ٹینسر نیٹ ورکس کے ساتھ توسیع پذیر اور لچکدار کلاسیکی شیڈو ٹوموگرافی۔

ٹینسر نیٹ ورکس کے ساتھ توسیع پذیر اور لچکدار کلاسیکی شیڈو ٹوموگرافی۔

ماخذ نوڈ: 2699822

Ahmed A. Akhtar1, Hong-Ye Hu1,2, and Yi-Zhuang You1

1Department of Physics, University of California San Diego, La Jolla, CA 92093, USA
2Department of Physics, Harvard University, 17 Oxford Street, Cambridge, MA 02138, USA

اس کاغذ کو دلچسپ لگتا ہے یا اس پر بات کرنا چاہتے ہیں؟ SciRate پر تبصرہ کریں یا چھوڑیں۔.

خلاصہ

Classical shadow tomography is a powerful randomized measurement protocol for predicting many properties of a quantum state with few measurements. Two classical shadow protocols have been extensively studied in the literature: the single-qubit (local) Pauli measurement, which is well suited for predicting local operators but inefficient for large operators; and the global Clifford measurement, which is efficient for low-rank operators but infeasible on near-term quantum devices due to the extensive gate overhead. In this work, we demonstrate a scalable classical shadow tomography approach for generic randomized measurements implemented with finite-depth local Clifford random unitary circuits, which interpolates between the limits of Pauli and Clifford measurements. The method combines the recently proposed locally-scrambled classical shadow tomography framework with tensor network techniques to achieve scalability for computing the classical shadow reconstruction map and evaluating various physical properties. The method enables classical shadow tomography to be performed on shallow quantum circuits with superior sample efficiency and minimal gate overhead and is friendly to noisy intermediate-scale quantum (NISQ) devices. We show that the shallow-circuit measurement protocol provides immediate, exponential advantages over the Pauli measurement protocol for predicting quasi-local operators. It also enables a more efficient fidelity estimation compared to the Pauli measurement.

Classical shadow tomography is a powerful randomized measurement protocol for predicting many properties of a quantum state with few measurements. The measurement protocol is defined in terms of a unitary ensemble that is applied to the state of interest before measurement, and different choices of unitary ensemble produce efficient protocols for different types of operators. In this work, we demonstrate a scalable classical shadow tomography approach for generic randomized measurements implemented with local, finite-depth random Clifford circuits. Using this framework, we show that the shallow-circuit measurement protocol provides immediate, exponential advantages over random, single-qubit measurements for predicting quasi-local operators and performing fidelity estimation.

► BibTeX ڈیٹا

► حوالہ جات

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کی طرف سے حوالہ دیا گیا

[1] Hong-Ye Hu, Soonwon Choi, and Yi-Zhuang You, "Classical Shadow Tomography with Locally Scrambled Quantum Dynamics", آر ایکس سی: 2107.04817, (2021).

[2] Christian Bertoni, Jonas Haferkamp, Marcel Hinsche, Marios Ioannou, Jens Eisert, and Hakop Pashayan, "Shallow shadows: Expectation estimation using low-depth random Clifford circuits", آر ایکس سی: 2209.12924, (2022).

[3] Gregory Boyd and Bálint Koczor, "Training Variational Quantum Circuits with CoVaR: Covariance Root Finding with Classical Shadows", جسمانی جائزہ X 12 4, 041022 (2022).

[4] Mirko Arienzo, Markus Heinrich, Ingo Roth, and Martin Kliesch, "Closed-form analytic expressions for shadow estimation with brickwork circuits", آر ایکس سی: 2211.09835, (2022).

[5] Minh C. Tran, Daniel K. Mark, Wen Wei Ho, and Soonwon Choi, "Measuring Arbitrary Physical Properties in Analog Quantum Simulation", جسمانی جائزہ X 13 1, 011049 (2023).

[6] Matteo Ippoliti, "Classical shadows based on locally-entangled measurements", آر ایکس سی: 2305.10723, (2023).

[7] Katherine Van Kirk, Jordan Cotler, Hsin-Yuan Huang, and Mikhail D. Lukin, "Hardware-efficient learning of quantum many-body states", آر ایکس سی: 2212.06084, (2022).

[8] Arnaud Carignan-Dugas, Dar Dahlen, Ian Hincks, Egor Ospadov, Stefanie J. Beale, Samuele Ferracin, Joshua Skanes-Norman, Joseph Emerson, and Joel J. Wallman, "The Error Reconstruction and Compiled Calibration of Quantum Computing Cycles", آر ایکس سی: 2303.17714, (2023).

[9] Matthias C. Caro, "Learning Quantum Processes and Hamiltonians via the Pauli Transfer Matrix", آر ایکس سی: 2212.04471, (2022).

[10] Hong-Ye Hu, Soonwon Choi, and Yi-Zhuang You, "Classical shadow tomography with locally scrambled quantum dynamics", جسمانی جائزہ تحقیق 5 2، 023027 (2023).

[11] Yusen Wu, Bujiao Wu, Yanqi Song, Xiao Yuan, and Jingbo B. Wang, "Complexity analysis of weakly noisy quantum states via quantum machine learning", آر ایکس سی: 2303.17813, (2023).

[12] Matteo Ippoliti, Yaodong Li, Tibor Rakovszky, and Vedika Khemani, "Operator relaxation and the optimal depth of classical shadows", آر ایکس سی: 2212.11963, (2022).

[13] Markus Heinrich, Martin Kliesch, and Ingo Roth, "General guarantees for randomized benchmarking with random quantum circuits", آر ایکس سی: 2212.06181, (2022).

[14] Hans Hon Sang Chan, Richard Meister, Matthew L. Goh, and Bálint Koczor, "Algorithmic Shadow Spectroscopy", آر ایکس سی: 2212.11036, (2022).

[15] Haoxiang Wang, Maurice Weber, Josh Izaac, and Cedric Yen-Yu Lin, "Predicting Properties of Quantum Systems with Conditional Generative Models", آر ایکس سی: 2211.16943, (2022).

[16] Zi-Jian Zhang, Kouhei Nakaji, Matthew Choi, and Alán Aspuru-Guzik, "A composite measurement scheme for efficient quantum observable estimation", آر ایکس سی: 2305.02439, (2023).

[17] Zheng An, Jiahui Wu, Muchun Yang, D. L. Zhou, and Bei Zeng, "Unified Quantum State Tomography and Hamiltonian Learning Using Transformer Models: A Language-Translation-Like Approach for Quantum Systems", آر ایکس سی: 2304.12010, (2023).

مذکورہ بالا اقتباسات سے ہیں۔ SAO/NASA ADS (آخری بار کامیابی کے ساتھ 2023-06-04 11:01:39)۔ فہرست نامکمل ہو سکتی ہے کیونکہ تمام ناشرین مناسب اور مکمل حوالہ ڈیٹا فراہم نہیں کرتے ہیں۔

On Crossref کی طرف سے پیش خدمت کاموں کے حوالے سے کوئی ڈیٹا نہیں ملا (آخری کوشش 2023-06-04 11:01:37)۔

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