22 January 2024 – Day 1 Agenda

Event Agenda: 22 January 2024 – Day 1



08:30 - 09:00


09:05 - 09:10

Welcome address

09:10 - 09:50

From quantum supremacy to quantum utility

Nature allows the storage and manipulation of data in new and powerful ways using quantum mechanics. I will explain the basic concepts behind the exponential power of this technology and how to build a quantum computer using superconductors. I will show experimental data from Google on a “quantum supremacy” experiment with 53 qubits that runs much faster than state-of-the-art classical supercomputers. I will also discuss the next important milestone of quantum utility - solving useful computations faster than classical computers. Although there are two schools of thought for accomplishing this, via near-term (NISQ) algorithms or error correction, both require significant improvements in qubit quality that lowers errors to the 10^-3 to 10^-4 range. I will also describe a vision of how superconducting qubits should be fabricated in the upcoming years.

09:50 - 10:30

Extracting and evaluating performance of NISQ Optimization experiments: beyond angle-parameter setting

Owing to advancements in hardware quality and control software, there have been several recent demonstrations of experimental runs on gate-model noisy quantum processors, showcasing the use of 20+ qubits in regimes where simulations become challenging. In this talk, we will discuss insights gained from the DARPA ONISQ program, where NASA, USRA, and Rigetti Computing employed an array of techniques to combat noise while aiming to solve fully-connected binary optimization problems. We will delve into the impacts of the discovered techniques, which encompass ansatz approximations, swap-network synthesis, over parametrization, categorical parameters like ordering and symmetry transformations, and iterative decompositions. We will also explore how these can be amalgamated into a cohesive algorithm-tuning strategy that can be executed on-the-fly, achieving high approximation ratios within a few thousand runs for problems with 50+ variables encoded in as many qubits.

10:30 - 11:00

Coffee Break

11:00 - 11:30

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.

11:30 - 12:00

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.

12:00 - 12:30

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

12:30 - 13:30


13:30 - 14:00

Quantum Optimization in the Era of Quantum Utility

During this talk, we first discuss the potential advantage of quantum computing in optimization from a complexity theoretic perspective and highlight the importance of quantum optimization heuristics. The ascent of quantum computers with 100+ qubits allows us to develop and test such heuristics in practice at a non-trivial scale. To this extent, we discuss Quantum Error Mitigation in the context of optimization and show recent results of a demonstration on 127 qubits.

14:00 - 14:30

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.

14:30 - 15:00

Quantum neural networks and applications

Neural networks are now ubiquitous in Finance for data analysis, pricing, hedging, modelling, etc. We investigate their quantum counterparts, namely parameterised quantum circuits and prove some new form of universal approximation theorem with error bounds. This in turn provides solid foundations for their use, and we highlight a few of them, in particular in the context of (quantum) GAN and to solve (high-dimensional) PDEs that arise in Finance.

15:00 - 15:30

Coffee Break

15:30 - 16:00

QUBO on Groq: running Quantum Computer programs on a large scale Dataflow computer

Quantum Computing is a wide field, bringing many advances in particle physics. A subset of Quantum Computers, using quantum annealing, solve Quadratic Unconstrained Binary Optimization (QUBO) problems which can also be computed on classical (deterministic dataflow) computers. A key challenge in Quantum Computing is the scarcity and small size of existing Quantum Computers, coupled with limited connectivity between QBits. The Groq QUBO platform enables Quantum researchers to run larger denser problems, resulting in QUBO matrices up to 200K by 200K on Groq machines, many years before the availability of physical Quantum Computers with the equivalent capacity. In this talk, I will show our recent results of running QUBO problems on Groq Language Processing Units (LPUs) for applications from optimization of Wind farms to High Frequency Trading. In the future, QUBOs, and maybe even Quantum Computers could also be used to train Artificial Intelligence.

16:00 - 16:30

Quantum-topological global framework for bioinformatic sequence alignment

The alignment of biological sequences is a cornerstone in bioinformatics, essential for a wide range of applications from phylogenetic analysis to structural genomics and personalized medicine. Classical algorithms like Needleman-Wunsch and Smith-Waterman have been used. However, the computational costs associated with these algorithms scale poorly with increasing sequence length, rendering them impractical for real-world sequences that span thousands to millions of base pairs. This limitation poses significant challenges in the era of high-throughput sequencing technologies. We introduce a quantum computing framework to tackle these limitations, both in the NISQ era and in the fault-tolerant QC regime. Furthermore, we draw connections with algebraic geometry through the Mayer-Vietoris sequence, which is traditionally used to integrate local information into a global context. We argue that this interdisciplinary approach holds the promise of developing more efficient alignment methods, thereby accelerating the pace of scientific discovery and healthcare delivery.