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Paper 1

A Divide-and-Conquer Approach to Dicke State Preparation

Shamminuj Aktar, Andreas Bärtschi, Abdel-Hameed A. Badawy, Stephan Eidenbenz

Year

2021

Journal

arXiv preprint

DOI

arXiv:2112.12435

arXiv

2112.12435

We present a divide-and-conquer approach to deterministically prepare Dicke states $\lvert D_k^n\rangle$ (i.e., equal-weight superpositions of all $n$-qubit states with Hamming Weight $k$) on quantum computers. In an experimental evaluation for up to $n=6$ qubits on IBM Quantum Sydney and Montreal devices, we achieve significantly higher state fidelity compared to previous results [Mukherjee and others, TQE'2020], [Cruz and others, QuTe'2019]. The fidelity gains are achieved through several techniques: Our circuits first "divide" the Hamming weight between blocks of $n/2$ qubits, and then "conquer" those blocks with improved versions of Dicke state unitaries [Bärtschi and others, FCT'2019]. Due to the sparse connectivity on IBM's heavy-hex-architectures, these circuits are implemented for linear nearest neighbor topologies. Further gains in (estimating) the state fidelity are due to our use of measurement error mitigation and hardware progress.

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Paper 2

Energy Landscape Structure of Small Graph Isomorphism Under Variational Optimization

Turbasu Chatterjee, Shah Ishmam Mohtashim, Akash Kundu

Year

2021

Journal

arXiv preprint

DOI

arXiv:2111.09821

arXiv

2111.09821

We investigate a quadratic unconstrained binary optimization (QUBO) formulation of the graph isomorphism problem using the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE). For small graph instances, we observe that isomorphic pairs exhibit consistent clustering in variational energies, indicating that the Hamiltonian successfully encodes structural features. However, we demonstrate that low variational energy alone is an unreliable certifier of isomorphism due to the high probability of converging to infeasible states that violate bijection constraints. To address this, we analyze optimization trajectories rather than final energies; consistently outperform naive energy thresholding, though absolute performance remains limited. Our results characterize the current limits of variational algorithms for graph isomorphism, positioning energy landscape analysis as a diagnostic tool rather than a scalable decision procedure in the NISQ regime.

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