Quantum Accelerator Stack: A Research Roadmap
Not on the map. Why?
Abstract (arXiv)
This paper presents the definition and implementation of a quantum computer architecture to enable creating a new computational device - a quantum computer as an accelerator In this paper, we present explicitly the idea of a quantum accelerator which contains the full stack of the layers of an accelerator. Such a stack starts at the highest level describing the target application of the accelerator. Important to realise is that qubits are defined as perfect qubits, implying they do not decohere and perform good quantum gate operations. The next layer abstracts the quantum logic outlining the algorithm that is to be executed on the quantum accelerator. In our case, the logic is expressed in the universal quantum-classical hybrid computation language developed in the group, called OpenQL. We also have to start thinking about how to verify, validate and test the quantum software such that the compiler generates a correct version of the quantum circuit. The OpenQL compiler translates the program to a common assembly language, called cQASM. We need to develop a quantum operating system that manages all the hardware of the micro-architecture. The layer below the micro-architecture is responsible of the mapping and routing of the qubits on the topology such that the nearest-neighbour-constraint can be be respected. At any moment in the future when we are capable of generating multiple good qubits, the compiler can convert the cQASM to generate the eQASM, which is executable on a particular experimental device incorporating the platform-specific parameters. This way, we are able to distinguish clearly the experimental research towards better qubits, and the industrial and societal applications that need to be developed and executed on a quantum device.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Feb 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 1.
4 papers.
- 2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
- 2023A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Aheadcited by 29
- 2021Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlowcited by 13
- 2021Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designscited by 11
- 2024Demystifying Chains, Trees, and Graphs of Thoughts25 Jan 2024
- 2023A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead16 Oct 2023
- 2021Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs8 Sep 2021
- 2021Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow8 Sep 2021