Scaling a Trapped-Ion Quantum Computer

    Sara Mouradian

    • Department of Electrical and Computer Engineering, University of Washington, Seattle, WA

&bula; physics 16, 209

Major technical advances in a quantum computer based on trapped ions may bring a larger version closer to reality.

Picture 1: Moises and colleagues present a quantum computer in which ions (red) move around a racetrack-like structure [1]. Ions are stored in the green regions, sorted in the upper blue regions, and entangled (yellow) in the lower blue regions. The gray loops are radio-frequency electrodes.

Scientists are exploring different platforms for future large-scale quantum computation. Among the leading contenders, those in which quantum bits (qubits) are trapped ions stand out for their low error operation. However, scaling up such platforms to the millions of qubits required for utility-scale quantum computing is a daunting task. Now Steven Moses of Quantinuum in Colorado and colleagues have described an impressive new confined quantum computer, the Quantinuum System Model H2, in which they have been able to increase the number of qubits (from 20 to 32 ) without increasing the error rate [1]. The researchers put this system through its paces with a full level of component testing, a set of industry standard tests, and a set of various applications.

In a typical trapped-ion quantum computer, a linear chain of ions is confined to an electric potential using direct-current (dc) and radio-frequency (rf) fields. While the ion-trap apparatus can be made at any temperature, the ions themselves must be cooled by the laser to near their ground state. Their motion can be quantized, and the resulting motional mode can be used to insert any pair of ions in the chain—a prerequisite for performing quantum operations. However, controlling individual ions in a long chain comes with its own technical difficulties, and it is unlikely that a million qubits — if necessary to build a universal, error-tolerant quantum computer . [2]- can be trapped in a potential.

In 2002, a group of scientists proposed a so-called quantum charge-coupled device (QCCD) architecture, in which short linear chains of ions are linked by physically quenching the ions between the storage and interaction regions. [3]. A quantum computer based on this architecture consists of several ion traps, each with a set of divided electrodes. By varying the voltages of these electrodes, one (or groups of) ions can be transported around the system to interact with ions in other areas. In this way, the computer can be divided into many short linear ion chains, and the dynamic arrangement of ions enables arbitrary connections.

Since the QCCD architecture was proposed, there has been a lot of work to realize the dream of a large QCCD confined quantum computer. Quantinum scientists previously reported results for one-dimensional geometries [4]. However, integrating all the electrical, optical, and computational building blocks needed to control ions is not trivial. Furthermore, for true scalability, all of this must be done in a way that ensures that the per-qubit error rate does not increase as more qubits are added.

Moses and colleagues report a QCCD confined quantum computer in which ions move around a racetrack-like structure (Fig. 1). This system combines and improves aspects of previous demonstrations from Quantinuum and other groups and combines three key features. First, the rf electrodes are routed under the upper part of the device, which leads to more customization for the electrode geometry. Second, a set of dc voltages is applied to multiple electrodes in parallel, reducing the number of individual control voltages that must be sent to the vacuum chamber in which the device is housed—an important consideration in increasing complexity. in the trap. Third, the ions are loaded into the device from a cloud of cold neutral atoms in a magneto-optical trap (rather than from a hot vapor as is often done), enabling more fast ion loading and thus reducing the time taken to start an experiment.

These hardware advances supported by classical computing infrastructure allowed Moses and colleagues to perform fully automated calibration of their system and tracking of characteristic qubit phases. The researchers also implemented “midcomputation” measurements and real-time feedback—an important feature for future error-tolerance demonstrations. Although these features have previously been demonstrated separately by this team and others, their combination forms a formidable device that can operate at a state-of-the-art level.

The engineering work that made this device possible was undoubtedly a tour de force. However, it was the whole character of Moses and the companions that distinguished their study. They begin by identifying every possible part of a quantum algorithm: single-qubit operations, two-qubit operations, state preparation and measurement, and ion transport. With this information, the researchers were able to fully catalog all sources of error, knowing that the reliability of their system was limited to errors related to two-qubit operations and to -prepare and measure the state.

The researchers didn’t stop there: they also performed system-level benchmark tests. Although the single-operation characterization gives a good first guess of what a machine will do, the whole operation of the system can be worse due to crosstalk, for example. Surprisingly, the team’s identified error rates from component-level testing match well (if not perfectly) with those from system-level benchmark tests. One of these metrics is quantum volume—an industry standard that characterizes the computational power of a quantum system. The researchers achieved a quantum volume of 216. This amount was a record for any machine when the result was first reported, but it was recently beaten by scientists at Quantinuum with another device. [5]. Finally, Moses and colleagues put their system through its paces by implementing a set of algorithms, each of which validates a different capability of the device.

Although the work of Moses and colleagues advances trapped-ion quantum computing and sets a formidable precedent for future efforts, there is still much work to be done before we have utility-scale version of these devices. First, as the researchers point out, building a truly two-dimensional architecture presents new challenges—such as achieving low-error ion transport through the junctions and scaling up the necessary power control signals. [6].

Second, Moses and colleagues state that only 1%–2% of computation time is spent performing quantum operations; the rest is spent trapping the ions and cooling them. This percentage is not enough for the future quantum computer, and a lot of effort should be done to improve it. One possible way forward is to increase the number of ions in each chain. Although this increases technical overhead, it reduces how many shuttling operations are required. It will be interesting to see how this balancing act plays out in future work from this team and others.


  1. OF Moses and so on.A race-track trapped-ion quantum processor, Phys. Rev. X 13041052 (2023).
  2. I AM Beverland and so on.Assessing the requirements to scale to practical quantum advantage, arXiv:2211.07629.
  3. D. Kielpinski and so on.Architecture for a large ion-trap quantum computer, NATURE 417 (2002).
  4. JM Pino and so on.Demonstration of trapped ion quantum CCD computer architecture, NATURE 592 (2021).
  5. Quantinuum, Quantinuum H-Series quantum computer accelerates through 3 more performance records for quantum volume: 217, 218and 219June 30, 2023.
  6. M. Malinowski and so on.How to wire a 1000-qubit trapped-ion quantum computer, PRX Quantum 4040313 (2023).

About the Author

Photo by Sara Mouradian

Sara Mouradian is an assistant professor of electrical and computer engineering at the University of Washington, Seattle. He received his BS, MEng, and PhD from the Massachusetts Institute of Technology, working on quantum technologies in optical and solid-state systems. He was previously an Intelligence Community postdoctoral fellow at the University of California, Berkeley, helping to demonstrate the control of rotational modes of ions and realizing a new sensing technique capable of demonstrating a true quantum advantage. At the University of Washington, his lab is focused on trapped-ion quantum information processing, including optical control with integrated photonics, optimizing multiqubit operations, and creating next-generation trap designs. .

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