arXiv subjects

Gary J Mooney

Publications and source records attributed to Gary J Mooney.

4 recordsLinked to original sources

arXiv2026

Optimisation-Free Recursive QAOA for the Binary Paint Shop Problem

The Quantum Approximate Optimisation Algorithm (QAOA) is a leading candidate for near-term quantum advantage, yet its practical impact is hindered by limited performance on symmetric local Hamiltonians and the costly optimisation of variational parameters. The Recursive-QAOA (RQAOA) introduced by Bravyi et al. Phys. Rev. Lett. (2020), addresses the first limitation while also reducing circuit size, and parameter transfer techniques can be used to effectively bypass the optimisation loop. In this work, we combine these two ideas to develop an optimisation-free RQAOA and evaluate its performance on the Binary Paint Shop Problem (BPSP) -- an optimisation problem found in manufacturing where a sequence of cars must be painted under constraints while minimising the number of colour changes. The BPSP can be formulated as an Ising ground state problem with a symmetric local Hamiltonian in the form of MAX-CUT and properties well-suited for the application of QAOA parameter transfer. We benchmark QAOA and RQAOA with parameter transfer against classical solvers and heuristics, and investigate their resilience to suboptimal parameters. For circuit optimisation, we use reverse causal cones (RCC) and introduce a method of trimming outer two-qubit gates. To estimate the classical resources needed to simulate these quantum algorithms, we compute entanglement entropy and bond dimensions using matrix product state methods. We also compare circuit sizes and measurement counts across implementations. Our results show that RQAOA is inherently robust to parameter deviations, maintaining near-optimal solutions without noticeable degradation under parameter transfer while substantially reducing quantum resource requirements compared to QAOA. This highlights a viable route toward scalable quantum optimisation without the overhead of the classical optimisation loop and its challenges with barren plateaus.

quant-ph↗

arXiv2026

Adaptable Weighted Token Swapping Algorithm for Optimal Multi-Qubit Pathfinding

Quantum computing promises breakthroughs in simulating and solving complex, classically intractable problems. However, current noisy intermediate-scale quantum (NISQ) devices are relatively small and error-prone, prohibiting large-scale computations. To achieve quantum advantage in this regime, it is crucial to minimise the impact of noise from qubit decoherence and two-qubit gates. A direct approach is to optimise quantum circuit compilation, particularly by improving how circuits are mapped onto hardware. This work targets multi-qubit pathfinding (MQPF), a key subproblem in quantum circuit mapping, formulated as a variant of the token swapping problem. We propose an adaptable algorithm, modelled as a binary integer linear program, that routes $K$ teams of qubits on hardware graphs using swap operations. The algorithm minimises SWAP-gate depth and accumulated gate and idle errors, effectively solving a weighted version of the parallel ($K+1$)-coloured token swapping problem. We benchmark performance across various hardware layouts, comparing runtimes, SWAP depths, gate counts, and errors. Our results show that the proposed MQPF algorithm offers significantly improved runtime scaling and lower accumulated errors over a state-of-the-art exact SMT-CBS-based method. Potential applications include precomputing optimal routing for circuit mappers, benchmarking heuristics, and informing quantum hardware design by analysing pathfinding behaviour.

quant-ph↗

arXiv2025

Hybrid quantum optimization in the context of minimizing traffic congestion

Traffic optimization on roads is a highly complex problem, with one important aspect being minimization of traffic congestion. By mapping to an Ising formulation of the traffic congestion problem, we benchmark solutions obtained from the Quantum Approximate optimization Algorithm (QAOA), a hybrid quantum-classical algorithm. In principle, as the number of QAOA layers approaches infinity, the solutions should reach optimality. On the other hand, short-depth QAOA circuits are known to have limited performance. We show that using tailored initialization techniques encourages the convergence to the desired solution state at lower circuit depths with two and three QAOA layers, thus highlighting the importance of adapting quantum algorithms in the noisy intermediate scale (NISQ) quantum computing era. Moreover, for NISQ devices with limited qubit connectivity and circuit depth, we introduce a heuristic noise-resilient variant of QAOA predicated on the elimination of long-range 2-qubit interactions in the QAOA layers whilst the cost function is unaltered. Our results show that this QAOA variant is surprisingly effective, outperforming QAOA on a physical IBM Quantum computer device.

quant-ph↗

arXiv2024

Characterization of entanglement on superconducting quantum computers of up to 414 qubits

As quantum technology advances and the size of quantum computers grow, it becomes increasingly important to understand the extent of quality in the devices. As large-scale entanglement is a quantum resource crucial for achieving quantum advantage, the challenge in its generation makes it a valuable benchmark for measuring the performance of universal quantum devices. In this work, we study entanglement in Greenberger-Horne-Zeilinger (GHZ) and graph states prepared on the range of IBM Quantum devices. We generate GHZ states and investigate their coherence times with respect to state size and dynamical decoupling techniques. A GHZ fidelity of $0.519 \pm 0.014$ is measured on a 32-qubit GHZ state, certifying its genuine multipartite entanglement (GME). We show a substantial improvement in GHZ decoherence rates for a 7-qubit GHZ state after implementing dynamical decoupling, and observe a linear trend in the decoherence rate of $α=(7.13N+5.54)10^{-3}μs^{-1}$ for up to $N=15$ qubits, confirming the absence of superdecoherence. Additionally, we prepare and characterize fully bipartite entangled native graph states on 22 superconducting quantum devices with qubit counts as high as 414 qubits, all active qubits of the 433-qubit IBM Osprey device. Analysis of the decay of 2-qubit entanglement within the prepared states shows suppression of coherent noise signals with the implementation of dynamical decoupling techniques. Additionally, we observe that the entanglement in some qubit pairs oscillates over time, which is likely caused by residual ZZ-interactions. Characterizing entanglement in native graph states, along with detecting entanglement oscillations, can be an effective approach to low-level device benchmarking that encapsulates 2-qubit error rates along with additional sources of noise, with possible applications to quantum circuit compilation.

quant-ph↗