Hybrid Quantum Computation Advances Phase Estimation in Noisy Intermediate Systems.

Researchers successfully implemented the Phase Estimation algorithm using a hybrid quantum system combining a Rydberg atom qubit and a superconducting flux qubit. Analysis of Hamiltonian dynamics identified noise sources, and optimal control techniques (GRAPE) refined gate construction, revealing performance tradeoffs between control parameters and noise levels.

The pursuit of scalable quantum computation necessitates exploration beyond single quantum technologies. Distributed quantum computing (DQC) offers a potential pathway, leveraging interconnected quantum processors to enhance computational power. However, realising DQC across different physical platforms remains a significant challenge. Researchers at TNO, Centrum Wiskunde & Informatica, and Maastricht University have now demonstrated a key quantum algorithm – phase estimation – implemented on a hybrid system combining a Rydberg atom qubit with a superconducting flux qubit. This work, detailed in their article “Non-Local Phase Estimation with a Rydberg-Superconducting Qubit Hybrid”, by Juan C. Boschero, Niels M.P. Neumann, Ward van der Schoot, and Frank Phillipson, investigates the Hamiltonian dynamics of this system, analyses noise sources, and employs optimal control techniques (GRAPE – Gradient Ascent Pulse Engineering) to refine gate construction, revealing performance trade-offs inherent in the process.

Hybrid Quantum System Advances Distributed Computation

Universities and research institutions are increasingly focused on distributed quantum computation (DQC) as a means of expanding quantum processing capacity within the current Noisy Intermediate-Scale Quantum (NISQ) era. Recent work details an implementation of the Phase Estimation algorithm utilising a hybrid quantum system, combining a Rydberg atom qubit with a superconducting flux qubit. This approach addresses a current research gap, as most DQC investigations focus on systems built using a single quantum technology, rather than integrating disparate platforms. Researchers characterised Hamiltonian dynamics – the study of energy and its transformation – to identify noise sources inherent in the hybrid system, understanding these mechanisms being critical for reliable quantum operations.

The study employed the Gradient-based Robust Adaptive Pulse Engineering (GRAPE) algorithm to optimise the construction of quantum gates. GRAPE facilitates the design of control pulses that minimise errors and maximise gate fidelity – a measure of how accurately a quantum gate performs its intended operation. Results demonstrate a clear relationship between GRAPE parameters – specifically step size and the number of iterations – and the system’s susceptibility to noise. Smaller step sizes and increased iterations generally improve robustness against noise, but at the cost of increased computational time. Conversely, larger step sizes accelerate optimisation but may amplify the impact of noise.

This research contributes to the development of practical strategies for realising DQC by highlighting the importance of careful control pulse design and noise mitigation techniques within hybrid quantum architectures. Researchers leveraged the QuTiP software package, a widely used Python framework for simulating the dynamics of open quantum systems, to model and optimise the control pulses, allowing them to explore a wide range of parameter settings and identify the optimal configuration for minimising gate errors.

Furthermore, the work acknowledges the importance of asynchronous operations in distributed quantum systems. The ability to perform operations on different qubits independently and at different times is crucial for scaling up quantum computations. Researchers employed the qoosim simulation environment, designed to facilitate asynchronous operations on qubits, to model the behaviour of the hybrid system and assess the impact of timing uncertainties on the overall performance of the Phase Estimation algorithm. This holistic approach, combining detailed Hamiltonian dynamics simulations, GRAPE optimal control, and asynchronous operation modelling, provides valuable insights into the challenges and opportunities of realising DQC across heterogeneous quantum platforms.