Is Quantum Advantage the Right Goal for Quantum Machine Learning?

Published in

PRX Quantum(2022)

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TL;DR

It is argued that these challenges call for a critical debate on whether quantum advantage and the narrative of 'beating' classical machine learning should continue to dominate the literature the way it does, and examples for how other perspectives in existing research provide an important alternative to the focus on advantage.

Abstract

Machine learning is frequently listed among the most promising applications for quantum computing. This is in fact a curious choice: Today's machine learning algorithms are notoriously powerful in practice, but remain theoretically difficult to study. Quantum computing, in contrast, does not offer practical benchmarks on realistic scales, and theory is the main tool we have to judge whether it could become relevant for a problem. In this perspective we explain why it is so difficult to say something about the practical power of quantum computers for machine learning with the tools we are currently using. We argue that these challenges call for a critical debate on whether quantum advantage and the narrative of 'beating' classical machine learning should continue to dominate the literature the way it does, and highlight examples for how other perspectives in existing research provide an important alternative to the focus on advantage.

Authors

M. Schuld

7 papers

N. Killoran

4 papers

References129 items

The complexity of quantum support vector machines

Uncovering Instabilities in Variational-Quantum Deep Q-Networks

Quantum machine learning with subspace states

Quantum advantage in learning from experiments

Generalization in quantum machine learning from few training data

Is Quantum Advantage the Right Goal for Quantum Machine Learning?

Published in

PRX Quantum(2022)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

M. Schuld

7 papers

N. Killoran

4 papers

References129 items

The complexity of quantum support vector machines

Uncovering Instabilities in Variational-Quantum Deep Q-Networks

Quantum machine learning with subspace states

Quantum advantage in learning from experiments

Generalization in quantum machine learning from few training data

Representation Learning via Quantum Neural Tangent Kernels

Quantum tangent kernel

A Case for Noisy Shallow Gate-based Circuits in Quantum Machine Learning

Quantum-classical hybrid neural networks in the neural tangent kernel regime

Entangled Datasets for Quantum Machine Learning

Generalized quantum circuit differentiation rules

General parameter-shift rules for quantum gradients

A quantum algorithm for training wide and deep classical neural networks

Analytic gradients in variational quantum algorithms: Algebraic extensions of the parameter-shift rule to general unitary transformations

The Inductive Bias of Quantum Kernels

Deep learning: a statistical viewpoint

Understanding deep learning (still) requires rethinking generalization

Generalization in Quantum Machine Learning: a Quantum Information Perspective

Structure optimization for parameterized quantum circuits

Quantum machine learning models are kernel methods

Machine learning of high dimensional data on a noisy quantum processor

Expressivity of quantum neural networks

Information-theoretic bounds on quantum advantage in machine learning

Connecting ansatz expressibility to gradient magnitudes and barren plateaus

Anomalous collapses of Nares Strait ice arches leads to enhanced export of Arctic sea ice

Variational quantum algorithms

A feasible approach for automatically differentiable unitary coupled-cluster on quantum computers

Focus beyond Quadratic Speedups for Error-Corrected Quantum Advantage

Power of data in quantum machine learning

The power of quantum neural networks

Quantum Deformed Neural Networks

A rigorous and robust quantum speed-up in supervised machine learning

Effect of data encoding on the expressive power of variational quantum-machine-learning models

Estimating the gradient and higher-order derivatives on quantum hardware

On the Quantum versus Classical Learnability of Discrete Distributions

Expressive power of parametrized quantum circuits

Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule

Operator Sampling for Shot-frugal Optimization in Variational Algorithms

A non-review of Quantum Machine Learning: trends and explorations

Quantum embeddings for machine learning

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Continuous variable quantum perceptron

An Adaptive Optimizer for Measurement-Frugal Variational Algorithms

Quantum Natural Gradient

Data re-uploading for a universal quantum classifier

Benign overfitting in linear regression

Parameterized quantum circuits as machine learning models

Implicit Regularization in Deep Matrix Factorization

Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition

Training deep quantum neural networks

Calculus on parameterized quantum circuits.

Evaluating analytic gradients on quantum hardware

An artificial neuron implemented on an actual quantum processor

Quantum convolutional neural networks

A quantum-inspired classical algorithm for recommendation systems

Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Continuous-variable quantum neural networks

Supervised learning with quantum-enhanced feature spaces

Differentiable Learning of Quantum Circuit Born Machine

Circuit-centric quantum classifiers

Barren plateaus in quantum neural network training landscapes

Quantum Machine Learning in Feature Hilbert Spaces.

Quantum circuit learning

Classification with Quantum Neural Networks on Near Term Processors

Unitary quantum perceptron as efficient universal approximator

Learning relevant features of data with multi-scale tensor networks

Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines

Quantum Neuron: an elementary building block for machine learning on quantum computers

The Implicit Bias of Gradient Descent on Separable Data

Dealing with the positive publication bias: Why you should really publish your negative results

Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers

Quantum machine learning: a classical perspective

Inductive Supervised Quantum Learning.

Open quantum generalisation of Hopfield neural networks

Generalized coherent states, reproducing kernels, and quantum support vector machines

Quantum machine learning

Quantum-enhanced machine learning

Hybrid Quantum-Classical Approach to Quantum Optimal Control.

Optimal Quantum Sample Complexity of Learning Algorithms

Solving the quantum many-body problem with artificial neural networks

Quantum Perceptron Models

Quantum Boltzmann Machine

90 years of the Ising model

Read the fine print

Adam: A Method for Stochastic Optimization

Simulating a perceptron on a quantum computer

An introduction to quantum machine learning

Quantum Machine Learning: What Quantum Computing Means to Data Mining

Quantum Inference on Bayesian Networks

Quantum speedup for active learning agents

Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

An Introduction to Statistical Learning

Quantum speed-up for unsupervised learning

A measure of intelligence

Quantum algorithm for data fitting.

Quantum learning without quantum memory

Training a Binary Classifier with the Quantum Adiabatic Algorithm

Support vector machines

100

Random Features for Large-Scale Kernel Machines

101

Pattern Recognition and Machine Learning

102

Machine Learning in a Quantum World

103

Equivalences and Separations Between Quantum and Classical Learnability

104

Quantum Pattern Recognition

105

Pattern recognition on a quantum computer

106

A QUANTUM PERCEPTRON

107

Quantum neural network

108

Quantum associative memory

109

The Lack of A Priori Distinctions Between Learning Algorithms

110

Learning DNF over the uniform distribution using a quantum example oracle

111

On Quantum Neural Computing

112

Learning representations by back-propagating errors

113

The perceptron: a probabilistic model for information storage and organization in the brain.

115

Evaluating Generalization in Classical and Quantum Generative Models

116

Machine Learning with Quantum Computers

117

Enhancing Combinatorial Optimization with Quantum Generative Models

118

Re: Andrew Vickers, Sigrid V. Carlsson, Matthew Cooperberg. Routine Use of Magnetic Resonance Imaging for Early Detection of Prostate Cancer Is Not Justified by the Clinical Trial Evidence. Eur Urol. In press. https://doi.org/10.1016/j.eururo.2020.04.016: Prebiopsy MRI: Through the Looking Glass

119

Continuous once backward —reusing computations cached from the forward pass — to get the entire gradient vector

121

Perceptrons with Heb-bian learning based on wave ensembles in spatially patterned potentials

122

Quantum support vector machine for big data classification

123

Toward the Implementation of a Quantum RBM

124

A survey of quantum learning

125

Learning representations by back-propagation errors, nature

126

Generalized Coherent States

127

This Paper Is Included in the Proceedings of the 12th Usenix Symposium on Operating Systems Design and Implementation (osdi '16). Tensorflow: a System for Large-scale Machine Learning Tensorflow: a System for Large-scale Machine Learning

128

There are many diﬀerent ways to formalise the notion of learning, and as usual in

Field of Study

Physics

Journal Information

Name

PRX Quantum

Venue Information

Name

PRX Quantum

Type

journal

URL

https://journals.aps.org/prxquantum

TL;DR

Abstract

Authors

References129 items

The complexity of quantum support vector machines

Uncovering Instabilities in Variational-Quantum Deep Q-Networks

Quantum machine learning with subspace states

Quantum advantage in learning from experiments

Generalization in quantum machine learning from few training data

TL;DR

Abstract

Authors

References129 items

The complexity of quantum support vector machines

Uncovering Instabilities in Variational-Quantum Deep Q-Networks

Quantum machine learning with subspace states

Quantum advantage in learning from experiments

Generalization in quantum machine learning from few training data

Representation Learning via Quantum Neural Tangent Kernels

Quantum tangent kernel

A Case for Noisy Shallow Gate-based Circuits in Quantum Machine Learning

Quantum-classical hybrid neural networks in the neural tangent kernel regime

Entangled Datasets for Quantum Machine Learning

Generalized quantum circuit differentiation rules

General parameter-shift rules for quantum gradients

A quantum algorithm for training wide and deep classical neural networks

Analytic gradients in variational quantum algorithms: Algebraic extensions of the parameter-shift rule to general unitary transformations

The Inductive Bias of Quantum Kernels

Deep learning: a statistical viewpoint

Understanding deep learning (still) requires rethinking generalization

Generalization in Quantum Machine Learning: a Quantum Information Perspective

Structure optimization for parameterized quantum circuits

Quantum machine learning models are kernel methods

Machine learning of high dimensional data on a noisy quantum processor

Expressivity of quantum neural networks

Information-theoretic bounds on quantum advantage in machine learning

Connecting ansatz expressibility to gradient magnitudes and barren plateaus

Anomalous collapses of Nares Strait ice arches leads to enhanced export of Arctic sea ice

Variational quantum algorithms

A feasible approach for automatically differentiable unitary coupled-cluster on quantum computers

Focus beyond Quadratic Speedups for Error-Corrected Quantum Advantage

Power of data in quantum machine learning

The power of quantum neural networks

Quantum Deformed Neural Networks

A rigorous and robust quantum speed-up in supervised machine learning

Effect of data encoding on the expressive power of variational quantum-machine-learning models

Estimating the gradient and higher-order derivatives on quantum hardware

On the Quantum versus Classical Learnability of Discrete Distributions

Expressive power of parametrized quantum circuits

Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule

Operator Sampling for Shot-frugal Optimization in Variational Algorithms

A non-review of Quantum Machine Learning: trends and explorations

Quantum embeddings for machine learning

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Continuous variable quantum perceptron

An Adaptive Optimizer for Measurement-Frugal Variational Algorithms

Quantum Natural Gradient

Data re-uploading for a universal quantum classifier

Benign overfitting in linear regression

Parameterized quantum circuits as machine learning models

Implicit Regularization in Deep Matrix Factorization

Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition

Training deep quantum neural networks

Calculus on parameterized quantum circuits.

Evaluating analytic gradients on quantum hardware

An artificial neuron implemented on an actual quantum processor

Quantum convolutional neural networks

Framework

A quantum-inspired classical algorithm for recommendation systems

Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Continuous-variable quantum neural networks

Supervised learning with quantum-enhanced feature spaces

Differentiable Learning of Quantum Circuit Born Machine

Circuit-centric quantum classifiers

Barren plateaus in quantum neural network training landscapes

Quantum Machine Learning in Feature Hilbert Spaces.

Quantum circuit learning

Classification with Quantum Neural Networks on Near Term Processors

Unitary quantum perceptron as efficient universal approximator

Learning relevant features of data with multi-scale tensor networks