Harnessing quantum back-action for time-series processing

• URL: http://arxiv.org/abs/2411.03979v1
• Date: Wed, 06 Nov 2024 15:15:04 GMT
• Title: Harnessing quantum back-action for time-series processing
• Authors: Giacomo Franceschetto, Marcin Płodzień, Maciej Lewenstein, Antonio Acín, Pere Mujal,
• Abstract summary: 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.
• Score: 0.0
• License:
• Abstract: Quantum measurements affect the state of the observed systems via back-action. While projective measurements extract maximal classical information, they drastically alter the system. In contrast, weak measurements balance information extraction with the degree of disturbance. Considering the prevalent use of projective measurements in quantum computing and communication protocols, the potential benefits of weak measurements in these fields remain largely unexplored. In this work, we demonstrate that incorporating weak measurements into a quantum machine-learning protocol known as quantum reservoir computing provides advantages in both execution time scaling and overall performance. We analyze different measurement settings by varying the measurement strength across two benchmarking tasks. Our results reveal that carefully optimizing both the reservoir Hamiltonian parameters and the measurement strength can significantly improve the quantum reservoir computing algorithm performance. This work provides a comprehensive and practical recipe to promote the implementation of weak measurement-based protocols in quantum reservoir computing. Moreover, our findings motivate further exploration of experimental protocols that leverage the back-action effects of weak measurements.

Related papers

• Experimental decoherence mitigation using a weak measurement-based scheme and the duality quantum algorithm [2.715284063484557]
  We experimentally demonstrate a weak measurement and measurement reversal-based scheme on an NMR quantum processor. The duality quantum algorithm is used to efficiently implement the required non-unitary quantum operations. Our experimental results clearly demonstrate the success of the weak measurement-based decoherence mitigation scheme.
  arXiv  Detail & Related papers  (2024-09-19T13:15:28Z)
• 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)
• Transition Role of Entangled Data in Quantum Machine Learning [51.6526011493678]
  Entanglement serves as the resource to empower quantum computing. Recent progress has highlighted its positive impact on learning quantum dynamics. We establish a quantum no-free-lunch (NFL) theorem for learning quantum dynamics using entangled data.
  arXiv  Detail & Related papers  (2023-06-06T08:06:43Z)
• 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)
• 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)
• Neural network enhanced measurement efficiency for molecular groundstates [63.36515347329037]
  We adapt common neural network models to learn complex groundstate wavefunctions for several molecular qubit Hamiltonians. We find that using a neural network model provides a robust improvement over using single-copy measurement outcomes alone to reconstruct observables.
  arXiv  Detail & Related papers  (2022-06-30T17:45:05Z)
• Measuring NISQ Gate-Based Qubit Stability Using a 1+1 Field Theory and Cycle Benchmarking [50.8020641352841]
  We study coherent errors on a quantum hardware platform using a transverse field Ising model Hamiltonian as a sample user application. We identify inter-day and intra-day qubit calibration drift and the impacts of quantum circuit placement on groups of qubits in different physical locations on the processor. This paper also discusses how these measurements can provide a better understanding of these types of errors and how they may improve efforts to validate the accuracy of quantum computations.
  arXiv  Detail & Related papers  (2022-01-08T23:12:55Z)
• Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms [0.0]
  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.
  arXiv  Detail & Related papers  (2021-04-01T15:49:05Z)
• Efficient qubit measurement with a nonreciprocal microwave amplifier [0.0]
  We demonstrate the efficient measurement of a superconducting qubit using a nonreciprocal parametric amplifier. In addition to providing tools for further improving the fidelity of strong projective measurement, this work creates a testbed for the experimental study of ideal weak measurements.
  arXiv  Detail & Related papers  (2020-09-18T14:40:38Z)
• Neural network quantum state tomography in a two-qubit experiment [52.77024349608834]
  Machine learning inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states. We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states greatly improves the quality of the reconstructed states.
  arXiv  Detail & Related papers  (2020-07-31T17:25:12Z)
• Direct Characterization of Quantum Measurements using Weak Values [19.009425676277974]
  We propose and experimentally demonstrate the direct tomography of a measurement apparatus by taking the backward direction of weak measurement formalism. Our work provides new insight on the symmetry between quantum states and measurements, as well as an efficient method to characterize a measurement apparatus.
  arXiv  Detail & Related papers  (2020-05-21T02:00:37Z)

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.