Paper-QSP

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From Problem to Solution: A General Pipeline to Solve Optimisation Problems on Quantum Hardware

From Problem to Solution: A General Pipeline to Solve Optimisation Problems on Quantum Hardware

Tobias Rohe, Simon Grätz, Michael Kölle, Sebastian Zielinski, Jonas Stein, and Claudia Linnhoff-Popien

Abstract

On account of the inherent complexity and novelty of quantum computing (QC), as well as the expected lack of expertise of many of the stakeholders involved in its development, QC software development projects are exposed to the risk of being conducted in a crowded and unstructured way, lacking clear guidance and understanding. This paper presents a comprehensive quantum optimisation development pipeline, novel in its depth of 22 activities across multiple stages, coupled with project management insights, uniquely targeted to the late noisy intermediate-scale quantum (NISQ) and early post-NISQ eras. We have extensively screened literature and use-cases, interviewed experts, and brought in our own expertise to develop this general quantum pipeline. The proposed solution pipeline is divided into five stages: Use-case Identification, Solution Draft, Pre-Processing, Execution and Post-Processing. Additionally, the pipeline contains two review points to address the project management view, the inherent risk of the project and the current technical maturity of QC technology. This work is intended as an orientation aid and should therefore increase the chances of success of quantum software projects.

In progress

Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting

Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting

Gerhard Stenzel, Sebastian Zielinski, Michael Kölle, Philipp Altmann, Jonas Nüßlein, and Thomas Gabor

Abstract

To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution. Quantum gate matrix caching reduces the overhead of repeated applications of the Kronecker product when applying a gate matrix to the state vector by storing decomposed partial matrices for each gate. Circuit splitting divides the circuit into sub-circuits with fewer gates by constructing a dependency graph, enabling parallel or sequential execution on disjoint subsets of the state vector. These techniques are implemented using the PyTorch machine learning framework. We demonstrate the performance of our approach by comparing it to other PyTorch-compatible quantum state-vector simulators. Our implementation, named Qandle, is designed to seamlessly integrate with existing machine learning workflows, providing a user-friendly API and compatibility with the OpenQASM format.

In progress

Quantum Circuit Construction and Optimization through Hybrid Evolutionary Algorithms

Quantum Circuit Construction and Optimization through Hybrid Evolutionary Algorithms

Leo Sünkel, Philipp Altmann, Michael Kölle, Gerhard Stenzel, Thomas Gabor, and Claudia Linnhoff-Popien

Abstract

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In progress

Challenges for Reinforcement Learning in Quantum Circuit Design

Challenges for Reinforcement Learning in Quantum Circuit Design

Philipp Altmann, Jonas Stein, Michael K¨olle, Adelina B¨arligea, Maximilian Zorn, Thomas
Gabor, Thomy Phan, Sebastian Feld, and Claudia Linnhoff-Popien

Abstract

Quantum computing (QC) in the current NISQ era is still limited in size and precision. Hybrid applications mitigating those shortcomings are prevalent to gain early insight and advantages. Hybrid quantum machine learning (QML) comprises both the application of QC to improve machine learning (ML) and ML to improve QC architectures. This work considers the latter, leveraging reinforcement learning (RL) to improve quantum circuit design (QCD), which we formalize by a set of generic objectives. Furthermore, we propose qcd-gym, a concrete framework formalized as a Markov decision process, to enable learning policies capable of controlling a universal set of continuously parameterized quantum gates. Finally, we provide benchmark comparisons to assess the shortcomings and strengths of current state-of-the-art RL algorithms.

In progress

The UQ Platform: A Unified Approach To Quantum Annealing

The UQ Platform: A Unified Approach To Quantum Annealing

Thomas Gabor, Sebastian Zielinski, Christoph Roch, Sebastian Feld, Claudia Linnhoff-Popien
Abstract

Quantum Annealing is an algorithm for solving instances of quadratic unconstrained binary optimization (QUBO) that is implemented in hardware utilizing quantum effects to quickly find approximate solutions. However, QUBO can obviously also be solved by any classical optimization technique, for which various implementations exist. The UQ platform provides a unified interface to various means of solving QUBO that allows for a seamless switch between classical and quantum methods while implementing features such as load and user management.

IEEE 5th International Conference on Computer and Communication Systems (ICCCS 2020)

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