Quantum Volume
@inproceedings{Bishop2017QuantumV, title={Quantum Volume}, author={L. Bishop and S. Bravyi and A. Cross and J. Gambetta and J. Smolin and March}, year={2017} }
As we build larger quantum computing devices capable of performing more complicated algorithms, it is important to quantify their power. The origin of a quantum computer’s power is already subtle, and a quantum computer’s performance depends on many factors that can make assessing its power challenging. These factors include: 1. The number of physical qubits; 2. The number of gates that can be applied before errors make the device behave essentially classically; 3. The connectivity of the… CONTINUE READING
21 Citations
NISQ+: Boosting quantum computing power by approximating quantum error correction
- Computer Science, Physics
- 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA)
- 2020
- 3
- PDF
Establishing the Quantum Supremacy Frontier with a 281 Pflop/s Simulation
- Computer Science, Physics
- ArXiv
- 2019
- 44
- PDF
A generative modeling approach for benchmarking and training shallow quantum circuits
- Computer Science, Mathematics
- 2018
- 84
- PDF
Understanding Quantum Control Processor Capabilities and Limitations through Circuit Characterization
- Computer Science, Physics
- 2020 International Conference on Rebooting Computing (ICRC)
- 2020
- 7
- PDF
A Roadmap for Automating the Selection of Quantum Computers for Quantum Algorithms
- Computer Science, Physics
- 2020
- 3
- PDF
Experimental quantum homodyne tomography via machine learning
- Computer Science, Physics
- ArXiv
- 2019
- 11
- PDF
Simulating Noisy Quantum Circuits with Matrix Product Density Operators
- Mathematics, Computer Science
- ArXiv
- 2020
- 1
- PDF
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