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Physical Realizations: Ion Traps and Superconductors
In this post we look at two leading platforms for building quantum hardware as of 2020: trapped ions and superconducting circuits. Both systems have advantages and trade‑offs in coherence time, control, and scaling.
Trapped-Ion Qubits
Trapped-ion qubits use electrically charged atoms (ions) confined by electromagnetic fields. Common choices include ytterbium (Yb⁺) and calcium (Ca⁺) ions. Key features:
- Encoding: Qubit states are two stable electronic levels of the ion [1].
- Control: Laser pulses drive state transitions and entangling gates via shared motional modes [2].
- Coherence: Ions exhibit long coherence times (seconds) thanks to excellent isolation [3].
- Two-Qubit Gates: Mølmer–Sørensen and Cirac–Zoller gates use collective motion to entangle pairs [4].
- Scalability: Linear chains of up to ~20–50 ions demonstrated, with challenges in control cross-talk and mode density [5].
Major trapped‑ion efforts by companies like IonQ and academic groups at NIST and MIT have achieved high‑fidelity gates (>99%) and multi‑ion entanglement demonstrations approaching 20 qubits [6].
Superconducting Qubits
Superconducting platforms use circuits cooled to millikelvin temperatures, where superconductivity allows coherent quantum states in Josephson junctions. Typical architectures include the transmon qubit.
- Encoding: Qubit levels correspond to different charge or flux states in a nonlinear resonator [7].
- Control: Microwave pulses tune frequency and perform X/Y rotations; tunable couplers enable two‑qubit gates [8].
- Coherence: T₁ times improved from microseconds to ~100 μs between 2015–2020 through materials and design optimizations [9].
- Two-Qubit Gates: Cross-resonance and parametrically driven CZ gates reach fidelities above 99% [10].
- Scalability: IBM, Google, and Rigetti have built processors with 50–70+ qubits by 2020, leveraging lithographic fabrication and microwave control networks [11].
Comparison and Outlook
| Feature | Trapped Ions | Superconducting Circuits |
|---|---|---|
| Coherence Time | Seconds | Tens to hundreds of μs |
| Gate Fidelity | >99.9% (single), >99% (two-qubit) | >99.9% (single), ~99% (two-qubit) |
| Gate Speed | ~10–100 μs | ~10–100 ns |
| Scaling Approach | Modular traps, photonic links | 2D chip arrays, cryo‑electronics |
By mid‑2020, both platforms showed promise: ions excel in coherence and uniformity; superconductors lead in gate speed and integration. Future progress hinges on error correction and system integration.
References
[1] Wineland, D. J., et al. (1998). Experimental Issues in Coherent Quantum-State Manipulation of Trapped Atomic Ions. Journal of Research of the National Institute of Standards and Technology, 103(3), 259-328.
[2] Monroe, C., & Kim, J. (2013). Scaling the Ion Trap Quantum Processor. Science, 339(6124), 1164-1169.
[3] Harty, T. P., et al. (2014). High-Fidelity Preparation, Gates, Memory, and Readout of a Trapped-Ion Quantum Bit. Physical Review Letters, 113(22), 220501.
[4] Sørensen, A., & Mølmer, K. (1999). Quantum computation with ions in thermal motion. Physical Review Letters, 82(9), 1971-1974.
[5] Debnath, S., et al. (2016). Demonstration of a small programmable quantum computer with atomic qubits. Nature, 536(7614), 63-66.
[6] Wright, K., et al. (2019). Benchmarking an 11-qubit quantum computer. Nature Communications, 10(1), 5464.
[7] Koch, J., et al. (2007). Charge-insensitive qubit design derived from the Cooper pair box. Physical Review A, 76(4), 042319.
[8] Blais, A., et al. (2020). Circuit quantum electrodynamics. Reviews of Modern Physics, 93(2), 025005.
[9] Gambetta, J. M., et al. (2017). Building logical qubits in a superconducting quantum computing system. Nature, 519(7541), 66-69.
[10] Sheldon, S., et al. (2016). Characterizing errors on qubit operations via gate set tomography. Physical Review A, 93(1), 012301.
[11] Arute, F., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505-510.