Multiparameter quantum estimation under dephasing noise
- URL: http://arxiv.org/abs/2004.00720v1
- Date: Wed, 1 Apr 2020 22:02:59 GMT
- Title: Multiparameter quantum estimation under dephasing noise
- Authors: Le Bin Ho, Hideaki Hakoshima, Yuichiro Matsuzaki, Masayuki Matsuzaki,
Yasushi Kondo
- Abstract summary: We present a framework of the simultaneous estimation of multiple parameters with quantum sensors in a certain noisy environment.
We show that there is an optimal sensing time in the noisy environment and the sensitivity can beat the standard quantum limit when the noisy environment is non-Markovian.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Simultaneous quantum estimation of multiple parameters has recently become
essential in quantum metrology. Although the ultimate sensitivity of a
multiparameter quantum estimation in noiseless environments can beat the
standard quantum limit that every classical sensor is bounded by, it is unclear
whether the quantum sensor has an advantage over the classical one under
realistic noise. In this work, we present a framework of the simultaneous
estimation of multiple parameters with quantum sensors in a certain noisy
environment. Our multiple parameters to be estimated are three components of an
external magnetic field, and we consider the noise that causes only dephasing.
We show that there is an optimal sensing time in the noisy environment and the
sensitivity can beat the standard quantum limit when the noisy environment is
non-Markovian.
Related papers
- Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise [49.97673761305336]
Noise remains a major obstacle to achieving reliable quantum algorithms.<n>We present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers.
arXiv Detail & Related papers (2025-05-24T02:51:34Z) - Quantum Cramer-Rao Precision Limit of Noisy Continuous Sensing [0.6990493129893112]
We establish a numerically efficient method to determine the quantum Cramer-Rao bound (QCRB) for continuously monitored quantum sensors subject to general environmental noise.
Our method provides a rigorous and practical framework for assessing and enhancing the sensor performance in realistic settings.
arXiv Detail & Related papers (2025-04-16T18:06:10Z) - Hybrid quantum network for sensing in the acoustic frequency range [1.7485293435157372]
Applicability of quantum optical sensing is often restricted by fixed wavelengths of available photonic quantum sources.
Here we demonstrate a novel tool for broadband quantum sensing by performing quantum state processing that can be applied to a wide range of the optical spectrum.
Other possible applications include continuous-variable quantum repeaters and distributed quantum sensing.
arXiv Detail & Related papers (2024-12-16T14:45:44Z) - Bayesian Quantum Amplitude Estimation [49.1574468325115]
We introduce BAE, a noise-aware Bayesian algorithm for quantum amplitude estimation.
We show that BAE achieves Heisenberg-limited estimation and benchmark it against other approaches.
arXiv Detail & Related papers (2024-12-05T18:09:41Z) - Enhanced Quantum Metrology with Non-Phase-Covariant Noise [0.5906031288935516]
We show that phase-covariant (PC) noise either degrades or remains neutral to sensing precision, whereas non-phase-covariant (NPC) noise can potentially enhance parameter estimation.
This implies that a non-Hermitian quantum sensor may outperform its Hermitian counterpart in terms of sensing performance.
arXiv Detail & Related papers (2024-04-12T12:41:45Z) - Lindblad-like quantum tomography for non-Markovian quantum dynamical maps [46.350147604946095]
We introduce Lindblad-like quantum tomography (L$ell$QT) as a quantum characterization technique of time-correlated noise in quantum information processors.
We discuss L$ell$QT for the dephasing dynamics of single qubits in detail, which allows for a neat understanding of the importance of including multiple snapshots of the quantum evolution in the likelihood function.
arXiv Detail & Related papers (2024-03-28T19:29:12Z) - 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) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - Quantum metrology in the noisy intermediate-scale quantum era [5.640610268831498]
Quantum metrology pursues the physical realization of higher-precision measurements to physical quantities.
It has potential applications in developing next-generation frequency standards, magnetometers, radar, and navigation.
However, the ubiquitous decoherence in the quantum world degrades the quantum resources and forces the precision back to or even worse than the classical limit.
arXiv Detail & Related papers (2023-07-15T04:05:47Z) - Variational quantum metrology for multiparameter estimation under
dephasing noise [0.8594140167290099]
We present a hybrid quantum-classical variational scheme to enhance precision in quantum metrology.
We discuss specific applications to 3D magnetic field sensing under several dephasing noise modes.
arXiv Detail & Related papers (2023-05-15T01:09:58Z) - Quantum Conformal Prediction for Reliable Uncertainty Quantification in
Quantum Machine Learning [47.991114317813555]
Quantum models implement implicit probabilistic predictors that produce multiple random decisions for each input through measurement shots.
This paper proposes to leverage such randomness to define prediction sets for both classification and regression that provably capture the uncertainty of the model.
arXiv Detail & Related papers (2023-04-06T22:05:21Z) - Self-consistent noise characterization of quantum devices [0.0]
We develop an approach to reduce the quantum environment causing single-qubit dephasing to a simple yet predictive noise model.
We demonstrate the power and limits of our approach by characterizing, with nanoscale spatial resolution, the noise experienced by two electronic spins in diamond.
arXiv Detail & Related papers (2022-10-17T19:10:56Z) - Quantum Noise Sensing by generating Fake Noise [5.8010446129208155]
We propose a framework to characterize noise in a realistic quantum device.
Key idea is to learn about the noise by mimicking it in a way that one cannot distinguish between the real (to be sensed) and the fake (generated) one.
We find that, when applied to the benchmarking case of Pauli channels, the SuperQGAN protocol is able to learn the associated error rates even in the case of spatially and temporally correlated noise.
arXiv Detail & Related papers (2021-07-19T09:42:37Z) - Quantum noise protects quantum classifiers against adversaries [120.08771960032033]
Noise in quantum information processing is often viewed as a disruptive and difficult-to-avoid feature, especially in near-term quantum technologies.
We show that by taking advantage of depolarisation noise in quantum circuits for classification, a robustness bound against adversaries can be derived.
This is the first quantum protocol that can be used against the most general adversaries.
arXiv Detail & Related papers (2020-03-20T17:56:14Z) - Multi-level Quantum Noise Spectroscopy [40.434546680037606]
Existing quantum noise spectroscopy protocols measure an aggregate amount of noise affecting a quantum system.
We propose and experimentally validate a spin-locking-based QNS protocol that exploits the multi-level energy structure of a superconducting qubit.
arXiv Detail & Related papers (2020-03-05T17:31:30Z)
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.