Optimizing Parameters of Quantum Circuits with Sparsity-Inducing Coordinate Descent
Authors: Rudy Raymond, Zichang He
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide theoretical analyses and demonstrate experiments showing the effectiveness of Rotolasso to solve instances of combinatorial optimization problems. [...] We show the efficacy of CD-based model selection by experimenting on instances of Max Cut and LABS, two hard problems with promising near-term quantum algorithms. |
| Researcher Affiliation | Industry | Rudy Raymond , Zichang He Global Technology Applied Research (GTAR), JPMorgan Chase & Co., New York, NY 10001, USA EMAIL |
| Pseudocode | Yes | Coordinate Descent (CD) Evaluate VQC or QAOA at 2m + 1 different rotation values to obtain the tuples {(θj,l, ˆf(θj,l))}2m+1 l=1 . Build a system of linear equations as Eq. (9) for obtaining (c0,j, c1,j, . . . , c2m,j) with classical computers from {(θj,l, ˆf(θj,l))}2m+1 l=1 . Compute θ j = arg minθj ˆf(θj) by classical computers. |
| Open Source Code | No | Supplementary Material is available at 10.5281/zenodo.15425252. The paper does not explicitly state that code is provided in the supplementary material or provide a direct link to a code repository. |
| Open Datasets | Yes | Experiments demonstrating the role of sparsity-inducing CD in PQCs are conducted using qujax [Duffield et al., 2023] for classical simulation of quantum circuits of both QAOA and VQC for LABS and Max Cut (3-regular graphs) instances. [...] Another hard problem we consider is the so-called low-autocorrelation binary sequences (LABS) [Shaydulin et al., 2024] whose objective is to minimize the sum of the squares of autocorrelations of z { 1, 1}n formulated as min z { 1,1}n Pn 1 k=1 ( Pn k i=1 zizi+k)2. |
| Dataset Splits | No | The paper focuses on combinatorial optimization problems (Max Cut and LABS) and evaluates performance on different problem instances and initial parameter settings. It does not describe dataset splits (e.g., train/test/validation) in the context of machine learning. The text refers to 'random initial θ s' and 'random graphs' which relate to initialization and instance generation, not data splitting. |
| Hardware Specification | No | Experiments demonstrating the role of sparsity-inducing CD in PQCs are conducted using qujax [Duffield et al., 2023] for classical simulation of quantum circuits of both QAOA and VQC for LABS and Max Cut (3-regular graphs) instances. No specific hardware (e.g., GPU/CPU models, cloud resources) used for these classical simulations is mentioned in the paper. |
| Software Dependencies | No | Experiments demonstrating the role of sparsity-inducing CD in PQCs are conducted using qujax [Duffield et al., 2023] for classical simulation of quantum circuits of both QAOA and VQC for LABS and Max Cut (3-regular graphs) instances. The paper mentions 'qujax' but does not specify its version number. |
| Experiment Setup | Yes | Following the standard usage of L1-regularized optimizer in conventional machine learning, we iteratively run Rotolasso on a randomly initialized θ(0) with the regularization factor λ initialized to λstart, and update each parameter cyclically to obtain a (sub)optimal θ . The value of λ is then reduced by a factor while utilizing the previously obtained θ as the new initial parameter set. This is repeated until λ becomes very close to 0. [...] Each data point is derived from 5 random initial θ s. [...] Each point is derived from 5 initial θ s and 5 random graphs. [...] Each datapoint is derived from 50 random initial θ. |