China

&quot;HqHop: Hybrid Quantum Hopfield Neural Network for Portfolio Selection

RP Mahajan 
 
Faculty at Dept. of Computer Science &amp; Engineering, SIRT, SAGE University, India
E-mail: rpmahajan.sage@gmail.com ; rpmahajan@yahoo.com

 
Abstract: Classical Hopfield neural networks (CHNN) are known for solving portfolio selection problem for asset management. A Hybrid Quantum Hopfield Neural Network (HqHop) is proposed to solve portfolio selection problem based on Markowitz mean variance approach. Due to the large number of portfolios, the CHNN requires huge memory and processing power. The inbuilt parallelism in CHNN makes it more suitable for quantum based system. This paper details an approach, perhaps the first time, for the solution of portfolio selection problem using the involved concept. The developed HqHop can be used for solving other optimization problems as well.

&quot;

Quantum 2020

­­­­­­­­­­­­HqHop: Hybrid Quantum Hopfield Neural Network for Portfolio Selection

quantum computation.

portfolio optimization

portfolio selection

qnn in financial engineering

hybrid quantum hopfield neural network

quantum hopfield neural network

quantum neural network (qnn)

The Institute of Physics and IOP Publishing, in partnership with the Chinese Physical Society (CPS) and the University of Science and Technology of China (USTC) are proud to have brought together the international quantum science community online for this inaugural conference.


Early career and leading researchers from universities, industry and government worldwide came together to share, learn and collaborate on the latest research and emerging areas of high interest across quantum science and technology. The broad themes covered by Quantum 2020 included:

Quantum communication;
Quantum computing and quantum simulation;
Quantum sensing, metrology and imaging;
Quantum information theory, software and algorithms;
Quantum foundations;
Quantum technology in industry.
The four-day event featured talks from world-renowned scientists, interactive poster sessions and two exciting panel discussions on the themes of Quantum Technology in Industry and Quantum Technology Programmes Around the World.


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The Institute of Physics and IOP Publishing, in partnership with the Chinese Physical Society (CPS) and the University of Science and Technology of China (USTC) are proud to have brought together the international quantum science community online for this inaugural conference.

Early career and leading researchers from universities, industry and government worldwide came together to share, learn and collaborate on the latest research and emerging areas of high interest across quantum science and technology. 

"HqHop: Hybrid Quantum Hopfield Neural Network for Portfolio Selection

RP Mahajan 
 
Faculty at Dept. of Computer Science & Engineering, SIRT, SAGE University, India
E-mail: rpmahajan.sage@gmail.com ; rpmahajan@yahoo.com

 
Abstract: Classical Hopfield neural networks (CHNN) are known for solving portfolio selection problem for asset management. A Hybrid Quantum Hopfield Neural Network (HqHop) is proposed to solve portfolio selection problem based on Markowitz mean variance approach. Due to the large number of portfolios, the CHNN requires huge memory and processing power. The inbuilt parallelism in CHNN makes it more suitable for quantum based system. This paper details an approach, perhaps the first time, for the solution of portfolio selection problem using the involved concept. The developed HqHop can be used for solving other optimization problems as well.

"

poster

An efficient TensorFlow solver for large-scale quantum PageRank and quantum stochastic walks

Quantum Day 1 - first 8 hours

The atomic approach to quantum computing

Beyond Classical Computation

Towards Universal Quantum Computation with Bosonic Qubits

Petar Jurcevic: Quantum Volume - IBM

Michael Bierchuk: Using Q-CTRL's BOULDER OPAL to improve the performance of cloud quantum computers - Q-CTRL

Designing Quantum Networks

Large-scale quantum computing with quantum teleportation

Quantum-dot single photon sources: Progresses and Applications

technical paper

keynote

invited talk

https://d3smihljt9218e.cloudfront.net/transcript/2270/subtitle/7975bdacc9693b8191e475d5c77a9fff.vtt

English (automatic)

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An efficient TensorFlow solver for large-scale quantum PageRank and quantum stochastic walks

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Premium content

­­­­­­­­­­­­HqHop: Hybrid Quantum Hopfield Neural Network for Portfolio Selection

Next from Quantum 2020

An efficient TensorFlow solver for large-scale quantum PageRank and quantum stochastic walks

Similar lecture

Multiqubit entanglement for public randomness testing vs Google's quantum supremacy

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

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HqHop: Hybrid Quantum Hopfield Neural Network for Portfolio Selection