Q-Pilot: Field Programmable Qubit Array Compilation with Flying Ancillas

TL;DR

Q-Pilot introduces flying ancillas for field programmable qubit arrays (FPQA), enabling fixed data qubits to be bridged by movable ancillas and a dynamic, high-parallelism routing flow. The framework includes a generic high-parallelism router plus domain-specific strategies for quantum simulation and QAOA, achieving substantial circuit-depth and 2-Q gate reductions across random, quantum-simulation, and QAOA benchmarks. The work demonstrates scalable compilation times up to thousands of qubits and discusses error, parallelism distribution, and hardware implications, establishing FPQA as a viable platform for near-term and scalable quantum computation. Overall, flying ancillas provide a principled routing mechanism that leverages FPQA’s reconfigurable coupling graph to dramatically reduce runtime overheads and improve circuit fidelity in practice.

Abstract

Neutral atom arrays have become a promising platform for quantum computing, especially the field programmable qubit array (FPQA) endowed with the unique capability of atom movement. This feature allows dynamic alterations in qubit connectivity during runtime, which can reduce the cost of executing long-range gates and improve parallelism. However, this added flexibility introduces new challenges in circuit compilation. Inspired by the placement and routing strategies for FPGAs, we propose to map all data qubits to fixed atoms while utilizing movable atoms to route for 2-qubit gates between data qubits. Coined flying ancillas, these mobile atoms function as ancilla qubits, dynamically generated and recycled during execution. We present Q-Pilot, a scalable compiler for FPQA employing flying ancillas to maximize circuit parallelism. For two important quantum applications, quantum simulation and the Quantum Approximate Optimization Algorithm (QAOA), we devise domain-specific routing strategies. In comparison to alternative technologies such as superconducting devices or fixed atom arrays, Q-Pilot effectively harnesses the flexibility of FPQA, achieving reductions of 1.4x, 27.7x, and 6.3x in circuit depth for 100-qubit random, quantum simulation, and QAOA circuits, respectively.

Figures (16)

• Figure 1: (a) The coupling graph of a QPU. (b) Qubit mapping and routing. The initial mapping is annotated at the beginning of each wire/qubit. A SWAP gate changes the mapping. (c) Using an ancilla and two more CNOT s to implement $\texttt{CZ}(q_0,q_2)$.
• Figure 2: Field Programmable Qubit Array (FPQA).
• Figure 3: The general case of routing CZ s with ancillas. The 3 CZ s on the right can be executed simultaneously.
• Figure 4: The flowchart of the FPQA compilation framework.
• Figure 5: Routing process of an example circuit using the generic router. The router adds as many as possible to one stage to execute them simultaneously.