Contributed Session: Quantum Computing
Paper ID: 28
Authors: Alexander Popov, Nico Meyer, Daniel D. Scherer and Guido Dietl
Title: Optimized Compilation of Logical Clifford Circuits
Abstract: Fault-tolerant quantum computing hinges on efficient logical compilation, in particular, translating high-level circuits into code-compatible implementations. Gate-by-gate compilation often yields deep circuits, requiring significant overhead to ensure fault-tolerance. As an alternative, we investigate the compilation of primitives from quantum simulation as single blocks. We focus our study on the [[n, n−2, 2]] code family, which allows for the exhaustive comparison of potential compilation primitives on small circuit instances. Based upon that, we then introduce a methodology that lifts these primitives into size-invariant, depth-efficient compilation strategies. This recovers known methods for circuits with moderate Hadamard counts and yields improved realizations for sparse and dense placements. Simulations show significant error-rate reductions in the compiled circuits. We envision the approach as a core component of peephole-based compilers. Its flexibility and low hand-crafting burden potentially enable extension to other circuit structures and code families.
Paper ID: 48
Authors: Marek Gluza, Jeongrak Son, Bi Hong Tiang, René Zander, Raphael Seidel, Yudai Suzuki, Zoë Holmes and Nelly H. Y. Ng
Title: Double-bracket quantum algorithms for quantum imaginary-time evolution
Abstract: Efficiently preparing approximate ground-states of large, strongly correlated systems on quantum hardware is challenging and yet nature is innately adept at this. This has motivated the study of thermodynamically inspired approaches to ground-state preparation that aim to replicate cooling processes via imaginary-time evolution. However, synthesizing quantum circuits that efficiently implement imaginary-time evolution is itself difficult, with prior proposals generally adopting heuristic variational approaches or using deep block encodings. Here, we use the insight that quantum imaginary-time evolution is a solution of Brockett's double-bracket flow and synthesize circuits that implement double-bracket flows coherently on the quantum computer. We prove that our Double-Bracket Quantum Imaginary-Time Evolution (DB-QITE) algorithm inherits the cooling guarantees of imaginary-time evolution. Concretely, each step is guaranteed to i) decrease the energy of an initial approximate ground-state by an amount proportion to the energy fluctuations of the initial state and ii) increase the fidelity with the ground-state. We provide gate counts for DB-QITE through numerical simulations in Qrisp which demonstrate scenarios where DB-QITE outperforms quantum phase estimation. Thus DB-QITE provides a means to systematically improve the approximation of a ground-state using shallow circuits.
Paper ID: 34
Authors: Jan Schneider, Tom Könecke and Michael Hanss
Title: Incorporating Prior Knowledge of Coherent Control Errors in Quantum Algorithms via Possibility Theory
Abstract: Robustness analysis of quantum algorithms against coherent control errors is essential for the reliable deployment of quantum computing. While existing approaches often assume either systematic errors or only upper bounds, this work develops a framework to incorporate (partial) prior knowledge about error distributions using possibility theory, allowing multiple information sources to be combined. We derive worst-case fidelity bounds for individually bounded coherent control errors by generalizing Lipschitz-based fidelity estimates. Furthermore, we propose an approximation for confidence levels on different fidelities using the concept of α-cuts, and project these bounds onto expectation values of projective measurements, fusing the information into a unified bound. Finally, we apply the framework to two quantum algorithms from the literature to illustrate its practical utility.
Paper ID: 35
Authors: Ugo Nzongani, Dylan Laplace Mermoud and Arthur Braida
Title: Scaling QAOA: transferring optimal adiabatic schedules from small-scale to large-scale variational circuits
Abstract: The Quantum Approximate Optimization Algorithm (QAOA) is a leading approach for combinatorial optimization on near-term quantum devices, yet its scalability is limited by the difficulty of optimizing 2p variational parameters for a large number p of layers. Recent empirical studies indicate that optimal QAOA angles exhibit concentration and transferability across problem sizes. Leveraging this observation, we propose a schedule-learning framework that transfers spectral-gap-informed adiabatic control strategies from smallscale instances to larger systems. Our method extracts the spectral gap profile of small problems and constructs a continuous schedule governed by ∂ts = κgq(s), where g(s) is the instantaneous gap and (κ, q) are global hyperparameters. Discretizing this schedule yields closed-form expressions for all QAOA angles, reducing the classical optimization task from 2p parameters to only 2, independent of circuit depth. This drastic parameter compression mitigates classical optimization overhead and reduces sensitivity to barren plateau phenomena. Numerical simulations on random QUBO and 3-regular MaxCut instances demonstrate that the learnt schedules transfer effectively to larger systems while achieving competitive approximation ratios. Our results suggest that gap-informed schedule transfers provide a scalable and parameter-efficient strategy for QAOA.
Paper ID: 91
Authors: Yabo Wang, Bo Qi and Daoyi Dong
Title: From Trainable to Reliable: A Unified Framework for Hybrid Quantum-Classical Computation
Abstract: Hybrid quantum-classical computation is widely regarded as a viable path toward practical quantum advantage on current noisy intermediate-scale quantum devices. However, its practical utility is hindered by three fundamental challenges: trainability, reliable learning capability, and noise resilience. This work systematically addresses these challenges. First, we propose a reduced-domain initialization strategy and an entanglement-variational hardware-efficient ansatz ， both of which improve trainability without sacrificing expressivity. Second, for quantum machine learning, we provide the first rigorous convergence rates for both training and generalization error within the quantum AdaBoost framework, establishing provable predictive guarantees. Finally, we present the first systematical characterization of prediction performance in noisy quantum kernel methods, identifying the critical conditions under which predictive power collapses. Taken together, our results demonstrate that the path to quantum utility hinges not on expressivity alone, but on a principled co-design of trainability, generalizability, and noise resilience.
Paper ID: 86
Authors: Rémi Robin
Title: Lasalle principle for convergence of solutions of infinite-dimensional Lindblad master equations towards the codespace
Abstract: This talk presents a new tool inspired by LaSalle principle to prove convergence of the solutions of infinite-dimensional Lindblad master equations towards the codespace. In the context of bosonic codes, the codespace is a linear manifold in the kernel of the Lindbladian, and we aim to show that it is attractive: the support of any initial condition converges to this subspace (or more abstractly, the projector on this codespace converges towards the Identity). A key challenge is that the codespace typically consists of density matrices with low rank, which complicates the use of standard convergence techniques that often require faithfulness and uniqueness of the invariant state. We introduce a novel approach based on compactness and density hypotheses, which we successfully applied to the dissipative cat-qubit+buffer system to prove convergence in the context of reservoir engineering. We will also present recent results on the convergence of dissipative tiger codes using this technique. This talk is based on doi.org/10.1007/s00023-024-01481-8 and an ongoing work on tiger codes.