The EDHEC Quantum Technologies for Finance and Insurance Initiative
Quantum computing will not replace the models that run global finance overnight, but it will reshape how the hardest problems in risk analysis, portfolio construction, and asset pricing are approached. The EDHEC Quantum Institute studies where these technologies can create genuine value in finance and insurance, grounded in EDHEC’s long-standing expertise in quantitative investment and risk management.
Finance is one of the first industries putting quantum technologies to a serious test. Its core problems, from portfolio construction to derivative pricing and large-scale risk simulation, are precisely the workloads where quantum methods are expected to help first. The commercial interest is now measurable. For a bank, an asset manager or an insurer, the strategic question is therefore no longer whether quantum matters, but where to allocate resources.
The Institute’s research is organized around four families of financial problems where quantum methods look most promising, always tested against a realistic view of what today’s hardware can and cannot do.
Quantum computing could bring significant improvements to financial problem-solving.
In plain terms, building a portfolio involves two distinct problems: selecting which assets to hold, and deciding how much to allocate to each, under a variety of financial and non-financial constraints. As the number of assets and constraints grows, the number of admissible combinations explodes, and classical solvers struggle. This is one of the most actively studied candidates for a quantum advantage in finance.
In the area of portfolio optimization, quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) on the gate-based side, as well as Adiabatic Quantum Optimization (AQO) and Quantum Annealing (QA) algorithms on the simulation side, are expected to solve complex construction problems involving high-dimensional combinatorial constraints more efficiently than classical algorithms.
Risk measures such as Value at Risk and the price of complex derivatives are usually estimated by running very large numbers of simulated scenarios. The quantum approach aims to reach the same statistical accuracy with fewer samples, which would shorten the time and cost of these computations.
Quantum-enhanced Monte Carlo methods such as Quantum Amplitude Estimation (QAE) offer the potential for faster and more accurate results, especially in pricing derivatives and estimating extreme risk measures.
Machine learning is already used across finance to detect patterns in large datasets. Quantum machine learning investigates whether some of these patterns can be represented or learned more efficiently, with potential applications in fraud detection, credit scoring and signal discovery.
Many financial models ultimately reduce to large matrix and vector computations. Quantum linear algebra provides, in principle, faster ways to manipulate very high-dimensional data, which is why it is relevant to risk modeling, factor analysis and scenario generation.
Quantum linear algebra algorithms such as the Harrow-Hassidim-Lloyd (HHL) algorithm or quantum principal component analysis (qPCA) make it possible in principle to manipulate high-dimensional vectors and matrices of relevance in financial applications efficiently, by encoding them into quantum states and transforming them through unitary operations.
The quantum transition is also a security problem, and not only for finance: it concerns every industry that holds long-lived confidential data. In finance it is particularly acute. Fault-tolerant quantum computers that are expected to accelerate optimization will, in time, break the public-key cryptography schemes that protect payments, settlement and confidential data. The timeline is uncertain, but the threat is already present: encrypted data harvested today can be stored and decrypted once such a machine exists. Standards bodies and central banks have already begun preparing the migration to post-quantum cryptography: the Bank for International Settlements, with the Banque de France and the Bundesbank, has tested this shift through Project Leap. This is where the Institute’s finance work meets its broader research on quantum technologies for information and communication.
Beyond computational aspects, quantum communication also has significant implications for finance. Quantum Key Distribution (QKD) offers key exchange that enables ultra-secure communication, which is critical for transmitting sensitive financial data.
In the near term, the use of quantum computing in finance will be focused on hybrid approaches, combining classical and quantum methods. The objective for the time being is not to achieve a quantum advantage, but to build capabilities, identify use cases, and develop an operational understanding of the technology. In the medium term, as hardware improves, one can expect more significant advances in areas such as optimization, simulation, and machine learning. At the same time, the emergence of quantum threats to existing cryptographic systems will force a transition toward post-quantum infrastructures. While claims of exponential speedups or universal quantum advantage are not operationally meaningful at this stage, experimentation with hybrid pipelines and problem reformulations already provides strategic value, both in terms of skill acquisition and algorithmic innovation.
Several financial institutions have published the results of experiments run with quantum technology providers. They remain small in scale and rely on hybrid quantum-classical workflows, but they show where the first operational gains are being tested.
None of these experiments yet demonstrates a durable quantum advantage in production. That is why the Institute focuses on benchmarking quantum approaches against the best classical methods.
The EDHEC Quantum Institute is developing research on the application of quantum technologies in portfolio construction, asset pricing, and risk management, drawing on EDHEC’s heritage in quantitative asset and risk management built over two decades at the former EDHEC-Risk Institute. That heritage spans portfolio construction, factor investing and risk measurement, which the initiative now extends to the quantum setting.
As the quantum finance field is still in its early stages, the Institute aims to influence the direction of research and standard-setting within the industry, and to play a part in policy development and industry regulation. At a time when the ethical, legal, and social implications of quantum technologies are still being defined, it intends, through informed research and engagement, to help establish responsible frameworks for their use in finance.
The initiative is directed by Professor Lionel Martellini, Founding Director of the EDHEC Quantum Institute. Contributing researchers, research associates and the initiative’s peer-reviewed publications are listed here.
Explore Quantum for Executives for leadership programs, the Institute’s research on Quantum Technologies for Information and Communication for the security dimension, and its plain-language glossary of quantum concepts.
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