Quantum advantage in learning from experiments

@article{Huang2022QuantumAI,
  title={Quantum advantage in learning from experiments},
  author={Hsin-Yuan Huang and Mick Broughton and Jordan S. Cotler and Sitan Chen and Jerry Zheng Li and Masoud Mohseni and Hartmut Neven and Ryan Babbush and Richard Kueng and John Preskill and Jarrod R. McClean},
  journal={Science},
  year={2022},
  volume={376 6598},
  pages={
          1182-1186
        }
}
Quantum technology promises to revolutionize how we learn about the physical world. An experiment that processes quantum data with a quantum computer could have substantial advantages over conventional experiments in which quantum states are measured and outcomes are processed with a classical computer. We proved that quantum machines could learn from exponentially fewer experiments than the number required by conventional experiments. This exponential advantage is shown for predicting… 
Quantum Computing 2022
—Quantum technology is full of figurative and literal noise obscuring its promise. In this overview, we will attempt to provide a sober assessment of the promise of quantum technology with a focus on
Quantum neural networks force fields generation
TLDR
A direct connection between classical and quantum solutions for learning neural network potentials is established and a quantum neural network architecture is designed and applied successfully to different molecules of growing complexity, pointing towards potential quantum advantages in natural science applications via quantum machine learning.
An Empirical Review of Optimization Techniques for Quantum Variational Circuits
TLDR
A large number of problems and optimizers evaluated yields strong empirical guidance for choosing optimizers for QVCs that is currently lacking, and includes both classical and quantum data based optimization routines.
Optimisation-free Classification and Density Estimation with Quantum Circuits
TLDR
A variational quantum circuit approach that could leverage quantum advantage for the implementation of a novel machine learning framework for probability density estimation and classification using quantum circuits is discussed.
Transfer Learning in Quantum Parametric Classifiers: An Information-Theoretic Generalization Analysis
TLDR
This paper studies a transfer learning setting in which classical-to-quantum embedding is carried out by an arbitrary parametric quantum circuit that is pre-trained based on data from a source task, and demonstrates that the average excess risk can be bounded in terms of two Rényi mutual information terms under source and target tasks.
Perceval: A Software Platform for Discrete Variable Photonic Quantum Computing
We introduce Perceval , an evolutive open-source software platform for simulating and interfacing with discrete-variable photonic quantum computers, and describe its main features and components. Its
Implementation of quantum stochastic walks for function approximation, two-dimensional data classification, and sequence classification
TLDR
The results show that quantum stochastic walks may be a useful resource to implement a quantum neural network and the coherent QSNN is more robust against both label noise and device noise, compared with the decoherentQSNN.
Avoiding barren plateaus using classical shadows
TLDR
This work defines a notion of weak barren plateaus (WBP) based on the entropies of local reduced density matrices and demonstrates that decreasing the gradient step size allows to avoid WBPs during the optimization process.
Tight Bounds for Quantum State Certification with Incoherent Measurements
TLDR
This work focuses on algorithms which use incoherent measurements, i.e. which only measure one copy of ρ at a time, and can be implemented without persistent quantum memory and thus represent a large class of protocols that can be run on current or near-term devices.
Unentangled quantum reinforcement learning agents in the OpenAI Gym
Classical reinforcement learning (RL) has generated excellent results in different regions ; however, its sample inefficiency remains a critical issue. In this paper, we provide concrete numerical
...
...

References

SHOWING 1-10 OF 46 REFERENCES
Quantum Computing in the NISQ era and beyond
TLDR
Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future, and the 100-qubit quantum computer will not change the world right away - but it should be regarded as a significant step toward the more powerful quantum technologies of the future.
Quantum supremacy using a programmable superconducting processor
TLDR
Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute.
Information-theoretic bounds on quantum advantage in machine learning
TLDR
It is proven that for any input distribution D(x), a classical ML model can provide accurate predictions on average by accessing E a number of times comparable to the optimal quantum ML model, and it is proved that the exponential quantum advantage is possible.
Quantum algorithmic measurement
There has been recent promising experimental and theoretical evidence that quantum computational tools might enhance the precision and efficiency of physical experiments. However, a systematic
Exponential Separations Between Learning With and Without Quantum Memory
TLDR
It is proved that to estimate absolute values of all $n-qubit Pauli observables, algorithms with k < n qubits of quantum memory require at least $\Omega(2^{(n-k)/3})$ samples, but there is an algorithm using $n$-qu bit quantum memory which only requires $\mathcal{O}(n)$ samples.
Quantum sensing
“Quantum sensing” describes the use of a quantum system, quantum properties or quantum phenomena to perform a measurement of a physical quantity. Historical examples of quantum sensors include
Optical quantum memory
Quantum memory is essential for the development of many devices in quantum information processing, including a synchronization tool that matches various processes within a quantum computer, an
Provably efficient machine learning for quantum many-body problems
TLDR
It is proved that classical ML algorithms can efficiently predict ground state properties of gapped Hamiltonian in finite spatial dimensions, after learning from data obtained by measuring other Hamiltonians in the same quantum phase of matter.
Quantum Machine Learning
TLDR
This review focuses on the supervised classification quantum algorithm of nearest centroid, presented in [11], which helps to overcome the main bottleneck of the algorithm: calculation of the distances between the vectors in highly dimensional space.
Quantum Principal Component Analysis
TLDR
The amplitude amplification together with the phase estimation algorithm to an operator is shown how to obtain a combination of the eigenvectors associated to the largest eigenvalues and so can be used to do principal component analysis on quantum computers.
...
...