Shadow measurements for feedback-based quantum optimization

• URL: http://arxiv.org/abs/2502.20366v1
• Date: Thu, 27 Feb 2025 18:36:30 GMT
• Title: Shadow measurements for feedback-based quantum optimization
• Authors: Leticia Bertuzzi, João P. Engster, Evandro C. R. da Rosa, Eduardo I. Duzzioni,
• Abstract summary: We present an implementation of the recently introduced Feedback-based algorithm for quantum optimization (FALQON) with the Ket quantum programming platform.<n>We employ classical shadows for the feedback routine of parameter estimation and compare this approach with the direct estimation of observables.<n>Our results show that depending on the graph geometry for the MaxCut problem, the number of measurements required to estimate expected values of observables with classical shadows can be up to 16 times lower than with direct observable estimation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Improving the performance of quantum algorithms is a fundamental task to achieve quantum advantage. In many cases, extracting information from quantum systems poses an important challenge for practical implementations in real-world quantum computers, given the high resource cost of performing state tomography. In this scenario, randomized measurements emerged as a promising tool. In particular, the classical shadows protocol allows one to retrieve expected values of low-weight Pauli observables by performing only local measurements. In this paper, we present an implementation of the recently introduced Feedback-based algorithm for quantum optimization (FALQON) with the Ket quantum programming platform, for solving the MaxCut optimization problem. We employ classical shadows for the feedback routine of parameter estimation and compare this approach with the direct estimation of observables. Our results show that depending on the graph geometry for the MaxCut problem, the number of measurements required to estimate expected values of observables with classical shadows can be up to 16 times lower than with direct observable estimation. Furthermore, by analyzing complete graphs, we numerically confirm a logarithmic growth in the required number of measurements relative to the number of observables, reinforcing that classical shadows can be a useful tool for estimating low-locality Pauli observables in quantum algorithms.

Related papers

• Optimization for expectation value estimation with shallow quantum circuits [1.5733643545082079]
  Estimating linear properties of quantum states is a fundamental task in quantum information science. We propose a framework that optimize sample complexity for estimating the expectation value of any observable using a shallow parameterized quantum circuit. We numerically demonstrate the performance of our algorithm by estimating the ground energy of a sparse Hamiltonian and the inner product of two pure states.
  arXiv  Detail & Related papers  (2024-07-28T14:04:33Z)
• Power Characterization of Noisy Quantum Kernels [52.47151453259434]
  We show that noise may make quantum kernel methods to only have poor prediction capability, even when the generalization error is small. We provide a crucial warning to employ noisy quantum kernel methods for quantum computation.
  arXiv  Detail & Related papers  (2024-01-31T01:02:16Z)
• Quantum State Tomography with Locally Purified Density Operators and Local Measurements [17.38734393793605]
  An efficient representation of quantum states enables realizing quantum state tomography with minimal measurements. We propose an alternative approach to state tomography that uses tensor network representations of mixed states through locally purified density operators. Our study opens avenues in quantum state tomography for two-dimensional systems using tensor network formalism.
  arXiv  Detail & Related papers  (2023-07-31T03:14:31Z)
• Applicability of Measurement-based Quantum Computation towards Physically-driven Variational Quantum Eigensolver [17.975555487972166]
  Variational quantum algorithms are considered one of the most promising methods for obtaining near-term quantum advantages. The roadblock to developing quantum algorithms with the measurement-based quantum computation scheme is resource cost. We propose an efficient measurement-based quantum algorithm for quantum many-body system simulation tasks, called measurement-based Hamiltonian variational ansatz (MBHVA)
  arXiv  Detail & Related papers  (2023-07-19T08:07:53Z)
• End-to-end resource analysis for quantum interior point methods and portfolio optimization [63.4863637315163]
  We provide a complete quantum circuit-level description of the algorithm from problem input to problem output. We report the number of logical qubits and the quantity/depth of non-Clifford T-gates needed to run the algorithm.
  arXiv  Detail & Related papers  (2022-11-22T18:54:48Z)
• Quantum-enhanced mean value estimation via adaptive measurement [0.39102514525861415]
  Mean value estimation of observables is an essential subroutine in quantum computing algorithms. The quantum estimation theory identifies the ultimate precision of such an estimator, which is referred to as the quantum Cram'er-Rao (QCR) lower bound or equivalently the inverse of the quantum Fisher information. We propose a quantum-enhanced mean value estimation method in a depolarizing noisy environment that saturates the QCR bound in the limit of a large number of qubits.
  arXiv  Detail & Related papers  (2022-10-27T17:13:07Z)
• Analyzing Prospects for Quantum Advantage in Topological Data Analysis [35.423446067065576]
  We analyze and optimize an improved quantum algorithm for topological data analysis. We show that super-quadratic quantum speedups are only possible when targeting a multiplicative error approximation. We argue that quantum circuits with tens of billions of Toffoli can solve seemingly classically intractable instances.
  arXiv  Detail & Related papers  (2022-09-27T17:56:15Z)
• 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)
• 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)
• Generalization Metrics for Practical Quantum Advantage in Generative Models [68.8204255655161]
  Generative modeling is a widely accepted natural use case for quantum computers. We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance. Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
  arXiv  Detail & Related papers  (2022-01-21T16:35:35Z)
• Quantum scale estimation [0.0]
  Quantum scale estimation establishes the most precise framework for the estimation of scale parameters that is allowed by the laws of quantum mechanics. The new framework is exploited to generalise scale-invariant global thermometry, as well as to address the estimation of the lifetime of an atomic state. On a more conceptual note, the optimal strategy is employed to construct an observable for scale parameters, an approach which may serve as a template for a more systematic search of quantum observables.
  arXiv  Detail & Related papers  (2021-11-23T15:03:19Z)
• Experimental quantum state measurement with classical shadows [5.455606108893398]
  A crucial subroutine for various quantum computing and communication algorithms is to efficiently extract different classical properties of quantum states. We show how to project the quantum state into classical shadows and simultaneously predict $M$ different functions of a state with only $mathcalO(log M)$ measurements. Our experiment verifies the efficacy of exploiting (derandomized) classical shadows and sheds light on efficient quantum computing with noisy intermediate-scale quantum hardware.
  arXiv  Detail & Related papers  (2021-06-18T15:42:03Z)
• Robust shadow estimation [1.7205106391379026]
  We show how to mitigate errors in the shadow estimation protocol recently proposed by Huang, Kueng, and Preskill. By adding an experimentally friendly calibration stage to the standard shadow estimation scheme, our robust shadow estimation algorithm can obtain an unbiased estimate of the classical shadow of a quantum system.
  arXiv  Detail & Related papers  (2020-11-19T03:46:49Z)
• Measurements of Quantum Hamiltonians with Locally-Biased Classical Shadows [6.434709790375755]
  We consider the problem of estimating expectation values of molecular Hamiltonians, obtained on states prepared on a quantum computer. We propose a novel estimator for this task, which is locally optimised with knowledge of the Hamiltonian and a classical approximation to the underlying quantum state.
  arXiv  Detail & Related papers  (2020-06-29T03:08:03Z)

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.