We solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. The formulation incorporates transaction costs (including permanent and temporary market impact), and, significantly, the solution does not require the inversion of a covariance matrix. The discrete multi-period portfolio optimization problem we solve is significantly harder than the continuous variable problem. We present insight into how results may be improved using suitable software enhancements and why current quantum annealing technology limits the size of problem that can be successfully sol...
This paper proposes a highly efficient quantum algorithm for portfolio optimisation targeted at near...
An approach to solve combinatorial optimization problems on a D-Wave Quantum Annealer while using a ...
We briefly review various computational methods for the solution of optimization problems. First, se...
We solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We de...
In this work, we introduce a new workflow to solve portfolio optimization problems on annealing plat...
The first quantum computers are expected to perform well on quadratic optimisation problems. In this...
The prediction of financial crashes in a complex financial network is known to be an NP-hard problem...
In many real-life situations in engineering (and in other disciplines), we need to solve an optimiza...
"Quantum annealing employs quantum fluctuations in frustrated systems or networks to anneal the syst...
Recent developments in quantum annealing techniques have been indicating potential advantage of quan...
Quantum annealing is a quantum version of classical simulated annealing, but using quantum fluctuati...
We report on our efforts to solve a problem from airport planning with a D-Wave quantum annealer. Th...
We study the effect of the anneal path control per qubit, a new user control feature offered on the ...
The path integral Monte Carlo simulated quantum annealing algorithm is applied to the optimization o...
We review here some recent work in the field of quantum annealing, alias adiabatic quantum computati...
This paper proposes a highly efficient quantum algorithm for portfolio optimisation targeted at near...
An approach to solve combinatorial optimization problems on a D-Wave Quantum Annealer while using a ...
We briefly review various computational methods for the solution of optimization problems. First, se...
We solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We de...
In this work, we introduce a new workflow to solve portfolio optimization problems on annealing plat...
The first quantum computers are expected to perform well on quadratic optimisation problems. In this...
The prediction of financial crashes in a complex financial network is known to be an NP-hard problem...
In many real-life situations in engineering (and in other disciplines), we need to solve an optimiza...
"Quantum annealing employs quantum fluctuations in frustrated systems or networks to anneal the syst...
Recent developments in quantum annealing techniques have been indicating potential advantage of quan...
Quantum annealing is a quantum version of classical simulated annealing, but using quantum fluctuati...
We report on our efforts to solve a problem from airport planning with a D-Wave quantum annealer. Th...
We study the effect of the anneal path control per qubit, a new user control feature offered on the ...
The path integral Monte Carlo simulated quantum annealing algorithm is applied to the optimization o...
We review here some recent work in the field of quantum annealing, alias adiabatic quantum computati...
This paper proposes a highly efficient quantum algorithm for portfolio optimisation targeted at near...
An approach to solve combinatorial optimization problems on a D-Wave Quantum Annealer while using a ...
We briefly review various computational methods for the solution of optimization problems. First, se...