Portfolio Optimization with Digitized-Counterdiabatic Quantum Algorithms

• URL: http://arxiv.org/abs/2112.08347v1
• Date: Wed, 15 Dec 2021 18:55:02 GMT
• Title: Portfolio Optimization with Digitized-Counterdiabatic Quantum Algorithms
• Authors: N. N. Hegade, P. Chandarana, K. Paul, X. Chen, F. Albarr\'an-Arriagada, and E. Solano
• Abstract summary: We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum advantage for industrial applications in the NISQ era. Our analysis shows a drastic improvement in the success probabilities of the resulting digital quantum algorithm when approximate counterdiabatic techniques are introduced.
• Score: 1.1682745573995112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum advantage for industrial applications in the NISQ era. We apply this concept to investigate a discrete mean-variance portfolio optimization problem, showing its usefulness in a key finance application. Our analysis shows a drastic improvement in the success probabilities of the resulting digital quantum algorithm when approximate counterdiabatic techniques are introduced. Along these lines, we discuss the enhanced performance of our methods over variational quantum algorithms like QAOA and DC-QAOA.

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