# Quantum advantage in learning from experiments

@article{Huang2021QuantumAI, 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={ArXiv}, year={2021}, volume={abs/2112.00778} }

Hsin-Yuan Huang,1, 2, * Michael Broughton,3 Jordan Cotler,4, 5 Sitan Chen,6, 7 Jerry Li,8 Masoud Mohseni,3 Hartmut Neven,3 Ryan Babbush,3 Richard Kueng,9 John Preskill,1, 2, 10 and Jarrod R. McClean3, † Institute for Quantum Information and Matter, Caltech, Pasadena, CA, USA Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA Google Quantum AI, 340 Main Street, Venice, CA 90291, USA Harvard Society of Fellows, Cambridge, MA 02138 USA Black Hole Initiative, Cambridge…

## 16 Citations

Revisiting dequantization and quantum advantage in learning tasks

- Computer ScienceArXiv
- 2021

This research presents a probabilistic simulation of the black hole and its environment that allows for the simulation of supermassive black holes and their environment to be studied in detail.

Learning Noise via Dynamical Decoupling of Entangled Qubits

- Physics
- 2022

Trevor McCourt,1, 2 Charles Neill,3 Kenny Lee,3 Chris Quintana,3 Yu Chen,3 Julian Kelly,3 V. N. Smelyanskiy,3 M. I. Dykman,4 Alexander Korotkov,3 Isaac L. Chuang,1, 5 and A. G. Petukhov3 Department…

Quantum neural networks force fields generation

- Computer Science, PhysicsArXiv
- 2022

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.

Transfer Learning in Quantum Parametric Classifiers: An Information-Theoretic Generalization Analysis

- Computer ScienceArXiv
- 2022

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.

Quantum Signatures in Nonlinear Gravitational Waves

- Physics
- 2021

Thiago Guerreiro, ∗ Francesco Coradeschi, † Antonia Micol Frassino, ‡ Jennifer Rittenhouse West, § and Enrico Junior Schioppa ¶ Department of Physics, Pontifical Catholic University of Rio de…

Quantum Computing 2022

- Physics
- 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…

Unentangled quantum reinforcement learning agents in the OpenAI Gym

- Physics
- 2022

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…

Tight Bounds for Quantum State Certification with Incoherent Measurements

- Computer ScienceArXiv
- 2022

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.

An Empirical Review of Optimization Techniques for Quantum Variational Circuits

- Computer Science, PhysicsArXiv
- 2022

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.

Perceval: A Software Platform for Discrete Variable Photonic Quantum Computing

- Physics
- 2022

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…

## References

SHOWING 1-10 OF 46 REFERENCES

A Hierarchy for Replica Quantum Advantage

- Computer ScienceArXiv
- 2021

This paper presents a probabilistic simulation of the black hole using a convolutional approach to solve the inequality in theorems of LaSalle’s inequality.

Revisiting dequantization and quantum advantage in learning tasks

- Computer ScienceArXiv
- 2021

This research presents a probabilistic simulation of the black hole and its environment that allows for the simulation of supermassive black holes and their environment to be studied in detail.

Shadow tomography of quantum states

- Computer ScienceElectron. Colloquium Comput. Complex.
- 2017

Surprisingly, this work gives a procedure that solves the problem of shadow tomography by measuring only O( ε−5·log4 M·logD) copies, which means, for example, that the authors can learn the behavior of an arbitrary n-qubit state, on *all* accepting/rejecting circuits of some fixed polynomial size, by measuringonly nO( 1) copies of the state.

Exponential Separations Between Learning With and Without Quantum Memory

- Physics2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS)
- 2022

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 advantages for Pauli channel estimation

- Computer Science
- 2021

We show that entangled measurements provide an exponential advantage in sample complexity for Pauli channel estimation, which is both a fundamental problem and a practically important subroutine for…

TensorFlow Quantum: A Software Framework for Quantum Machine Learning

- Computer Science, PhysicsArXiv
- 2020

This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators.

Entanglement is Necessary for Optimal Quantum Property Testing

- Computer Science2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS)
- 2020

It is shown that with independent measurements, $\Omega(d^{4/3}/\epsilon^{2})$ is necessary, even if the measurements are chosen adaptively, which resolves a question posed in [7].

Quantum Principal Component Analysis Only Achieves an Exponential Speedup Because of Its State Preparation Assumptions.

- Computer SciencePhysical review letters
- 2021

With this model, classical analogues to Lloyd, Mohseni, and Rebentrost’s quantum algorithms for principal component analysis and nearest-centroid clustering are described and it is suggested that the exponential speedups of their quantum counterparts are simply an artifact of state preparation assumptions.

Gentle measurement of quantum states and differential privacy

- Computer ScienceElectron. Colloquium Comput. Complex.
- 2019

The connection theorem is proved, together with a quantum analog of the so-called private multiplicative weights algorithm of Hardt and Rothblum, to give a protocol to solve the problem of shadow tomography using order ( logm) 2 2 copies of ρ, compared to Aaronson’s previous bound of O(logm) 4( logd) .

Quantum Machine Learning

- Computer Science
- 2020

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.