Benchmarking Quantum Annealing Controls with Portfolio Optimization

Grant, Erica; Humble, Travis S.; Stump, Benjamin

doi:10.1103/physrevapplied.15.014012

Cited by 70 publications

(50 citation statements)

References 43 publications

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“…However, the hope is, that some of the samples obtained this way will reflect the minimal energy configuration of the problem's Ising Hamiltonian. Recent results of algorithmic benchmarking of QA architectures have been reported in [18]- [22], highlighting diverse application areas.…”

Section: B Quantum Annealingmentioning

confidence: 99%

Evaluation of Quantum Annealer Performance via the Massive MIMO Problem

Tabi¹,

Marosits

Kallus

et al. 2021

IEEE Access

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Quantum annealing offers an appealing route to handle large-scale optimization problems. Existing Quantum Annealing processing units are readily available via cloud platform access for solving Quadratic Unconstrained Binary Optimization (QUBO) problems. In particular, the novel D-Wave Advantage device has been recently released. Its performance is expected to improve upon the previous stateof-the-art D-Wave 2000Q annealer, due to higher number of qubits and the Pegasus topology. Here, we present a comparative study via an ensemble of Maximum Likelihood (ML) Channel Decoder problems for MIMO scenarios in Centralized Radio Access Network (C-RAN) architectures. The main challenge for exact optimization of ML decoders with ever-increasing demand for higher data rates is the exponential increase of the solution space with problem sizes. Since current 5G solutions mainly use approximate methodologies, Kim et al. leveraged Quantum Annealing for large MIMO problems with Phase Shift Keying and Quadrature Amplitude Modulation scenarios. Here, we extend their work and analyze experiments for more complex modulations and larger MIMO antenna array sizes. By implementing the extended QUBO formulae on the novel annealer architecture, we uncover the limits of state-of-the-art quantum optimization for the massive MIMO ML decoder. We report on the improvements and discuss the uncovered limiting factors learned from the 64-QAM extension. We include the enhanced evaluation of raw annealer sampling via implementation of post-processing methods in the comparative analysis between D-Wave 2000Q and D-Wave Advantage system.

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Section: B Quantum Annealingmentioning

confidence: 99%

Evaluation of Quantum Annealer Performance via the Massive MIMO Problem

Tabi¹,

Marosits

Kallus

et al. 2021

IEEE Access

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“…Although several problems can be modeled as QUBO (e.g., [9]- [11]), most optimization problems of practical interest contain both discrete and continuous variables. For instance, mixed-integer linear programming (MILP) problems are commonly used in diverse application areas, such as logistics [12], the coordination of unmanned aerial vehicles [13] and power systems [14].…”

Section: A Decomposition-based Hqc Algorithmsmentioning

confidence: 99%

Hybrid Quantum-Classical Multi-cut Benders Approach with a Power System Application

Paterakis¹

2021

Preprint

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Leveraging the current generation of quantum devices to solve optimization problems of practical interest necessitates the development of hybrid quantum-classical (HQC) solution approaches. In this paper, a multi-cut Benders decomposition (BD) approach that exploits multiple feasible solutions of the master problem (MP) to generate multiple valid cuts is adapted, so as to be used as an HQC solver for general mixed-integer linear programming (MILP) problems. The use of different cut selection criteria and strategies to manage the size of the MP by eliciting a subset of cuts to be added in each iteration of the BD scheme using quantum computing is discussed. The HQC optimization algorithm is applied to the Unit Commitment (UC) problem. UC is a prototypical use case of optimization applied to electrical power systems, a critical sector that may benefit from advances in quantum computing. The validity and computational viability of the proposed approach are demonstrated using the D-Wave Advantage 4.1 quantum annealer.

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“…Quantum annealing is also used to solve the portfolio optimization problem [51]- [53] before. Still, most of the literature only focuses on quantum computers' internal abstract performance and does not study the implication of using it in portfolio optimization.…”

Section: Introductionmentioning

confidence: 99%

Portfolio Optimization on Classical and Quantum Computers Using PortFawn

Owhadi-Kareshk¹,

Boulanger²

2021

Preprint

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Portfolio diversification is one of the most effective ways to minimize investment risk. Individuals and fund managers aim to create a portfolio of assets that not only have high returns but are also uncorrelated. This goal can be achieved by comparing the historical performance, fundamentals, predictions, news sentiment, and many other parameters that can affect the portfolio's value. One of the most well-known approaches to manage/optimize portfolios is the well-known mean-variance (Markowitz) portfolio. The algorithm's inputs are the expected returns and risks (volatility), and its output is the optimized weights for each asset in the target portfolio. Simplified unrealistic assumptions and constraints were used in its original version preventing its use in practical cases. One solution to improve its usability is by altering the parameters and constraints to match investment goals and requirements. This paper introduces PortFawn , an open-source Python library to create and backtest mean-variance portfolios. PortFawn provides simple-to-use APIs to create and evaluate mean-variance optimization algorithms using classical computing (real-valued asset weights) as well as quantum annealing computing (binary asset weights). This tool has many parameters to customize the target portfolios according to the investment goals. The paper introduces the background and limitations of the meanvariance portfolio optimization algorithm, its architecture, and a description of the functionalities of PortFawn . We also show how one can use this tool in practice using a simple investment scenario.

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Benchmarking Quantum Annealing Controls with Portfolio Optimization

Cited by 70 publications

References 43 publications

Evaluation of Quantum Annealer Performance via the Massive MIMO Problem

Evaluation of Quantum Annealer Performance via the Massive MIMO Problem

Hybrid Quantum-Classical Multi-cut Benders Approach with a Power System Application

Portfolio Optimization on Classical and Quantum Computers Using PortFawn

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