Variational quantum state preparation via quantum data buses

Abstract

<jats:p>We propose a variational quantum algorithm to prepare ground states of 1D lattice quantum Hamiltonians specifically tailored for programmable quantum devices where interactions among qubits are mediated by Quantum Data Buses (QDB). For trapped ions with the axial Center-Of-Mass (COM) vibrational mode as single QDB, our scheme uses resonant sideband optical pulses as resource operations, which are potentially faster than off-resonant couplings and thus less prone to decoherence. The disentangling of the QDB from the qubits by the end of the state preparation comes as a byproduct of the variational optimization. We numerically simulate the ground state preparation for the Su-Schrieffer-Heeger model in ions and show that our strategy is scalable while being tolerant to finite temperatures of the COM mode.</jats:p>

Probing phases of quantum matter with an ion-trap tensor-network quantum eigensolver

“…In this section, we briefly review the concept of the TN-VQE approach in trapped ions previously proposed in Ref. [46]. Further details on our experimental implementation are provided in Sec.…”

“…However, the accurate implementation requires precise calibration of the experimental setup as well as cooling of the phonon modes to zero temperature, as the effective Rabi frequency of the sideband operations heavily depends on the phonon population as ∼ √ n. Miscalibrations and any finite phonon population lead to imperfect gates and, as a result, residual entanglement between the QDB and the qubits at the end of a circuit. Recently, it was proposed [46] to tackle this problem by employing adaptive feedback-loop strategies, such as the Variational Quantum Eigensolver (VQE) [36][37][38][39][40]. VQE is a NISQ protocol which aims to prepare ground states of interacting Hamiltonians on programmable quantum hardware, while mitigating the imperfections.…”

“…Such hybrid methods aim to solve problems where implementing a given numerical task with specialized quantum resources can provide an advantage over the classical hardware. In this context, NISQ processors can be tailored to represent and sample TN states [42][43][44][45][46][47][48], e.g. by encoding specific classes of variational TNs as programmable quantum circuits.…”

“…1(b). In our approach, the interactions are optimized by a classical algorithm with the ultimate goal to realize a target many-body quantum state with the phonon mode being disentangled from the ions at the end of the circuit [46].…”

Quantum activation functions for quantum neural networks

“…However, encoding the inputs as coefficients of a superposition of quantum states requires an algorithm for generic quantum state preparations [33][34][35] or, alternatively, to directly feed quantum data to the network [36]. For instance quantum encoding methods such as Flexible Representation of Quantum Images (FRQI) [37] have been proposed.…”