Cristian Bonato

Professor at Heriot-Watt University

Engineering spin-based quantum technologies

 

Single spins are an excellent platform for the implementation of quantum memories/repeaters for secure quantum networks and nanoscale quantum sensors operating at the ultimate limits of spatial resolution. The deployment of these technologies into real applications requires engineering spin-based devices to make them reliable, robust and compatible with industrial-scale processing. In this talk, I will discuss two aspects of our recent work in engineering spin-based quantum technologies.

In the first part, I will describe some of our recent results in engineering silicon carbide quantum spintronic devices for quantum networking. Silicon carbide, as a semiconductor widely used in power electronics, offers a great opportunity in integrating spintronics, photonics and electronics on a single multi-functional platform. I will report the first observation of spin-dependent optical transitions (a key requirement for spin-photon interfaces) on single vanadium centres in SiC, which have recently attracted attention due to direct telecom-wavelength (O-band) emission and the availability of a coherent electron spin [1]. I will show that, by engineering the isotopic composition of the SiC matrix, we reduce the inhomogeneous spectral distribution of different emitters down to 100 MHz, significantly smaller than any other single quantum emitter [2]. This is important as the implementation of quantum networks requires all spin-photon interfaces to operate at exactly the same frequency.

In the second half of my talk, I will describe our effort to develop "smart" spin-based quantum sensors that self-optimise themselves to operate in the regime of maximum sensitivity [3-6]. I will present an adaptive approach, based on Bayesian estimation, to estimate the key decoherence timescales (T1, T2* and T2) and the corresponding decay exponent for a single qubit, using information gained in preceding experiments. This approach reduces the time required to reach a given uncertainty by a factor up to an order of magnitude, depending on the specific experiment, compared to curve fitting data taken on a pre-determined parameter range. Smart quantum architectures, that self-optimise themselves to automatically operate with optimal settings, will significantly facilitate the adoption of quantum technologies by non-expert users.

 

References
[1] J. Hendriks et al, “Coherent spin dynamics of hyperfine-coupled vanadium impurities in silicon carbide”, arXiv:2210.09942 (2022)
[2] P. Cilibrizzi et al, “Ultra-narrow inhomogeneous spectral distribution of telecom-wavelength vanadium centres in isotopically-enriched silicon carbide”, Nature Communications 14, 8448 (2023)
[3] C. Bonato et al, “Optimized quantum sensing with a single electron spin using real-time adaptive measurements”, Nature Nanotechnology 11, 247-252 (2016)
[4] H. Dinani et al, “Bayesian estimation for quantum sensing in the absence of single-shot detection”, Phys. Rev. B 99, 125413 (2019)
[5] M. J. Arshad et al, “Online adaptive estimation of decoherence timescales for a single qubit”, Physical Review Applied 21, 024026 (2024)
[6] V. Gebhart et al, “Learning Quantum Systems”, Nature Reviews Physics 5, 141 (2023)