Demonstration of a small programmable quantum computer with atomic qubits

By Shantanu Debnath, Norbert Matthias Linke, Caroline Figgatt, Kevin A. Landsman, Kenneth Wright, Christopher R. Monroe. Nature, 2016

789 citations recorded by the source corpus.

Quantum computers can solve certain problems more efficiently than any possible conventional computer. Small quantum algorithms have been demonstrated on multiple quantum computing platforms, many specifically tailored in hardware to implement a particular algorithm or execute a limited number of computational paths. Here we demonstrate a five-qubit trapped-ion quantum computer that can be programmed in software to implement arbitrary quantum algorithms by executing any sequence of universal quantum logic gates. We compile algorithms into a fully connected set of gate operations that are native to the hardware and have a mean fidelity of 98 per cent. Reconfiguring these gate sequences provides the flexibility to implement a variety of algorithms without altering the hardware. As examples, we implement the Deutsch–Jozsa and Bernstein–Vazirani algorithms with average success rates of 95 and 90 per cent, respectively. We also perform a coherent quantum Fourier transform on five trapped-ion qubits for phase estimation and period finding with average fidelities of 62 and 84 per cent, respectively. This small quantum computer can be scaled to larger numbers of qubits within a single register, and can be further expanded by connecting several such modules through ion shuttling or photonic quantum channels.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/67fb6da859b1a1bb62bcb9903cab4d54200920a1

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In Atomic and Molecular Physics, and Optics

Citations in the corpus

Automated optimization of large quantum circuits with continuous parameters (2017, methodology): “more expensive to implement than a single-qubit gate.(5,6) We accommodate this by considering the CNOT gate count and optimizing the number of the CNOT gates in our algorithms.”
Toward the first quantum simulation with quantum speedup (2017, background): “1 Introduction While a scalable quantum computer remains a long-term goal, recent experimental progress suggests that devices capable of outperforming classical computers will soon be available [10, 21, 28, 38, 73, 78].”
Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers (2019, neutral): “orrecting circuits. Our methods can also be extended to map programs to logical qubits based on their error properties. Our techniques can be adapted for other qubit technologies such as trapped ions [71] and other routing approaches such as teleportationbased communication [72] by choosing the appr”
Goal-Directed Planning via Hindsight Experience Replay (2022, background): “Due to hardware constraints and quantum disturbances (Linke et al., 2017; Maslov, 2017; Leibfried et al., 2007; Debnath et al., 2016), quantum computers require compilers to approximate any quantum transformations as ordered sequences of quantum gates that can be applied on the hardware.”
From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz (2017, background): “Beside superconducting architectures, ion [17] and neutral atom based [66] devices are also reaching the scale at which intermediate-size experiments would be feasible [56].”
Quantum optimization using variational algorithms on near-term quantum devices (2017, background): “es and physical qubit realizations are being considered. While quantum systems based on superconducting qubits [56,57,58, 34,59,60,61] at the moment seem to be leading the way, ion-trap-based systems [62,63] are close competitors. Furthermore, semiconductor-based spin qubits [64,65,66] and other qua”
Quantum error correction: an introductory guide (2019, background): “There is currently no preferred qubit technology; a variety of physical systems are being explored for use as qubits, including photons [5,6], trapped ions [7–10], superconducting circuits [11–13] and spins in semiconductors [14–16].”
Demonstration of qubit operations below a rigorous fault tolerance threshold with gate set tomography (2016, background): “the number of gauge parameters for this gate set (14).”
Classification with Quantum Neural Networks on Near Term Processors (2018, background): “In the near future we will have gate model quantum computers with a suﬃcient number of qubits and suﬃciently high gate ﬁdelity to run circuits with enough depth to perform tasks that cannot be simulated on classical computers [19][20][21][22][23].”