This paper proposes a highly efficient quantum algorithm for portfolio optimisation targeted at near-term noisy intermediate-scale quantum computers. Recent work by Hodson et al. (2019) explored potential application of hybrid quantum-classical algorithms to the problem of financial portfolio rebalancing. In particular, they deal with the portfolio optimisation problem using the Quantum Approximate Optimisation Algorithm and the Quantum Alternating Operator Ansatz. In this paper, we demonstrate substantially better performance using a newly developed Quantum Walk Optimisation Algorithm in finding high-quality solutions to the portfolio optimisation problem
Quantum walks are stochastic processes generated by a quantum evolution mechanism, allowing for spee...
The quantum approximate optimization algorithm was proposed as a heuristic method for solving combin...
Stock selection is the first problem that investors encounter when investing in the stock market and...
49 pages, 4 figuresQuantum computers are expected to have substantial impact on the finance industry...
The first quantum computers are expected to perform well on quadratic optimisation problems. In this...
Recently there has been increased interest on quantum algorithms and how they are applied to real li...
In this work, we introduce a new workflow to solve portfolio optimization problems on annealing plat...
In the last decade, public and industrial research funding has moved quantum computing from the earl...
This paper proposes a quantum-inspired evolutionary algorithm with neighborhood search (call-ed QEAN...
The development of quantum algorithms based on quantum versions of random walks is placed in the con...
Optimization problems are ubiquitous in but not limited to the sciences, engineering, and applied ma...
We solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We de...
We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum ...
Quantum Computing leverages the quantum properties of subatomic matter to enable computations faster...
Quantum computers are expected to surpass the computational capabilities of classical computers duri...
Quantum walks are stochastic processes generated by a quantum evolution mechanism, allowing for spee...
The quantum approximate optimization algorithm was proposed as a heuristic method for solving combin...
Stock selection is the first problem that investors encounter when investing in the stock market and...
49 pages, 4 figuresQuantum computers are expected to have substantial impact on the finance industry...
The first quantum computers are expected to perform well on quadratic optimisation problems. In this...
Recently there has been increased interest on quantum algorithms and how they are applied to real li...
In this work, we introduce a new workflow to solve portfolio optimization problems on annealing plat...
In the last decade, public and industrial research funding has moved quantum computing from the earl...
This paper proposes a quantum-inspired evolutionary algorithm with neighborhood search (call-ed QEAN...
The development of quantum algorithms based on quantum versions of random walks is placed in the con...
Optimization problems are ubiquitous in but not limited to the sciences, engineering, and applied ma...
We solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We de...
We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum ...
Quantum Computing leverages the quantum properties of subatomic matter to enable computations faster...
Quantum computers are expected to surpass the computational capabilities of classical computers duri...
Quantum walks are stochastic processes generated by a quantum evolution mechanism, allowing for spee...
The quantum approximate optimization algorithm was proposed as a heuristic method for solving combin...
Stock selection is the first problem that investors encounter when investing in the stock market and...