Iterative quantum phase estimation: a trade-off between number of iterations and circuit dimensions
Project Description:
All the potential advantages of quantum computing make it a research topic of tremendous interest in both industrial and academic communities. Consequently, significant efforts are underway to develop and enhance both quantum hardware and the algorithms essential for quantum applications. Most of the ground-breaking advantages of quantum computing are based on the development of so-called fault-tolerant quantum devices, which are characterized by having a substantial number of physical qubits combined into logical qubits, which are minimally affected by noise. However, such devices are not yet available, and the current ones suffer from high noise and have only a very limited number of qubits available. As a result, to effectively utilize current or near-future quantum machines, algorithms and routines must be tailored specifically to these devices.
One of the most promising quantum algorithms is the Quantum Phase Estimation (QPE), which finds eigenvalues and is used as a subroutine of many other algorithms. The literature version of this algorithm is expensive both in terms of qubits and gate count, making it unsuitable for near-term devices. To overcome this problem other versions of this algorithm have been proposed, one of them being the iterative quantum phase estimation, which requires both fewer qubits and fewer gates at the cost of a larger number of circuit executions.
In this project, carried out in the NNF Quantum Computing Programme’s ‘Algorithms & Applications’ team, different QPE variants will be studied, and the optimal balance between the number of qubits and gate count versus the number of iterations will be analysed for early fault-tolerant devices with different architectures.
Expected qualifications:
We are looking for an ambitious student who is interested in quantum computing, algorithms, and numerical simulations, with a background in physics, chemistry, mathematics, computer science, or similar.
Contacts:
- Gabriela Oliveira, Ph.D. Student, maria.oliveira@nbi.ku.dk
Nina Glaser, Assistant Professor, nina.glaser@nbi.ku.dk
How to apply:
Please apply by email to M. Gabriela Oliveira and Nina Glaser.
Your application must include:
- CV
- Application letter
- Transcript of studies