Maximum precision charging of multi-qubit quantum batteries
- URL: http://arxiv.org/abs/2601.12183v1
- Date: Sat, 17 Jan 2026 21:58:03 GMT
- Title: Maximum precision charging of multi-qubit quantum batteries
- Authors: Davide Rinaldi, Radim Filip, Dario Gerace, Giacomo Guarnieri,
- Abstract summary: We show how genuine quantum features, together with non-Gaussianity, can be the key elements to achieve the best of these three aspects during a quantum battery-charging process.<n>Our study allows to conclude that charging the battery through a sequential protocol involving a quantum non-Gaussian field state guarantees extremely high-performances.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Precision, robustness, and efficiency are crucial aspects in the design of quantum technologies. Here, we show how genuine quantum features, together with non-Gaussianity, can be the key elements to achieve the best of these three aspects during a quantum battery-charging process. Taking inspiration from a light-matter interaction paradigm, i.e., the Jaynes-Cummings model, we employ the Full Counting Statistics to study the stochastic exchanges of energy between an entire stack of qubits and a single-mode electromagnetic field (or mechanical oscillator). Our study allows to conclude that charging the battery through a sequential protocol involving a quantum non-Gaussian field state guarantees extremely high-performances in the charging process, whose precision is maximized even under sub-optimal operating conditions. These results highlight the potential of non-Gaussian quantum state charging to achieve a robust quantum precision advantage over Gaussian states of the field by suppressing detrimental quantum fluctuations, thus making it suitable to ultimate tasks for which a significant degree of accuracy is required.
Related papers
- Charging power enhancement at the phase transition of a non-integrable quantum battery [0.0]
A central question in this direction is whether quantum phase transitions can enhance the charging energy or the power.<n>Here, we investigate a one-dimensional Axial Next-Nearest-Neighbor Ising model as an example of non-integrable quantum battery charged via a quantum-quench protocol.<n>In contrast to integrable cases, we find that criticality in this setting can lead to a pronounced enhancement of the charging power.
arXiv Detail & Related papers (2026-03-03T10:15:44Z) - Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors [64.50565018996328]
We introduce the concept of predictive surrogates, designed to emulate the mean-value behavior of a given quantum processor with provably computational efficiency.<n>We use these surrogates to emulate a quantum processor with up to 20 programmable superconducting qubits, enabling efficient pre-training of variational quantum eigensolvers.<n> Experimental results reveal that the predictive surrogates not only reduce measurement overhead by orders of magnitude, but can also surpass the performance of conventional, quantum-resource-intensive approaches.
arXiv Detail & Related papers (2025-07-23T12:51:03Z) - Non-Gaussian enhancement of precision in quantum batteries [0.0]
We analyze how quantum coherence, non-Gaussianity, and entanglement affect the fluctuations in the energy output of bosonic quantum batteries.<n>This work highlights a tangible thermodynamic quantum advantage, demonstrating how quantum effects can be harnessed to improve the performance of practical energy conversion tasks.
arXiv Detail & Related papers (2025-05-30T13:54:10Z) - Reliable quantum advantage in quantum battery charging [0.0]
Energy fluctuations have a significant impact on the charging efficiency.<n>We study a model in which a flying qubit coherently interacts with a single mode optical cavity.<n>We show that preparing the latter in a genuinely quantum non-Gaussian Fock state leads to a definite and (in principle) measurable advantage.
arXiv Detail & Related papers (2024-12-19T19:11:50Z) - Diverse methods and practical aspects in controlling single semiconductor qubits: a review [1.1549572298362787]
Quantum control allows a wide range of quantum operations employed in molecular physics, nuclear magnetic resonance and quantum information processing.<n>Semiconducting qubits, where quantum information is encoded in spin or charge degree freedom of electrons or nuclei in semiconductor quantum dots, constitute a highly competitive candidate for scalable solid-state quantum technologies.
arXiv Detail & Related papers (2024-12-04T12:58:49Z) - The curse of random quantum data [62.24825255497622]
We quantify the performances of quantum machine learning in the landscape of quantum data.
We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits.
Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
arXiv Detail & Related papers (2024-08-19T12:18:07Z) - In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methods [30.565491081930997]
We develop an approach for estimating desired precision of kernel values, which is translated into the number of circuit runs.
We stress that quantum kernel methods should not only be considered from the machine learning performance perspective, but also from the context of the resource consumption.
arXiv Detail & Related papers (2024-07-22T16:29:35Z) - Bias-field digitized counterdiabatic quantum optimization [39.58317527488534]
We call this protocol bias-field digitizeddiabatic quantum optimization (BF-DCQO)
Our purely quantum approach eliminates the dependency on classical variational quantum algorithms.
It achieves scaling improvements in ground state success probabilities, increasing by up to two orders of magnitude.
arXiv Detail & Related papers (2024-05-22T18:11:42Z) - 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) - Variational-quantum-eigensolver-inspired optimization for spin-chain work extraction [39.58317527488534]
Energy extraction from quantum sources is a key task to develop new quantum devices such as quantum batteries.
One of the main issues to fully extract energy from the quantum source is the assumption that any unitary operation can be done on the system.
We propose an approach to optimize the extractable energy inspired by the variational quantum eigensolver (VQE) algorithm.
arXiv Detail & Related papers (2023-10-11T15:59:54Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - Quantum advantage in charging cavity and spin batteries by repeated
interactions [0.0]
Recently, an unconditional advantage has been demonstrated for the process of charging of a quantum battery in a collisional model.
We consider a model where the battery is modeled by a quantum harmonic oscillator or a large spin, charged via repeated interactions with a stream of non-equilibrium qubit units.
For both setups, we show that a quantum protocol can significantly outperform the most general adaptive classical schemes.
arXiv Detail & Related papers (2022-04-29T18:04:27Z)
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.