High-precision measurements on near-term quantum devices are important for advancing quantum computing applications. The high readout errors of the current quantum computers complicate any effort to perform quantum simulations with high accuracy. This thesis implements practical techniques to reduce overhead and error, achieving accuracies suitable for quantum chemistry applications. To reduce circuit overhead, we use repeated settings and parallel Quantum Detector Tomography (QDT), and for shot overhead, we use a locally biased randomized measurement scheme. QDT is also used to mitigate readout errors and blending is used to mitigate the effects of drift. These techniques were validated by estimating the expectation values of the energy of the BODIPY molecule on a Hartree-Fock state. All experiments were performed on an IBM Eagle r3 quantum computer, where we were able to reduce the measurement errors by an order of magnitude, from 1-5% to 0.16%. These methods enable reliable quantum computations, essential for precise molecular energy calculations.
High-precision measurements on near-term quantum devices are important for advancing quantum computing applications. The high readout errors of the current quantum computers complicate any effort to perform quantum simulations with high accuracy. This thesis implements practical techniques to reduce overhead and error, achieving accuracies suitable for quantum chemistry applications. To reduce circuit overhead, we use repeated settings and parallel Quantum Detector Tomography (QDT), and for shot overhead, we use a locally biased randomized measurement scheme. QDT is also used to mitigate readout errors and blending is used to mitigate the effects of drift. These techniques were validated by estimating the expectation values of the energy of the BODIPY molecule on a Hartree-Fock state. All experiments were performed on an IBM Eagle r3 quantum computer, where we were able to reduce the measurement errors by an order of magnitude, from 1-5% to 0.16%. These methods enable reliable quantum computations, essential for precise molecular energy calculations.
Achieving high-precision measurements on near-term quantum devices is critical for advancing quantum computing applications. Quantum computers suffer from high readout errors, making quantum simulations with high accuracy requirements particularly challenging. This paper implements practical techniques to reach accuracies essential for quantum chemistry by addressing key overheads and noise sources. Specifically, we leverage locally biased random measurements for reducing shot overhead, repeated settings with parallel quantum detector tomography for reducing circuit overhead and mitigating readout errors, and blended scheduling for mitigating time-dependent noise. We demonstrate these techniques via molecular energy estimation of the BODIPY molecule on a Hartree-Fock state on an IBM Eagle r3, obtaining a reduction in measurement errors by an order of magnitude from 1-5% to 0.16%. These strategies pave the way for more reliable quantum computations, particularly for applications requiring precise molecular energy calculations.
Measurement overhead and hardware noise present fundamental obstacles to the practical realization of quantum algorithms on near-term quantum devices. In many quantum applications, the dominant computational cost arises from repeated measurements needed to extract accurate information and from handling noise effects. This dissertation develops a unified framework for efficient measurement and error mitigation in quantum algorithms.In this work, measurement is treated as a controllable and designable component across hardware, algorithmic, and statistical levels, rather than as a fixed final step. By tailoring measurement schemes and estimators, we show that substantial reductions in measurement cost can be achieved without sacrificing accuracy. This approach is developed and validated across multiple settings, from hardware-level randomized measurement implementations to algorithm-level molecular energy estimation.At the hardware level, we investigate randomized measurement schemes based on photonic metasurfaces. We find that the device itself naturally realizes the required family of measurement bases without repeated active optical reconfiguration, providing an efficient platform for randomized measurements. We analyze the effect of realistic measurement noise and develop calibration-based error mitigation techniques that suppress noise-induced bias. Numerical studies show that this approach enables accurate estimation of state properties under realistic measurement noise.At the algorithmic level, we investigate measurement-efficient quantum eigen solvers through the Non-Orthogonal Quantum Eigensolver (NOQE). We develop a detailed implementation framework for NOQE, including circuit construction and simplification, analysis of measurement complexity, and resource estimation. Building on this understanding, we incorporate randomized measurements as an alternative to standard Hadamard-test-based schemes and develop shadow-tomography-enhanced variants of NOQE that significantly reduce circuit complexity and qubit requirements while maintaining favorable sample complexity. Shadow-based error mitigation methods, including shadow distillation, further improve robustness against noise.Finally, we investigate a more fundamental redesign of the NOQE measurement task by reformulating matrix-element estimation as a quantum amplitude estimation problem. By integrating iterative quantum amplitude estimation into the NOQE framework, we replace the original incoherent sampling-based estimation with coherent information accumulation through amplitude amplification and achieve near-Heisenberg scaling in measurement complexity, improving on the standard sampling-based methods. Numerical results for molecular energy estimation demonstrate that this approach reaches chemical accuracy with fewer queries than the original NOQE.Overall, this dissertation shows that measurement overhead is not a fixed limitation, but a resource bottleneck that can be identified and reduced at multiple levels in quantum applications. Across the settings studied here, improvements are achieved at different layers, including hardware-aware randomized measurements, algorithm-level redesign of measurement protocols, estimator-level error mitigation, and statistical improvements based on coherent information accumulation. Taken together, these results provide a unified framework for understanding and reducing measurement cost under realistic noise, and offer practical guidance for the design of quantum algorithms on near-term quantum devices.
Quantum computing promises to address problems in simulation, optimization, and chemistry, but current hardware operates in the noisy intermediate-scale (NISQ) regime where coherence limits, gate imperfections, and readout errors restrict performance. Improving calibration and mitigating errors therefore remain essential. This thesis analyzes error reduction on superconducting transmon qubits through calibration, gate analysis, and circuit-level mitigation. It compares single-qubit amplitude calibration methods and analyzes two-qubit gates based on the cross-resonance interaction, identifying differences in precision, convergence, and performance. In addition, it develops Inverted-Circuit Zero-Noise-Extrapolation (IC-ZNE) which measures effective error strength using inverted circuits. Compared to standard ZNE, IC-ZNE achieves results closer to ideal values and reduces observable errors. Overall, the presented methods improve the reliability of near-term quantum processors.
The field of quantum technologies has experienced explosive growth in the past decade. As hardware capabilities have advanced, quantum computing has exhibited a scaling trend analogous to Moore's law, often referred to as Rose's law, where the number of coherently controlled qubits roughly doubles every two years. Associated with such rapid growth is an equally growing need for benchmarking tools and error handling. While advancements in quantum hardware and software have mainly concentrated on enhancing gate fidelity and qubit connectivity, the progress in addressing readout errors has been relatively limited during the same period. This thesis tries to close this gap by advancing and developing new tools for information extraction in quantum systems. We do this in two different ways: In Part II, we focus on mitigating the effect of general readout errors in quantum systems. We developed a comprehensive protocol for readout-error-mitigated state tomography (REMST) and implemented it on a superconducting qubit system. The REMST protocol was designed to mitigate not only classical readout errors but also more challenging coherent and correlated readout errors. The protocol uses only a deterministic application of single-qubit readout, making it one of the most experimentally friendly approaches available. We developed a scalable version of the REMST protocol and showed numerically that it is suitable for systems with more than 100 qubits, matching the size of current state-of-the-art systems. The REMST protocol was applied to superconducting qubit experiments, where we saw an improvement in reconstruction accuracy by a factor of up to 30. In Part III, we move our focus to optimal measurement strategies. Adaptive measurement strategies promise a quadratic improvement in the statistical fluctuation of pure and close-to-pure state reconstruction by dynamically optimizing measurement settings during sampling. We investigated whether these adaptive measurement strategies were viable in realistic experimental settings. We demonstrated both analytically and in numerical simulations that going from ideal readout to noisy readout disproportionally deteriorates the quality of the adaptive measurement strategy, to a point where no asymptotic benefit exists for adaptive measurements. These results not only deepen our understanding of readout processes in superconducting qubits but also provide concrete implementations of more robust quantum measurement and tomography protocols, offering clearer insight into the interplay between readout noise and information extraction.
Quantum computing holds the potential to address problems that are currently considered intractable for classical processors by leveraging quantum mechanical effects for simulation, optimization, and chemistry. Despite recent technological progress, quantum hardware remains in the noisy intermediate-scale regime, where coherence limits, gate imperfections, and readout errors restrict circuit depth and algorithmic reliability. Although first demonstrations of small-scale Quantum Error Correction exist, logical processors with low logical error rates are still distant. In the near term, even error-corrected qubits are expected to operate with residual error, which means that improving calibration and mitigating errors at the physical and circuit level will remain essential for obtaining meaningful results. Established calibration routines and mitigation methods such as Zero-Noise-Extrapolation (ZNE) are valuable, but need to be further improved and more rigorously validated with respect to their underlying assumptions, stability and quantitative benefits. This thesis addresses error reduction techniques on transmon qubits through calibration, gate analysis, and circuit-level mitigation. First, a systematic comparison of two single-qubit amplitude calibration techniques is carried out (cross-point and ping-pong calibration), followed by an analysis of two implementations of a two-qubit gate, based on the cross-resonance interaction. The results show that cross-point calibration offers faster convergence, higher precision and a more favorable signal to noise ratio than ping-pong calibration. Hamiltonian tomography confirms the validity of a reduced model for the cross-resonance interaction, and algorithmic tests reveal that direct and echoed CNOT gates offer complementary advantages depending on the application. Second, the thesis develops and evaluates the method of Inverted-Circuit Zero-Noise-Extrapolation (IC-ZNE). Its main idea consists of measuring the effective error strength of a circuit through inverted circuits. This quantity is then used for extrapolation towards zero noise rather than relying on simple, approximate noise scaling assumptions as in standard ZNE. This approach constitutes a novel and original method. IC-ZNE consistently produces results closer to ideal reference values than standard ZNE. The method achieves up to 77% error reduction in observable error on the tested circuits compared to standard ZNE and simple post processing filters can yield up to 39% reduction in selected cases compared to standard ZNE. Further refinements introduce noise-adaptive circuit scaling strategies that enhance mitigation outcomes without significant additional cost. The performance of these techniques is examined across several circuits and in the context of variational quantum algorithms. Applications to the Quantum Approximate Optimization Algorithm and the Variational Quantum Eigensolver demonstrate that these techniques extend the useful operating regime of current devices, although their effectiveness decreases for very deep circuits or unfavorable variational ansätze. Taken together, the calibration and mitigation techniques investigated and developed in this thesis demonstrate practical routes toward enhancing the reliability of near-term quantum processors. While they cannot replace full fault-tolerant quantum computation, they offer useful and complementary capabilities that support progress toward scalable quantum technologies.
High Resolution Image Download MS PowerPoint Slide Decoherence and gate errors severely limit the capabilities of state-of-the-art quantum computers. This work introduces a strategy for reference-state error mitigation (REM) of quantum chemistry that can be straightforwardly implemented on current and near-term devices. REM can be applied alongside existing mitigation procedures, while requiring minimal postprocessing and only one or no additional measurements. The approach is agnostic to the underlying quantum mechanical ansatz and is designed for the variational quantum eigensolver. Up to two orders-of-magnitude improvement in the computational accuracy of ground state energies of small molecules (H 2, HeH +, and LiH) is demonstrated on superconducting quantum hardware. Simulations of noisy circuits with a depth exceeding 1000 two-qubit gates are used to demonstrate the scalability of the method.