arXiv subjects

Isaac L. Huidobro-Meezs

Publications and source records attributed to Isaac L. Huidobro-Meezs.

4 recordsLinked to original sources

arXiv2026

Reducing quantum measurements in qubit-based overlapping grouping methods for quantum energy estimation through better initializations

The measurement cost for estimating expectation values of Hamiltonians is a central bottleneck in variational quantum algorithms. Grouping strategies significantly reduce this cost, with overlapping techniques being the state of the art in the field. Overlapping grouping methods require i) a non-overlapping grouping of the Hamiltonian, typically obtained from the Sorted Insertion (SI) algorithm as initialization, and ii) the construction of covariance dictionaries from approximate wavefunctions to guide the optimization. It was recently shown that different initializations can potentially reduce measurement costs for overlapping methods. Motivated by these findings, we introduce variance-aware SI (VarSI), a family of covariance-informed non-overlapping Pauli grouping heuristics to reduce measurement counts. VarSI grouping leverages the covariance dictionaries, already required by overlapping methods, to construct better non-overlapping groups. We propose three variants: a global greedy grouping insertion rule, a variance-informed SI analog, and a local refinement step initialized from SI or our variance-informed variant. We showcase the use of groupings generated by our VarSI heuristic algorithms to initialize overlapping methods using the iterative coefficient-splitting (ICS) algorithm. Molecular benchmarks with 130 Hamiltonians demonstrate consistent, non-overlapping measurement improvements over SI of 38\% and enhanced downstream ICS results when initialized from VarSI groups. We find that the initializations considered here achieve up to 70\% measurement reductions for ICS, compared to the standard SI initialization with mean reductions of 9--15.3\% depending on qubit mappings and covariance dictionaries used. These results show that non-overlapping grouping remains a consequential design step even when the final estimator uses overlapping fragments.

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arXiv2026

Bottom-Up Design of Quantum Optical Experiments Using Discrete Generative Models

Designing quantum optical experiments requires searching over discrete circuit topologies and continuous parameters, often with multiple realizations of the same target state. Graph-based methods commonly address this problem by optimizing a dense graph and pruning it toward a single circuit. We introduce \texttt{Grinch}, a bottom-up reward-driven generative framework that learns to sample optical graphs directly from fidelity-based rewards without relying on a pre-existing training dataset. We demonstrate the framework on multipartite entangled states, graph and cluster states, and nonlocal photonic-gate targets, obtaining multiple high-fidelity optical graphs. We identify asymptotic solutions for states that cannot be generated exactly by graphs, as well as nonlocal Toffoli gate constructions requiring two ancillas. Our work presents hardware-constraint objectives, which lead to alternative constructions of CNOT and Toffoli gates when connections between ancilla nodes are restricted. These findings illustrate a reward-driven approach to quantum optical inverse design, exploring alternative circuit topologies while directly incorporating connectivity constraints into the search process.

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arXiv2026

Discrete Flow-Based Generative Models for Measurement Optimization in Quantum Computing

Estimating molecular Hamiltonians to chemical accuracy requires a large number of measurements. Hamiltonian overlapping grouping methods focus on reducing measurement counts, employing greedy initializations, such as sorted insertion (SI), leaving useful measurement/circuit trade-offs unexplored. Here, we formulate Hamiltonian grouping as a reward-driven generative search problem and introduce a Generative Flow Networks (GFlowNets)-based model that colors graph representations of molecular qubit Hamiltonians to sample non-overlapping commuting groupings. The reward function can combine measurement cost, circuit count, and compiled two-qubit-gate count, enabling multi-objective optimization without differentiable cost functions or model pretraining. Across molecular Hamiltonian benchmarks, the GFlowNets sampler finds fully commuting non-overlapping groupings with average measurement requirements 18% lower than SI, and produces Pareto sets that expose trade-offs among shots, circuits, and two-qubit resources. When used to initialize iterative coefficient splitting (ICS), GFlowNet-generated groupings reduce post-ICS measurement estimates by up to 40% for Jordan-Wigner-mapped fully commuting Hamiltonians relative to SI initialization. Composite rewards further identify lower two-qubit gate groupings, including cases with more than 100 fewer compiled two-qubit gates, while retaining comparable post-ICS measurement benefits. GFlowNets provide a flexible workflow for resource-aware measurement and quantum-resource optimization in quantum chemistry, replacing single-heuristic outputs with diverse candidate groupings that can be selected according to hardware-specific priorities. Our results show that our GFlowNets' generative policy framework not only reduces measurement and two-qubit gate costs but also provides flexibility for hardware-aware adaptations via its reward function.

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arXiv2024

GFlowNets for Hamiltonian decomposition in groups of compatible operators

Quantum computing presents a promising alternative for the direct simulation of quantum systems with the potential to explore chemical problems beyond the capabilities of classical methods. However, current quantum algorithms are constrained by hardware limitations and the increased number of measurements required to achieve chemical accuracy. To address the measurement challenge, techniques for grouping commuting and anti-commuting terms, driven by heuristics, have been developed to reduce the number of measurements needed in quantum algorithms on near-term quantum devices. In this work, we propose a probabilistic framework using GFlowNets to group fully (FC) or qubit-wise commuting (QWC) terms within a given Hamiltonian. The significance of this approach is demonstrated by the reduced number of measurements for the found groupings; 51% and 67% reduction factors respectively for FC and QWC partitionings with respect to greedy coloring algorithms, highlighting the potential of GFlowNets for future applications in the measurement problem. Furthermore, the flexibility of our algorithm extends its applicability to other resource optimization problems in Hamiltonian simulation, such as circuit design.

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