Praveen Viswanathan | Searcharxiv

arXiv2026

Buried germanium quantum well proximitised by magnetic field-resilient superconducting platinum iridium germanosilicide

Hybrid superconductor-semiconductor systems provide a versatile platform for quantum technologies, ranging from superconducting-spin interfaces to topological quantum devices. Progress toward scalable implementations requires superconductors that exhibit high critical fields ($>1$T) at accessible temperatures integrated with low-disorder semiconductor heterostructures. Here we demonstrate a superconducting platinum iridium germanosilicide (PtIrSiGe), with critical out-of-plane magnetic field up to $B_{\perp} = 1.9$T and critical temperature of $T_c\sim 1.85$K, integrated with planar germanium with mobility $\mu = 1.3\times 10^6$cm$^{2}$/Vs via top-down lithography fabrication. We show that the integrity of the germanium quantum well and mobility and density of the 2D hole gas are preserved despite annealing at $500\deg$C, a temperature comparable to that used for strained germanium epitaxy. We further demonstrate proximitisation of a buried germanium quantum well in a gate-defined Josephson junction/SQUID on a Ge/SiGe heterostructure.

cond-mat.mes-hall↗

arXiv2024

Hamiltonian-reconstruction distance as a success metric for the Variational Quantum Eigensolver

The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm for quantum simulation that can be run on near-term quantum hardware. A challenge in VQE -- as well as any other heuristic algorithm for finding ground states of Hamiltonians -- is to know how close the algorithm's output solution is to the true ground state, when the true ground state and ground-state energy are unknown. This is especially important in iterative algorithms, such as VQE, where one wants to avoid erroneous early termination. Recent developments in Hamiltonian reconstruction -- the inference of a Hamiltonian given an eigenstate -- give a metric can be used to assess the quality of a variational solution to a Hamiltonian-eigensolving problem. This metric can assess the proximity of the variational solution to the ground state without any knowledge of the true ground state or ground-state energy. In numerical simulations and in demonstrations on a cloud-based trapped-ion quantum computer, we show that for examples of both one-dimensional transverse-field-Ising (11 qubits) and two-dimensional J1-J2 transverse-field-Ising (6 qubits) spin problems, the Hamiltonian-reconstruction distance gives a helpful indication of whether VQE has yet found the ground state or not. Our experiments included cases where the energy plateaus as a function of the VQE iteration, which could have resulted in erroneous early stopping of the VQE algorithm, but where the Hamiltonian-reconstruction distance correctly suggests to continue iterating. We find that the Hamiltonian-reconstruction distance has a useful correlation with the fidelity between the VQE solution and the true ground state. Our work suggests that the Hamiltonian-reconstruction distance may be a useful tool for assessing success in VQE, including on noisy quantum processors in practice.

quant-ph↗