This thesis develops structure-preserving methods to control and compile quantum evolutions with provable guarantees. In quantum dynamics, a fundamental resource is coherence maintained within a designated subspace; leakage – population transfer out of that subspace – is a major source of error that demands robust methods for its suppression and control. For example, maintaining the evolution within the computational (logical) subspace in quantum computing is essential for practical quantum information processing. On the dynamics side, we study adiabatic evolution under weak perturbations through effective-Hamiltonian frames. Using Bloch wave operators and Schrieffer-Wolff constructions, we obtain block-diagonal generators and prove that their evolutions remain uniformly close to the true dynamics. Moreover, we establish time-independent (“eternal”) bounds on spectral leakage that scale as 𝑂(𝛾−1) in the weak-coupling regime, where 𝛾 is the dimensionless ratio between the strength of the strong system Hamiltonian and the weak drive. The analysis covers both finite-dimensional, time-dependent settings and a novel coarse-grained framework that accommodates continuous spectra and even unbounded Hamiltonians, enabling applications to banded tight-binding models and superconducting-qubit architectures. Optimising quantum algorithms for reduced depth or fewer non-error-corrected physical gates is also key to mitigating leakage. We therefore formulate quantum compiling as exact or approximate unitary synthesis up to a physically irrelevant global phase and solve it via mixed-integer linear programming (MILP). The formulation supports multiple objectives (depth, two-qubit gate count, non-Clifford cost) and provides theoretical guarantees of optimality. To the best of our knowledge, this is the first MILP formulation that targets circuit depth directly, rather than minimising gate counts and inferring depth retroactively. The model explores the algebraic structure of the problem in order to accelerate the solver through symmetry breaking, redundancy elimination, and other mathematically sound constraints. To scale beyond exact global optimisation, we introduce a rolling-horizon strategy that compiles circuits in overlapping windows. An open-source implementation, written in the Julia language and released as QuantumCircuitOpt.jl, provides these capabilities to facilitate further research. In both cases, similarity transformations and algebraic identities reduce complexity while focusing attention on the spectral/target content, and both yield quantitative guarantees. Collectively, these contributions confront leakage, the key threat to coherent, in-subspace computation, with the ultimate aim of getting error-resilient qubits to work.
This thesis develops structure-preserving methods to control and compile quantum evolutions with provable guarantees. In quantum dynamics, a fundamental resource is coherence maintained within a designated subspace; leakage – population transfer out of that subspace – is a major source of error that demands robust methods for its suppression and control. For example, maintaining the evolution within the computational (logical) subspace in quantum computing is essential for practical quantum information processing. On the dynamics side, we study adiabatic evolution under weak perturbations through effective-Hamiltonian frames. Using Bloch wave operators and Schrieffer-Wolff constructions, we obtain block-diagonal generators and prove that their evolutions remain uniformly close to the true dynamics. Moreover, we establish time-independent (“eternal”) bounds on spectral leakage that scale as 𝑂(𝛾−1) in the weak-coupling regime, where 𝛾 is the dimensionless ratio between the strength of the strong system Hamiltonian and the weak drive. The analysis covers both finite-dimensional, time-dependent settings and a novel coarse-grained framework that accommodates continuous spectra and even unbounded Hamiltonians, enabling applications to banded tight-binding models and superconducting-qubit architectures. Optimising quantum algorithms for reduced depth or fewer non-error-corrected physical gates is also key to mitigating leakage. We therefore formulate quantum compiling as exact or approximate unitary synthesis up to a physically irrelevant global phase and solve it via mixed-integer linear programming (MILP). The formulation supports multiple objectives (depth, two-qubit gate count, non-Clifford cost) and provides theoretical guarantees of optimality. To the best of our knowledge, this is the first MILP formulation that targets circuit depth directly, rather than minimising gate counts and inferring depth retroactively. The model explores the algebraic structure of the problem in order to accelerate the solver through symmetry breaking, redundancy elimination, and other mathematically sound constraints. To scale beyond exact global optimisation, we introduce a rolling-horizon strategy that compiles circuits in overlapping windows. An open-source implementation, written in the Julia language and released as QuantumCircuitOpt.jl, provides these capabilities to facilitate further research. In both cases, similarity transformations and algebraic identities reduce complexity while focusing attention on the spectral/target content, and both yield quantitative guarantees. Collectively, these contributions confront leakage, the key threat to coherent, in-subspace computation, with the ultimate aim of getting error-resilient qubits to work.
Quantum computers hold the promise of solving certain computational problems significantly faster than classical computers. Unlocking the full potential of quantum hardware depends on our ability to efficiently translate high-level quantum algorithms into executable sequences of quantum operations tailored to the specific constraints of a given device, a task known as quantum compilation. Among the most promising applications is the simulation of quantum systems, such as in quantum chemistry, where scalable, error resilient, and experimentally feasible Hamiltonian simulation techniques are essential. In the first part of this thesis, we present recent advances in the field of quantum compilation, emphasizing on efficient gate decomposition and circuit optimization. In particular, we analyze compilation techniques for Clifford circuits and general single-qubit rotations. Clifford operations play a central role in quantum error correction, the characterization of quantum devices and quantum cryptography. We explore asymptotically optimal decomposition methods, including constructions using either two-qubit gates or global entangling operations, the latter being especially well-suited for platforms like trapped ions. For single-qubit unitaries, we discuss approximation schemes that achieve the theoretically optimal scaling of gate count with respect to the desired precision. Beyond gate synthesis, we examine various hardware-aware optimization strategies aimed at reducing circuit depth and gate overhead. These include techniques for qubit routing in systems with limited connectivity, as well as methods for removing redundant operations. The second and primary, scientific contribution of this thesis focuses on implementation of effective many-body interactions on quantum hardware by combining the system’s native dynamics with control operations, which we call Hamiltonian engineering. Motivated by the central role of Hamiltonian simulation in quantum science, we introduce a general and efficient framework for synthesizing arbitrary target Hamiltonians under mild locality constraints. We propose a novel linear programming (LP) approach that computes pulse sequences consisting of single-qubit π or π/2 rotations applied at certain times during free evolution under the system’s native Hamiltonian. Our LP-based framework offers multiple advantages. First, its classical runtime depends only on the number and locality of the interactions in the system Hamiltonian, not directly on the overall system size. Second, we extend the method to include robustness against common error sources such as finite pulse time effects and systematic control errors, leveraging tools from average Hamiltonian theory and robust composite pulses. Our approach enables the efficient realization of high-fidelity Hamiltonian dynamics with minimal experimental overhead. The scalability of the approach are demonstrated through simulations, including the engineering of a random two-body Hamiltonian on a 225 qubit lattice in under a minute of classical preprocessing. In conclusion, this thesis contributes both theoretical insights and practical tools that bridge the gap between algorithm design and experimental implementation in quantum computing. The techniques developed here are directly applicable to a variety of platforms, including trapped ions, neutral atoms, and superconducting circuits. By advancing the frontiers of Hamiltonian engineering, this work enables more efficient, robust, and scalable quantum simulations on nearterm devices. Finally, it brings us closer to realizing quantum technologies, which are possibly capable of solving meaningful problems.
Practical realizations of quantum computers are poised to deliver outstanding computational capabilities far beyond the reach of any classical supercomputer. While classical systems are reliably implemented using CMOS technology, the fabrication of quantum hardware has not yet reached a commercially viable level of maturity. Nowadays, many different technologies are being developed as competing candidates to build the first error-corrected quantum computer. Among the software resources required to operate such a system, quantum compilers translate a high-level description of a quantum algorithm into low-level, technology-dependent quantum instructions. Many quantum algorithms, including Shor's and Grover's algorithms, require to compute some classical logic functions on the quantum computer. The first part of this thesis deals with the problem of compiling quantum circuits that perform such classical Boolean functions, called oracles, into a fault-tolerant set of instructions. Oracle circuits often demand many resources in terms of the number of qubits and gates. The focus is on compiling quantum circuits starting from multi-level logic networks representing large Boolean functions while minimizing the resource footprint of the obtained quantum circuits. At the same time, the trade-off between memory resources and the number of operations typical of such a compilation process is explored. As part of the effort in reducing the resources required to perform a given quantum program, I also present some quantum circuit optimization techniques. The researched algorithms leverage data structures and techniques borrowed and adapted from classical logic synthesis, e.g., SAT solvers, LUT mapping, and multi-level logic networks. I implemented all the compilation algorithms presented in this thesis as part of open-source projects. In particular, I develop and maintain caterpillar---a C++ header-only library dedicated to the quantum compilation of oracles and quantum memory management. The second part of this thesis describes how to equip quantum programming language compilers with automatic accuracy management. Despite the availability of many of such languages, resource estimation of quantum algorithms does not yet support taking approximation errors into account. This general methodology is applicable to any programming language and I demonstrate its integration into the Q# compiler. The technique consists of providing language support to ease the accuracy-aware programming task. The user can define accuracy parameters that will be automatically optimized according to a constraint and a cost function directly generated from the source code. In the presented practical evaluations, the constraint function is the overall allowed accuracy, while the cost function is application-dependent and related to the number of gates. During the optimization process, such functions are evaluated hundreds of times. For this reason, we extract them as (near-)symbolic expressions, whose evaluation time does not depend on the quantum algorithm size. The algorithms and the methodologies presented in this thesis are part of a widespread effort of the research community to build a complete and efficient software stack to program and control the first practical universal quantum computer.
The efficiency of compiling high-level quantum algorithms into instruction sets native to quantum computers defines the moment in the future when we will be able to solve interesting and important problems on quantum computers. In my work I focus on the new methods for compiling single qubit operations that appear in many quantum algorithms into single qubit operations natively supported by several popular architectures. In addition, I study several questions related to synthesis and optimization of multiqubit operations. \n \n \nWhen studying the single qubit case, I consider two native instruction sets. The first one is Clifford+T; it is supported by conventional quantum computers implementing fault tolerance protocols based on concatenated and surface codes, and by topological quantum computers based on Ising anyons. The second instruction set is the one supported by topological quantum computers based on Fibonacci anyons. I show that in both cases one can use the number theoretic structure of the problem and methods of computational algebraic number theory to achieve improvements over the previous state of the art by factors ranging from 10 to 1000 for instances of the problem interesting in practice. This order of improvement might make certain interesting quantum computations possible several years earlier. \n \n \nThe work related to multiqubit operations is on exact synthesis and optimization of Clifford+T and Clifford circuits. I show an exact synthesis algorithm for unitaries generated by Clifford+T circuits requiring exponentially less number of gates than previous state of the art. For Clifford circuits two directions are studied: the algorithm for finding optimal circuits acting on a small number of qubits and heuristics for larger circuits optimization. The techniques developed allows one to reduce the size of encoding and decoding circuits for quantum error correcting codes by 40-50\\% and also finds their applications in randomized benchmarking protocols.
Combinatorial optimisation constitutes a central class of computational problems with broad relevance across science and industry, combining foundational theoretical interest with significant real-world impact. Many problems of practical importance admit concise mathematical formulations, yet resist efficient classical solution methods due to their NP-hard structure. Quantum computing offers several algorithmic paradigms that promise structural speedups for such problems. Most quantum and hybrid frameworks fall into one of the following three categories: algorithms based on continuous adiabatic evolution, such as quantum annealing; circuit-based variational quantum algorithms; circuit-based approaches based on Grover's algorithm. These three algorithmic families are conceptualised for different kinds and generations of quantum architectures, often studied in isolation from each other and from preexisting sophisticated classical methods. This thesis develops a unified, predominantly circuit-based treatment of quantum algorithms for combinatorial optimisation, with a particular focus on principled design transfers between near-term parametric algorithms and far-term fault-tolerant approaches. Starting from the Boolean circuit model, reversible and quantum circuit formulations are derived that allow for precise size and depth analysis of classical control logic, reversible embeddings, and quantum realisations of Boolean and pseudo-Boolean functions. These constructions form a common backbone for optimisation algorithms based on adiabatic evolution, variational principles, and amplitude amplification. Adiabatic quantum computation is analysed within this circuit framework, leading to explicit convergence conditions and resource bounds for discretised adiabatic evolutions driven by pseudo-Boolean cost functions of bounded degree. Variational quantum algorithms are then revisited with an emphasis on feasibility-preserving operations and mixing families. In this context, a novel class of exhaustive variational quantum algorithms is introduced, characterised by finite parameterisations that provably reach optimal solutions. These constructions naturally induce state-preparation routines that guarantee to generate superpositions of all feasible solutions. Finally, Grover’s algorithm and its generalisations are studied as optimisation methods, highlighting their structural dependence on feasible-state preparation and their conceptual proximity to variational and adiabatic approaches. By establishing explicit design transfer principles between these paradigms, this thesis provides a unified perspective on quantum optimisation algorithms, accompanied by concrete circuit constructions and quantitative resource analyses.
The rapid evolution of Noisy Intermediate-Scale Quantum (NISQ) devices has intensified the need for robust compilation strategies capable of mitigating hardware-induced errors while maximizing computational efficiency. Quantum compilers play a pivotal role in translating high-level quantum algorithms into hardware-executable instructions, yet they face significant challenges related to qubit connectivity constraints, limited coherence times, and stochastic noise. This paper examines error-resilient quantum compiler design for efficient qubit mapping, gate optimization, and noise mitigation in NISQ-era devices, proposing a framework that integrates topology-aware mapping, adaptive gate synthesis, and probabilistic error suppression techniques. By synthesizing advances in quantum circuit optimization, hardware-aware scheduling, and hybrid classical–quantum error correction strategies, the study evaluates how compiler-level innovations can substantially enhance execution fidelity and resource efficiency. The findings suggest that intelligent compiler architectures are critical enablers of practical quantum advantage during the transitional NISQ phase, bridging theoretical algorithm design with real-world hardware constraints.
Determining whether a target unitary can be implemented within a prescribed quantum circuit architecture is a fundamental problem in quantum information, with direct implications for optimisation and compilation of quantum circuits, and hardware-efficient quantum computation. While existing synthesis and compilation methods are primarily constructive, they generally do not provide rigorous certificates that a unitary cannot be realised using given implementation resources. Here we introduce a general framework to define quantum circuit architecture witnesses, which certify the incompatibility of a unitary transformation with a specified quantum circuit architecture. We formulate the witness construction as a semidefinite program by maximising the fidelity between the Choi state of the target unitary and those of tested circuits. The resulting witnesses provide practical and quantitative certificates of incompatibility, implying lower bounds on implementation resources such as the gate count or circuit depth, and can also be used experimentally to benchmark quantum devices by certifying that an implemented unitary channel goes beyond the capabilities of a given circuit architecture. For Clifford unitaries, we exploit the stabiliser formalism to reduce the construction to linear programming, enabling both more efficient numerical certification for circuits containing on the order of seven two-qubit gates, and analytical witnesses for some families of architectures made of an arbitrary number of gates.