Exploring Quantum Technologies in Practical Applications

This special session is dedicated to the exploration and challenge of applying quantum technologies in practical settings, with a particu lar focus on quantum optimization. The rapid evolution of quantum computing has predominantly been within academic realms, but the imperative now is to extend these advancements to industrial applications, thereby unveiling significant economic benefits. Our session aims to narrow the gap between academic theories and real world applications, broadening the understanding and utilization of quantum technologies for sustainable growth. For this purpose, we have invited esteemed experts from JPMorgan, QuEra, LBNL and FinQ Tech, who are innovators in their respective fields. This session will present four in-depth talks: LBNL will research on the specialized compiler for the quantum optimization tasks. JP Morgan will explore the applications of quantum optimization in the financial sector, demonstrating how quantum technologies can revolutionize traditional financial models. QuEra will delve into a graph learning based strategies for predicting the performance of maximum independent set program, a critical aspect of determine if there is quantum advantage by using the quantum optimization for the task. Lastly, FinQ Tech will discuss the optimization of the quantum optimization, highlighting its potential to solve complex optimization problems more effectively than classical algorithms and potentially adopted by energy sector. By fostering an interdis ciplinary dialogue, this session aims to accelerate the progress of quantum technologies, ensuring they can be foundational to various industry sectors. The collaboration between academia and industry is crucial in overcoming current technological challenges and un locking novel solutions to global issues. This initiative promises to catalyze the practical adoption of quantum technologies, shaping them as a pivotal force in modern scientific and economic land scapes. 

  • Compiler Optimizations for QAOA

    Quantum Approximate Optimization Algorithm (QAOA) is a highly regarded variational algorithm for solving combinato rial optimization problems. However, current quantum circuits are limited by coherence time, making it challenging to handle very deep circuits. Therefore, when compiling a QAOA quantum cir cuit, we aim to allow the compiler to optimize circuit depth while obtaining a higher fidelity circuit. Despite the existence of many general-purpose compilers for quantum circuits, there is still a lack of application-specific compilers designed specifically for QAOA. In our work, we address the compilation problem of QAOA circuits efficiently by structurally combining various gate-level optimiza tion methods with QAOA-specific qubit mapping. This enhances the feasibility of QAOA applications in real-world scenarios.

  • GNN-Based Performance Prediction of Quan tum Optimization of Maximum Independent Set

    Maximum Independent Set (MIS) is an NP-hard opti mization problem with wide-ranging applications in science and technology. Recently, a super-linear speedup over classical simu lated annealing in solving MIS was experimentally observed using a Rydberg atom array (RAA) quantum computer. The extent of the observed speedup depended on the graph instance and the circuit depth of the quantum algorithm. Because classically simulating an RAA is intractable, it is highly beneficial to be able to efficiently predict quantum and classical algorithm performance on a given graph and circuit depth prior to running it on an RAA. In this work, we present an efficient and accurate graph neural network (GNN)- based performance predictor of the RAA and classical simulated annealing for MIS. Our experimental results achieve high accuracy with an average root mean squared error (RMSE) of 0.03 out of the range [0, 1]. To the best of our knowledge, this is the first work that uses deep learning techniques to predict the performance of such quantum computing devices. 

  • Towards the Parameter Setting of Quantum Ap proximate Optimization Algorithm

    Quantum Approximate Optimization Algorithm (QAOA) is a popular quantum algorithm proposed for combinatorial op timization problems, which has shown better scaling than state of-the-art classical solvers for some problems. However, the per formance of QAOA is highly dependent on its parameter setting. Although executed classically, the parameter optimization routine is usually very challenging. To enable the power of quantum com puting in practical optimization problems, the cost of QAOA param eter optimization must be minimized. Fortunately, there have been great recent progress in the parameter setting of QAOA, includ ing the parameter concentration, parameter transfer, and variants of customized optimizers. In this paper, we will highlight the re cent progress of setting QAOA parameters, offering an off-the-shelf solution to the practical usage of quantum optimization. 

  • A comparison on constrain encoding methods for quantum approximate optimization algorithm

    The Quantum Approximate Optimization Algorithm (QAOA) represents a significant opportunity for practical quan tum computing applications, particularly in the era before error correction is fully realized. This algorithm is especially relevant for addressing constraint satisfaction problems (CSPs), which are critical in various fields such as supply chain management, energy distribution, and financial modeling. In our study, we conduct a numerical comparison of three different strategies for incorporating linear constraints into QAOA: transforming them into an uncon strained format, introducing penalty dephasing, and utilizing the quantum Zeno effect. We evaluate the effectiveness of these meth ods across different types of constraints. Finally, we explore the potential of integrating these encoding techniques to tackle com plex real-world problems that involve multiple types of constraints simultaneously.