Cryogenic silicon surface ion trap

Trapped ions are pre-eminent candidates for building quantum information processors and quantum simulators. They have been used to demonstrate quantum gates and algorithms, quantum error correction, and basic quantum simulations. However, to realise the full potential of such systems and make scalable trapped-ion quantum computing a reality, there exist a number of practical problems which must be solved. These include tackling the observed high ion-heating rates and creating scalable trap structures which can be simply and reliably produced. Here, we report on cryogenically operated silicon ion traps which can be rapidly and easily fabricated using standard semiconductor technologies. Single $^{40}$Ca$^+$ ions have been trapped and used to characterize the trap operation. Long ion lifetimes were observed with the traps exhibiting heating rates as low as $\dot{\bar{n}}=$ 0.33 phonons/s at an ion-electrode distance of 230 $\mu$m. These results open many new avenues to arrays of micro-fabricated ion traps.Comment: 12 pages, 4 figures, 1 tabl

Individual addressing of ion qubits with counter-propagating optical frequency combs

We propose a new method of individual single-qubit addressing of linear trapped-ion chains utilizing two ultrastable femtosecond frequency combs. For that, we suggest implementing the single-qubit gates with two counter-propagating frequency combs overlapping on the target ion and causing the AC Stark shift between the qubit levels. With analytical calculations and numerical modeling, we show that the arbitrary single-qubit rotations can be indeed realized using only laser fields propagating along the ion chain. We analyze the error sources for the proposed addressing method and prove that it allows implementing the single-qubit gates with high fidelity

Mitigating Quantum Gate Errors for Variational Eigensolvers Using Hardware-Inspired Zero-Noise Extrapolation

Variational quantum algorithms have emerged as a cornerstone of contemporary quantum algorithms research. Practical implementations of these algorithms, despite offering certain levels of robustness against systematic errors, show a decline in performance due to the presence of stochastic errors and limited coherence time. In this work, we develop a recipe for mitigating quantum gate errors for variational algorithms using zero-noise extrapolation. We introduce an experimentally amenable method to control error strength in the circuit. We utilise the fact that gate errors in a physical quantum device are distributed inhomogeneously over different qubits and pairs thereof. As a result, one can achieve different circuit error sums based on the manner in which abstract qubits in the circuit are mapped to a physical device. We find that the estimated energy in the variational approach is approximately linear with respect to the circuit error sum (CES). Consequently, a linear fit through the energy-CES data, when extrapolated to zero CES, can approximate the energy estimated by a noiseless variational algorithm. We demonstrate this numerically and further prove that the approximation is exact if the two-qubit gates in the circuits are arranged in the form of a regular graph.Comment: 9 pages, 2 figure

A compact ion-trap quantum computing demonstrator

Quantum information processing is steadily progressing from a purely academic discipline towards applications throughout science and industry. Transitioning from lab-based, proof-of-concept experiments to robust, integrated realizations of quantum information processing hardware is an important step in this process. However, the nature of traditional laboratory setups does not offer itself readily to scaling up system sizes or allow for applications outside of laboratory-grade environments. This transition requires overcoming challenges in engineering and integration without sacrificing the state-of-the-art performance of laboratory implementations. Here, we present a 19-inch rack quantum computing demonstrator based on $^{40}\textrm{Ca}^+$ optical qubits in a linear Paul trap to address many of these challenges. We outline the mechanical, optical, and electrical subsystems. Further, we describe the automation and remote access components of the quantum computing stack. We conclude by describing characterization measurements relevant to digital quantum computing including entangling operations mediated by the Molmer-Sorenson interaction. Using this setup we produce maximally-entangled Greenberger-Horne-Zeilinger states with up to 24 ions without the use of post-selection or error mitigation techniques; on par with well-established conventional laboratory setups