Few technological ideas in recent memory have stirred as much excitement—or skepticism—as quantum computing. The promise is genuine: for certain computational tasks, a quantum machine can do in seconds what would take a classical supercomputer longer than the age of the universe. At the heart of realising that promise is the qubit, the quantum analogue of a classical bit, and among the many physical systems that can host a qubit, superconducting circuits have quietly become the frontrunner. They can be fabricated in the same cleanrooms used to make conventional chips, controlled with ordinary microwave electronics, and scaled up in ways that other approaches cannot yet match. This thesis traces the full arc from first principles to working code. We start with the quantum mechanics a reader needs—superposition, entanglement, measurement—and build from therethrough the physics of superconductivity to the engineering of real qubit circuits. A key character in that story is the Josephson junction: a thin sandwich of metal and insulator whose quirky nonlinear behaviour is what makes the whole enterprise of superconducting qubits possible. A major focus of this work is the surface code, a topological quantum error-correcting code widely regarded as the most promising approach for achieving fault-tolerant quantum computation. We provide a rigorous analysis of the surface code’s lattice structure, stabilizer formalism, syndrome extraction, and decoding algorithms—in particular the minimum-weight perfect matching (MWPM) algorithm. A full Python/Qiskit implementation of a distance-3 surface code is presented, including qubit initialization, syndrome measurement circuits, error simulation, and decoding logic. We close by looking ahead. Modular processors, 3D circuit integration, and the long road to fault-tolerant machines are all on the horizon, and we try to give an honest assessment of where things stand and what the real obstacles are. Throughout, the goal has been to bridge theory and practice—to be precise enough to be useful, but grounded enough to stay connected to what is actually happening in laboratories today.
Few technological ideas in recent memory have stirred as much excitement—or skepticism—as quantum computing. The promise is genuine: for certain computational tasks, a quantum machine can do in seconds what would take a classical supercomputer longer than the age of the universe. At the heart of realising that promise is the qubit, the quantum analogue of a classical bit, and among the many physical systems that can host a qubit, superconducting circuits have quietly become the frontrunner. They can be fabricated in the same cleanrooms used to make conventional chips, controlled with ordinary microwave electronics, and scaled up in ways that other approaches cannot yet match. This thesis traces the full arc from first principles to working code. We start with the quantum mechanics a reader needs—superposition, entanglement, measurement—and build from therethrough the physics of superconductivity to the engineering of real qubit circuits. A key character in that story is the Josephson junction: a thin sandwich of metal and insulator whose quirky nonlinear behaviour is what makes the whole enterprise of superconducting qubits possible. A major focus of this work is the surface code, a topological quantum error-correcting code widely regarded as the most promising approach for achieving fault-tolerant quantum computation. We provide a rigorous analysis of the surface code’s lattice structure, stabilizer formalism, syndrome extraction, and decoding algorithms—in particular the minimum-weight perfect matching (MWPM) algorithm. A full Python/Qiskit implementation of a distance-3 surface code is presented, including qubit initialization, syndrome measurement circuits, error simulation, and decoding logic. We close by looking ahead. Modular processors, 3D circuit integration, and the long road to fault-tolerant machines are all on the horizon, and we try to give an honest assessment of where things stand and what the real obstacles are. Throughout, the goal has been to bridge theory and practice—to be precise enough to be useful, but grounded enough to stay connected to what is actually happening in laboratories today.
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.
For the first time in history, we are seeing a branching point in computing paradigms with the emergence of quantum processing units (QPUs). Extracting the full potential of computation and realizing quantum algorithms with a super-polynomial speedup will most likely require major advances in quantum error correction technology. Meanwhile, achieving a computational advantage in the near term may be possible by combining multiple QPUs through circuit knitting techniques, improving the quality of solutions through error suppression and mitigation, and focusing on heuristic versions of quantum algorithms with asymptotic speedups. For this to happen, the performance of quantum computing hardware needs to improve and software needs to seamlessly integrate quantum and classical processors together to form a new architecture that we are calling quantum-centric supercomputing. In the long term, we see hardware that exploits qubit connectivity in higher than 2D topologies to realize more efficient quantum error correcting codes, modular architectures for scaling QPUs and parallelizing workloads, and software that evolves to make the intricacies of the technology invisible to the users and realize the goal of ubiquitous, frictionless quantum computing.
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.
Quantum computing is a novel paradigm for information processing that promises computational speedup for certain computational tasks while not being driven by Moore's law. Superconducting circuits based on Josephson junctions are one of the leading approaches for implementing the quantum bits, or qubits, in a quantum processor. Most superconducting qubit architectures involve circuit quantum electrodynamics, where the qubits are analogous to artificial atoms that are coupled to superconducting linear resonant cavities. This scheme allows for the readout of individual qubits, the manipulation of single-qubit states, and the generation of entanglement between qubits. The performance of current state-of-the-art systems is limited by decoherence of the qubits and associated gate errors. Nonetheless, experiments have been performed that already demonstrate a significant computational speedup compared to classical processing. Future implementations of fault-tolerant quantum computers will require the development of quantum error correction. Initial tests of elements of error correction in superconducting qubit arrays are underway.
Quantum computers can solve selected problems more efficiently than classical computers, but current devices are limited by high physical error rates. Quantum Error Correction (QEC) mitigates this by encoding many physical qubits into a logical qubit, with the surface code among the most widely studied approaches. Since syndrome measurements are produced continuously, the decoder must process them fast enough to avoid becoming a system bottleneck, making real-time decoding essential for fault-tolerant quantum computing. While most state-of-the-art real-time decoders rely on Minimum-Weight Perfect Matching (MWPM), we instead use a high-accuracy Graph Neural Network (GNN) that trades higher computational cost for lower logical error rates. To make this GNN practical for real-time decoding, we apply algorithm-hardware co-design. We reduce complexity through hardware-guided pruning and retraining, producing two hardware-friendly models that reduce parameter count by 3.1× and 6.5×. These target, respectively, an average decoding latency of one syndrome cycle and a worst-case latency within one syndrome cycle. We further reduce hardware cost using input-graph filtering and post-training quantization. Based on these optimized models, we propose an FPGA-based architecture for low-latency inference and real-time decoding. Evaluated on surface codes up to distance 7 under circuit-level noise at physical error rate p=10−3, our decoder outperforms MWPM in decoding accuracy for both average-latency and max-latency settings. It reduces logical error rate by 40% at 1μs average latency, and by 13% under a strict 1μs deadline.
Fault-tolerant quantum computing relies on Quantum Error Correction (QEC), which encodes logical qubits into data and parity qubits. Error decoding is the process of translating the measured parity bits into types and locations of errors. To prevent a backlog of errors, error decoding must be performed in real-time (i.e., within 1μs on superconducting machines). Minimum Weight Perfect Matching (MWPM) is an accurate decoding algorithm for surface code, and recent research has demonstrated real-time implementations of MWPM (RT-MWPM) for a distance of up to 9. Unfortunately, beyond d=9, the number of flipped parity bits in the syndrome, referred to as the Hamming weight of the syndrome, exceeds the capabilities of existing RT-MWPM decoders. In this work, our goal is to enable larger distance RT-MWPM decoders by using adaptive predecoding that converts high Hamming weight syndromes into low Hamming weight syndromes, which are accurately decoded by the RT-MWPM decoder.