{"id":44,"date":"2020-02-24T11:38:16","date_gmt":"2020-02-24T16:38:16","guid":{"rendered":"https:\/\/quantum-computing.lehigh.edu\/?page_id=44"},"modified":"2025-12-22T01:14:05","modified_gmt":"2025-12-22T06:14:05","slug":"research","status":"publish","type":"page","link":"https:\/\/quantum-computing.lehigh.edu\/index.php\/research\/","title":{"rendered":"Research"},"content":{"rendered":"\n<div class=\"wp-block-media-text alignwide is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"698\" src=\"https:\/\/quantum-computing.lehigh.edu\/wp-content\/uploads\/2020\/02\/Screenshot-2020-02-24-10.43.24-1024x698.png\" alt=\"\" class=\"wp-image-58 size-full\" srcset=\"https:\/\/quantum-computing.lehigh.edu\/wp-content\/uploads\/2020\/02\/Screenshot-2020-02-24-10.43.24-1024x698.png 1024w, https:\/\/quantum-computing.lehigh.edu\/wp-content\/uploads\/2020\/02\/Screenshot-2020-02-24-10.43.24-300x205.png 300w, https:\/\/quantum-computing.lehigh.edu\/wp-content\/uploads\/2020\/02\/Screenshot-2020-02-24-10.43.24-768x524.png 768w, https:\/\/quantum-computing.lehigh.edu\/wp-content\/uploads\/2020\/02\/Screenshot-2020-02-24-10.43.24.png 1176w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<h3 class=\"wp-block-heading has-text-align-center\">Quantum Interior Point Methods<\/h3>\n\n\n\n<p class=\"has-text-align-left\">Making use of Quantum Linear Solvers and Block Encodings to achieve a quantum speedup over classical runtimes by solving the Newton system more efficiently. <\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-media-text alignwide has-media-on-the-right is-stacked-on-mobile\" style=\"grid-template-columns:auto 39%\"><div class=\"wp-block-media-text__content\">\n<h3 class=\"wp-block-heading has-text-align-center\">Noise in NISQ Devices<\/h3>\n\n\n\n<p class=\"has-text-align-left\">Building up a description of physical errors in quantum computers from both individual gate and integrated quantum circuit aspects.&nbsp;<\/p>\n<\/div><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"405\" height=\"398\" src=\"https:\/\/quantum-computing.lehigh.edu\/wp-content\/uploads\/2020\/02\/Pic2-Muqing.png\" alt=\"\" class=\"wp-image-59 size-full\" srcset=\"https:\/\/quantum-computing.lehigh.edu\/wp-content\/uploads\/2020\/02\/Pic2-Muqing.png 405w, https:\/\/quantum-computing.lehigh.edu\/wp-content\/uploads\/2020\/02\/Pic2-Muqing-300x295.png 300w\" sizes=\"auto, (max-width: 405px) 100vw, 405px\" \/><\/figure><\/div>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<div class=\"wp-block-media-text alignwide is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"524\" height=\"387\" src=\"https:\/\/quantum-computing.lehigh.edu\/wp-content\/uploads\/2020\/02\/Slide_Plot-2.jpg\" alt=\"\" class=\"wp-image-111 size-full\" srcset=\"https:\/\/quantum-computing.lehigh.edu\/wp-content\/uploads\/2020\/02\/Slide_Plot-2.jpg 524w, https:\/\/quantum-computing.lehigh.edu\/wp-content\/uploads\/2020\/02\/Slide_Plot-2-300x222.jpg 300w\" sizes=\"auto, (max-width: 524px) 100vw, 524px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<h4 class=\"wp-block-heading\">Solving Combinatorial Problems in NISQ devices <\/h4>\n\n\n\n<p> Development and study of implementable QUBO formulations of combinatorial problems using QAOA algorithms.  <\/p>\n<\/div><\/div>\n<\/div><\/div>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Publications<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Wu, Z., Sampourmahani, P., Mohammadisiahroudi, M. &amp; Terlaky, T. (2025). A Quantum Dual Logarithmic Barrier Method for Linear Optimization. <em>INFORMS Journal on Optimization<\/em>, Ahead of Print. <a href=\"https:\/\/doi.org\/10.1287\/ijoo.2024.0062\">https:\/\/doi.org\/10.1287\/ijoo.2024.0062<\/a><\/li>\n\n\n\n<li>Mohammadisiahroudi, M., Fakhimi, R., Wu, Z. &amp; Terlaky, T. (2025). An Inexact Feasible Interior Point Method for Linear Optimization with High Adaptability to Quantum Computers. <em>SIAM Journal on Optimization, 35<\/em>(4), pp. 2203-2233. <a href=\"https:\/\/doi.org\/10.1137\/23M1589414\">https:\/\/doi.org\/10.1137\/23M1589414<\/a><\/li>\n\n\n\n<li>Wu, Z., Yang, X. &amp; Terlaky, T. (2025). A Preconditioned Inexact Infeasible Quantum Interior Point Method for Linear Optimization. <em>Computational Optimization and Applications<\/em>. <a href=\"https:\/\/doi.org\/10.1007\/s10589-025-00750-4\">https:\/\/doi.org\/10.1007\/s10589-025-00750-4<\/a><\/li>\n\n\n\n<li>Mohammadisiahroudi, M., Wu, Z., Augustino, B., Carr, A. &amp; Terlaky, T. (2025). Improvements to Quantum Interior Point Method for Linear Optimization. <em>ACM Transactions on Quantum Computing, 6<\/em>(1), Article 8. <a href=\"https:\/\/doi.org\/10.1145\/3702244\">https:\/\/doi.org\/10.1145\/3702244<\/a><\/li>\n\n\n\n<li>Mohammadisiahroudi, M., Augustino, B., Sampourmahani, P. &amp; Terlaky, T. (2025). Quantum Computing Inspired Iterative Refinement for Semidefinite Optimization. <em>Mathematical Programming<\/em>. <a href=\"https:\/\/doi.org\/10.1007\/s10107-024-02183-z\">https:\/\/doi.org\/10.1007\/s10107-024-02183-z<\/a><\/li>\n\n\n\n<li>Harkness, A. Krawec, W. O. &amp; Wang, B. (2024). Security of partially corrupted quantum repeater networks. <em>Quantum Sci. Technol., 10<\/em>(1), Article 5. <a href=\"https:\/\/doi.org\/10.1088\/2058-9565\/ad7882\">https:\/\/doi.org\/10.1088\/2058-9565\/ad7882<\/a><\/li>\n\n\n\n<li>Mohammadisiahroudi, M., Fakhimi, R. &amp; Terlaky, T. (2024). Efficient Use of Quantum Linear System Algorithms in Inexact Infeasible IPMs for Linear Optimization. <em>Journal of Optimization Theory and Applications<\/em>, <em>202<\/em>(1), pp. 146-183. <a href=\"https:\/\/doi.org\/10.1007\/s10957-024-02452-z\">https:\/\/doi.org\/10.1007\/s10957-024-02452-z<\/a><\/li>\n\n\n\n<li>Augustino, B., Nannicini, G., Terlaky, T. &amp; Zuluaga, L. F. (2023). Quantum Interior Point Methods for Semidefinite Optimization. <em>Quantum, 7<\/em>, p. 1110. <a href=\"https:\/\/doi.org\/10.22331\/q-2023-09-11-1110\">https:\/\/doi.org\/10.22331\/q-2023-09-11-1110<\/a><\/li>\n\n\n\n<li>Augustino, B. &amp; Terlaky, T. (2023). Quantum Interior Point Methods for Conic Linear Optimization. In: Pardalos, P.M., Prokopyev, O.A. (eds) Encyclopedia of Optimization. Springer, Cham. <a href=\"https:\/\/doi.org\/10.1007\/978-3-030-54621-2_852-1\">https:\/\/doi.org\/10.1007\/978-3-030-54621-2_852-1<\/a><\/li>\n\n\n\n<li>Mohammadisiahroudi, M. &amp; Terlaky, T. (2023). Quantum IPMs for Linear Optimization. In: Pardalos, P.M., Prokopyev, O.A. (eds) Encyclopedia of Optimization. Springer, Cham. <a href=\"https:\/\/doi.org\/10.1007\/978-3-030-54621-2_851-1\">https:\/\/doi.org\/10.1007\/978-3-030-54621-2_851-1<\/a><\/li>\n\n\n\n<li>Zheng, M. &amp; Yang, X. (2023). Error Modeling in NISQ Devices. In: Pardalos, P.M., Prokopyev, O.A. (eds) Encyclopedia of Optimization. Springer, Cham. <a href=\"https:\/\/doi.org\/10.1007\/978-3-030-54621-2_850-1\">https:\/\/doi.org\/10.1007\/978-3-030-54621-2_850-1<\/a><\/li>\n\n\n\n<li>Muqing, Z., Li, A., Terlaky, T. &amp; Yang, X. (2023). A Bayesian Approach for Characterizing and Mitigating Gate and Measurement Errors. <em>ACM Transactions on Quantum Computing, 4<\/em>(2), Article 11. <a href=\"https:\/\/doi.org\/10.1145\/3563397\">https:\/\/doi.org\/10.1145\/3563397<\/a><\/li>\n\n\n\n<li>Wu, Z., Mohammadisiahroudi, M., Augustino, B., Yang, X. &amp; Terlaky, T. (2023). An Inexact Feasible Quantum Interior Point Method for Linearly Constrained Quadratic Optimization. <em>Entropy, 25<\/em>(2), p. 330. <a href=\"https:\/\/doi.org\/10.3390\/e25020330\">https:\/\/doi.org\/10.3390\/e25020330<\/a><\/li>\n\n\n\n<li>Mohammadisiahroudi, M., Wu, Z., Augustino, B., Terlaky, T. &amp; Carr, A. (2022). Quantum-enhanced Regression Analysis Using State-of-the-art QLSAs and QIPMs.&nbsp;<em>2022 IEEE\/ACM 7th Symposium on Edge Computing (SEC)<\/em>, Seattle, WA, USA, 2022, pp. 375-380, <a href=\"https:\/\/doi.org\/10.1109\/SEC54971.2022.00055\">https:\/\/doi.org\/10.1109\/SEC54971.2022.00055<\/a><\/li>\n\n\n\n<li>Quintero, R. A. &amp; Zuluaga, L. F. (2022). QUBO Formulations of Combinatorial Optimization Problems for Quantum Computing Devices. In: Pardalos, P.M., Prokopyev, O.A. (eds) Encyclopedia of Optimization. Springer, Cham. <a href=\"https:\/\/doi.org\/10.1007\/978-3-030-54621-2_853-1\">https:\/\/doi.org\/10.1007\/978-3-030-54621-2_853-1<\/a><\/li>\n\n\n\n<li>Quintero, R., Bernal, D., Terlaky, T.&nbsp;&amp; Zuluaga, L. F. (2022).&nbsp;Characterization of QUBO reformulations for the maximum&nbsp;<em>k<\/em>-colorable subgraph problem.&nbsp;<em>Quantum Information Processing, 21<\/em>(89). <a href=\"https:\/\/doi.org\/10.1007\/s11128-022-03421-z\">https:\/\/doi.org\/10.1007\/s11128-022-03421-z<\/a><\/li>\n\n\n\n<li>Pirhooshyaran, M. &amp; Terlaky, T. (2021). Quantum circuit design search.&nbsp;<em>Quantum Machine Intelligence, 3<\/em>, Article 25. <a href=\"https:\/\/doi.org\/10.1007\/s42484-021-00051-z\">https:\/\/doi.org\/10.1007\/s42484-021-00051-z<\/a><\/li>\n<\/ul>\n<\/div><\/div>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Preprints, Presentations &amp; Reports<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Harkness, A., Validi, H., Fakhimi, R., Hicks, I. V., Terlaky, T. &amp; Zuluaga, L. F. (2025). Characterizing QUBO Reformulations of the Max-k-Cut Problem for Quantum Computing. <a href=\"https:\/\/arxiv.org\/abs\/2511.01108\">arXiv:2511.01108<\/a><\/li>\n\n\n\n<li>Tu, Y., Dubynskyi, M., Mohammadisiahroudi, M., Riashchentceva, E., Cheng, J.,  Ryashchentsev, D., Terlaky, T. &amp; Liu, J. (2025). Towards identifying possible fault-tolerant advantage of quantum linear system algorithms in terms of space, time and energy. <a href=\"https:\/\/arxiv.org\/abs\/2502.11239\">arXiv:2502.11239<\/a><\/li>\n\n\n\n<li>Harkness, A., Saltovets, K., Mohammadisiahroudi, M. &amp; Terlaky, T. (2025). Benchmarking Refined Quantum Linear Systems Algorithms. Presented at Purdue QAI Symposium 2025. <a href=\"https:\/\/engineering.lehigh.edu\/sites\/engineering.lehigh.edu\/files\/_DEPARTMENTS\/ise\/pdf\/tech-papers\/25\/25T_019.pdf\">25T-019<\/a><\/li>\n\n\n\n<li>Mohammadisiahroudi, M., Wu, Z., Sampourmahani, P., Harkness, A. &amp; Terlaky, T. (2025). Quantum Interior Point Methods: A Review of Developments and An Optimally Scaling Framework. Presented at Purdue QAI Symposium 2025. <a href=\"https:\/\/arxiv.org\/abs\/2512.06224\">arXiv:2512.06224<\/a><\/li>\n\n\n\n<li>Mohammadisiahroudi, M., Wu, Z., Sampourmahani, P., You, J. &amp; Terlaky, T. (2025). Optimal Scaling Quantum Interior Point Method for Linear Optimization. Presented at IEEE QCE 2025. <a href=\"https:\/\/arxiv.org\/abs\/2512.04510\">arXiv:2512.04510<\/a><\/li>\n\n\n\n<li>Quintero, R. A., Vera, J. C. &amp; Zuluaga, L. F. (2024). Lagrangian Reformulation for Nonconvex Optimization: Tailoring Problems to Specialized Solvers. <a href=\"https:\/\/arxiv.org\/abs\/2410.24111\">arXiv:2410.24111<\/a><\/li>\n\n\n\n<li>Wu, Z., Misra, S., Terlaky, T., Yang, X. &amp; Vuffray, M. (2024). An Efficient Quantum Algorithm for Linear System Problem in Tensor Format. <a href=\"https:\/\/arxiv.org\/abs\/2403.19829\">arXiv:2403.19829<\/a><\/li>\n\n\n\n<li>Augustino, B., Leng, J., Nannicini, G., Terlaky, T. &amp; Wu, X. (2023). A quantum central path algorithm for linear optimization. <a href=\"https:\/\/arxiv.org\/abs\/2311.03977\">arXiv:2311.03977<\/a><\/li>\n\n\n\n<li>Augustino, Nannicini, G., Terlaky, T. &amp; Zuluaga, L. (2023). Solving the semidefinite relaxation of QUBOs in matrix multiplication time, and faster with a quantum computer. <a href=\"https:\/\/arxiv.org\/abs\/2301.04237\">arXiv:2301.04237<\/a><\/li>\n\n\n\n<li>Fakhimi, R., Validi, H., Hicks, I. V., Terlaky, T. &amp; Zuluaga, L. F. (2023). On Hamiltonians of the Max k-cut Problem. <a href=\"https:\/\/engineering.lehigh.edu\/sites\/engineering.lehigh.edu\/files\/_DEPARTMENTS\/ise\/pdf\/tech-papers\/23\/23T_020.pdf\">23T-020<\/a><\/li>\n\n\n\n<li>Sampourmahani, P., Mohammadisiahroudi, M. &amp; Terlaky, T. (2023). On Semidefinite Representations of Second-Order Conic Optimization Problems. <a href=\"https:\/\/arxiv.org\/abs\/2301.12007\">arXiv:2301.12007<\/a><\/li>\n\n\n\n<li>Mohammadisiahroudi, M., Augustino, B., Nannicini, G. &amp; Terlaky, T. (2023). Accurately Solving Linear Systems with Quantum Oracles. Presented at APS March Meeting 2023. <a href=\"https:\/\/engineering.lehigh.edu\/sites\/engineering.lehigh.edu\/files\/_DEPARTMENTS\/ise\/pdf\/tech-papers\/23\/23T-006.pdf\">23T-006<\/a><\/li>\n\n\n\n<li>Mohammadisiaroudi, M., Augustino, B., Fakhimi, R., Nannicini, Giacomo. &amp; Terlaky, T. (2023). Exponentially more precise tomography for quantum linear system solutions via iterative refinement. <a href=\"https:\/\/engineering.lehigh.edu\/sites\/engineering.lehigh.edu\/files\/_DEPARTMENTS\/ise\/pdf\/tech-papers\/23\/23T-007.pdf\">23T-007<\/a><\/li>\n\n\n\n<li>Quintero, R. A., Terlaky, T. &amp; Zuluaga, L. F. (2021). Characterizing and Benchmarking QUBO Reformulations of the Knapsack Problem. <a href=\"https:\/\/engineering.lehigh.edu\/sites\/engineering.lehigh.edu\/files\/_DEPARTMENTS\/ise\/pdf\/tech-papers\/21\/21T_028.pdf\">21T-028<\/a><\/li>\n\n\n\n<li>Fakhimi, R., Validi, H., Hicks, I. V., Terlaky, T. &amp; Zuluaga, L. F. (2021). Quantum-inspired formulations for the max k-cut problem. <a href=\"https:\/\/engineering.lehigh.edu\/sites\/engineering.lehigh.edu\/files\/_DEPARTMENTS\/ise\/pdf\/tech-papers\/21\/21T_007.pdf\">21T-007<\/a><\/li>\n\n\n\n<li>Augustino, B., Nannicini, G., Terlaky, T. &amp; Zuluaga, L. F. (2021). Quantum interior point methods for semidefinite optimization. <a href=\"https:\/\/engineering.lehigh.edu\/sites\/engineering.lehigh.edu\/files\/_DEPARTMENTS\/ise\/pdf\/tech-papers\/21\/21T_009.pdf\">21T-009<\/a><\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Quantum Interior Point Methods Making use of Quantum Linear Solvers and Block Encodings to achieve a quantum speedup over classical&hellip;<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-44","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/quantum-computing.lehigh.edu\/index.php\/wp-json\/wp\/v2\/pages\/44","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/quantum-computing.lehigh.edu\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/quantum-computing.lehigh.edu\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/quantum-computing.lehigh.edu\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/quantum-computing.lehigh.edu\/index.php\/wp-json\/wp\/v2\/comments?post=44"}],"version-history":[{"count":24,"href":"https:\/\/quantum-computing.lehigh.edu\/index.php\/wp-json\/wp\/v2\/pages\/44\/revisions"}],"predecessor-version":[{"id":434,"href":"https:\/\/quantum-computing.lehigh.edu\/index.php\/wp-json\/wp\/v2\/pages\/44\/revisions\/434"}],"wp:attachment":[{"href":"https:\/\/quantum-computing.lehigh.edu\/index.php\/wp-json\/wp\/v2\/media?parent=44"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}