Boosting on the shoulders of giants in quantum device calibration (2020-05-13T00:00:00.000000Z)

TL;DR

This work shows its efficacy in predicting the entire energy spectrum of a Hamiltonian on a superconducting quantum device, a key task in present quantum computer calibration, and demonstrates how artificial intelligence can be further enhanced by "standing on the shoulders of giants."

Abstract

Traditional machine learning applications, such as optical character recognition, arose from the inability to explicitly program a computer to perform a routine task. In this context, learning algorithms usually derive a model exclusively from the evidence present in a massive dataset. Yet in some scientific disciplines, obtaining an abundance of data is an impractical luxury, however; there is an explicit model of the domain based upon previous scientific discoveries. Here we introduce a new approach to machine learning that is able to leverage prior scientific discoveries in order to improve generalizability over a scientific model. We show its efficacy in predicting the entire energy spectrum of a Hamiltonian on a superconducting quantum device, a key task in present quantum computer calibration. Our accuracy surpasses the current state-of-the-art by over $20\%.$ Our approach thus demonstrates how artificial intelligence can be further enhanced by "standing on the shoulders of giants."

Authors

A. Wozniakowski

1 papers

Jayne Thompson

1 papers

M. Gu

1 papers

TL;DR

Abstract

Authors

References74 items

Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks

David Montgomery: Growing a revolution: bringing our soil back to life

Growth and preservation of entanglement in a many-body localized system

AI Feynman: A physics-inspired method for symbolic regression

Machine learning and the physical sciences

Toward an AI Physicist for Unsupervised Learning

Exploratory data analysis

Multi-target prediction: a unifying view on problems and methods

Gradient and Newton Boosting for Classification and Regression

Discovering physical concepts with neural networks

Machine learning for molecular and materials science

Machine learning & artificial intelligence in the quantum domain: a review of recent progress

A high-bias, low-variance introduction to Machine Learning for physicists

Physical qubit calibration on a directed acyclic graph

Quantum Machine Learning

LightGBM: A Highly Efficient Gradient Boosting Decision Tree

Spectroscopic signatures of localization with interacting photons in superconducting qubits

A blueprint for demonstrating quantum supremacy with superconducting qubits

Active learning machine learns to create new quantum experiments

A Unified Approach to Interpreting Model Predictions

Mutual information, neural networks and the renormalization group

Neural-network quantum state tomography

Experimental comparison of two quantum computing architectures

Why Does Deep and Cheap Learning Work So Well?

Machine learning phases of matter

XGBoost: A Scalable Tree Boosting System

Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine Learning Approach

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

A survey on multi‐output regression

Explaining prediction models and individual predictions with feature contributions

Practical Lessons from Predicting Clicks on Ads at Facebook

Qubit Architecture with High Coherence and Fast Tunable Coupling.

Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit

Quantum mechanics can reduce the complexity of classical models

Scikit-learn: Machine Learning in Python

On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

Why Does Unsupervised Pre-training Help Deep Learning?

Distilling Free-Form Natural Laws from Experimental Data

A Treatise of Human Nature

BOOSTING ALGORITHMS: REGULARIZATION, PREDICTION AND MODEL FITTING

Generalized Additive Models

The Elements of Statistical Learning

Kernel Matching Pursuit

Incorporating Prior Knowledge into Boosting

Greedy function approximation: A gradient boosting machine.

Special Invited Paper-Additive logistic regression: A statistical view of boosting

A Model of Inductive Bias Learning

Boosting Algorithms as Gradient Descent

Computational Mechanics: Pattern and Prediction, Structure and Simplicity

A decision-theoretic generalization of on-line learning and an application to boosting

Multitask Learning

No free lunch theorems for optimization

Cryptographic limitations on learning Boolean formulae and finite automata

Matching pursuits with time-frequency dictionaries

Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications

Toward Efficient Agnostic Learning

Efficient distribution-free learning of probabilistic concepts

Boosting a weak learning algorithm by majority

The Strength of Weak Learnability

Quantifying Inductive Bias: AI Learning Algorithms and Valiant's Learning Framework

Interpretable machine learning: a guide for making black box models explainable. christophm.github.io/interpretable-ml-book

Scikit-physlearn. github.com/awozniakowski/scikit-physlearn

Machine learning yearning. deeplearning.ai project

Stacked Generalization

The Need for Biases in Learning Generalizations

The Plastic Human Brain Cortex

Stacked regressions

Importance Sampled Learning Ensembles

Predicting Multivariate Responses in Multiple Linear Regression

ARCING THE EDGE

Distribution-free Learning of Probabilistic Concepts

Learning boolean formulae or finite automata is as hard as factoring

cal Report TR-14-88

Radial basis functions for multivariable interpolation: a review

Field of Study

Journal Information