In this thesis, we aim to answer one research question: What is the algorithmic role of classical computing in scaling up quantum computing applicability? Quantum computers have promising potential in offering more efficient solutions to some of the most demanding computational problems in the industry. There has been phenomenal progress in the theory and realization of quantum computers in the last few decades. However, quantum computing is a nascent field of technology and scaling up its computing capacity remains impeded by many open engineering/scientific challenges. One emerging solution to alleviate some of the present limitations is finding clever use of classical pre- and post-processing techniques in tandem with quantum algorithms. This helps to advantageously use scarce quantum computing resources for only a portion of a problem that benefits the most from a quantum computer. In other words, it leads to a hybrid framework in which quantum computers are considered special-purpose co-processors for accelerating specific computational tasks. We aim to develop a hybrid theoretical framework for the role of classical algorithms in scaling up quantum computing. We investigate this by focusing on three distinct limitations. We review each limitation in the context of a different application and propose an original hybrid classical-quantum solution to it. First, we focus on classical algorithms' role in a more resource-efficient compilation and embedding of quantum programs. We propose a classical algorithm that helps to embed larger optimization problems in a quantum annealer. Then we explore how a problem decomposition technique and preprocessing of the input problem can help solve significantly larger optimization problems on a quantum computer. Finally, we propose a completely hybrid quantum algorithm for preparing ground states of quantum Hamiltonians. We achieve orders of magnitude faster convergence by combining classical optimization with variational quantum state preparation.
In this thesis, we aim to answer one research question: What is the algorithmic role of classical computing in scaling up quantum computing applicability? Quantum computers have promising potential in offering more efficient solutions to some of the most demanding computational problems in the industry. There has been phenomenal progress in the theory and realization of quantum computers in the last few decades. However, quantum computing is a nascent field of technology and scaling up its computing capacity remains impeded by many open engineering/scientific challenges. One emerging solution to alleviate some of the present limitations is finding clever use of classical pre- and post-processing techniques in tandem with quantum algorithms. This helps to advantageously use scarce quantum computing resources for only a portion of a problem that benefits the most from a quantum computer. In other words, it leads to a hybrid framework in which quantum computers are considered special-purpose co-processors for accelerating specific computational tasks. We aim to develop a hybrid theoretical framework for the role of classical algorithms in scaling up quantum computing. We investigate this by focusing on three distinct limitations. We review each limitation in the context of a different application and propose an original hybrid classical-quantum solution to it. First, we focus on classical algorithms' role in a more resource-efficient compilation and embedding of quantum programs. We propose a classical algorithm that helps to embed larger optimization problems in a quantum annealer. Then we explore how a problem decomposition technique and preprocessing of the input problem can help solve significantly larger optimization problems on a quantum computer. Finally, we propose a completely hybrid quantum algorithm for preparing ground states of quantum Hamiltonians. We achieve orders of magnitude faster convergence by combining classical optimization with variational quantum state preparation.
Computational methods are central to modern physics, yet many problems, especially those involving Quantum Many-Body Systems (QMBSs), remain challenging at scale. The core difficulty of simulating QMBSs is the exponential growth of the solution space with system size. This renders exact treatments intractable and pushes approximate methods to their limits when tackling two- and higher-dimensional systems and long-time dynamics. Quantum computers offer a quantum-native platform for representing and manipulating such systems. Recent algorithmic and technological advancements have enabled experiments on quantum processors in the quantum utility regime, a scale at which exact classical reproduction is no longer feasible. This represents the first step towards realizing quantum advantage, where quantum computers solve specific, practically relevant problems more efficiently than any classical approach. While advancing towards the goal of quantum advantage, useful computations on quantum processors are expected to be realised through hybrid quantum-classical algorithms, which combine quantum experiments and classical computational resources. A prominent example are variational quantum algorithms, where a Parametrized Quantum Circuit (PQC) prepares a trial quantum state whose parameters are iteratively updated by a classical optimisation routine to minimize a target cost function (e.g., the energy of a target Hamiltonian). However, the optimisation of such PQCs is affected by trainability issues, such as barren plateaus in the parameter landscape, which can render the optimisation increasingly difficult for larger system sizes. This motivates the development of novel hybrid approaches overcoming the limits of PQCs. In this thesis, we introduce a new class of approaches based on hybrid Tensor Networks (TNs). A hybrid TN is obtained by integrating quantum states prepared on a quantum processor within TNs -- one of the state-of-the-art approaches for simulating quantum systems. We first show how hybrid TNs can be used for ground-state searches within a variational setting. Then, we further extend the framework to non-variational hybrid TN ansätze for which the quantum tensors are prepared via quantum annealing and subsequently post-processed by classical TNs. In the context of quantum machine learning, which also relies on PQCs, we further investigate heuristic best practices that mitigate the same exponential concentration effects in kernel-based models for anomaly detection. Another class of hybrid algorithms are quantum error-mitigation workflows, where classical post-processing is used to reconstruct expectation values from noise-affected circuit executions. Such methods generally feature a trade-off between the bias and the variance: the price to pay for mitigating noise in a bias-free fashion is that the number of measurements usually grows exponentially with the depth of the circuit in order to achieve a fixed accuracy. In this thesis, we develop a low-resource error-mitigation strategy that reduces the measurement overhead in practical digital Hamiltonian-simulation workflows. We apply this method to Trotterized dynamics in a lattice-gauge-theory model, enabling the extraction of expectation values with improved accuracy at time-scales that allow the observation of post-collision scattering dynamics.
Quantum computers hold the potential of solving problems that are intractable using classical means. Yet, to date no quantum computer has actually solved a problem of general interest that could not have been solved otherwise. Bridging this gap from promised theoretical usefulness to practical relevance is an interdisciplinary challenge, requiring experimental progress, solutions of classical software challenges as well as advances in quantum algorithm design. It is these final two points in which this thesis aims to contribute. One of the most promising quantum computing architectures is based on trapping ions in surface-electrode ion-traps. In order to productively operate such trapped-ion quantum computers, we develop and benchmark a compiler that optimizes ion shuttling sequences in order to efficiently map abstract quantum circuits onto a physical trapped-ion chip. This is a necessary component of the infrastructure of a quantum computing setup, however it is alone not sufficient to execute arbitrary quantum algorithms. In particular, a very promising class of quantum algorithms are so-called hybrid quantum-classical algorithms which leverage the computational resources of both quantum and classical computers. To effectively implement such algorithms, a suitable software setup is necessary to allow for tight interplay between the two types of computers without wasting precious coherence time. To solve this challenge, we propose, implement and benchmark our setup for Interpreted Quantum-Classical Computations (IQCCs), demonstrating its feasibility for trapped-ion quantum computers. For the second part of this thesis we focus on the development of quantum algorithms that make use of this setup. As a first application, we investigate quantum simulations of gauge theories. Such theories are prevalent throughout modern physics and are characterized by local constraints that the dynamics are subject to. Errors in noisy simulations of such theories results in these constraints being violated and the simulations becoming unphysical. We show that this problem can be remedied by measuring gauge operators, decoding possible errors on a classical computer and actively correcting them, which reduces simulation errors. Additionally we interpret the scheme as a sympathetic cooling setup, which allows thermal control of the simulation. We finally turn to the task of preparing ground states of frustration-free Hamiltonians in quantum computers. Studying ground states of various systems is of paramount importance for solving optimization problems and understanding phenomena such as magnetism, superconductivity and superfluidity. We frame the ground-state preparation problem as an error correction problem, wherein energy is measured locally, measurements are decoded and corrections are applied to reduce the energy. In physically relevant examples we show up to exponentially improved runtimes over previous, similar algorithms and give a general run-time bound in terms of the Hamiltonian gap.
Hybrid computing models, which integrate both classical and quantum systems, represent a promising frontier in enhancing computational power and efficiency. Classical computing systems, while highly efficient for a broad range of tasks, encounter significant limitations when addressing complex, large-scale problems such as optimization, cryptography, and molecular simulations. Quantum computing, on the other hand, offers exponential speedup for specific algorithms, particularly in areas like factorization, search, and simulation of quantum systems. However, quantum systems are not yet mature enough to replace classical systems entirely, due to issues like error rates, qubit coherence, and the need for cryogenic environments. As a result, hybrid models are emerging as the most viable solution, combining the strengths of both paradigms to tackle computationally intensive tasks more efficiently. This paper provides a comprehensive analysis of the development and application of hybrid computing models. It explores how these models leverage the parallel processing power of quantum systems while utilizing the robustness and data handling capabilities of classical systems. We discuss various architectures proposed for hybrid computing, focusing on their scalability, efficiency, and real-world applications across industries such as healthcare, finance, cryptography, and artificial intelligence. Additionally, we examine the challenges involved in integrating classical and quantum systems, including synchronization, resource management, and algorithm development. The paper concludes with future directions for hybrid computing, emphasizing the potential for these models to revolutionize fields that rely on high-performance computing by offering unprecedented computational capabilities. In summary, hybrid computing models stand at the intersection of classical and quantum technologies, providing a synergistic approach to solving some of the most challenging computational problems faced today.
Quantum computers acting as ML accelerators have attracted considerable interest as a promising avenue for addressing the limitations of classical computing confronted with solving complex, high-dimensional problems and handling large data sets. The most exciting aspect of quantum computing lies in its quantum advantage via techniques such as quantum parallelism, superposition, and entanglement which allows not only for rapid data processing, but also a means to resolve complex optimization problems beyond the capacity of classical algorithms. Nevertheless, the usage of quantum computing in machine learning is still a fledgling domain, with challenges concerning quantum noise, hardware restrictions, and algorithmic development. In particular, this research is concerned with the development of and study on hybrid quantum-classical algorithms that target to improve ML models in terms of computational capabilities by making use of the best of both worlds. To address this, researchers are developing hybrid algorithms that leverage the strengths of quantum computing for specific tasks, like optimization and pattern recognition, while still relying on classical computing for more flexible and larger-scale processing, ultimately achieving more efficient machine learning systems that can handle large datasets and complex problem-solving more effectively. It includes potential hybrid approaches like quantum-enhanced optimization algorithms, quantum neural networks, and quantum support vector machines, and examines how such algorithms can be used for practical ML problems including data classification, regression and clustering. Furthermore, the paper proposes that hybrid quantum algorithms offer several advantages over their classical counterparts, such as faster convergence rates, higher accuracy in high-dimensional spaces, and improved handling of non-linear and complex problem landscapes. Key advancements in the area of data processing speed and efficiency in problem-solving with and without quantum in the machine learning workflow are also highlighted in the research through comparative analysis and simulation studies. Lastly, the paper concludes by outlining the obstacles yet to be surmounted and the opportunities that lie ahead for hybrid quantum-classical algorithms, such as further improvement of quantum hardware and error mitigation methods, as well as the design of new algorithmic paradigms that seamlessly integrates the classical and quantum computing spaces.
One of the primary focuses in the rapidly changing scenery of quantum computing is one of the most promising methodologies for transgressing the current feat of quantum technologies: a better mix of quantum and classical paradigms of computations.This paper suggests a new hybrid quantum-classical processor architecture that augments the computational speed and enhances efficiency by utilizing powers emanating from the quantum and classical processing units.The architecture proposed in the study incorporates quantum processing units with classical processing units, with CPUs interfaced tightly to support dynamic task allocation with a view toward computational requirements.Hybrid processor design caters to the performance of quantum algorithms that usually involve classical preprocessing and postprocessing, such as the Variational Quantum Eigensolver and Quantum Approximate Optimization Algorithm.These algorithms are essential for any complicated optimization, the simulation of quantum systems, and applications in machine learning.We provide an insightful processor architecture design, understanding the communication protocol details between QPU and CPU, the scheduling mechanism for hybrid tasks, and the optimization techniques employed to minimize data transfer overhead while maximizing computational throughput.A lot of these implementation challenges, like quantum error correction, managing the coherence time, and classical-quantum data conversion, are something that innovations in hardware design and software algorithms can achieve.In this paper, the performance of the hybrid processor is, for the first time, fully benchmarked based on simulation and experimental setups.Resultsshow that this hybrid approach outperforms standalone quantum and classical processors on specific computational tasks by ten times in optimization problems and five times for machine learning.These include case studies showing that for some problems, the processor can solve real-world problems much more efficiently than any existing quantum or classical system.Such case studies include quantum chemistry simulations, cryptographic tasks, and training machine learning models.We turn to the scalability and flexibility of the hybrid processor to extend the horizon of these case studies and to bridge the gap between the current quantum computing capabilities and the race toward fully functional quantum computers.This new hybrid quantum-classical processor architecture makes significant progress in quantum computing by providing a pragmatic, workable solution for harnessing quantum mechanics but using the maturity of classical computing.The results show how to proceed with the research involving the hybrid system and the many potential applications of such systems across optimization, cryptography, and artificial intelligence.This study is significant as it offers a practical approach to overcoming the limitations of current quantum and classical processors, providing a pathway to more efficient and scalable computational solutions.
Quantum computing has emerged as a transformative paradigm, leveraging principles of quantum mechanics to tackle computationally intractable problems. This manuscript explores the potential of quantum computing for solving complex engineering problems—specifically optimization, simulation, and data analysis tasks—using technologies and methodologies available up to 2022. We examine both gate-based and quantum annealing approaches, outline experimental frameworks, and compare performance against classical counterparts. Results from simulated benchmark problems demonstrate promising speedups in select cases, though practical adoption is constrained by hardware limitations such as qubit coherence times and noise. The study concludes by highlighting avenues for integrating hybrid quantum-classical workflows in engineering, along with a discussion of current constraints and future directions. Over the past decade, research efforts have focused on understanding how quantum phenomena like superposition and entanglement can be harnessed to realize computational advantages for specific problem classes. In particular, combinatorial optimization problems—ubiquitous in engineering design and logistics—offer fertile ground for quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing. Additionally, quantum simulation techniques, including the Variational Quantum Eigensolver (VQE), promise to accelerate the modeling of physical systems at the molecular and materials level, potentially informing the design of next-generation structural and functional materials. Beyond raw algorithmic performance, this paper carefully assesses the end‑to‑end workflow, accounting for overheads in problem encoding, error mitigation, and classical post‑processing. We also present a detailed analysis of resource utilization—quantified by qubit counts, circuit depths, and annealing schedules—highlighting trade‑offs between solution quality and execution time. Ultimately, our findings underscore that, while pure quantum speedups remain rare in 2022, hybrid approaches harnessing both quantum and classical resources can yield practical benefits for small‑to‑medium scale engineering tasks. This expanded evaluation provides a roadmap for researchers and practitioners interested in applying quantum computing to real‑world engineering challenges.