Neutral atom computing represents a promising pathway towards scalable quantum computation, but preparing the necessary initial state of the atoms presents a significant challenge. Jonas Winklmann and Martin Schulz, both from Technical University of Munich, alongside their colleagues, have developed a new algorithm, termed HiPARS, that dramatically improves the speed and efficiency of atom rearrangement. Current methods struggle with either limited parallel processing or slow execution times, hindering the creation of the precise atomic configurations required for quantum circuits. HiPARS overcomes these limitations by employing highly-parallel, composite moves, simultaneously repositioning multiple atoms across considerable distances, and demonstrates superior performance for near-term devices with up to one thousand qubits, paving the way for potentially scaling to several thousand with further refinement.

Scalable Neutral Atom Array Assembly Challenges

Research focuses on efficiently assembling large, defect-free arrays of neutral atoms, such as Rubidium or Cesium, for use as qubits in quantum computing and simulation. Creating these arrays quickly and reliably, while minimizing defects, is crucial for scaling up quantum systems. Several methods are currently employed, each with limitations. Optical tweezers are precise but slow, while optical lattices struggle with arbitrary configurations or defect correction. Spatial light modulators (SLMs) offer a promising approach, requiring efficient algorithms and fast technology.

Current research highlights the use of SLMs for atom array assembly, with a particular emphasis on algorithms to minimize sorting moves and correct defects. Techniques are explored to assemble arrays in parallel, accelerating the process, and some research aims for constant-time overhead assembly. Linear phase interpolation improves light pattern quality created by the SLM, and software tools automate the assembly process. Researchers are also leveraging artificial intelligence to accelerate assembly and achieve constant-time overhead, pushing the boundaries of neutral atom quantum computing by developing more efficient and scalable methods for assembling large, defect-free atom arrays.

Parallel Atom Rearrangement Using Composite Moves

Researchers have developed a novel algorithm to significantly improve the rearrangement of neutral atoms in quantum computing setups, a crucial step for preparing qubits for computation. Recognizing limitations in the speed and parallelizability of current rearrangement schemes, the team focused on enhancing the efficiency of moving atoms into a predetermined configuration. The study pioneers a method based on highly-parallel “composite moves”, where multiple atoms are simultaneously picked up and maneuvered towards their target positions, even over comparatively large distances. The experimental setup utilizes acousto-optic deflectors (AODs) to create movable traps for the neutral atoms, initially loaded stochastically into an array.

The team’s algorithm addresses efficiently sorting these atoms into a fully occupied sub-area. Instead of relying on simple heuristics, the researchers implemented a greedy algorithm that dynamically selects the best possible move at each step, maximizing the number of target sites filled per unit of execution time. This involves a configurable cost function that accurately estimates the time required for each potential move, considering both distance and the number of atoms involved. The core innovation lies in the algorithm’s ability to suggest both complex, parallel moves and faster, direct moves, adapting to the specific demands of each stage of the sorting process. This advancement promises to reduce the initialization time of quantum computations, shifting the bottleneck away from atom sorting and towards actual computation.

Faster Qubit Rearrangement via Composite Moves

Scientists have developed a novel rearrangement algorithm for neutral atom quantum computers, significantly improving the speed of preparing qubits for computation. This work addresses a critical bottleneck in neutral atom computing, where atoms must be precisely positioned within an array before quantum circuits can begin. The team’s algorithm focuses on maximizing parallel movement of atoms, enabling the simultaneous transfer of multiple qubits, a substantial improvement over existing methods. The algorithm operates by identifying complex, composite moves where numerous atoms are picked up and maneuvered towards their target locations in a single step.

For example, a single move within the algorithm can fill eight sites in a target area, whereas a sequential approach would require at least thirteen separate operations to achieve the same result. Measurements confirm that the new algorithm saves more than 50% of the move-execution time compared to currently available methods. The team implemented the algorithm in C++ for fast run times, with a Python wrapper for easy integration into existing quantum computing software, and made it publicly available. This breakthrough delivers a substantial improvement in the efficiency of neutral atom quantum computers, paving the way for faster and more complex quantum computations.

Scalable Neutral Atom Rearrangement Algorithm Demonstrated

This work introduces a novel algorithm designed to improve the rearrangement of neutral atoms in quantum computing systems, a crucial step in preparing qubits for computation. The team successfully demonstrated that their approach enhances parallelizability compared to existing methods, particularly for near-term devices with up to around 1000 qubits, and suggests potential scalability to several thousand qubits with further refinement. The algorithm operates by simultaneously moving multiple atoms to potentially distant target locations, streamlining the rearrangement process. Results indicate the algorithm’s success rate closely aligns with the probability of having a sufficient number of atoms in the source array, confirming its reliability when adequate resources are available. The authors note that the chosen cost function facilitates comparison with other algorithms, and that more conservative estimates would increase the reported execution times. Future work will likely focus on optimizing the algorithm for physical implementation and exploring its performance with larger qubit numbers, potentially unlocking significant advancements in neutral atom quantum computing.