Direct implementation of a perceptron in superconducting circuit quantum hardware - Pechal, Marek; Roy, Federico; Wilkinson, Samuel; Salis, Gian; Werninghaus, Max; Hartmann, Michael; Filipp, Stefan (Physical Review Research, 2022)
The utility of classical neural networks as universal approximators suggests that their quantum analogues could play an important role in quantum generalizations of machine-learning methods. Inspired by the proposal in Torrontegui and Garcia-Ripoll [Europhys. Lett. 125, 30004 (2019)], we demonstrate a superconducting qubit implementation of a controlled gate, which generalizes the action of a classical perceptron as the basic building block of a quantum neural network. In a two-qubit setup we show full control over the steepness of the perceptron activation function, the input weight and the bias by tuning the gate length, the coupling between the qubits, and the frequency of the applied drive, respectively. In its general form, the gate realizes a multiqubit entangling operation in a single step, whose decomposition into single-and two-qubit gates would require a number of gates that is exponential in the number of qubits. Its demonstrated direct implementation as perceptron in quantum hardware may therefore lead to more powerful quantum neural networks when combined with suitable additional standard gates.
CRIS-Team WoS Importer:2022-09-23

Superconducting quantum many-body circuits for quantum simulation and computing - Wilkinson, Samuel; Hartmann, Michael (Applied Physics Letters, 2020)
Quantum simulators are attractive as a means to study many-body quantum systems that are not amenable to classical numerical treatment. A versatile framework for quantum simulation is offered by superconducting circuits. In this perspective, we discuss how superconducting circuits allow the engineering of a wide variety of interactions, which, in turn, allows the simulation of a wide variety of model Hamiltonians. In particular, we focus on strong photon-photon interactions mediated by nonlinear elements. This includes on-site, nearest-neighbor, and four-body interactions in lattice models, allowing the implementation of extended Bose-Hubbard models and the toric code. We discuss not only the present state in analog quantum simulation but also future perspectives of superconducting quantum simulation, which open up when concatenating quantum gates in emerging quantum computing platforms.
CRIS-Team WoS Importer:2020-07-03

Variational Hamiltonian simulation for translational invariant systems via classical pre-processing - Mansuroglu, Refik; Eckstein, Timo; Nützel, Ludwig; Wilkinson, Samuel; Hartmann, Michael (Quantum Science and Technology, 2023)
The simulation of time evolution of large quantum systems is a classically challenging and in general intractable task, making it a promising application for quantum computation. A Trotter-Suzuki approximation yields an implementation thereof, where a higher approximation accuracy can be traded for an increased gate count. In this work, we introduce a variational algorithm which uses solutions of classical optimizations to predict efficient quantum circuits for time evolution of translationally invariant quantum systems. Our strategy can improve upon the Trotter-Suzuki accuracy by several orders of magnitude. It translates into a reduction in gate count and hence gain in overall fidelity at the same algorithmic accuracy. This is important in noisy intermediate scale quantum-applications where the fidelity of the output state decays exponentially with the number of gates. The performance advantage of our classical assisted strategy can be extended to open boundaries with translational symmetry in the bulk. We can extrapolate our method to beyond classically simulatable system sizes, maintaining its total fidelity advantage over a Trotter-Suzuki approximation making it an interesting candidate for beyond classical time evolution.
CRIS-Team Scopus Importer:2023-02-17
Keywords: Hamiltonian simulation; noisy intermediate scale quantum; time evolution; translational invariance; Trotter-Suzuki decomposition; variational algorithms