SYNTHESIS: QUANTUM RESERVOIR COMPUTING, MACHINE LEARNING, AND ASTROMETRY.

Dr. Stephen Vintskevich

There are multiple challenging issues one must address to boost further the nascent field of quantum technologies. The most common are reducing noises’ affection on a given quantum protocol’s performance, performing well-controlled quantum operations, and developing general frameworks for mapping various practical problems into quantum algorithms performed in different quantum devices.

The synthesis of methods based on machine learning, tensor network language, and adjacent areas such as Reservoir Computing demonstrates great potential to resolve the abovementioned issues and enhance the abilities of quantum devices. Combining approaches from these areas gives rise to interdisciplinary directions and developments of novel approaches in modern quantum physics, from quantum machine learning to quantum metrology.

The talk overs several seemingly directly unrelated topics. However, as we will see, these topics have strong bonds via similar hybrid approaches. First, we will focus on applying tensor networks and machine learning-based methods to analyze a simulation complexity and perform efficient simulations of open quantum dynamics [1, 2]. Second, we will explore a novel and rapidly developing paradigm of computations and information processing - quantum reservoir computing and quantum reservoir processing (QRC/P). The QRC/P paradigm has already demonstrated great potential in applications such as quantum state tomography, preparation, characterization, entanglement detection, and performing quantum operations. As we will see, machine learning and tensor networks play a crucial role in developing novel heuristic approaches to performance optimization of several quantum reservoirs connected into a network via two-qubit quantum channels [3]. Third, we will discuss how these hybrid methods can be combined with methods used in quantum metrology and their employment in quantum-enhanced sensor networks with possible applications in quantum astrometry. Finally, we will briefly discuss how "synthesized" methods from all these applications can boost the further development of quantum technologies [4, 5].

References

[1] I. A. Luchnikov, S. V. Vintskevich, H. Ouerdane, and S. N. Filippov, “Simulation complexity of open quantum dynamics: Connection with tensor networks,” Phys. Rev. Lett., vol. 122, p. 160401, Apr 2019.

[2] I. A. Luchnikov, S. V. Vintskevich, D. A. Grigoriev, and S. N. Filippov, “Machine learning non-markovian quantum dynamics,” Phys. Rev. Lett., vol. 124, p. 140502, Apr 2020.

[3] S. Vintskevich and D. Grigoriev, “Computing with two quantum reservoirs connected via optimized two-qubit nonselective measurements,” arXiv2201.07969, 2022.

[4] S. Vintskevich, N. Bao, A. Nomerotski, P. Stankus, and D. Grigoriev, “Classification of four-qubit entangled states via machine learning,” arXiv preprint arXiv:2205.11512, 2022.

[5] P. Stankus, A. Nomerotski, A. Slosar, and S. Vintskevich, “Two-photon amplitude interferometry for precision astrometry,” 2020.

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