Quantum Machine Learning for Finance

arXiv:2109.04298 · quant-ph, cs.LG · Submitted 2021-09-09 · Read on arXiv

quant-ph, cs.LG

Submitted: 2021-09-09

Updated: 2021-09-09

DOI: 10.1109/ICCAD51958.2021.9643469

License: http://creativecommons.org/licenses/by/4.0/

The gist: Quantum computers are expected to surpass the computational capabilities of classical computers during this decade, and achieve disruptive impact on numerous industry sectors, particularly finance.

Terminology

• Clustering — 25× in this paper · explained in 2 episodes
• Machine Learning — 12× in this paper
• Reinforcement Learning — 10× in this paper · explained in 4 episodes
• Quantum Circuits — 7× in this paper · explained in 2 episodes
• Feature Extraction — 5× in this paper · explained in 2 episodes
• Feature Selection — 5× in this paper · explained in 4 episodes
• Hilbert Space — 5× in this paper · explained in 2 episodes
• Natural Language Processing — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Dynamic Quantum Clustering — 3× in this paper
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Gibbs State — 3× in this paper · explained in 2 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Differentially Private — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Hamming Distance — 2× in this paper · explained in 2 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Adiabatic Evolution — 1× in this paper · explained in 2 episodes
• Adversarial Training — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fingerprinting — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Imaginary Time Evolution — 1× in this paper
• Instantaneous Quantum Polynomial — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Long Short Term Memory — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Markov Decision Process — 1× in this paper
• Principal Component Analysis — 1× in this paper
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Quantum Annealing — 1× in this paper
• Quantum Annealing (QA) — 1× in this paper · explained in 2 episodes
• Quantum Random Access Memory — 1× in this paper
• Query Complexity — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Reproducing Kernel Hilbert Space — 1× in this paper
• Reproducing Kernel Hilbert Space (RKHS) — 1× in this paper · explained in 3 episodes
• Restricted Boltzmann Machine (RBM) — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Spectral clustering — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Abstract

Sources

• Loading Classical Data into a Quantum Computer
• The power of block-encoded matrix powers: improved regression techniques via faster Hamiltonian simulation
• Quantum-inspired sublinear classical algorithms for solving low-rank linear systems
• Quantum Long Short-Term Memory
• Deep Learning in Asset Pricing
• Quantum Differentially Private Sparse Regression Learning
• Application of deep quantum neural networks to finance
• Nearest Centroid Classification on a Trapped Ion Quantum Computer
• Quantum K-nearest neighbor classification algorithm based on Hamming distance
• Quantum $k$-nearest neighbors algorithm
• A Quantum Algorithm for Finding $k$-Minima
• Adam: A Method for Stochastic Optimization
• Trainability of Dissipative Perceptron-Based Quantum Neural Networks
• A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver
• Supervised quantum machine learning models are kernel methods
• Neural Tangent Kernel: Convergence and Generalization in Neural Networks
• Analyzing Big Data with Dynamic Quantum Clustering
• q-means: A quantum algorithm for unsupervised machine learning
• Coreset Clustering on Small Quantum Computers
• K-Means Clustering on Noisy Intermediate Scale Quantum Computers

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