Bao Gia Bach

Trong Duong, Sang T. Truong, Minh Tam et al.

Quantum neural networks are promising for a wide range of applications in the Noisy Intermediate-Scale Quantum era. As such, there is an increasing demand for automatic quantum neural architecture search. We tackle this challenge by designing a quantum circuits metric for Bayesian optimization with Gaussian process. To this goal, we propose a new quantum gates distance that characterizes the gates' action over every quantum state and provide a theoretical perspective on its geometrical properties. Our approach significantly outperforms the benchmark on three empirical quantum machine learning problems including training a quantum generative adversarial network, solving combinatorial optimization in the MaxCut problem, and simulating quantum Fourier transform. Our method can be extended to characterize behaviors of various quantum machine learning models.

Bao Bach, Ilya Safro, Ed Younis

With the growth of quantum platforms for gate-based quantum computation, compilation holds a crucial role in deciding the success of the implementation. While there has been rich research in compilation techniques for the superconducting-qubit regime. The trapped-ion architectures, currently leading in robust quantum computations for their reliable operations, still lack competitive compilation strategies. This work introduces a unifying hardware abstraction, the ``position graph'', representing various hardware architectures. With this abstraction, we model trapped-ion Quantum Charge-Coupled Device (QCCD) architectures, enabling high-quality, scalable compilation methods. Specifically, we propose scheduling algorithms called SHuttling-Aware PERmutative (SHAPER) and SHuttling-AWare (SHAW) heuristic searches to tackle the complex constraints and dynamics of trapped-ion machines, with the cooperation of state-of-the-art permutation-aware mapping. These approaches generate executable circuits and native instructions that respect the physical constraints of shuttling-based architectures. We evaluate proposed algorithms across theorized and real architectures using the position graph framework. For completeness, we also introduce a linear program of trapped-ion scheduling that yields the optimal schedules, enabling a direct comparison with our heuristics. Our algorithm can successfully compile programs for extreme architectures where priori algorithms fail. When the baseline does complete, our produced schedules are $1.45$ times faster on average, with best-case speedups up to $4$ times faster.

Kien X. Nguyen, Bao Bach, Ilya Safro

Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising candidates to achieve the quantum advantage in solving combinatorial optimization problems. The process of finding a good set of variational parameters in the QAOA circuit has proven to be challenging due to multiple factors, such as barren plateaus. As a result, there is growing interest in exploiting parameter transferability, where parameter sets optimized for one problem instance are transferred to another that could be more complex either to estimate the solution or to serve as a warm start for further optimization. But can we transfer parameters from one class of problems to another? Leveraging parameter sets learned from a well-studied class of problems could help navigate the less studied one, reducing optimization overhead and mitigating performance pitfalls. In this paper, we study whether pretrained QAOA parameters of MaxCut can be used as is or to warm start the Maximum Independent Set (MIS) circuits. Specifically, we design machine learning models to find good donor candidates optimized on MaxCut and apply their parameters to MIS acceptors. Our experimental results show that such parameter transfer can significantly reduce the number of optimization iterations required while achieving comparable approximation ratios.