Hybrid quantum-classical approach to enhanced quantum metrology

• URL: http://arxiv.org/abs/2008.06466v2
• Date: Wed, 20 Jan 2021 13:56:35 GMT
• Title: Hybrid quantum-classical approach to enhanced quantum metrology
• Authors: Xiaodong Yang, Xi Chen, Jun Li, Xinhua Peng and Raymond Laflamme
• Abstract summary: We introduce adjustable controls into the encoding process and then utilize a hybrid quantum-classical approach to automatically optimize the controls online. We report the first experimental demonstration of this promising scheme for the task of finding optimal probes for frequency estimation on a nuclear magnetic resonance (NMR) processor.
• Score: 13.1056933958821
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum metrology plays a fundamental role in many scientific areas. However, the complexity of engineering entangled probes and the external noise raise technological barriers for realizing the expected precision of the to-be-estimated parameter with given resources. Here, we address this problem by introducing adjustable controls into the encoding process and then utilizing a hybrid quantum-classical approach to automatically optimize the controls online. Our scheme does not require any complex or intractable off-line design, and it can inherently correct certain unitary errors during the learning procedure. We also report the first experimental demonstration of this promising scheme for the task of finding optimal probes for frequency estimation on a nuclear magnetic resonance (NMR) processor. The proposed scheme paves the way to experimentally auto-search optimal protocol for improving the metrology precision.

Related papers

• Simulation of a feedback-based algorithm for quantum optimization for a realistic neutral atom system with an optimized small-angle controlled-phase gate [0.0]
  We present a scheme to implement an optimally tuned small-angle controlled-phase gate. We show that the performance of FALQON implemented with small-angle controlled-phase gates exceeds that of FALQON utilizing CZ gates.
  arXiv  Detail & Related papers  (2024-05-16T21:36:50Z)
• Reinforcement learning pulses for transmon qubit entangling gates [0.0]
  We utilize a continuous-control reinforcement learning algorithm to design entangling two-qubit gates for superconducting qubits. We demonstrate the capability to generate novel pulse sequences that outperform the standard cross-resonance gates.
  arXiv  Detail & Related papers  (2023-11-07T03:19:19Z)
• Control-enhanced quantum metrology under Markovian noise [10.626708718934022]
  We propose a control-enhanced quantum metrology scheme to defend against realistic noises. As a demonstration, we apply it to the problem of frequency estimation under several typical Markovian noise channels. We show that our scheme performs better and can improve the estimation precision up to around one order of magnitude.
  arXiv  Detail & Related papers  (2022-11-03T13:39:47Z)
• Retrieving space-dependent polarization transformations via near-optimal quantum process tomography [55.41644538483948]
  We investigate the application of genetic and machine learning approaches to tomographic problems. We find that the neural network-based scheme provides a significant speed-up, that may be critical in applications requiring a characterization in real-time. We expect these results to lay the groundwork for the optimization of tomographic approaches in more general quantum processes.
  arXiv  Detail & Related papers  (2022-10-27T11:37:14Z)
• Automatic and effective discovery of quantum kernels [43.702574335089736]
  Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data. We present a different approach, which employs optimization techniques, similar to those used in neural architecture search and AutoML. The results obtained by testing our approach on a high-energy physics problem demonstrate that, in the best-case scenario, we can either match or improve testing accuracy with respect to the manual design approach.
  arXiv  Detail & Related papers  (2022-09-22T16:42:14Z)
• Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks [68.8204255655161]
  We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. We consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator. We observe high quantum fidelities and resilience to noise, despite the algorithm being trained in the ideal noise-free setting.
  arXiv  Detail & Related papers  (2022-06-29T14:34:00Z)
• Dynamical learning of a photonics quantum-state engineering process [48.7576911714538]
  Experimentally engineering high-dimensional quantum states is a crucial task for several quantum information protocols. We implement an automated adaptive optimization protocol to engineer photonic Orbital Angular Momentum (OAM) states. This approach represents a powerful tool for automated optimizations of noisy experimental tasks for quantum information protocols and technologies.
  arXiv  Detail & Related papers  (2022-01-14T19:24:31Z)
• Experimental Bayesian calibration of trapped ion entangling operations [48.43720700248091]
  We develop and characterize an efficient calibration protocol to automatically estimate and adjust experimental parameters of the widely used Molmer-Sorensen entangling gate operation. We experimentally demonstrate a median gate infidelity of $1.3(1)cdot10-3$, requiring only $1200pm500$ experimental cycles, while completing the entire gate calibration procedure in less than one minute.
  arXiv  Detail & Related papers  (2021-12-02T16:59:00Z)
• Error mitigation and quantum-assisted simulation in the error corrected regime [77.34726150561087]
  A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations. We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
  arXiv  Detail & Related papers  (2021-03-12T20:58:41Z)
• Probe optimization for quantum metrology via closed-loop learning control [11.156127985996417]
  We propose a practical controlled sequential scheme for quantum metrology. Purity loss of the probe state, which relates to quantum Fisher information, is measured efficiently as the fitness to guide the learning loop.
  arXiv  Detail & Related papers  (2020-06-17T09:07:02Z)

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.