Sub-Quantum Fisher Information
- URL: http://arxiv.org/abs/2101.10144v2
- Date: Thu, 24 Jun 2021 15:22:08 GMT
- Title: Sub-Quantum Fisher Information
- Authors: M. Cerezo, Akira Sone, Jacob L. Beckey, Patrick J. Coles
- Abstract summary: We analyze a lower bound on the Quantum Fisher Information (QFI)
The sub-QFI is based on the super-fidelity, an upper bound on Uhlmann's fidelity.
We provide additional meaning to the sub-QFI as a measure of coherence, asymmetry, and purity loss.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Quantum Fisher Information (QFI) plays a crucial role in quantum
information theory and in many practical applications such as quantum
metrology. However, computing the QFI is generally a computationally demanding
task. In this work we analyze a lower bound on the QFI which we call the
sub-Quantum Fisher Information (sub-QFI). The bound can be efficiently
estimated on a quantum computer for an $n$-qubit state using $2n$ qubits. The
sub-QFI is based on the super-fidelity, an upper bound on Uhlmann's fidelity.
We analyze the sub-QFI in the context of unitary families, where we derive
several crucial properties including its geometrical interpretation. In
particular, we prove that the QFI and the sub-QFI are maximized for the same
optimal state, which implies that the sub-QFI is faithful to the QFI in the
sense that both quantities share the same global extrema. Based on this
faithfulness, the sub-QFI acts as an efficiently computable surrogate for the
QFI for quantum sensing and quantum metrology applications. Finally, we provide
additional meaning to the sub-QFI as a measure of coherence, asymmetry, and
purity loss.
Related papers
- Extracting Many-Body Quantum Resources within One-Body Reduced Density
Matrix Functional Theory [0.0]
Quantum Fisher information (QFI) is a central concept in quantum sciences used to quantify the ultimate precision limit of parameter estimation.
Here we combine ideas from functional theories and quantum information to develop a novel functional framework for the QFI of fermionic and bosonic ground states.
Our results provide the first connection between the one-body reduced density matrix functional theory and the quantum Fisher information.
arXiv Detail & Related papers (2023-11-21T13:33:53Z) - QKSAN: A Quantum Kernel Self-Attention Network [53.96779043113156]
A Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM.
A Quantum Kernel Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which ingeniously incorporates the Deferred Measurement Principle (DMP) and conditional measurement techniques.
Four QKSAN sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary classification on MNIST and Fashion MNIST.
arXiv Detail & Related papers (2023-08-25T15:08:19Z) - Quantum Fisher kernel for mitigating the vanishing similarity issue [0.9404723842159504]
The quantum kernel method is a machine learning model exploiting quantum computers to calculate the quantum kernels (QKs) that measure the similarity between data.
Despite the potential quantum advantage, the commonly used fidelity-based QK suffers from a detrimental issue.
We propose a new class of QKs called the quantum Fisher kernels (QFKs) that take into account the geometric structure of the data source.
arXiv Detail & Related papers (2022-10-29T12:00:17Z) - Symmetric Pruning in Quantum Neural Networks [111.438286016951]
Quantum neural networks (QNNs) exert the power of modern quantum machines.
QNNs with handcraft symmetric ansatzes generally experience better trainability than those with asymmetric ansatzes.
We propose the effective quantum neural tangent kernel (EQNTK) to quantify the convergence of QNNs towards the global optima.
arXiv Detail & Related papers (2022-08-30T08:17:55Z) - QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
Self-Attention Mechanism (SAM) is good at capturing the internal connections of features.
A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
arXiv Detail & Related papers (2022-07-14T12:22:51Z) - Theory of Quantum Generative Learning Models with Maximum Mean
Discrepancy [67.02951777522547]
We study learnability of quantum circuit Born machines (QCBMs) and quantum generative adversarial networks (QGANs)
We first analyze the generalization ability of QCBMs and identify their superiorities when the quantum devices can directly access the target distribution.
Next, we prove how the generalization error bound of QGANs depends on the employed Ansatz, the number of qudits, and input states.
arXiv Detail & Related papers (2022-05-10T08:05:59Z) - Quantum circuit architecture search on a superconducting processor [56.04169357427682]
Variational quantum algorithms (VQAs) have shown strong evidences to gain provable computational advantages for diverse fields such as finance, machine learning, and chemistry.
However, the ansatz exploited in modern VQAs is incapable of balancing the tradeoff between expressivity and trainability.
We demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique to enhance VQAs on an 8-qubit superconducting quantum processor.
arXiv Detail & Related papers (2022-01-04T01:53:42Z) - A Statistical Framework for Low-bitwidth Training of Deep Neural
Networks [70.77754244060384]
Fully quantized training (FQT) uses low-bitwidth hardware by quantizing the activations, weights, and gradients of a neural network model.
One major challenge with FQT is the lack of theoretical understanding, in particular of how gradient quantization impacts convergence properties.
arXiv Detail & Related papers (2020-10-27T13:57:33Z) - Variational Quantum Algorithm for Estimating the Quantum Fisher
Information [0.0]
We present a variational quantum algorithm called Variational Quantum Fisher Information Estimation (VQFIE)
By estimating lower and upper bounds on the QFI, based on bounding the fidelity, VQFIE outputs a range in which the actual QFI lies.
This result can then be used to variationally prepare the state that maximizes the QFI, for the application of quantum sensing.
arXiv Detail & Related papers (2020-10-20T17:44:55Z) - Generalized Measure of Quantum Fisher Information [0.0]
We present a lower bound on the quantum Fisher information (QFI) which is efficiently computable on near-term quantum devices.
We show that it satisfies the canonical criteria of a QFI measure.
arXiv Detail & Related papers (2020-10-06T17:36:30Z) - Asymptotic theory of quantum channel estimation [3.3852463130297448]
We show that a simple criterion can determine whether the scaling is linear or quadratic.
When the scaling is linear, we show the QFI is still in general larger than $N$ times the single-channel QFI.
arXiv Detail & Related papers (2020-03-23T21:50:12Z)
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.