Optimization of quantum optical devices with machine learning

Sunil Pai, Ben Bartlett, Olav Solgaard +1 more

Networks of tunable, integrated optical interferometers support quantum information processing and machine learning with much better energy efficiency than standard electronics. A network's gridlike structure and imperfections localize optical signals propagating through the device, which ultimately slows training by gradient-based optimization. Here this problem is solved by proper initialization, combined with redundant and remotely interacting interferometers. The authors' approach improves the convergence time of gradient-based optimization to random target operators by at least two orders of magnitude, at the scale of practical machine-learning applications (10${}^{4}$ to 10${}^{6}$ nodes).

M. Praba, Ahmad Abdelhafiz Ali Samhan, V. Samuthira Pandi +3 more

Hybridizing machine learning (ML) with quantum computing has become an attractive strategy to increase computational power on Noisy Intermediate Scale Quantum (NISQ) devices. Although these can be potentially quantum advantage devices, their qubit numbers and noise are bounded and there are challenges when it comes to practical quantum circuit realization. This work concentrates on the creation of optimized quantum circuits via ML-based strategies, to enhance the performance of the circuits, to reduce the error rate and to accommodate the particular noise profile of the hardware of NISQ devices. The resulting suboptimal mappings with little or no consideration of noise and hardware topology in traditional quantum compilers can ruin the fidelity of quantum algorithm. Leveraging machine learning, and specifically reinforcement and supervised learning methods, our method learns to optimize compilation strategy for specific quantum hardware architecture. The paper proposes a framework that uses machine learning models to predict qubit mappings, gate decompositions, and transpilation paths that minimize gate depth and error accumulation. We also investigate transfer learning methods for propagating the learned compilation strategies to novel NISQ machines. Such methods are empirically validated on both simulated and real quantum hardware backends, providing evidence of circuit depth, execution time and error reductions. The results show that ML-assisted compilation can achieve better success probabilities for benchmark quantum algorithms like VQE and QAOA than conventional compilers. Furthermore, the work examines the interpretability of ML models providing hardware-aware optimization recommendations and understanding the noise patterns and methods of error correction. Our work highlights the key role of ML for transferring between theoretical quantum algorithms and their practical NISQ implementations. This integration boosts the immediate applicability of existing quantum devices and provides a basis for adaptive and scalable likelihood-based compilation for the era of fault-tolerant quantum computation. Our results motivate further investigation of hybrid classical-quantum workflows, in which ML iteratively improves compilation in accordance with changing hardware noise and capabilities. By improving quantum circuit compilation onNISQ computers, this work is taking steps toward the realization of quantum advantage in application scenarios such as optimization, cryptography, and quantum simulation without needing to wait for fully fault-tolerant large-scale quantum computers.

Shahnawaz Ahmed, Carlos Sánchez Muñoz, Franco Nori +1 more

Natalia Ares

Blake Wilson, Yuheng Chen, Sabre Kais +3 more

We present recent advances and future perspectives in using machine learning for characterization, fabrication, and inverse design for device applications, such as hybrid quantum-classical optimization of nanostructures, hypothesis learning for automated discovery, and pre-characterization binning.