Siyuan (Bruce) JIN 金思远
  • Siyuan (Bruce) JIN 金思远
  • May 01, 2023

Literature on Quantum Finance

 

Quantum Computing is emerging, and finance could be one of the first industry sectors to benefit from it. This is a new field where new ideas come out every day! That is why we create a collection ...

Quantum Computing is emerging, and finance could be one of the first industry sectors to benefit from it. This is a new field where new ideas come out every day! That is why we create a collection to follow the up-to-now papers in this field! Welcome to join and update the collection! You may check updates from: https://github.com/siyuan-bruce/Quantum-Finance

The paper format follows the style: Paper Title (Author) [Institution]

Some papers are conducted by many institutions together, and we can only include the corresponding institution.

Portfolio Optimization

  • [2018] “Quantum computational finance: quantum algorithm for portfolio optimization.” (P. Rebentrost and S. Lloyd) [CQT - NUS, MIT], arXiv.
    • First to present a quantum algorithm for portfolio optimization
    • Gate-based quantum computer
  • [2019] “Quantum-inspired algorithms in practice.” (Arrazola, J. M., Delgado, A., Bardhan, B. R., & Lloyd, S.)arXiv
  • [2019] “Reverse Quantum Annealing Approach to Portfolio Optimization Problems.” (D. Venturelli and A. Kondratyev) [USRA, Standard Chartered Bank], Quantum Machine Intelligence.
    • investigate a hybrid quantum-classical solution method to the mean-variance portfolio optimization problems
  • [2019] “Quantum Algorithms for Portfolio Optimization.” (I. Kerenidis, A. Prakash, and D. Szilágyi) [CNRS], Proceedings of the 1st ACM Conference on Advances in Financial Technologies.
    • develop the first quantum algorithm for the constrained portfolio optimization problem
    • Gate-based quantum computer
  • [2019] “Portfolio rebalancing experiments using the Quantum Alternating Operator Ansatz.” (M. Hodson, B. Ruck, H. Ong, D. Garvin, and S. Dulman) [Rigetti, Commonwealth Bank of Australia], arXiv.
    • design a novel problem encoding and hard constraint mixers for the Quantum Alternating Operator Ansatz
    • Gate-based quantum computer
  • [2020] “Portfolio Optimization of 40 Stocks Using the DWave Quantum Annealer.”(J. Cohen, A. Khan, and C. Alexander) [Chicago Quantum], arXiv.
    • implement in a D-Wave quantum computer to solve the equilibrium state of a complex financial network (NP-Hard)
    • use DWaves 2,041 qubit quantum annealer through a repeatable research and business process.
    • pick 40 assets, which creates a solution space of 240 or 1.1 trillion portfolios from which to select.
  • [2021] “Portfolio Optimization on Classical and Quantum Computers Using PortFawn.” (M. Owhadi-Kareshk and P. Boulanger) [University of Alberta], arXiv.
  • [2021] “Quantum walk-based portfolio optimisation.” (N. Slate, E. Matwiejew, S. Marsh, and J. B. Wang) [The University of Western Australia], Quantum.
  • [2021] “Quantum Portfolio Optimization with Investment Bands and Target Volatility.” (Samuel Palmer, Serkan Sahin, Rodrigo Hernandez, Samuel Mugel, Roman Orus) [Multiverse Computing], arXiv.
  • [2022] “NISQ-HHL: Portfolio Optimization for Near-Term Quantum Hardware.” (R. Yalovetzky, P. Minssen, D. Herman, and M. Pistoia) [JP Morgan Chase Bank], arXiv.
    • the first hybrid formulation of HHL suitable for the end-to-end execution of small-scale portfolio-optimization problems on NISQ devices
    • Gate-based quantum computer
  • [2022] “Constrained Quantum Optimization for Extractive Summarization on a Trapped-ion Quantum Computer.” (P. Niroula et al.) [JP Morgan Chase Bank, University of Maryland], Scientific Reports.

  • [2022] A Quantum Online Portfolio Optimization Algorithm.” (D. Lim and P. Rebentrost) [CQT - NUS], arXiv.

Asset Pricing

  • [2018] “Quantum computational finance: Monte Carlo pricing of financial derivatives.” (P. Rebentrost, B. Gupt, and T. R. Bromley) [Xanadu], Physical Review A.
    • First to present a quantum algorithm for option pricing
  • [2020] “Option Pricing using Quantum Computers.” ( N. Stamatopoulos et al.) [JPMorgan Chase, IBM, ETH Zurich], Quantum.
    • implementation of the quantum circuits with the input states and operators needed by amplitude estimation to price the different option types
  • [2021] “Towards Pricing Financial Derivatives with an IBM Quantum Computer.” (A. Martin et al.) [University of the Basque Country UPV/EHU, et al.], Physical Review Research.
  • [2021] “A Quantum Walk Model of Financial Options.” (D. Orrell) [Systems Forecasting], Wiley Online Library.
  • [2021] “Quantum algorithm for stochastic optimal stopping problems with applications in finance.” (P. Rebenstrost et. al)[CQT], 17th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2022).
  • [2021] “Simulating option price dynamics with exponential quantum speedup.” (Javier Gonzalez-Conde et. al)[University of the Basque Country], arXiv.
  • [2022] “Pricing Multi-Asset Derivatives by Finite-Difference Method on a Quantum Computer.” (K. Miyamoto and K. Kubo) [Osaka University], IEEE Transactions on Quantum Engineering.
  • [2022] “Quantum advantage for multi-option portfolio pricing and valuation adjustments.” (Jeong Yu Han, Patrick Rebentrost)[CQT], arXiv.
  • [2022] “Quantum computational finance: martingale asset pricing for incomplete markets.” (P. Rebentrost, A. Luongo, S. Bosch, and S. Lloyd) [CQT, MIT], arXiv.

Fraud Detection

  • [2021] “Unsupervised quantum machine learning for fraud detection.” (Oleksandr Kyriienko and Einar B. Magnusson)[University of Exeter, HSBC], arXiv.
  • [2022] “Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection.” (M. Grossi et al.) [CERN, IBM], IEEE Transactions on Quantum Engineering .
    • present a first end-to-end application of a Quantum Support Vector Machine (QSVM) algorithm for a classification problem in the financial payment industry.
  • [2022] “Approximate complex amplitude encoding algorithm and its application to classification problem in financial operations.” (Naoki Mitsuda, et. al)[Sumitomo Mitsui Trust Bank, Keio University] arXiv.

Risk Management

  • [2019] “Quantum Risk Analysis.” (S. Woerner and D. J. Egger) [IBM Research - Zurich], npj Quantum Information.
    • present a quantum algorithm that analyzes risk more efficiently than Monte Carlo simulations traditionally used on classical computers (financial risk for a two-asset portfolio )
  • [2020] “Improving Variational Quantum Optimization using CVaR.” (P. K. Barkoutsos, G. Nannicini, A. Robert, I. Tavernelli, and S. Woerner) [IBM Research, PSL University], Quantum.
  • [2021] “Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms.” (N. Stamatopoulos, G. Mazzola, S. Woerner, and W. J. Zeng) [Goldman Sachs, IBM], Quantum.
  • [2021] “Credit Risk Analysis Using Quantum Computers.” (Daniel J. Egger , Ricardo Garcıa Gutierrez, Jordi Cahu e Mestre, and Stefan Woerner) [IBM], IEEE Transactions on Quantum Engineering.
  • [2021] “Quantum speedup of Monte Carlo integration with respect to the number of dimensions and its application to finance.” (Kazuya Kaneko, Koichi Miyamoto, Naoyuki Takeda, Kazuyoshi Yoshino)[Mizuho-DL Financial Technology Co, Center for Quantum Information and Quantum biology], Quantum Information Processing. —

Blockchain

  • [2018] “Quantum-secured Blockchain.” (E. O. Kiktenko et al.) [Russian Quantum Center], Quantum Science and Technology.
  • [2021] “Quantum-resistance in blockchain networks.”(M. Allende et al.) [IDB, 2LACChain, CQC, Tecnologico de Monterrey], arXiv.
  • [2021] “Post-Quantum Blockchain Proofs of Work.” (A. Cojocaru, J. Garay, A. Kiayias, F. Song, and P. Wallden) [Inria, etc.], arXiv.
  • [2022] “Quantum Proof of Work with Parametrized Quantum Circuits (M. Y. Shalaginov and M. Dubrovsky) [MIT], arXiv.
    • introduce a QPoW that involes a random quantum circuits which can not be simulated efficiently by classical computers.

Quantum Money

  • [1960+] “Conjugate coding.” (Wiesner, Stephen) [Columbia university], ACM Sigact News.
  • [2013] “Quantum Money from Hidden Subspaces.” (Aaronson, Scott, and Paul Christiano) [MIT], STOC ‘12: Proceedings of the forty-fourth annual ACM symposium on Theory of computing.
  • [2016] “Quantum Tokens for Digital Signatures.” (Ben-David, Shalev, and Or Sattath) [MIT, The Hebrew University], arXiv.
  • [2017] “Quantum Lightning Never Strikes the Same State Twice.” (M. Zhandry) [Princeton University], arXiv.
  • [2018] “Experimental preparation and verification of quantum money.” (Guan, Jian-Yu, Juan Miguel Arrazola, Ryan Amiri, Weijun Zhang, Hao Li, Lixing You, Zhen Wang, Qiang Zhang, and Jian-Wei Pan.) [USTC, CQT], arXiv.
  • [2018] “Experimental investigation of practical unforgeable quantum money.” (Bozzio, Mathieu, Adeline Orieux, Luis Trigo Vidarte, Isabelle Zaquine, Iordanis Kerenidis, and Eleni Diamanti.) [CNRS, CQT] npj Quantum Information.
  • [2019] “Practically feasible robust quantum money with classical verification.” (Kumar, Niraj) [University of Edinburgh], Cryptography.
  • [2019] “Gentle measurement of quantum states and differential privacy.” (Aaronson, Scott, and Rothblum, Guy N.) Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing.
  • [2020] “A quantum money solution to the blockchain scalability problem.” (Coladangelo, Andrea, and Or Sattath) [Caltech, Ben-Gurion University] Quantum.
  • [2020] “One-shot signatures and applications to hybrid quantum/classical authentication.” (Ryan Amos, Marios Georgiou, Aggelos Kiayias, Mark Zhandry) [Princetion U, City U of New York, U of Edinburgh, etc] Quantum.
  • [2021] “Public-Key Quantum Money with a Classical Bank.” (O. Shmueli) [Tel Aviv University], STOC 2022: Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing.
  • [2021] “Quantum money from quaternion algebras.” (Kane, Daniel M., Shahed Sharif, and Alice Silverberg.) [UCSD, California State University, University of California, Irvine], arXiv.
  • [2022] “Publicly verifiable quantum money from random lattices.” (A. B. Khesin, J. Z. Lu, and P. W. Shor)[MIT, Harvard], arXiv.
  • [2022] “Another Round of Breaking and Making Quantum Money: How to Not Build It from Lattices, and More.” (Montgomery, Hart, Jiahui Liu, and Mark Zhandry.)[UTA, Linux & Fujitsu, NTT], arXiv.
  • [2023] “Quantum tokens for digital signatures.” (Ben-David, Shalev, and Sattath, Or) Quantum 7.

Quantum Inspired Algorithm (Norm-Sampling)

  • [2018] “A quantum-inspired classical algorithm for recommendation systems.” (Ewin Tang) arXiv.
  • [2019] “Quantum-inspired algorithms in practice.” (Juan Miguel Arrazola, Alain Delgado, Bhaskar Roy Bardhan, Seth Lloyd) arXiv.
  • [2019] “A quantum-inspired classical algorithm for recommendation systems using the L2-norm.” (Ewin Tang)** ACM Digital Library.
  • [2019] “Faster Quantum-inspired Algorithms for Solving Linear Systems.” (Changpeng Shao, Ashley Montanaro) ACM Digital Library
  • [2020] “Quantum Algorithms for Machine Learning and Optimization.” (Sangeetha P; Prameela Kumari) IEEE.
  • [2021] “Quantum-inspired algorithms for multivariate analysis: from classical to quantum and back.” (Juan José García-Ripoll) Quantum Journal
  • [2021] “Quantum-inspired support vector machine.” (Ding, C., Bao, T. Y., & Huang, H. L. ) IEEE Transactions on Neural Networks and Learning Systems
  • [2022] “Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning.” (Chia, N. H., Gilyén, A. P., Li, T., Lin, H. H., Tang, E., & Wang, C.) Journal of the ACM
  • [2022] “An improved quantum-inspired algorithm for linear regression.” (Gilyén, A., Song, Z., & Tang, E. ) Quantum
  • [2023] “A CS guide to the quantum singular value transformation.” (Tang, E., & Tian, K.) arXiv

Others

Predicting Financial Crashes

  • [2019] “Forecasting financial crashes with quantum computing.” (R. Orus, S. Mugel, and E. Lizaso) [Multiverse Computing, etc.], Physical Review A
    • implement on near-term quantum processors and provide a potentially more efficient way to assess financial equilibrium and predict financial crashes.
  • [2021] “Towards Prediction of Financial Crashes with a D-Wave Quantum Computer.” (Y. Ding et al.) [Shanghai University, University of the Basque Country UPV/EHU, etc.], arXiv.

Transaction Settlement

  • [2021] “Quantum Algorithms for Mixed Binary Optimization Applied to Transaction Settlement.” (L. Braine, D. J. Egger, J. Glick, and S. Woerner) [Barclays, IBM Quantum], IEEE Transactions on Quantum Engineering.
    • combine binary decision variables with continuous decision variables enables the modelling of inequality constraints via slack variables

Quantum Accounting

  • [2022] “Entropy, Double Entry Accounting and Quantum Entanglement.” (J. C. Fellingham, H. Lin, and D. Schroeder) [Fisher College of Business, The Ohio State University], Foundations and Trends® in Accounting.
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