Use Cases

Quantum-safe Cryptography

Quantum computing poses a security risk for the commonly used RSA encryption.
New encryption schemes have been developed and are already available (see, e.g., https://openquantumsafe.org/).
Are quantum-safe cryptography solutions ready for adoption?
What does it need to integrate quantum-safe encryption into current systems?
What are the implications regarding run-time, effort, and security?

Quantum Risk Analysis

Risk analysis is concerned with the evaluation of statistical properties given some complex financial product.
Quantum risk analysis can leverage the quantum computer’s ability to efficiently operate on probability amplitudes.
Multiple approaches have been investigated (see, e.g., Quantum risk analysis | (nature.com)).
How do benchmarking and scaling properties of such algorithms look like?

Quantum-enhanced Anomaly Detection

Generative adversarial networks represent suitable methods for anomaly detection.
One possible variation involves replacing the classical generator with a quantum neural network.
Central motivation is Google’s quantum supremacy experiment | (nature.com): NISQ devises are good for sampling.
But how much training is needed for such an approach?
Can we achieve similarly good results as with classical methods?

Quantum Deep Hedging

Quantum deep hedging is an approach to learning a hedging strategy with quantum neural networks.
Its benefits have been shown by papers (see, e.g., Deep Hedging | (arxiv.org) or Quantum Deep Hedging | (arxiv.org)).
These methods can be applied to arbitrary market settings and complex payoff functions.
Both classical and quantum deep hedging outperform standard hedging approaches.
How do the expressivities of quantum and classical deep hedging approaches compare?

Quantum Optimization

Optimization problems are ubiquitous. Finance examples are asset allocation, optimal arbitrage opportunities, or option pricing.
Finding a good optimizer for a given problem is a difficult task and generally involves complex modelling of the problem.
Quantum optimizers help traverse the cost landscape more efficiently because of tunnelling effects.
There exist promising quantum-inspired optimizers that are not restricted by nearest-neighbor and pairwise interactions (see, e.g., Simulated Quantum Annealing | (github.com)).
Are there differences in the performance of classical, quantum and quantum-inspired optimizers?

Explainable AI for Quantum Models

Quantum circuits are difficult to simulate at large scales, which impedes their explainability.
These explainers still take a long time to be evaluated.
Instead, a viable procedure could be classical surrogates for quantum models.
A classical surrogate is a model that approximates the quantum model up to a small error.
Fourier-based surrogates are promising and can be used as explainers (see, e.g., Classical surrogates for quantum learning models | (arxiv.org))