Superconducting circuits form one of the most promising hardware platforms for building a quantum computer. As the quantum computing system gets more complex as we increase the size, employing simple circuit designs and control strategies can make the task of building a large scale quantum computer easier. This thesis describes a novel control strategy that utilises spin-echo techniques and native interaction in superconducting circuits, which reduces the cost of calibrating pulsed two-qubit gates. Spin-echo pulses are used to rescale the always-on Hamiltonian, and the timings of spin-echo pulses encode the effective coupling strengths. In collaboration with the NMR group in Oxford, two methods for scaling this technique to large numbers of qubits were explored. In the first approach, pulse sequences for an all-to-all coupled system are obtained numerically using linear programming, and it finds the time-optimal solution for up to twenty qubits and the near time-optimal solution for up to hundreds of qubits. Another approach based on graph colouring finds the near time-optimal pulse sequence analytically, allowing pulse sequences for any number of qubits. An idea based on the Hamiltonian rescaling technique was applied to implementing the variational quantum eigensolver algorithm and error mitigation on two superconducting qubits. In contrast to previous studies, the residual dispersive coupling between qubits was used for computation instead of regarding it as a source of error. Lastly, the detailed dynamics of the residual dispersive coupling in superconducting circuits were investigated to predict the practicality of spin-echo-based quantum computing on superconducting circuits. The Hamiltonian rescaling protocol assumes the always-on coupling to be diagonal, such as Ising Hamiltonian, but deviation from the pure Ising interaction was observed in the strongly coupled superconducting qubits. The origin of the deviation was identified analytically, and the circuit design criteria to suppress the deviation are presented.
Superconducting circuits form one of the most promising hardware platforms for building a quantum computer. As the quantum computing system gets more complex as we increase the size, employing simple circuit designs and control strategies can make the task of building a large scale quantum computer easier. This thesis describes a novel control strategy that utilises spin-echo techniques and native interaction in superconducting circuits, which reduces the cost of calibrating pulsed two-qubit gates. Spin-echo pulses are used to rescale the always-on Hamiltonian, and the timings of spin-echo pulses encode the effective coupling strengths. In collaboration with the NMR group in Oxford, two methods for scaling this technique to large numbers of qubits were explored. In the first approach, pulse sequences for an all-to-all coupled system are obtained numerically using linear programming, and it finds the time-optimal solution for up to twenty qubits and the near time-optimal solution for up to hundreds of qubits. Another approach based on graph colouring finds the near time-optimal pulse sequence analytically, allowing pulse sequences for any number of qubits. An idea based on the Hamiltonian rescaling technique was applied to implementing the variational quantum eigensolver algorithm and error mitigation on two superconducting qubits. In contrast to previous studies, the residual dispersive coupling between qubits was used for computation instead of regarding it as a source of error. Lastly, the detailed dynamics of the residual dispersive coupling in superconducting circuits were investigated to predict the practicality of spin-echo-based quantum computing on superconducting circuits. The Hamiltonian rescaling protocol assumes the always-on coupling to be diagonal, such as Ising Hamiltonian, but deviation from the pure Ising interaction was observed in the strongly coupled superconducting qubits. The origin of the deviation was identified analytically, and the circuit design criteria to suppress the deviation are presented.
Quantum interactions are strikingly different from their classical counterparts, and they lead to counter-intuitive effects such as quantum entanglement. The advances in quantum science and technology over the past two decades have shown that such interactions can be harnessed in quantum devices to address hard computational problems. The field of quantum computation has emerged, and superconducting circuits have become a leading platform for quantum processors. Because of the large design space and the difficulty to simulate design prototypes on a classical computer, a key challenge in quantum processor engineering is to translate desired quantum characteristics into a circuit design. A particular obstacle has been the implementation of interactions between multiple quantum bits (qubits) in one coupling mechanism. Multi-qubit interactions are necessary for a number of applications in quantum annealing, analog quantum simulation, and digital quantum computation. In this work, we develop classical and quantum computational methods to address the challenge of implementing such interactions. We present a computational method for automated design of superconducting circuits. Our method performs a parallelized, closed-loop optimization to design superconducting circuit diagrams that match predefined properties, such as dispersion features and noise sensitivities. We employ it to design a coupler for superconducting flux qubits that mediates 4-qubit interactions. The coupler circuit is then realized experimentally. We find that it has the desired ground state dispersion properties versus flux and that it does not adversely affect the lifetime of a connected flux qubit. Moreover, the coupling concept can be extended by systematically adding loops to the coupler. In a second iteration, we implement a circuit architecture in which the coupler mediates interactions between three flux qubits. The system Hamiltonian is estimated via multi-qubit pulse sequences that implement Ramsey-type interferometry between all neighboring excitation manifolds. We verify that the multi-qubit system exhibits a 3-qubit coupling that is coherently tunable, which is important for practical applications. Finally, we address the challenge that complex multi-qubit circuits are difficult to simulate accurately on a classical computer as they become larger. As a solution, we propose quantum algorithms to design and test the performance of next-generation quantum hardware on a quantum computer. We have therefore addressed the challenge of quantum device design both from a classical optimization and from a quantum computational angle.
An exquisite control over the dynamics of a quantum system is essential for realizing any quantum technology. This thesis presents some practical and efficient strategies for coherent quantum control: a paradigm for controlling quantum dynamics using electromagnetic fields. Coherent control is a common framework for describing the physical implementation of quantum logic gates in several quantum information processing platforms including superconducting qubits and trapped ions. For the purpose of demonstration, however, liquid-state Nuclear Magnetic Resonance (NMR) is used in this thesis as it is an ideal test-bed for developing, testing and benchmarking coherent control tools and techniques. Coherent control was revitalised in 2005 by the development of Gradient Ascent Pulse Engineering (GRAPE), a powerful optimal control technique that is widely used for designing shaped pulses for a variety of tasks. GRAPE, like any other closed-loop optimal control algorithm, requires multiple evaluations of the computationally expensive matrix exponential and the full time-evolution propagator. To this end, I present strategies to sidestep the explicit evaluation of matrix exponentials, thereby offering substantial speed-ups over conventional implementations of the GRAPE algorithm. The results are demonstrated experimentally in NMR, but also in simulations of ultracold molecules and superconducting qubits. Beyond GRAPE, this thesis also considers methods for designing spin-echo sequences for implementing any arbitrary network of controlled-Z gates in a system of qubits with zz-couplings. First, a method based on linear-programming provides near time-optimal sequences in fully-coupled systems having up to a few hundred qubits. Further, an analytic method based on graph colouring finds near time-optimal spin echo sequences in engineered systems with any number of qubits having only nearest and next-nearest neighbour-couplings.
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.
The discovery of quantum mechanics in the 20th century forms the basis of many of the technologies that define our lives today. The ability to engineer and manipulate individual quantum systems -- even create artificial atoms -- promises a similar revolutionary leap in technology. A quantum technology of particular interest is quantum computing, which has the potential to solve problems that are intractable for classical computers, opening up new domains of computation. Meanwhile, an attractive approach to creating engineered quantum systems is circuit quantum electrodynamics (cQED). Where research initially focused on understanding the physics of cQED devices, focus has shifted to building systems capable of performing useful computations. However, this remains extremely challenging, in part due to the inherently fragile nature of the individual quantum bits, but also due to difficulties in controlling and scaling up these systems. This thesis focuses on the control aspects of building an extensible full-stack quantum computer based on superconducting transmon qubits. We define the demonstration of quantum fault-tolerance as our target application to give focus to our efforts. The QuSurf architecture for a full-stack quantum computer presented in this thesis is designed with this application in mind. We provide a detailed study of the error sources present in this system and give an overview of the relevant characterization techniques. In the second part of this thesis, we address several key challenges in the control of a quantum computer. To realize high-fidelity coherence limited gates, we present a novel tuneup protocol that achieves a tenfold speedup over the state-of-the-art. This is realized by eliminating the need for qubit initialization. We demonstrate this protocol by calibrating single-qubit gates to a coherence limited Clifford fidelity of 99.9% in one minute. Performing repeated parity checks, as is required for quantum error correction, requires reusing qubits quickly after they have been measured. By introducing a numerically optimized depletion pulse we are able to speeds up the depletion of measurement photons in a readout resonator without having to rely on specific symmetry conditions. Using this technique speed up photon depletion by more than six inverse resonator linewidths, reducing the error rate in an emulated ancilla parity check by a factor 75. Flux-pulsing based two-qubit gates are the fastest two-qubit gates. However, they are also very technically demanding. The key challenge in performing these gates is addressing the distortions that control signals experience as they traverse various electrical components. We have developed Cryoscope (short for cryogenic oscilloscope) to characterize and correct these distortions. Cryoscope is an in-situ technique that uses the qubit to sample control pulses of arbitrary shape. Even when correcting distortions to within sim0.1 two-qubit gates are history-dependent due to the long timescale upon which some of these distortions act. We have invented Net-Zero, a new type of flux-pulsing based two-qubit gate, to address this problem. It makes use of a symmetry condition of the transmon to have net-zero integral, making the gate resilient to long-timescale distortions. The gate suppresses leakage out of the computational subspace to 0.1% by making use of leakage interference and has a built-in echo effect that enhances the coherence of the gate, achieving a two-qubit gate fidelity of 99.1%. Custom software is required to perform the physics experiments needed to build and operate a quantum computer. PycQED is an open-source software framework we have developed for this purpose. We discuss the design choices and concepts of PycQED before turning our focus to characterization and calibration. Here we introduced dependency graphs as a useful abstraction and system emulation as an essential development tool for automating the characterization and calibration process. We conclude the thesis by reflecting on the limitations of our architecture and providing an outlook on the grand challenges of building a useful kilo-qubit sized quantum computer. We define these challenges as The Application Problem, The Fabrication Problem, and The Calibration Problem.
This thesis explores novel strategies for advancing superconducting quantum computing through a series of experimental investigations and device innovations. Grounded in the development of circuit quantum electrodynamics, the work addresses the challenges posed by the limitations of coherent control, coherence time and scaling of superconducting qubits. Focusing on transmon qubits, the thesis first reviews the foundational principles of circuit quantum electrodynamics and the critical role of the transmon in achieving robust qubit performance. Three experimental studies form the core of this work. The first study demonstrates a universal quantum gate set for strongly coupled transmons. By exploiting the residual ZZ interaction, the work introduces a novel two-axis gate protocol for single-qubit operations and implements both CZ and CNOT gates with high fidelities, highlighting the potential for fast, low-error quantum operations in the strong coupling regime. The second study investigates a dual-rail qubit architecture using parametrically coupled transmons, showcasing a hardware-efficient approach to quantum error correction. This experiment converts single-photon loss errors into detectable erasures and leverages mid-circuit detection to enhance qubit coherence, thereby advancing fault-tolerant quantum computing schemes in the NISQ era. The final study focuses on the development of merged-element transmons (MET) and their FinMET variants. By integrating the Josephson junction and shunt capacitor into a single trilayer device, these innovations achieve a dramatic reduction in device footprint while addressing loss channels through advanced materials engineering, ultimately paving the way for scalable, high-coherence quantum circuits. Collectively, these contributions demonstrate significant progress in quantum gate design, error mitigation, and qubit architecture, offering promising routes toward the realization of largescale, fault-tolerant quantum computers
Quantum computing promises to revolutionize several scientific and technological domains through fundamentally new ways of processing information. Among its most compelling applications is digital quantum simulation, where quantum computers are used to replicate the behavior of other quantum systems. This could enable the study of problems that are otherwise intractable on classical computers, transforming fields such as quantum chemistry, condensed matter physics, and materials science. Despite this potential, realizations of practical quantum advantage for relevant problems are hindered by imperfections of current devices. This also affects quantum hardware based on superconducting circuits which is among the most advanced and scalable platforms. The envisaged long-term solution of fault-tolerant quantum computers that correct their own errors remains out of reach mainly due to the associated qubit number overhead. As a result, the field has developed strategies that combine quantum and classical resources, exploit hardware-native operations, and employ error mitigation techniques to extract meaningful results from noisy data. This doctoral thesis contributes to this broader effort by exploring methods for advancing quantum simulation across the full computational stack, including hardware-level innovations, refined techniques for noise modeling and error mitigation, and algorithmic improvements enabled by efficient measurement processing.