Quantum Computing
Our research in quantum computing focuses on developing practical algorithms, optimization techniques, and software tools to accelerate the transition from NISQ (Noisy Intermediate-Scale Quantum) devices to fault-tolerant quantum computers.
QuMod: Parallel Quantum Job Scheduling on Modular QPUs using Circuit Cutting
2026Presents QuMod, a parallel quantum job scheduling framework for modular QPUs leveraging circuit cutting to improve throughput on heterogeneous quantum hardware.
Efficient Transpilation of OpenQASM 3.0 Dynamic Circuits to CUDAQ: Performance and Expressiveness Advantages
2026Presents an efficient transpilation approach for converting OpenQASM 3.0 dynamic circuits to CUDAQ, demonstrating performance and expressiveness advantages.
QuFlex: Parallel Quantum Job Scheduling Using Adaptive Circuit-Cutting
2025Parallel 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.
HOPPS: Hardware-Aware Optimal Phase Polynomial Synthesis with Blockwise Optimization for Quantum Circuits
2025Blocks 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.
QGroup: Parallel Quantum Job Scheduling Using Dynamic Programming
2024Scheduling quantum circuits across multiple QPUs requires efficient algorithms that minimize idle time while respecting hardware constraints. QGroup uses dynamic programming to optimally group and schedule quantum circuits across multiple QPUs, maximizing throughput and minimizing idle time through principled combinatorial optimization. Evaluated on realistic quantum workloads, QGroup achieves improved scheduling efficiency compared to greedy and heuristic-based baseline approaches.
Efficient Circuit Wire Cutting Based on Commuting Groups
2024Current 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.
Accelerating VQE Algorithms via Parameters and Measurement Reuse
2023Variational 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.
Online Detection of Golden Circuit Cutting Points
2023Quantum 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.
Quantum Noise in the Flow of Time: A Temporal Study of the Noise in Quantum Computers
2022Quantum noise in quantum computers is not static but evolves over time, yet most error characterization treats noise as temporally fixed. This paper conducts a temporal study of noise characteristics in quantum computers, revealing how quantum noise patterns change over time and analyzing the implications for circuit fidelity and error mitigation strategies. The findings provide insights for developing more effective time-aware calibration and error mitigation approaches for near-term quantum hardware.
Pinpointing the System Reliability Degradation in NISQ Machines
2022Noise in quantum hardware causes significant reliability degradation in NISQ machines, but the systematic patterns of this degradation are not well understood. This paper investigates the sources and temporal patterns of reliability degradation in NISQ machines, identifying when and where noise causes significant performance drops in quantum circuits. The analysis provides guidance for developing error mitigation strategies targeted at the most impactful reliability degradation patterns in near-term quantum hardware.
Practical Implications of Dequantization on Machine Learning Algorithms
2022Quantum computing algorithms offer theoretical speedups for certain machine learning tasks, but dequantization results show that classical algorithms can sometimes achieve comparable performance. This paper examines the practical implications of dequantization on machine learning algorithms, providing a systematic analysis of when quantum approaches offer genuine advantages versus when classical alternatives are sufficient. The work offers guidance for practitioners on determining which ML tasks are promising candidates for quantum speedup versus those where dequantization renders quantum approaches redundant.
Approximate Quantum Circuit Reconstruction
2022Current and imminent quantum hardware lacks reliability due to noise and limited qubit counts, and quantum circuit cutting—which divides large circuits into smaller subcircuits—faces exponential classical post-processing overhead. This paper introduces approximate circuit reconstruction using a sampling-based method (MCMC) to probabilistically select high-probability bit strings during reconstruction, avoiding excessive calculations for the full probability distribution. Results show that this sampling-based post-processing holds great potential for fast and reliable circuit reconstruction in the NISQ era and beyond.
TQEA: Temporal Quantum Error Analysis
2021Quantum errors in NISQ hardware vary temporally, but most error analysis tools treat noise as time-invariant. TQEA (Temporal Quantum Error Analysis) characterizes how quantum errors evolve over time by systematically measuring and modeling the temporal dynamics of noise in quantum computers. The framework provides insights for improving error mitigation strategies that account for drift and time-varying noise characteristics, supporting progress toward more reliable quantum computing.