Problem-size-independent angles for a Grover-driven quantum approximate optimization algorithm

Headley, David; Wilhelm, Frank K.

doi:10.1103/physreva.107.012412

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…With each of the p iterations, the potential for probably amplitude concentration at |i⟩ corresponding to high-quality solutions increases-given suitable values for the variational parameters γ and t [44,45]. Optimal variational parameters γ and t are obtained using a classical optimiser that minimises with respect to the objective function f (γ, t) = ⟨γ, t|Q|γ, t⟩, (10) such that a lowering of f(γ, t) entails an increased probability of measuring a quantum state that corresponds to a high-quality solution.…”

Section: Qwoa For Combinatorial Optimizationmentioning

confidence: 99%

Experimental implementation of quantum-walk-based portfolio optimization

Qu,

Matwiejew,

Wang

et al. 2024

Quantum Sci. Technol.

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The application of quantum algorithms has attracted much attention as it holds the promise of solving practical problems that are intractable to classical algorithms. One such application is the recent development of a quantum-walk-based optimisation algorithm approach to portfolio optimisation under the modern portfolio theory framework. In this paper, we demonstrate an experimental realisation of the alternating phase-shift and continuous-time quantum walk unitaries that underpin this quantum algorithm using optical networks and single photons. The experimental analysis conﬁrms that the probability of states corresponding to high-quality solutions is eﬃciently ampliﬁed by increasing the number of phase-shift and quantum walk iterations. This work provides strong evidence for practical applications of quantum-walk-based algorithms such as ﬁnancial portfolio optimisation.

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Section: Qwoa For Combinatorial Optimizationmentioning

confidence: 99%

Experimental implementation of quantum-walk-based portfolio optimization

Qu,

Matwiejew,

Wang

et al. 2024

Quantum Sci. Technol.

View full text Add to dashboard Cite

show abstract

$$\mathrm{Q(AI)}^2$$: Quantum Artificial Intelligence for the Automotive Industry

Stollenwerk,

Bhattacharya,

Cattelan

et al. 2024

Künstl Intell

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Advancements in Quantum Computing—Viewpoint: Building Adoption and Competency in Industry

Pfaendler,

Konson,

Greinert

2024

Datenbank Spektrum

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The narrative around quantum computing is evolving quickly. First reports of quantum computers able to solve certain scientific problems on-par with the precision of High-Performance Computers are persuading end-users and industry leaders to shift from passive observation to active exploration. Insights are provided here to enable organization and technology leaders in the database and data science community an entry into the field of quantum computing. This article provides an introduction of key concepts, insights into the rapid advancement of the state-of-the-art, an overview of how companies in Germany are approaching the development of competency and adoption of quantum computing, including an overview of European activities and learning materials in the German language.

show abstract

Problem-size-independent angles for a Grover-driven quantum approximate optimization algorithm

Cited by 5 publications

References 25 publications

Experimental implementation of quantum-walk-based portfolio optimization

Experimental implementation of quantum-walk-based portfolio optimization

$$\mathrm{Q(AI)}^2$$: Quantum Artificial Intelligence for the Automotive Industry

Advancements in Quantum Computing—Viewpoint: Building Adoption and Competency in Industry

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