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Variational-state quantum metrology

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TLDR
This work comprehensively explores systems consisting of up to 9 qubits and finds new highly entangled states that are not symmetric under permutations and non-trivially outperform previously known states up to a constant factor 2.
Abstract
Quantum technologies exploit entanglement to enhance various tasks beyond their classical limits including computation, communication and measurements. Quantum metrology aims to increase the precision of a measured quantity that is estimated in the presence of statistical errors using entangled quantum states. We present a novel approach for finding (near) optimal states for metrology in the presence of noise, using variational techniques as a tool for efficiently searching the high-dimensional space of quantum states, which would be classically intractable. We comprehensively explore systems consisting of up to 9 qubits and find new highly entangled states that are not symmetric under permutations and non-trivially outperform previously known states up to a constant factor $2$. We consider a range of environmental noise models; while passive quantum states cannot achieve a fundamentally superior scaling (as established by prior asymptotic results) we do observe a significant absolute quantum advantage. We finally outline a possible experimental setup for variational quantum metrology which can be implemented in near-term hardware.

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Citations
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Journal ArticleDOI

Cost function dependent barren plateaus in shallow parametrized quantum circuits.

TL;DR: In this paper, the authors rigorously prove two results, assuming V(θ) is an alternating layered ansatz composed of blocks forming local 2-designs, and they explore the feasibility of training depending on the type of cost functions, showing that local ones are less prone to the barren plateau problem.
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Noisy intermediate-scale quantum algorithms

TL;DR: In this article , the authors discuss what is possible in this ''noisy intermediate scale'' quantum (NISQ) era, including simulation of many-body physics and chemistry, combinatorial optimization, and machine learning.
Posted Content

Noise-Induced Barren Plateaus in Variational Quantum Algorithms

TL;DR: This work rigorously proves a serious limitation for noisy VQAs, in that the noise causes the training landscape to have a barren plateau, and proves that the gradient vanishes exponentially in the number of qubits n if the depth of the ansatz grows linearly with n.
References
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Book

Modern Quantum Mechanics

TL;DR: Modern Quantum Mechanics as mentioned in this paper is a classic graduate level textbook, covering the main quantum mechanics concepts in a clear, organized and engaging manner, and introduces topics that extend the text's usefulness into the twenty-first century, such as advanced mathematical techniques associated with quantum mechanical calculations.
Book

Quantum Theory Of Angular Momemtum

TL;DR: In this article, the authors present a collection of useful formulas besides those related to angular momentum, and compare different notations used by previous authors, and present results relating to different aspects of the angular momentum theory.
Journal ArticleDOI

On the Theory of the Brownian Motion

TL;DR: In this paper, the mean values of all the powers of the velocity $u$ and the displacement $s$ of a free particle in Brownian motion are calculated and the exact expressions for the square of the deviation of a harmonically bound particle in the Fokker-Planck partial differential equation as a function of the time and the initial deviation are obtained.
Journal ArticleDOI

A variational eigenvalue solver on a photonic quantum processor

TL;DR: The proposed approach drastically reduces the coherence time requirements and combines this method with a new approach to state preparation based on ansätze and classical optimization, enhancing the potential of quantum resources available today and in the near future.
Journal ArticleDOI

Advances in quantum metrology

TL;DR: Quantum metrology is the use of quantum techniques such as entanglement to yield higher statistical precision than purely classical approaches as discussed by the authors, where the central limit theorem implies that the reduction is proportional to the square root of the number of repetitions.
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