Abstract: This study investigates the feasibility of exploiting macroscopic quantum tunnelling (MQT) in superconducting circuits as a constructive resource for fault-tolerant quantum computing. While MQT has historically been treated as a decoherence pathway associated with metastable escape and switching events, recent advances in circuit engineering suggest that controlled access to tunnelling dynamics can be integrated into protected qubit modalities and error-mitigating architectures. The present framework synthesizes quantum escape theory, circuit Hamiltonian models, and contemporary fault-tolerance criteria to propose an experimentally realistic pathway for harnessing MQT-driven transitions as calibrated, monitorable events that can support syndrome extraction and bias-preserving logical operations. A methods blueprint is developed that combines cryogenic device characterization, time-resolved switching statistics, and noise spectroscopy with scalable surface-code-inspired performance projections. Using literature-aligned representative device parameters, the results illustrate how barrier engineering and bias-point optimization can reshape the MQT rate landscape while maintaining coherence metrics compatible with microsecond-scale error-correction cycles. The analysis indicates that decoupling the tunnelling coordinate from dominant low-frequency noise sources and implementing fast, quantum-limited readout may enable an error budget in which MQT transitions become part of a controlled operational envelope rather than a catastrophic failure mode. The study concludes with design rules, limitations, and a roadmap for validating MQT-assisted fault-tolerant primitives in next-generation superconducting quantum processors. Keywords: macroscopic quantum tunnelling; superconducting circuits; fluxonium; fault tolerance; surface code; noise spectroscopy; Josephson junctions; quantum error correction.
Abstract: This study investigates the feasibility of exploiting macroscopic quantum tunnelling (MQT) in superconducting circuits as a constructive resource for fault-tolerant quantum computing. While MQT has historically been treated as a decoherence pathway associated with metastable escape and switching events, recent advances in circuit engineering suggest that controlled access to tunnelling dynamics can be integrated into protected qubit modalities and error-mitigating architectures. The present framework synthesizes quantum escape theory, circuit Hamiltonian models, and contemporary fault-tolerance criteria to propose an experimentally realistic pathway for harnessing MQT-driven transitions as calibrated, monitorable events that can support syndrome extraction and bias-preserving logical operations. A methods blueprint is developed that combines cryogenic device characterization, time-resolved switching statistics, and noise spectroscopy with scalable surface-code-inspired performance projections. Using literature-aligned representative device parameters, the results illustrate how barrier engineering and bias-point optimization can reshape the MQT rate landscape while maintaining coherence metrics compatible with microsecond-scale error-correction cycles. The analysis indicates that decoupling the tunnelling coordinate from dominant low-frequency noise sources and implementing fast, quantum-limited readout may enable an error budget in which MQT transitions become part of a controlled operational envelope rather than a catastrophic failure mode. The study concludes with design rules, limitations, and a roadmap for validating MQT-assisted fault-tolerant primitives in next-generation superconducting quantum processors. Keywords: macroscopic quantum tunnelling; superconducting circuits; fluxonium; fault tolerance; surface code; noise spectroscopy; Josephson junctions; quantum error correction.
Superconducting qubits are a leading platform for quantum computation, yet decoherence and scalability challenges continue to limit progress toward fault-tolerant systems. This review synthesizes research on architectures, noise sources, and error correction to clarify performance trade-offs and guide development. The transition from Transmon to Fluxonium has dramatically improved qubit performance, achieving millisecond-scale coherence times. However, this success reveals a fundamental limit of coherence now being primarily restricted by the physical properties of the materials, specifically dielectric losses occurring at the Josephson junction leads and within the surrounding materials. As overcoming these material limits becomes increasingly difficult, system-level engineering determines whether high-coherence qubits can scale.Scaling quantum systems is difficult because current cooling and control systems create too much heat and this forces a shift to integrated digital superconducting circuits and cryogenic light-based links. While these hardware challenges restrict scaling, experiments in error correction show that logical qubits can already outperform physical ones. For example, a distance-7 surface code improved logical qubit lifetime by 2.4×. Achieving practical fault tolerance, however, requires moving beyond simple error models. Future systems must combine techniques like Dynamical Decoupling and Machine Learning-based Optimal Control with dynamic classical control capable of handling complex, correlated faults such as Multi-Bit Burst Errors. Progress in materials, cryogenic system integration for control, and adaptive error management must advance together to realize scalable, high-fidelity quantum hardware.
Quantum computing holds the promise of solving problems that are intractable for classical computers, with applications ranging from optimization and materials simulation to cryptography. It achieves this by leveraging the exponentially scaling dimensionality of the space encoding quantum information and by executing algorithms on quantum registers in superposition states – a feature often referred to as quantum parallelism. However, while encoding information in computational units capable of exploring a continuous set of quantum states enables exponential scaling of computational power with system size, quantum systems are also more susceptible to errors than their classical counterparts. These errors take the form of both control errors, where miscalibration of the control pulses causes coherent errors on the qubit states, as well as incoherent errors due to energy relaxation and dephasing. Scaling quantum systems to run useful algorithms involving hundreds or thousands of logical qubits will require quantum error correction. In this approach, quantum information is redundantly encoded across an entangled state of many physical qubits. This has enabled experimental demonstrations across various platforms – including ion traps, superconducting circuits, and neutral atoms – showing that encoded quantum states can be preserved beyond the coherence times of the constituting physical qubits. However, quantum state preservation is only one component needed for scalable quantum computing. To execute algorithms, it is also necessary to implement quantum gates on and between encoded qubits, in a way that maintains fault tolerance by preserving the ability to correct errors as they occur during the computation. In the first part of this thesis, we demonstrate the implementation of an error-corrected quantum operation that entangles two logical qubits. This is achieved by performing lattice surgery on a distance-three surface code qubit, resulting in a logical Bell state encoded in distance-three repetition codes, which enable the correction of single bit-flip errors. We further introduce techniques to benchmark and characterize the performance of logical operations, including applying the protocol to arbitrary logical input states and performing logical quantum process tomography. Together, these results constitute one of the first realizations of error-corrected lattice surgery in superconducting circuits. While quantum error correction can correct errors provided they occur with sufficiently low frequency, it may fail when many qubits experience errors simultaneously. In the second part of this thesis, we therefore shift focus to a specific error mechanism known to cause correlated errors in superconducting circuits – quasiparticle-induced decoherence. In this case, various forms of energy deposition on the quantum device can break Cooper pairs into quasiparticles, which may subsequently diffuse to the tunnel junction and interact with the qubit state, thereby causing decoherence. In this thesis, we investigate the impact of quasiparticle tunneling on qubit performance, with a focus on infrared-induced quasiparticles. We compare the sensitivity of niobium-based qubits to those made from tantalum – a material that has gained interest in recent years following demonstrations of qubits with improved coherence times based on this material. Using a thermal radiator source, we show that the performance of tantalum qubits may be limited by infrared-induced quasiparticles in typical superconducting circuit experimental setups. By employing both ambient and in-line infrared filters, we investigate the effectiveness of different filtering strategies and develop best practices for engineering cryogenic environments for superconducting quantum processors. The results presented in this thesis contribute to the advancement of quantum computing based on superconducting circuits by implementing and characterizing quantum error-corrected operations, and by investigating quasiparticles as a source of decoherence, along with strategies to improve infrared filtering and signal conditioning in cryogenic environments for future fault-tolerant quantum computing.
Quantum error correction (QEC) represents the cornerstone technology for realizing fault-tolerant quantum computing, addressing the fundamental challenge of quantum state decoherence in noisy intermediate-scale quantum (NISQ) devices. This comprehensive review examines recent advances in QEC implementations from 2020-2024, spanning theoretical foundations to practical hardware demonstrations. We analyze the evolution from pioneering stabilizer codes to modern topological approaches, with particular emphasis on surface codes and low-density parity-check (LDPC) codes that have demonstrated below-threshold error correction in superconducting and trapped-ion systems. The paper provides a comparative analysis of classical and machine learning-based decoder algorithms, evaluating their performance through complexity analysis and threshold comparisons in real-time error correction scenarios. Through examination of breakthrough experiments by Google, IBM, Quantinuum, and emerging platforms, we assess the current state of fault-tolerant quantum computing and identify critical engineering challenges including correlated noise models, cryogenic control systems, and scalable logical gate implementations. Our quantitative analysis reveals that while significant progress has been made toward practical fault-tolerance thresholds, achieving the resource efficiency and reliability required for large-scale quantum computation remains challenging. We provide detailed resource estimates for key applications including Shor's algorithm and optimization problems, along with analysis of industry roadmaps toward thousand-logical-qubit systems. This review serves as a comprehensive guide for researchers and engineers working toward the next generation of fault-tolerant quantum computers, with particular focus on information-theoretic perspectives relevant to quantum information entropy and syndrome processing.
The pursuit of a universal, fault-tolerant quantum computer has transformed quantum computing from a predominantly theoretical discipline into a rapidly diversifying hardware ecosystem. This review presents a comprehensive and technically grounded analysis of the principal physical qubit modalities: superconducting circuits, trapped ions, photonic platforms, neutral atoms, semiconductor spin qubits, and topological approaches, focusing on their underlying operating principles, performance characteristics, and system-level constraints. We examine how these qubit technologies are embedded within scalable quantum processor architectures, including control and readout infrastructures, connectivity topologies, and quantum error correction strategies. The review provides a comparative study of key performance metrics, such as coherence times, gate fidelities, error rates, and scalability, reveals fundamental trade-offs between speed, robustness, and operational complexity that shape distinct technological roadmaps and target applications. We further highlight the ongoing transition from noisy intermediate-scale quantum (NISQ) devices toward architectures centred on logical qubits, driven by advances in materials engineering, precision control, and quantum information theory. Together, these developments sketch the emerging routes toward fault-tolerant quantum computing and practical quantum advantage, while offering readers a coherent entry point and reference guide to the current quantum hardware landscape.
Quantum error correction (QEC) represents the cornerstone technology for realizing fault-tolerant quantum computing, addressing the fundamental challenge of quantum state decoherence in noisy intermediate-scale quantum (NISQ) devices. This comprehensive review examines recent advances in QEC implementations from 2020-2024, spanning theoretical foundations to practical hardware demonstrations. We analyze the evolution from pioneering stabilizer codes to modern topological approaches, with particular emphasis on surface codes and low-density parity-check (LDPC) codes that have demonstrated below-threshold error correction in superconducting and trapped-ion systems. The paper provides a comparative analysis of classical and machine learning-based decoder algorithms, evaluating their performance through complexity analysis and threshold comparisons in real-time error correction scenarios. Through examination of breakthrough experiments by Google, IBM, Quantinuum, and emerging platforms, we assess the current state of fault-tolerant quantum computing and identify critical engineering challenges including correlated noise models, cryogenic control systems, and scalable logical gate implementations. Our quantitative analysis reveals that while significant progress has been made toward practical fault-tolerance thresholds, achieving the resource efficiency and reliability required for large-scale quantum computation remains challenging. We provide detailed resource estimates for key applications including Shor's algorithm and optimization problems, along with analysis of industry roadmaps toward thousand-logical-qubit systems. This review serves as a comprehensive guide for researchers and engineers working toward the next generation of fault-tolerant quantum computers, with particular focus on information-theoretic perspectives relevant to quantum information entropy and syndrome processing.
Quantum processing units (QPUs) built on superconducting Josephson junctions remain the most industrially mature route to fault-tolerant quantum computing, but the field has moved substantially since early 2025. This paper provides an updated overview of Josephson-junction QPUs, grounded in the quantum-mechanical principles, superposition, entanglement, and decoherence, that any qubit implementation must contend with, and in the physics of Cooper pairing and quantum tunneling that make a Josephson junction behave as a controllable qubit. We examine the engineering challenges of scaling these devices, including crosstalk, classical control-interface bottlenecks, and quantum error correction, and discuss the first below-threshold surface-code demonstrations on superconducting hardware. We survey recent materials advances, near-term computational results, and applications beyond computing, and compare Josephson-junction QPUs against trapped-ion, photonic, and neutral-atom alternatives, the latter having emerged as a serious fourth architecture since our original analysis. We close with the current public roadmaps toward fault-tolerant superconducting quantum computers and a calibrated assessment of how much progress those roadmaps still require.