Predicting human-generated bitstreams using classical and quantum models Article Swipe
Alex Bocharov
,
Michael Freedman
,
Eshan Kemp
,
Martin Roetteler
,
Krysta M. Svore
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.04671
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.04671
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.
Related Topics
Concepts
Computer science
Bitstream
Context (archaeology)
Qubit
Parameterized complexity
Quantum
Predictability
Inference
Binary number
Binary decision diagram
Theoretical computer science
Simple (philosophy)
Artificial intelligence
Electronic circuit
Algorithm
Decoding methods
Mathematics
Arithmetic
Epistemology
Philosophy
Physics
Biology
Statistics
Electrical engineering
Paleontology
Quantum mechanics
Engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2004.04671
- https://arxiv.org/pdf/2004.04671
- OA Status
- green
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3015938445
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3015938445Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2004.04671Digital Object Identifier
- Title
-
Predicting human-generated bitstreams using classical and quantum modelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-04-09Full publication date if available
- Authors
-
Alex Bocharov, Michael Freedman, Eshan Kemp, Martin Roetteler, Krysta M. SvoreList of authors in order
- Landing page
-
https://arxiv.org/abs/2004.04671Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2004.04671Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2004.04671Direct OA link when available
- Concepts
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Computer science, Bitstream, Context (archaeology), Qubit, Parameterized complexity, Quantum, Predictability, Inference, Binary number, Binary decision diagram, Theoretical computer science, Simple (philosophy), Artificial intelligence, Electronic circuit, Algorithm, Decoding methods, Mathematics, Arithmetic, Epistemology, Philosophy, Physics, Biology, Statistics, Electrical engineering, Paleontology, Quantum mechanics, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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