This work introduces a self-learning protocol that incorporates measurement and feedback into variational quantum circuits for efficient quantum state preparation. By combining projective measurements with conditional feedback, the protocol learns state preparation strategies that extend beyond unitary-only methods, leveraging measurement-based shortcuts to reduce circuit depth. Using the spin-1 Affleck-Kennedy-Lieb-Tasaki state as a benchmark, the protocol learns high-fidelity state preparation by overcoming a family of measurement induced local minima through adjustments of parameter update frequencies and ancilla regularization. Despite these efforts, optimization remains challenging due to the highly non-convex landscapes inherent to variational circuits. The approach is extended to larger systems using translationally invariant ansätze and recurrent neural networks for feedback, demonstrating scalability. Additionally, the successful preparation of a specific AKLT state with desired edge modes highlights the potential to discover new state preparation protocols where none currently exist. These results indicate that integrating measurement and feedback into variational quantum algorithms provides a promising framework for quantum state preparation.
This work introduces a self-learning protocol that incorporates measurement and feedback into variational quantum circuits for efficient quantum state preparation. By combining projective measurements with conditional feedback, the protocol learns state preparation strategies that extend beyond unitary-only methods, leveraging measurement-based shortcuts to reduce circuit depth. Using the spin-1 Affleck-Kennedy-Lieb-Tasaki state as a benchmark, the protocol learns high-fidelity state preparation by overcoming a family of measurement induced local minima through adjustments of parameter update frequencies and ancilla regularization. Despite these efforts, optimization remains challenging due to the highly non-convex landscapes inherent to variational circuits. The approach is extended to larger systems using translationally invariant ansätze and recurrent neural networks for feedback, demonstrating scalability. Additionally, the successful preparation of a specific AKLT state with desired edge modes highlights the potential to discover new state preparation protocols where none currently exist. These results indicate that integrating measurement and feedback into variational quantum algorithms provides a promising framework for quantum state preparation.
This thesis introduces and systematically analyzes novel measurement-based quantum state preparation protocols in one dimension, leveraging non-unitary dynamics, quantum measurements, and adaptive feedback mechanisms. Additionally, a stochastic sampling-based approach for ground state search using Projected Entangled Pair States (PEPS) in two dimensions is investigated, further expanding the computational toolkit for simulating complex quantum states. A central contribution is the development of a protocol that employs periodic measurement and resetting of ancilla systems, explicitly applied to the preparation of the one-dimensional Affleck-Kennedy-Lieb-Tasaki (AKLT) state. We identify an optimal reset interval, balancing entanglement generation and convergence speed, and demonstrate robustness against realistic experimental noise such as dephasing. Furthermore, this thesis introduces a self-learning adaptive measurement-feedback framework within variational quantum circuits (VQCs). The proposed framework autonomously discovers efficient, constant-depth strategies for deterministic preparation of specific AKLT edge states, outperforming existing analytical protocols. This analysis uncovers and mitigates previously unknown optimization challenges unique to measurement-feedback-based variational quantum circuits. The thesis also advances two-dimensional ground state search by investigating a sampling-based optimization approach for finite PEPS. It demonstrates that PEPS representing physically realistic states are computationally tractable within this sampling framework, in contrast to random states, whose complexity makes them prohibitively difficult to contract. To quantitatively distinguish between computationally easy and hard-to-contract states, a novel diagnostic measure is introduced. The developed methodology is successfully applied to physically significant states, including chiral spin liquids and Rydberg atom arrays with long-range interactions, highlighting its effectiveness in simulating complex states relevant to state-of-the-art cold atom experiments. Overall, the thesis provides methodological and conceptual insights into measurement-based quantum state preparation, optimization landscapes, and tensor network simulations.
Dynamic quantum circuits (DQCs) incorporate mid-circuit measurements and gates conditioned on these measurement outcomes. DQCs can prepare certain long-range entangled states in constant depth, making them a promising route to preparing complex quantum states on devices with a limited coherence time. Almost all constructions of DQCs for state preparation have been formulated analytically, relying on special structure in the target states. In this work, we approach the problem of state preparation variationally, developing scalable tensor network algorithms which find high-fidelity DQC preparations for generic states. We apply our algorithms to critical states, random matrix product states, and subset states. We consider both DQCs with a fixed number of ancillae and those with an extensive number of ancillae. Even in the few ancillae regime, the DQCs discovered by our algorithms consistently prepare states with lower infidelity than a static quantum circuit of the same depth. Notably, we observe constant fidelity gains across system sizes and circuit depths. For DQCs with an extensive number of ancillae, we introduce scalable methods for decoding measurement outcomes, including a neural network decoder and a real-time decoding protocol. Our work demonstrates the power of an algorithmic approach to generating DQC circuits, broadening their scope of applications to new areas of quantum computing.
Variational quantum algorithms (VQA) combine the advantages of classical and near-term quantum computation for solving problems on today's noisy quantum devices. Variational Quantum Eigensolver (VQE) is one of the widely used VQAs, which aims to find the approximate ground state energy of a given Hamiltonian. While traditional VQE implementation is promising for ansatz-based trial state preparation, it requires an initial fiducial state or a reference state, which can be infeasible for large quantum systems. In this paper, we propose a novel approach for trial state preparation in VQE algorithms. This method leverages passive steering, a circuit-based approach with repeated measurements, eliminating the need for an initial fidu-cial state or a reference state. Experimental results demonstrate that passive steering-based state preparation provides improved accuracy and scalability of VQE compared to traditional ansatz-based solutions. Our proposed solution can also be effectively combined with the existing ansatz-based methods, where passive steering prepares the reference state while ansatz prepares the trial state, facilitating a robust and scalable state preparation for variational quantum algorithms.
Variational quantum sensing offers a promising route to high-precision parameter estimation, but its performance depends strongly on the circuit architectures used for probe preparation and measurement. Existing approaches typically optimize continuous parameters within predefined ansätze, restricting the accessible design space and limiting adaptation to sensing tasks and hardware constraints. Here, we introduce \textsc{AutoQSense}, a reinforcement-learning framework that searches circuit architectures using Fisher-information-based objectives. For few-qubit systems, a single agent sequentially constructs preparation and measurement circuits. For larger systems, a distributed formulation assigns local circuit design to subsystem agents and inter-block entanglement to a budgeted agent. Numerical results show that the learned architectures recover known benchmark strategies, adapt to dephasing noise, and outperform fixed hardware-efficient ansätze while using fewer entangling gates. These results establish \textsc{AutoQSense} as a resource-aware approach to adaptive and hardware-compatible quantum sensing.
Quantum computation holds immense promise for solving problems intractable for classical computers. However, the inherent fragility of quantum states to environmental noise poses a significant challenge. This work proposes a novel approach to mitigating noise in quantum circuits through the implementation of measurement-based feedback. The core concept involves designing quantum circuits with strategically placed measurement points that, upon observing correlations in the measured quantum state, dynamically adjust circuit parameters. This creates a self-correcting mechanism, enabling the circuit to adapt to and compensate for errors in real-time. The theoretical framework outlines the design considerations, the feedback control algorithm, and the mathematical formalism underlying this measurement-based approach. The presented method addresses a critical gap in current quantum error correction strategies by directly integrating feedback into the circuit architecture, offering a potentially more efficient and robust solution for noisy quantum computations. This paper details the key components of this system and suggests future research directions.
Quantum state tomography (QST) is a fundamental technique in quantum information science, aiming to reconstruct the density matrix of an unknown quantum state from experimental measurements. However, traditional QST methods often suffer from significant computational demands and are highly susceptible to measurement noise, hindering their practical application, particularly with near-term quantum computers. This work proposes a novel approach to QST utilizing variational quantum algorithms (VQAs). VQAs iteratively optimize a parameterized quantum circuit to minimize a cost function related to the fidelity between the desired state and the state predicted by the circuit. This approach dramatically reduces the number of required measurements and offers the potential for achieving high-fidelity state reconstruction with lower quantum resource requirements. We detail the theoretical framework, outlining the core algorithms and mathematical formulations. The key advantage lies in the ability to learn the state directly, mitigating the limitations of traditional methods. We explore the optimization landscape and discuss strategies for improving convergence and robustness. The results demonstrate a promising path toward scalable and robust QST on quantum devices.