Xinpeng Li

Presents QuMod, a parallel quantum job scheduling framework for modular QPUs leveraging circuit cutting to improve throughput on heterogeneous quantum hardware.

Parallel quantum job scheduling across multiple QPUs is critical for maximizing throughput in heterogeneous quantum computing environments. QuFlex introduces an adaptive circuit-cutting approach that dynamically partitions quantum circuits based on available QPU resources, enabling efficient parallel scheduling across heterogeneous quantum hardware. The framework demonstrates improved QPU utilization and reduced job completion times compared to static partitioning approaches.

Blocks composed of CNOT and Rz gates are ubiquitous in modern quantum applications such as QAOA ansatzes and quantum adders, but after compilation they often exhibit large CNOT counts or depths that lower fidelity. This paper introduces HOPPS, a SAT-based hardware-aware optimal phase polynomial synthesis algorithm that generates CNOT/Rz blocks with CNOT count or depth optimality under hardware topology constraints. To address scalability for large circuits, an iterative blockwise optimization strategy partitions large circuits into smaller blocks and optimally refines each—achieving CNOT count reductions up to 50% and depth reductions up to 57.1% when used as a peephole optimizer.

Current quantum devices face challenges with large circuits due to increasing error rates as circuit size and qubit count grow. Inspired by ancilla-assisted quantum process tomography and MUBs-based grouping for simultaneous measurement, this paper proposes a new circuit wire cutting approach that uses ancillary qubits to transform quantum input initializations into quantum output measurements, allowing multiple measurements to be grouped and executed simultaneously. The technique significantly reduces subcircuit execution overhead and classical reconstruction complexity compared to standard wire cutting.

Variational Quantum Eigensolver algorithms require many quantum circuit executions to converge, creating significant overhead on current quantum hardware. This paper accelerates VQE by reusing parameters and measurement results across iterations, reducing the number of quantum circuit executions required for convergence without sacrificing solution quality. The approach is validated on standard molecular simulation benchmarks, demonstrating meaningful reduction in quantum resource requirements.

Quantum circuit cutting enables large circuits to run on small quantum devices, but reconstructing measurement statistics requires computational resources that grow exponentially with the number of cuts. This paper introduces the concept of a golden cutting point—circuit structures that induce negligible basis components during reconstruction, allowing those downstream computations to be avoided entirely. A hypothesis-testing scheme is proposed for online detection of golden cutting points, with robustness results for low-probability test failures, and demonstrated applicability on Qiskit's Aer simulator achieving reduced wall time from identifying and avoiding obsolete measurements.