arXiv subjects

Hyunho Cha

Publications and source records attributed to Hyunho Cha.

22 records · Page 2Linked to original sources

arXiv2026

Binary integer programming for optimizing ebit cost in distributed quantum circuits with fixed module allocation

Modular and networked quantum architectures can scale beyond the qubit count of a single device, but executing a circuit across modules requires implementing non-local two-qubit gates using shared entanglement (ebits) and classical communication, making ebit cost a central resource in distributed execution. The resulting distributed quantum circuit (DQC) problem is combinatorial, motivating prior heuristic approaches such as hypergraph partitioning. In this work, we decouple module allocation from distribution. For a fixed module allocation (i.e., assignment of each qubit to a specific Quantum Processing Unit), we formulate the remaining distribution layer as an exact binary integer programming (BIP). This yields solver-optimal distributions for the fixed-allocation subproblem and can be used as a post-processing step on top of any existing allocation method. We derive compact BIP formulations for four or more modules and a tighter specialization for three modules. Across a diverse benchmark suite, BIP post-processing reduces ebit cost by up to 20\% for random circuits and by more than an order of magnitude for some arithmetic circuits. While the method incurs offline classical overhead, it is amortized when circuits are executed repeatedly.

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arXiv2025

Towards Optimizing the Expected Performance of Sampling-Based Quantum-Inspired Algorithms

Quantum-inspired classical algorithms has received much attention due to its exponential speedup compared to existing algorithms, under certain data storage assumptions. The improvements are noticeable in fundamental linear algebra tasks. In this work, we analyze two major subroutines in sampling-based quantum-inspired algorithms, specifically, inner product estimation and sampling from a linear combination of vectors, and discuss their possible improvements by generalizing the data structure. The idea is to consider the average behavior of the subroutines under certain assumptions regarding the data elements. This allows us to determine the optimal data structure, and the high-dimensional nature of data makes our assumptions reasonable. Experimental results from recommendation systems also highlight a consistent preference for our proposed data structure. Motivated by this observation, we tighten the upper bound on the number of required measurements for direct fidelity estimation. We expect our findings to suggest optimal implementations for various quantum and quantum-inspired machine learning algorithms that involve extremely high-dimensional operations, which has potential for many applications.

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arXiv2025

Scalable bayesian shadow tomography for quantum property estimation with set transformers

A scalable Bayesian machine learning framework is introduced for estimating scalar properties of an unknown quantum state from measurement data, which bypasses full density matrix reconstruction. This work is the first to integrate the classical shadows protocol with a permutation-invariant set transformer architecture, enabling the approach to predict and correct bias in existing estimators to approximate the true Bayesian posterior mean. Measurement outcomes are encoded as fixed-dimensional feature vectors, and the network outputs a residual correction to a baseline estimator. Scalability to large quantum systems is ensured by the polynomial dependence of input size on system size and number of measurements. On Greenberger-Horne-Zeilinger state fidelity and second-order Rényi entropy estimation tasks -- using random Pauli and random Clifford measurements -- this Bayesian estimator always achieves lower mean squared error than classical shadows alone, with more than a 99\% reduction in the few copy regime.

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arXiv2025

Efficient sampling for Pauli-measurement-based shadow tomography in direct fidelity estimation

A constant number of random Clifford measurements allows the classical shadow protocol to perform direct fidelity estimation (DFE) with high precision. However, estimating properties of an unknown quantum state is expected to be more feasible with random Pauli measurements than with random Clifford measurements in the near future. Inspired by the importance sampling technique applied to sampling Pauli measurements for DFE, we show that similar strategies can be derived from classical shadows. Specifically, we describe efficient methods using only local Pauli measurements to perform DFE with GHZ, W, and Dicke states, establishing tighter bounds (by factor of $14.22$ and $16$ for GHZ and W, respectively) on the number of measurements required for desired precision. These protocols are derived by adjusting the distribution of observables. Notably, they require no preprocessing steps other than the sampling algorithms.

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