Hybrid quantum-classical systems make it possible to utilize existing quantum computers to their fullest extent. Within this framework, parameterized quantum circuits can be thought of as machine learning models with remarkable expressive power. This Review presents components of these models and discusses their application to a variety of data-driven tasks such as supervised learning and generative modeling. With experimental demonstrations carried out on actual quantum hardware, and with software actively being developed, this rapidly growing field could become one of the first instances of quantum computing that addresses real world problems.

Negative differential resistance remains an attractive but elusive functionality, so far only finding niche applications. Atom scale entities have shown promising properties, but viability of device fabrication requires fuller understanding of electron dynamics than has been possible to date. Using an all-electronic time-resolved scanning tunneling microscopy technique and a Green's function transport model, we study an isolated dangling bond on a hydrogen terminated silicon surface. A robust negative differential resistance feature is identified as a many body phenomenon related to occupation dependent electron capture by a single atomic level. We measure all the time constants involved in this process and present atomically resolved, nanosecond timescale images to simultaneously capture the spatial and temporal variation of the observed feature.

Quantum computers have the potential to advance material design and drug discovery by performing costly electronic structure calculations. A critical aspect of this application requires optimizing the limited resources of the quantum hardware. Here, we experimentally demonstrate an end-to-end pipeline that focuses on minimizing quantum resources while maintaining accuracy. Using density matrix embedding theory as a problem decomposition technique, and an ion-trap quantum computer, we simulate a ring of 10 hydrogen atoms without freezing any electrons. The originally 20-qubit system is decomposed into 10 two-qubit problems, making it amenable to currently available hardware. Combining this decomposition with a qubit coupled cluster circuit ansatz, circuit optimization, and density matrix purification, we accurately reproduce the potential energy curve in agreement with the full configuration interaction energy in the minimal basis set. Our experimental results are an early demonstration of the potential for problem decomposition to accurately simulate large molecules on quantum hardware.

We report on tuning the carrier capture events at a single dangling bond (DB) midgap state by varying the substrate temperature, doping type, and doping concentration. All-electronic time-resolved scanning tunneling microscopy (TR-STM) is employed to directly measure the carrier capture rates on the nanosecond time scale. A characteristic negative differential resistance (NDR) feature is evident in the scanning tunneling microscopy (STM) and scanning tunneling spectroscopy (STS) measurements of DBs on both n and p-type doped samples. It is found that a common model accounts for both observations. Atom-specific Kelvin probe force microscopy (KPFM) measurements confirm the energetic position of the DB's charge transition levels, corroborating STS studies. It is shown that under different tip-induced fields the DB can be supplied from two distinct reservoirs: the bulk conduction band and/or the valence band. We measure the filling and emptying rates of the DBs in the energy regime where electrons are supplied by the bulk valence band. By adding point charges in the vicinity of a DB, Coulombic interactions are shown to shift observed STS and NDR features.

Quantum computers have the potential to perform accurate and efficient electronic structure calculations, enabling the simulation of properties of materials. However, today’s noisy, intermediate-scale quantum (NISQ) devices have a limited number of qubits and gate operations due to the presence of errors. Here, we propose a systematically improvable end-to-end pipeline to alleviate these limitations. Our proposed resource-efficient pipeline combines problem decomposition techniques for compact molecular representations, circuit optimization methods for compilation, solving the eigenvalue problem on advanced quantum hardware, and error-mitigation techniques in post-processing the results. Using the density matrix embedding theory for compact representation, and an ion-trap quantum computer, we simulate a ring of 10 hydrogen atoms taking into account all electrons equally and explicitly in the electronic structure calculation. In our experiment, we simulated the largest molecular system on a quantum computer within chemical accuracy with respect to total molecular energy calculated by the full CI method. Our methods reduce the number of qubits required for high-accuracy quantum simulations by an order of magnitude in the present work, enabling the simulation of larger, more industrially relevant molecules using NISQ devices. They are further systematically improvable as devices’ computational capacity continues to grow.