The computational power of a quantum computer is limited by the number of qubits available for information processing. Increasing this number within a single device is difficult; it is widely accepted that distributed modular architectures are the solution to large scale quantum computing. The major challenge in implementing such architectures is the need to exchange quantum information between modules. In this work, we show that a distributed quantum computing architecture with limited capacity to exchange information between modules can accurately solve quantum computational problems. Using the example of a variational quantum eignesolver with an ansatz designed for a two-module (dual-core) architecture, we show that three inter-module operations provide a significant advantage over no inter-module (or serially executed) operations. These results provide a strong indication that near-term modular quantum processors can be an effective alternative to their monolithic counterparts.

Interconnecting clusters of qubits will be an essential element of scaling up future quantum computers. Operations between quantum processing units (QPUs) are usually significantly slower and costlier than those within a single QPU, so usage of the interconnect must be carefully managed. This is loosely analogous to the need to manage shared caches or memory in classical multi-CPU machines. Unlike classical clusters, however, quantum data is subject to the no-cloning theorem, which necessitates a rethinking of cache coherency strategies. Here, we consider a simple strategy of using EPR-mediated remote gates and teleporting qubits between clusters as necessary. Crucially, we develop optimizations at compile-time that leverage local structure in a quantum circuit, so as to minimize inter-cluster operations at runtime. We benchmark our approach against existing quantum compilation and optimization routines, and find significant improvements in circuit depth and interconnect usage.

No abstract

No abstract