Direct classical algorithms for determining the risk-return curve and other properties of the optimal portfolio take time {m poly}(N) {m poly}(N) and we discuss potential quantum speedups in light of the recent works on efficient classical sampling approaches....
Moreover, we believe that the use of distributed quantum computer architectures, such as those discussed in Ref.3, could accelerate even more the implementation of this type of quantum computing algorithms to solve real-life financial mathematical problems....
We present a detailed study of portfolio optimization using different versions of the quantum approximate optimization algorithm (QAOA). For a given list o
Nonetheless, NISQ devices are still quite limited at the time of solving certain problems of relevance, such as complex optimization problems. This is true with the current implementation of quantum optimization algorithms such as Variational Quantum Eigensolvers (VQE)6 and Quantum Approximate ...
Quantum Algorithms for Financial Portfolio Optimization Introduction Portfolio optimization is the process of selecting the best combination of assets to achieve a desired investment objective. The goal is to find the allocation of assets that maximizes the return while minimizing the risk. Traditional po...
It has been demonstrated that computation and complex machine learning algorithms for learning and optimization can be accelerated by quantum parallelization and associated memories (Dasari, Im, & Beshaj, 2020; Purushothaman & Karayiannis, 1997) A critical look at the quantum algorithms that have ...
Many financial services activities, from securities pricing to portfolio optimization, require the ability to assess a range of potential outcomes. To do this, banks use algorithms and models that calculate statistical probabilities. These are fairly effective but are not infallible, as was ...
Quantum computing has unique advantages in optimization algorithms, capable of solving complex optimization problems that traditional algorithms struggle with. For instance, quantum annealing algorithms can be used for portfolio optimization, helping investors find the best asset allocation. ...
processing horsepower, new error correction capabilities to ensure accuracy and reliability, greater accessibility through cloud-based services, and toolkits and software that simplify the design of quantum algorithms and workloads are among the latest developments causing an uptick in commercial applications...
(2) Since the correlation matrix is positive-definite and symmetric, the utility function 𝑄𝑐Qc is convex, so that a solution to this optimization problem can be found in polynomial time with linear and quadratic algorithms (Kolm et al. 2014)....