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Abstract

Quantum computing holds the promise of solving problems intractable for classical systems, yet current hardware is constrained by qubit scarcity, limited circuit depth, and high error rates. These challenges necessitate efficient resource management and circuit optimization to unlock quantum advantage. This thesis addresses these limitations by developing techniques that optimize quantum circuit execution, enable dynamic qubit reuse, and enhance measurement fidelity, offering practical solutions for noisy intermediate-scale quantum (NISQ) devices. The first contribution introduces Rotation-Inspired Circuit Cut Optimization (RICCO), which employs parameterized unitary rotations to align quantum states at cut locations, minimizing measurement overhead. This reduces the number of circuit executions and classical postprocessing required for accurate state reconstruction. RICCO’s effectiveness was demonstrated on the variational quantum eigensolver (VQE) to simulate the ground state energy of a hydrogen molecule. The second contribution, Graph-based Identification of Qubit Network (GidNET) for Qubit Reuse, maximizes qubit reuse by modeling circuits as directed acyclic graphs (DAGs). GidNET identifies optimal reuse sequences, reducing physical qubit requirements without compromising performance. It achieved up to a 21% reduction in circuit width and a 97.4% reduction in classical runtime on benchmark circuits, significantly enhancing scalability on qubit-limited devices compared to other methods. The third contribution develops a hybrid framework for high-fidelity generalized measurements using dynamic circuits. By combining Naimark’s dilation with binary search techniques, this method implements multiqubit positive operator-valued measures (POVMs) with midcircuit measurements, feed-forward control, and conditional readout error mitigation (CREM). It achieved a fidelity of 70.4% for two-qubit symmetric informationally complete (SIC) POVMs, outperforming standalone traditional approaches. Together, these contributions offer a comprehensive framework for circuit cutting, qubit reuse, and high-fidelity measurements. By addressing critical bottlenecks in scalable quantum computing, the methods enhance the capabilities of NISQ devices and establish a foundation for future advancements in adaptive algorithms, quantum error correction, and resource-efficient computation. These independent approaches collectively represent a significant step toward bridging the gap between current quantum hardware and the realization of fault-tolerant quantum systems.