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!

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
• Asset Pricing
• Fraud Detection
• Risk Management
• Blockchain
• Quantum Money
• Others

• [2018] "Quantum computational finance: quantum algorithm for portfolio optimization." (P. Rebentrost and S. Lloyd) [CQT - NUS, MIT]
  • First to present a quantum algorithm for portfolio optimization
  • Gate-based quantum computer
• [2019] "Reverse Quantum Annealing Approach to Portfolio Optimization Problems." (D. Venturelli and A. Kondratyev) [USRA, Standard Chartered Bank]
  • 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]
  • 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]
  • 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]
  • 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]
• "Quantum walk-based portfolio optimisation." (N. Slate, E. Matwiejew, S. Marsh, and J. B. Wang) [The University of Western Australia]
• [2022] "NISQ-HHL: Portfolio Optimization for Near-Term Quantum Hardware." (R. Yalovetzky, P. Minssen, D. Herman, and M. Pistoia) [JP Morgan Chase Bank]
  • 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]
• [2022] A Quantum Online Portfolio Optimization Algorithm." (D. Lim and P. Rebentrost) [CQT - NUS]

• [2018] "Quantum computational finance: Monte Carlo pricing of financial ." (P. Rebentrost, B. Gupt, and T. R. Bromley) [Xanadu]
  • First to present a quantum algorithm for option pricing
• [2020] "Option Pricing using Quantum Computers." ( N. Stamatopoulos et al.) [JPMorgan Chase, IBM, ETH Zurich]
  • 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.]
• [2021] A Quantum Walk Model of Financial Options." (D. Orrell) [Systems Forecasting]
• [2022] "Pricing Multi-Asset Derivatives by Finite-Difference Method on a Quantum Computer." (K. Miyamoto and K. Kubo) [Osaka University]
• [2022] "Quantum computational finance: martingale asset pricing for incomplete markets." (P. Rebentrost, A. Luongo, S. Bosch, and S. Lloyd) [CQT, MIT]

• [2022] "Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection." (M. Grossi et al.) [CERN, IBM]
  • present a first end-to-end application of a Quantum Support Vector Machine (QSVM) algorithm for a classification problem in the financial payment industry.

• [2019] "Quantum risk analysis." (S. Woerner and D. J. Egger) [IBM Research - Zurich]
  • 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]
• [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]
  • extend this to a quadratic error scaling advantage in market risk computation

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

• [1983] "Conjugate coding." (Wiesner, Stephen) [Columbia university]
• [2013] "Quantum money from hidden subspaces." (Aaronson, Scott, and Paul Christiano. ) [MIT], Theory of Computing.
• [2016] "Quantum tokens for digital signatures." (Ben-David, Shalev, and Or Sattath) [MIT, The Hebrew University]
• [2017] "Quantum Lightning Never Strikes the Same State Twice." (M. Zhandry) [Princeton University]
• [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]
• [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]
• [2020] "A quantum money solution to the blockchain scalability problem." (Coladangelo, Andrea, and Or Sattath) [Caltech, Ben-Gurion University] Quantum.
• [2021] "Public-Key Quantum Money with a Classical Bank." (O. Shmueli) [Tel Aviv University]
• [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]

• [2019] "Forecasting financial crashes with quantum computing." (R. Orus, S. Mugel, and E. Lizaso) [Multiverse Computing, etc.]

  • 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.]

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

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