On small synthetic portfolio problems, noiseless QAOA fits the known ground-state energy well, but noisy QAOA fails while QITE, pretrained on noiseless simulators, still identifies the optimal portfolio on IBM hardware.
Portfolio Optimization of 40 Stocks Using the DWave Quantum Annealer
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abstract
We investigate the use of quantum computers for building a portfolio out of a universe of U.S. listed, liquid equities that contains an optimal set of stocks. Starting from historical market data, we look at various problem formulations on the D-Wave Systems Inc. D-Wave 2000Q(TM) System (hereafter called DWave) to find the optimal risk vs return portfolio; an optimized portfolio based on the Markowitz formulation and the Sharpe ratio, a simplified Chicago Quantum Ratio (CQR), then a new Chicago Quantum Net Score (CQNS). We approach this first classically, then by our new method on DWave. Our results show that practitioners can use a DWave to select attractive portfolios out of 40 U.S. liquid equities.
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Benchmarking Quantum Solvers in Noisy Digital Simulations for Financial Portfolio Optimization
On small synthetic portfolio problems, noiseless QAOA fits the known ground-state energy well, but noisy QAOA fails while QITE, pretrained on noiseless simulators, still identifies the optimal portfolio on IBM hardware.