Quantum Computing for Finance: State-of-the-Art and Future Prospects

Egger, Daniel; Gambella, Claudio; Mareček, Jakub; McFaddin, Scott; Mevissen, Martin; Raymond, Rudy; Simonetto, Andrea; Woerner, Stefan; Yndurain, Elena

doi:10.1109/tqe.2020.3030314

Cited by 259 publications

(140 citation statements)

References 88 publications

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“…Further, we emphasize that the resource estimation approach here can be fruitfully applied to analyze thresholds in other financially relevant applications. As summarized in two recent reviews [19,29] there are many potential areas for quantum advantage in finance where advantage thresholds would provide useful targets for both industry and the research community.…”

Section: Discussionmentioning

confidence: 99%

A Threshold for Quantum Advantage in Derivative Pricing

Chakrabarti

Krishnakumar

Mazzola

et al. 2021

Quantum

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120

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We give an upper bound on the resources required for valuable quantum advantage in pricing derivatives. To do so, we give the first complete resource estimates for useful quantum derivative pricing, using autocallable and Target Accrual Redemption Forward (TARF) derivatives as benchmark use cases. We uncover blocking challenges in known approaches and introduce a new method for quantum derivative pricing – the re-parameterization method – that avoids them. This method combines pre-trained variational circuits with fault-tolerant quantum computing to dramatically reduce resource requirements. We find that the benchmark use cases we examine require 8k logical qubits and a T-depth of 54 million. We estimate that quantum advantage would require executing this program at the order of a second. While the resource requirements given here are out of reach of current systems, we hope they will provide a roadmap for further improvements in algorithms, implementations, and planned hardware architectures.

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Section: Discussionmentioning

confidence: 99%

A Threshold for Quantum Advantage in Derivative Pricing

Chakrabarti

Krishnakumar

Mazzola

et al. 2021

Quantum

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120

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“…Gate-based quantum computers are expected to help solve problems in quantum chemistry [1][2][3], machine learning [4,5], financial simulation [6][7][8][9][10][11][12][13] and combinatorial optimization [14,15]. The quantum approximate optimization algorithm (QAOA) [16][17][18], inspired by a Trotterization of adiabatic quantum computing [19][20][21], runs on gate-based quantum computers [22,23].…”

Section: Introductionmentioning

confidence: 99%

Warm-starting quantum optimization

Egger

Mareček

Woerner

2021

Quantum

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165

111

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There is an increasing interest in quantum algorithms for problems of integer programming and combinatorial optimization. Classical solvers for such problems employ relaxations, which replace binary variables with continuous ones, for instance in the form of higher-dimensional matrix-valued problems (semidefinite programming). Under the Unique Games Conjecture, these relaxations often provide the best performance ratios available classically in polynomial time. Here, we discuss how to warm-start quantum optimization with an initial state corresponding to the solution of a relaxation of a combinatorial optimization problem and how to analyze properties of the associated quantum algorithms. In particular, this allows the quantum algorithm to inherit the performance guarantees of the classical algorithm. We illustrate this in the context of portfolio optimization, where our results indicate that warm-starting the Quantum Approximate Optimization Algorithm (QAOA) is particularly beneficial at low depth. Likewise, Recursive QAOA for MAXCUT problems shows a systematic increase in the size of the obtained cut for fully connected graphs with random weights, when Goemans-Williamson randomized rounding is utilized in a warm start. It is straightforward to apply the same ideas to other randomized-rounding schemes and optimization problems.

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“…The main advantage of quantum computers is that this category of computers could potentially solve some complexity classes, which require excessive temporal, technical, and economic resources to be solved. This is a fascinating field, but one with considerable criticalities: both from a scientific perspective (there are still difficulties in demonstrating the effective superiority of quantum computing compared to classic approaches) and at the engineering level, given the fragility of quantum systems and the need to shield them from radiation, keep them in temperatures close to zero, and correct errors [32,33].…”

Section: Fractal Geometrymentioning

confidence: 99%

Fintech Frontiers in Quantum Computing, Fractals, and Blockchain Distributed Ledger: Paradigm Shifts and Open Innovation

Moşteanu

Faccia

2021

Journal of Open Innovation: Technology, Market, and Complexity

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Among the hot research topics, Fintech is leading the trend in terms of the newest technology applications. The relatively new emerging paradigms in various sciences, such as geometry (fractals), physics (quantum), and database systems (distributed ledger—blockchain), seem to potentially contribute to a greater shift in the framework of the finance industry, bringing also some concerns (cyber-threats). Consistent and extensive investigation of the reasonable potential impact of these new models (and their underlying technologies) is performed, and then tested through a SWOT analysis, as the main objective of this research. Threats and opportunities are always intrinsically driven by the introduction of technological advancements (revolutions). This research confirms that information availability and the increasing interconnection of crosswise applications of each discovery to the different fields of science is determining the rapid succession of revolutions identified by evident large shifts in economic paradigms. The growing computing capacity and the development of increasingly powerful predictive software are leading to a competitive, extremely dynamic, and challenging system. In this context, as shown by history, there is a high possibility of market concentration in which, however, only a few corporations—digital giants—can afford to develop these technologies, consolidating their dominance.

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Quantum Computing for Finance: State-of-the-Art and Future Prospects

Cited by 259 publications

References 88 publications

A Threshold for Quantum Advantage in Derivative Pricing

A Threshold for Quantum Advantage in Derivative Pricing

Warm-starting quantum optimization

Fintech Frontiers in Quantum Computing, Fractals, and Blockchain Distributed Ledger: Paradigm Shifts and Open Innovation

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