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A Survey of Quantum Computing for Finance

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arxiv 2201.02773 v4 pith:ZIWJGMSR submitted 2022-01-08 quant-ph q-fin.CP

classification quant-phq-fin.CP
keywords quantumfinancecomputerscomputingindustryfinancialmodelingonly
verification ladder T0 review T1 audit T2 compute T3 formal
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Quantum computers are expected to surpass the computational capabilities of classical computers during this decade and have transformative impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from quantum computing, not only in the medium and long terms, but even in the short term. This survey paper presents a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modeling, optimization, and machine learning, describing how these solutions, adapted to work on a quantum computer, can potentially help to solve financial problems, such as derivative pricing, risk modeling, portfolio optimization, natural language processing, and fraud detection, more efficiently and accurately. We also discuss the feasibility of these algorithms on near-term quantum computers with various hardware implementations and demonstrate how they relate to a wide range of use cases in finance. We hope this article will not only serve as a reference for academic researchers and industry practitioners but also inspire new ideas for future research.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 95 citations worldwide. Full citation record

  1. Fast-forwardability of Jordan-Wigner-transformed Fermion models based on Cartan decomposition

    quant-ph 2025-02 conditional novelty 7.0 of 10

    The dimension of the Hamiltonian algebra of Jordan-Wigner-transformed interacting fermion models grows exponentially with the number of sites, making Cartan-based fast-forwarding inefficient for the Hubbard and Anders...

  2. Benchmarking of Quantum and Classical Computing in Large-Scale Dynamic Portfolio Optimization Under Market Frictions

    math.OC 2025-02 reject novelty 4.0 of 10

    The authors formulate a multi-period portfolio problem with transaction costs and short selling as a QUBO/BQP benchmark and compare Gurobi, ABS2, D-Wave, and IBM quantum solvers on S&P 500 data.

  3. Novel Quantum Circuit Designs of Random Injection and Payoff Computation for Financial Risk Assessment

    quant-ph 2025-07 reject novelty 3.0 of 10

    A Qiskit demo of random XOR injection and threshold-based payoff averaging, with QAE speedup claimed but not implemented and accuracy improved by a fitted 1.57 scaling factor.

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