediction sets for both classification and regression that provably capture the uncertainty of the model.
  arXiv  Detail & Related papers  (2023-04-06T22:05:21Z)
• An Introduction to Quantum Machine Learning for Engineers [36.18344598412261]
  Quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers. This book provides a self-contained introduction to quantum machine learning for an audience of engineers with a background in probability and linear algebra.
  arXiv  Detail & Related papers  (2022-05-11T12:10:52Z)
• 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)
• Facial Expression Recognition on a Quantum Computer [68.8204255655161]
  We show a possible solution to facial expression recognition using a quantum machine learning approach. We define a quantum circuit that manipulates the graphs adjacency matrices encoded into the amplitudes of some appropriately defined quantum states.
  arXiv  Detail & Related papers  (2021-02-09T13:48:00Z)
• Maximal entropy approach for quantum state tomography [3.6344381605841187]
  Current quantum computing devices are noisy intermediate-scale quantum $($NISQ$)$ devices. Quantum tomography tries to reconstruct a quantum system's density matrix by a complete set of observables. We propose an alternative approach to quantum tomography, based on the maximal information entropy, that can predict the values of unknown observables.
  arXiv  Detail & Related papers  (2020-09-02T04:39:45Z)
• Reconstructing quantum states with quantum reservoir networks [4.724825031148412]
  We introduce a quantum state tomography platform based on the framework of reservoir computing. It forms a quantum neural network, and operates as a comprehensive device for reconstructing an arbitrary quantum state.
  arXiv  Detail & Related papers  (2020-08-14T14:01:55Z)

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.