Design of time-optimal multi-qubit gates using Lie-theoretic methods

Bao-Zhi Sun, Shao-Ming Fei, Naihuan Jing +1 more

Dirk Heimann, Felix Wiebe, Elie Mounzer +1 more

In quantum optimal control theory, gradient-based trajectory optimization techniques have proven versatile in designing multi-qubit quantum gates. Furthermore, incorporating the underlying Lie-group structure can accelerate the optimization process. In this work, we adapt the Lie-group formulation of the iterative linear quadratic regulator (iLQR) to the special unitary group SU(N) and apply it to quantum gate synthesis, systematically comparing it against the standard Euclidean iLQR formulation across multiple two- to five-qubit gates. We find that in the idealized, unconstrained setting, where all Lie-algebra basis elements are available as drive Hamiltonian terms, the Lie-group formulation converges faster than the Euclidean iLQR formulation. If drive terms are constrained to 2-local Hamiltonian terms, the Lie-group variant converges faster in early optimization iterations, but exhibits greater sensitivity to initialization and a stronger tendency towards local minima. These results demonstrate that incorporating Lie-group geometry into iLQR substantially improves convergence and highlight important next steps for improvements in constrained control settings.

Pascal Baßler, Matthias Zipper, Christopher Cedzich +4 more

We develop a method to synthesize a class of entangling multi-qubit gates for a quantum computing platform with fixed Ising-type interaction with all-to-all connectivity. The only requirement on the flexibility of the interaction is that it can be switched on and off for individual qubits. Our method yields a time-optimal implementation of the multi-qubit gates. We numerically demonstrate that the total multi-qubit gate time scales approximately linear in the number of qubits. Using this gate synthesis as a subroutine, we provide compilation strategies for important use cases: (i) we show that any Clifford circuit on n qubits can be implemented using at most 2 n multi-qubit gates without requiring ancilla qubits, (ii) we decompose the quantum Fourier transform in a similar fashion, (iii) we compile a simulation of molecular dynamics, and (iv) we propose a method for the compilation of diagonal unitaries with time-optimal multi-qubit gates, as a step towards general unitaries. As motivation, we provide a detailed discussion on a microwave controlled ion trap architecture with magnetic gradient induced coupling (MAGIC) for the generation of the Ising-type interactions.

Hyeong-Jin Kim

Harrison Ball, Per Liebermann, Andre R. R. Carvalho +5 more

Sahar Daraeizadeh, Shavindra Premaratne, A. Y. Matsuura

In this paper, we present a machine learning framework to design high-fidelity multi-qubit gates for quantum processors based on quantum dots in silicon, with qubits encoded in the spin of single electrons. In this hardware architecture, the control landscape is vast and complex, so we use the deep reinforcement learning method to design optimal control pulses to achieve high fidelity multi-qubit gates. In our learning model, a simulator models the physical system of quantum dots and performs the time evolution of the system, and a deep neural network serves as the function approximator to learn the control policy. We evolve the Hamiltonian in the full state-space of the system, and enforce realistic constraints to ensure experimental feasibility.