Demonstration of two-dimensional connectivity for a scalable error-corrected ion-trap quantum processor architecture
arXiv:2406.02406 · doi:10.1103/b9s1-6r44
Abstract
A major hurdle for building a large-scale quantum computer is increasing the number of qubits while maintaining connectivity between them. In trapped-ion devices, this connectivity can be achieved by moving subregisters consisting of a few ions across the processor. Here, we focus on an architecture, which we refer to as the Quantum Spring Array (QSA), that is based on a rectangular two-dimensional lattice of linear strings of ions. Connectivity between adjacent ion strings can be controlled by adjusting their separation. This requires control of trapping potentials along two directions, one along the axis of the ion string and one radial to it. In this work, we investigate key elements of the QSA architecture along both directions: We show that the coupling rate between neighboring lattice sites increases with the number of ions per site and the motion of the coupled system can be resilient to electrical noise, both being key requisites for fast and high-fidelity quantum gate operations. The coherence of the coupling is assessed and an entangling gate between qubits stored in radially separated trapping regions is demonstrated. Moreover, we demonstrate control over radio-frequency signals to adjust the radial separation, and thus the coupling rate, between strings. We further present constructions for the implementation of parallelized, transversal gate operations, and map the QSA architecture to code primitives for fault-tolerant quantum error correction, providing a step towards a quantum processor architecture that is optimized for large-scale operation.
35 pages, 27 figures (19 in main text, 8 in appendices), 6 appendices
References in corpus (35)
- Logical quantum processor based on reconfigurable atom arrays
- Topological Quantum Distillation
- Fault-Tolerant Quantum Dynamical Decoupling
- Demonstration of the trapped-ion quantum-CCD computer architecture
- Experimental Quantum Computations on a Topologically Encoded Qubit
- A compact ion-trap quantum computing demonstrator
- A Race Track Trapped-Ion Quantum Processor
- Quantum Low-Density Parity-Check Codes
- Demonstration of fault-tolerant universal quantum gate operations
- Fault-tolerant quantum computation against biased noise
- High-Fidelity Bell-State Preparation with Ca Optical Qubits
- Large Scale Quantum Computation in an Anharmonic Linear Ion Trap
- Optimal Surface-Electrode Trap Lattices for Quantum Simulation with Trapped Ions
- Concatenated Control Sequences based on Optimized Dynamic Decoupling
- Tunable spin-spin interactions and entanglement of ions in separate wells
- Phase-modulated decoupling and error suppression in qubit-oscillator systems
- Demonstration of fault-tolerant Steane quantum error correction
- Heating and ion transport in a Y-junction surface-electrode trap
- Experimental Demonstration of Logical Magic State Distillation
- Scaling and logic in the color code on a superconducting quantum processor
- Heating of a trapped ion induced by dielectric materials
- Dynamics and control of fast ion crystal splitting in segmented Paul traps
- Quantum Magnetism of Spin-Ladder Compounds with Trapped-Ion Crystals
- A high fidelity light-shift gate for clock-state qubits
- Designing Filter Functions of Frequency-Modulated Pulses for High-Fidelity Two-Qubit Gates in Ion Chains
- Transport of multispecies ion crystals through a junction in an RF Paul trap
- Two-dimensional linear trap array for quantum information processing
- Toward a 2D Local Implementation of Quantum LDPC Codes
- Quantum LDPC Codes for Modular Architectures
- A Wavelength-Insensitive, Multispecies Entangling Gate for Group-2 Atomic Ions
- Coherent rotations of qubits within a multi-species ion-trap quantum computer
- Ideal intersections for radio-frequency trap networks
- Motional heating of spatially extended ion crystals
- High-Fidelity Transport of Trapped-Ion Qubits in a Multi-Layer Array
- Optimized continuous dynamical decoupling via differential geometry and machine learning