Welcome address

Advances in superconducting qubit technology

Hybrid quantum-classical reservoir computing for solving chaotic systems

Inspired by the reservoir computing classical paradigm of machine learning, and its ability to exploit non-linear dynamics to learn linear mapping of such dynamics to complex functions, we look at opportunity to explore the dynamics of quantum systems, as represented by sequential runs of quantum circuits specified via measurement results, for short term forecasting of time-series (nowcasting). We observe that even few noisy qubits on superconducting processors and simple entangling circuits could provide the richness of dynamics required for nowcasting chaotic system benchmarks. This work could pave the way to more complex applications in precipitation forecasting.

Scalable quantum algorithms for large scale constrained portfolio optimization

We introduce a quantum framework that utilizes Quadratic Unconstrained Binary Optimization (QUBO) to optimize dynamic trading strategies, taking into account transaction costs, integer restrictions, short selling and scalability. While dynamic trading strategies in modern portfolio optimization have been extensively studied, their practical implementation often faces considerable challenges due to market frictions and computing limitations, which adversely affect computational efficiency. In light of this, our work explores the potential of quantum solutions to transform the field of portfolio optimization. We present quantum algorithms that can tackle the complexities of real-world portfolio optimization and ensure optimal results. By doing so, we aim to bridge the gap between quantum finance and practical portfolio optimization.

Quantum algorithms for risk management in finance

We explore the use of quantum algorithms applied to credit and market risk: credit scoring for small and medium size businesses (SMEs) and rare events in capital markets. For credit scoring a quantum/classical hybrid approach has been used to experiment with several quantum neural network (QNN) models with a range of parameters. Results are shown from the best model, using two quantum classifiers and a classical neural network, applied to data for companies in Singapore. We observe significantly more efficient training for the quantum models over the classical models for comparable prediction performance. For rare events in capital markets, we estimate the probability of an occurrence of a rare event in a specified time frame using an innovative quantum hidden Markov model (QHMM) on financial time-series data. We compare to exact calculations and classical models using S&P500 time-series and artificially created data. First results show the scaling of tracking error and the behaviour of the QHMM. Other factors for the future practical implementation of quantum systems are also discussed.

Quantum computing for financial portfolio, option pricing, and risk analysis

Within the realm of finance, numerous challenges demand intensive computational resources and timely solutions. Consider financial portfolio optimization, a process aimed at maximizing returns while minimizing risks by strategically selecting asset distributions. incorporating real-world constraints, such as limitations on concurrent asset holdings, significantly escalates the computational costs associated with portfolio optimization. Similarly, some option valuation problems exhibit frustratingly slow resolution on current computing systems, even when dealing with a small number of underlying assets. Furthermore, existing methods for risk analysis, mandated by regulatory bodies and institutional risk management, are computationally demanding. Our goal is to explore the transformative potential of quantum computation in addressing these intricate challenges pervasive in the financial sector

Towards quantum advantage in optimization and applications in finance

The need for Quantum Blockchain

The advent of quantum computing fundamentally threatens many important digital technologies. Blockchain, despite being at the forefront of digital technology for its potential to provide transparency, resilience and security for financial and other transactions, will quickly become obsolete in a quantum world. Quantum algorithms have already been developed which can easily solve the mainstays of blockchain security: public-key cryptography and hash functions. Consequently, traditional blockchain platforms are unsuitable for the quantum era, hence the urgent and inevitable need to develop a quantum version of blockchain that can secure it against both classical and quantum attacks.

[TBC]

Panel discussion