Instruction scheduling is a key compiler optimization in quantum computing, just as it is for classical computing. Current schedulers optimize for data parallelism by allowing simultaneous execution of instructions, as long as their qubits do not overlap. However, on many quantum hardware platforms, instructions on overlapping qubits can be executed simultaneously through global interactions. For example, while fan-out in traditional quantum circuits can only be implemented sequentially when viewed at the logical level, global interactions at the physical level allow fan-out to be achieved in one step. We leverage this simultaneous fan-out primitive to optimize circuit synthesis for NISQ (Noisy Intermediate-Scale Quantum) workloads. In addition, we introduce novel quantum memory architectures based on fan-out.Our work also addresses hardware implementation of the fan-out primitive. We perform realistic simulations for trapped ion quantum computers. We also demonstrate experimental proof-of-concept of fan-out with superconducting qubits. We perform depth (runtime) and fidelity estimation for NISQ application circuits and quantum memory architectures under realistic noise models. Our simulations indicate promising results with an asymptotic advantage in runtime, as well as 7–24% reduction in error.
This content will become publicly available on November 1, 2022
Reoptimization of Quantum Circuits via Hierarchical Synthesis
The current phase of quantum computing is in the Noisy Intermediate-Scale Quantum (NISQ) era. On NISQ devices, two-qubit gates such as CNOTs are much noisier than single-qubit gates, so it is essential to minimize their count. Quantum circuit synthesis is a process of decomposing an arbitrary unitary into a sequence of quantum gates, and can be used as an optimization tool to produce shorter circuits to improve overall circuit fidelity. However, the time-to-solution of synthesis grows exponentially with the number of qubits. As a result, synthesis is intractable for circuits on a large qubit scale. In this paper, we propose a hierarchical, block-by-block opti-mization framework, QGo, for quantum circuit optimization. Our approach allows an exponential cost optimization to scale to large circuits. QGo uses a combination of partitioning and synthesis: 1) partition the circuit into a sequence of independent circuit blocks; 2) re-generate and optimize each block using quantum synthesis; and 3) re-compose the final circuit by stitching all the blocks together. We perform our analysis and show the fidelity improvements in three different regimes: small-size circuits on real devices, medium-size circuits on noisy simulations, and large-size circuits on analytical models. Our technique can be applied after existing optimizations to more »
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- International Conference on Rebooting Computing (ICRC), 2021
- Sponsoring Org:
- National Science Foundation
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