Predicting human-generated bitstreams using classical and quantum models

04/09/2020
by   Alex Bocharov, et al.
0

A school of thought contends that human decision making exhibits quantum-like logic. While it is not known whether the brain may indeed be driven by actual quantum mechanisms, some researchers suggest that the decision logic is phenomenologically non-classical. This paper develops and implements an empirical framework to explore this view. We emulate binary decision-making using low width, low depth, parameterized quantum circuits. Here, entanglement serves as a resource for pattern analysis in the context of a simple bit-prediction game. We evaluate a hybrid quantum-assisted machine learning strategy where quantum processing is used to detect correlations in the bitstreams while parameter updates and class inference are performed by classical post-processing of measurement results. Simulation results indicate that a family of two-qubit variational circuits is sufficient to achieve the same bit-prediction accuracy as the best traditional classical solution such as neural nets or logistic autoregression. Thus, short of establishing a provable "quantum advantage" in this simple scenario, we give evidence that the classical predictability analysis of a human-generated bitstream can be achieved by small quantum models.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

06/18/2019

Parameterized quantum circuits as machine learning models

Hybrid quantum-classical systems make it possible to utilize existing qu...
05/30/2020

QuLBIT: Quantum-Like Bayesian Inference Technologies for Cognition and Decision

This paper provides the foundations of a unified cognitive decision-maki...
11/08/2021

Representation Learning via Quantum Neural Tangent Kernels

Variational quantum circuits are used in quantum machine learning and va...
10/19/2020

Predicting toxicity by quantum machine learning

In recent years, parameterized quantum circuits have been regarded as ma...
09/05/2017

Speeding-up the decision making of a learning agent using an ion trap quantum processor

We report a proof-of-principle experimental demonstration of the quantum...
07/21/2020

Quantum and Classical Hybrid Generations for Classical Correlations

We consider two-stage hybrid protocols that combine quantum resource and...
11/14/2019

A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer

We propose a regression algorithm that utilizes a learned dictionary opt...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.