Efficient quantum state tomography with two complementary projective measurements
- URL: http://arxiv.org/abs/2609.15662v1
- Date: Mon, 14 Sep 2026 14:43:08 GMT
- Title: Efficient quantum state tomography with two complementary projective measurements
- Abstract summary: Quantum state tomography (QST) is of fundamental importance to characterize quantum systems in quantum information processing.<n>We present a novel QST protocol that utilizes Kirkwood-Dirac (KD) quasiprobability to reconstruct quantum states.<n>The full reconstruction of randomly generated 15-qubit mixed-state instances can be accomplished within 20 minutes under the GPU implementation.
- Score: 15.143727690396195
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Quantum state tomography (QST) is of fundamental importance to characterize quantum systems in quantum information processing, but its practical implementation is severely hindered by the exponential scaling of measurement and computational costs. In this paper, we present a novel QST protocol that utilizes Kirkwood-Dirac (KD) quasiprobability to reconstruct quantum states. First, it enables state reconstruction with only two complementary rank-one projective measurements, thus significantly reducing the measurement cost. Then, a complex logistic regression estimator is proposed to process collected KD data, together with a projected gradient algorithm to mitigate numerical instability and to accelerate convergence. The product-operator structure of KD quasiprobability is further exploited to reduce the computational cost. Finally, extensive experiments are implemented to confirm the validity of our protocol. Notably, the full reconstruction of randomly generated 15-qubit mixed-state instances can be accomplished within 20 minutes under the GPU implementation. These results suggest a promising route toward scalable QST and benchmarking large-scale quantum systems.
Related papers
- Statistical and Algorithmic Foundations of Probing Quantum Systems with Compressive Measurements: A Review [23.123362765639214]
Quantum state tomography aims to reconstruct unknown quantum states from measurement data.<n>Prior structure, such as low-rankness, tensor-network representations, shallow quantum circuits, and neural quantum states, can substantially reduce the effective degrees of freedom.
arXiv Detail & Related papers (2026-05-26T15:44:42Z) - Towards Efficient Verification of Computation in Quantum Devices [12.146871607856037]
Traditional methods of comprehensively verifying quantum devices, such as quantum process tomography, face significant limitations because of the exponential growth in computational resources.<n>In this paper, we investigate the structure of computations on the hardware, focusing on the layered interruptible quantum circuit model.<n>Our method completely reconstructs the circuits within a time complexity of $O(d2 t log (n/delta))$, guaranteeing success with a probability of at least $1-delta$.<n>Our approach significantly reduces execution time for completely verifying computations in quantum devices, achieving double logarithmic scaling in the problem size.
arXiv Detail & Related papers (2025-08-01T02:10:06Z) - Efficient quantum state tomography with auxiliary systems [5.577276175518491]
Quantum state tomography is a technique in quantum information science used to reconstruct the density matrix of an unknown quantum state.<n>As the size of the quantum system increases, the number of measurement settings and sampling requirements for quantum state tomography grow exponentially with the number of qubits.<n>This study proposes a state tomography method based on auxiliary systems to reduce measurement settings and improve sampling efficiency.
arXiv Detail & Related papers (2025-07-22T12:40:14Z) - 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) - 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) - Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [62.46800898243033]
Recent progress in quantum learning theory prompts a question: can linear properties of a large-qubit circuit be efficiently learned from measurement data generated by varying classical inputs?<n>We prove that the sample complexity scaling linearly in $d$ is required to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.<n>We propose a kernel-based method leveraging classical shadows and truncated trigonometric expansions, enabling a controllable trade-off between prediction accuracy and computational overhead.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - 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) - Improved Quantum Algorithms for Fidelity Estimation [77.34726150561087]
We develop new and efficient quantum algorithms for fidelity estimation with provable performance guarantees.
Our algorithms use advanced quantum linear algebra techniques, such as the quantum singular value transformation.
We prove that fidelity estimation to any non-trivial constant additive accuracy is hard in general.
arXiv Detail & Related papers (2022-03-30T02:02:16Z) - Reducing the cost of energy estimation in the variational quantum
eigensolver algorithm with robust amplitude estimation [50.591267188664666]
Quantum chemistry and materials is one of the most promising applications of quantum computing.
Much work is still to be done in matching industry-relevant problems in these areas with quantum algorithms that can solve them.
arXiv Detail & Related papers (2022-03-14T16:51:36Z) - 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) - As Accurate as Needed, as Efficient as Possible: Approximations in
DD-based Quantum Circuit Simulation [5.119310422637946]
Decision Diagrams (DDs) have previously shown to reduce the required memory in many important cases by exploiting redundancies in the quantum state.
We show that this reduction can be amplified by exploiting the probabilistic nature of quantum computers to achieve even more compact representations.
Specifically, we propose two new DD-based simulation strategies that approximate the quantum states to attain more compact representations.
arXiv Detail & Related papers (2020-12-10T12:02:03Z)
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.