Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms

• URL: http://arxiv.org/abs/2104.00569v2
• Date: Fri, 3 Dec 2021 08:57:37 GMT
• Title: Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms
• Authors: Guillermo Garc\'ia-P\'erez, Matteo A. C. Rossi, Boris Sokolov, Francesco Tacchino, Panagiotis Kl. Barkoutsos, Guglielmo Mazzola, Ivano Tavernelli and Sabrina Maniscalco
• Abstract summary: We present an algorithm that optimize informationally complete positive operator-valued measurements (POVMs) on the fly. We show its advantage by improving the efficiency of the variational quantum eigensolver in calculating ground-state energies of molecular Hamiltonians. In addition, the informational completeness of the approach offers a crucial advantage, as the measurement data can be reused to infer other quantities of interest.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many prominent quantum computing algorithms with applications in fields such as chemistry and materials science require a large number of measurements, which represents an important roadblock for future real-world use cases. We introduce a novel approach to tackle this problem through an adaptive measurement scheme. We present an algorithm that optimizes informationally complete positive operator-valued measurements (POVMs) on the fly in order to minimize the statistical fluctuations in the estimation of relevant cost functions. We show its advantage by improving the efficiency of the variational quantum eigensolver in calculating ground-state energies of molecular Hamiltonians with extensive numerical simulations. Our results indicate that the proposed method is competitive with state-of-the-art measurement-reduction approaches in terms of efficiency. In addition, the informational completeness of the approach offers a crucial advantage, as the measurement data can be reused to infer other quantities of interest. We demonstrate the feasibility of this prospect by reusing ground-state energy-estimation data to perform high-fidelity reduced state tomography.

Related papers

• Harnessing quantum back-action for time-series processing [0.0]
  We show that incorporating weak measurements into a quantum machine-learning protocol provides advantages in both execution time scaling and overall performance. This work provides a comprehensive and practical recipe to promote the implementation of weak measurement-based protocols in quantum reservoir computing.
  arXiv  Detail & Related papers  (2024-11-06T15:15:04Z)
• Optimal Quantum Overlapping Tomography [2.555222031881788]
  Partial tomography has emerged as a promising approach for characterizing complex quantum systems. We introduce a unified framework for optimal overlapping tomography by mapping the problem to clique cover model. We experimentally validate the feasibility of our schemes on practical nuclear spin processor.
  arXiv  Detail & Related papers  (2024-10-17T12:03:43Z)
• Practical techniques for high precision measurements on near-term quantum hardware: a Case Study in Molecular Energy Estimation [0.0]
  We show how to minimize shot overhead, circuit overhead, measurement noise, and time-dependent measurement noise. These strategies pave the way for more reliable and accurate quantum computations.
  arXiv  Detail & Related papers  (2024-09-04T09:52:14Z)
• Estimating molecular thermal averages with the quantum equation of motion and informationally complete measurements [0.0]
  We use the Variational Quantum Eigensolver (VQE) to compute thermal averages of quantum systems. A drawback of qEOM is that it requires measuring the expectation values of a large number of observables on the ground state of the system. In this work we focus on measurements through informationally complete positive operator-valued measures (IC-POVMs) to achieve a reduction in the measurements overheads.
  arXiv  Detail & Related papers  (2024-06-06T20:02:24Z)
• Adaptive measurement strategy for quantum subspace methods [0.0]
  We propose an adaptive measurement optimization method that is useful for the quantum subspace methods. The proposed method first determines the measurement protocol for classically simulatable states, and then adaptively updates the protocol of quantum subspace expansion. As a numerical demonstration, we have shown for excited-state simulation of molecules that we are able to reduce the number of measurements by an order of magnitude.
  arXiv  Detail & Related papers  (2023-11-14T04:00:59Z)
• Multimodal deep representation learning for quantum cross-platform verification [60.01590250213637]
  Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms. We introduce an innovative multimodal learning approach, recognizing that the formalism of data in this task embodies two distinct modalities. We devise a multimodal neural network to independently extract knowledge from these modalities, followed by a fusion operation to create a comprehensive data representation.
  arXiv  Detail & Related papers  (2023-11-07T04:35:03Z)
• Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing [93.83016310295804]
  AQCs allow to implement problems of research interest, which has sparked the development of quantum representations for computer vision tasks. In this work, we explore the potential of using this information for probabilistic balanced k-means clustering. Instead of discarding non-optimal solutions, we propose to use them to compute calibrated posterior probabilities with little additional compute cost. This allows us to identify ambiguous solutions and data points, which we demonstrate on a D-Wave AQC on synthetic tasks and real visual data.
  arXiv  Detail & Related papers  (2023-10-18T17:59:45Z)
• Accurate melting point prediction through autonomous physics-informed learning [52.217497897835344]
  We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. We demonstrate how incorporating physical models of the solid-liquid coexistence evolution enhances the algorithm's accuracy and enables optimal decision-making.
  arXiv  Detail & Related papers  (2023-06-23T07:53:09Z)
• Guaranteed efficient energy estimation of quantum many-body Hamiltonians using ShadowGrouping [55.47824411563162]
  Estimation of the energy of quantum many-body systems is a paradigmatic task in various research fields. We aim to find the optimal strategy with single-qubit measurements that yields the highest provable accuracy given a total measurement budget. We develop a practical, efficient estimation strategy, which we call ShadowGrouping.
  arXiv  Detail & Related papers  (2023-01-09T14:41:07Z)
• 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)
• Quantum Algorithms for Data Representation and Analysis [68.754953879193]
  We provide quantum procedures that speed-up the solution of eigenproblems for data representation in machine learning. The power and practical use of these subroutines is shown through new quantum algorithms, sublinear in the input matrix's size, for principal component analysis, correspondence analysis, and latent semantic analysis. Results show that the run-time parameters that do not depend on the input's size are reasonable and that the error on the computed model is small, allowing for competitive classification performances.
  arXiv  Detail & Related papers  (2021-04-19T00:41:43Z)

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.