Wide Quantum Circuit Optimization with Topology Aware Synthesis

• URL: http://arxiv.org/abs/2206.13645v2
• Date: Mon, 8 Aug 2022 21:22:45 GMT
• Title: Wide Quantum Circuit Optimization with Topology Aware Synthesis
• Authors: Mathias Weiden, Justin Kalloor, John Kubiatowicz, Ed Younis, Costin Iancu
• Abstract summary: Unitary synthesis is an optimization technique that can achieve optimal multi-qubit gate counts while mapping quantum circuits to restrictive qubit topologies. We present TopAS, a topology aware synthesis tool built with the emphBQSKit framework that preconditions quantum circuits before mapping.
• Score: 0.8469686352132708
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unitary synthesis is an optimization technique that can achieve optimal multi-qubit gate counts while mapping quantum circuits to restrictive qubit topologies. Because synthesis algorithms are limited in scalability by their exponentially growing run time and memory requirements, application to circuits wider than 5 qubits requires divide-and-conquer partitioning of circuits into smaller components. In this work, we will explore methods to reduce the depth (program run time) and multi-qubit gate instruction count of wide (16-100 qubit) mapped quantum circuits optimized with synthesis. Reducing circuit depth and gate count directly impacts program performance and the likelihood of successful execution for quantum circuits on parallel quantum machines. We present TopAS, a topology aware synthesis tool built with the \emph{BQSKit} framework that preconditions quantum circuits before mapping. Partitioned subcircuits are optimized and fitted to sparse qubit subtopologies in a way that balances the often opposing demands of synthesis and mapping algorithms. This technique can be used to reduce the depth and gate count of wide quantum circuits mapped to the sparse qubit topologies of Google and IBM. Compared to large scale synthesis algorithms which focus on optimizing quantum circuits after mapping, TopAS is able to reduce depth by an average of 35.2% and CNOT gate count an average of 11.5% when targeting a 2D mesh topology. When compared with traditional quantum compilers using peephole optimization and mapping algorithms from the Qiskit or $t|ket\rangle$ toolkits, our approach is able to provide significant improvements in performance, reducing CNOT counts by 30.3% and depth by 38.2% on average.

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