Ion-trap quantum computers are a type of quantum computing technology that uses ions (charged atoms) as qubits (quantum bits). The origin of ion-trap quantum computers can be traced back to the early 1990s when a team of scientists led by David Wineland at the National Institute of Standards and Technology (NIST) first demonstrated the ability to trap a single ion in a vacuum and use it as a qubit. Since then, ion-trap quantum computers have been extensively studied and developed, and they have emerged as one of the most promising approaches to building practical quantum computers.
Later in 1995, Ignacio Cirac and Peter Zoller proposed a trapped ion qubit system that uses ions trapped in a Paul trap as qubits. Gauss’s law states that it is impossible to trap a single charge in free space by static electric fields alone, but time-varying electromagnetic fields can be used to trap charges, allowing for the formation of a qubit register.
An ion is a charged atom. Ca+ Calcium ion and Sr+ Strontium ion are super controllable ions. We could use this quality of ions to store information to either one. Suppose Sr+ is initially in state 0. We can apply a pulse of energy to switch it to state 1. Later, for the readout, we can use a laser that would have no effect on state 0, but our ion in state 1 would absorb energy and emit it back as a particle of light which we can detect and send it around using fiber optics wires.
Ion trap technology uses electromagnetic fields to trap ions in a vacuum chamber. The ions are typically held in place by a combination of static electric fields, which provide a confining potential well, and oscillating electric fields, which provide a cooling mechanism to keep the ions at very low temperatures (near absolute zero). Cooling is necessary to prevent the ions from interacting with their environment, which can cause decoherence (the loss of quantum coherence).
Once the ions are trapped and cooled, they can be manipulated using laser beams. The lasers can be used to excite the ions to higher energy levels, which correspond to different qubit states. By carefully controlling the laser pulses, it is possible to perform quantum operations on the qubits and create entangled states between them.
One of the advantages of ion-trap quantum computers is that the ions can be individually addressed and controlled, which allows for high-fidelity quantum operations. Additionally, the long coherence times of the trapped ions make them well-suited for quantum error correction, which is a critical requirement for building practical quantum computers.
To build an ion trap QPU, scientists use a gold strip to hold the ions still by producing an electric field without physically touching them. Then a laser is used to manipulate the state of an ion to make it excited to emit a photon. We can use this structure to build a highly scalable array of ion-trapped qubits, wire them up or entangle them with desirable architecture.
The top two leading ion-trapped technologies in the quantum computing industry are:
Honeywell Quantum Solutions are now known as Quantinuum – Honeywell’s quantum computing division is one of the leading players in the ion trap technology space. They have initially developed a 10-qubit trapped-ion quantum computer, which they claim is the most powerful in the world. Honeywell has also partnered with multiple organizations to expand the use of its quantum technology, including Microsoft, JPMorgan Chase, and others. Quntinuum is progressing well with scaling up the ion-trapped QPU while focusing on Quantum Cybersecurity applications.
IonQ – IonQ is a startup that has been developing ion trap technology for quantum computing since its founding in 2015. They have initially developed a 32-qubit trapped-ion quantum processing unit, which they, too, claim is the most powerful commercial quantum computer to date. IonQ has partnerships with companies like Amazon Web Services and recently went public via a SPAC merger.
Hamed is an innovative and results-driven Chief Scientist with expertise in Quantum Science, Engineering, and AI. He has worked for leading tech companies in Silicon Valley and served as an Adjunct Professor at UC Berkeley and UCLA.