Enhancing the Harrow-Hassidim-Lloyd (HHL) algorithm in systems with large condition numbers
Although the Harrow-Hassidim-Lloyd (HHL) algorithm offers an exponential speedup in system size for treating linear equations of the form Ax[over ⃗]=b[over ⃗] on quantum computers when compared to their traditional counterparts, it faces a challenge related to the condition number (κ) scaling of the...
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| Format: | Article |
| Language: | English |
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American Physical Society
2025-06-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/msvx-1drx |
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| author | Peniel Bertrand Tsemo Akshaya Jayashankar K. Sugisaki Nishanth Baskaran Sayan Chakraborty V. S. Prasannaa |
| author_facet | Peniel Bertrand Tsemo Akshaya Jayashankar K. Sugisaki Nishanth Baskaran Sayan Chakraborty V. S. Prasannaa |
| author_sort | Peniel Bertrand Tsemo |
| collection | DOAJ |
| description | Although the Harrow-Hassidim-Lloyd (HHL) algorithm offers an exponential speedup in system size for treating linear equations of the form Ax[over ⃗]=b[over ⃗] on quantum computers when compared to their traditional counterparts, it faces a challenge related to the condition number (κ) scaling of the A matrix. In this work, we address the issue by introducing the postselection-improved HHL (Psi-HHL) framework that operates on a simple yet effective premise: subtracting mixed and wrong signals to extract correct signals while providing the benefit of optimal scaling in the condition number of A (denoted as κ) for large κ scenarios. This approach, which leads to minimal increase in circuit depth, has the important practical implication of having to use substantially fewer shots relative to the traditional HHL algorithm. The term “signal” refers to a feature of |x〉. We design circuits for overlap and expectation value estimation in the Psi-HHL framework. We demonstrate performance of Psi-HHL via numerical simulations. We carry out two sets of computations, where we go up to 26-qubit calculations, to demonstrate the ability of Psi-HHL to handle situations involving large-κ matrices via (a) a set of toy matrices, for which we go up to size 64×64 and κ values of up to ≈1×10^{6}, and (b) application to quantum chemistry, where we consider matrices up to size 256×256 that reach κ of about 393. The molecular systems that we consider are Li_{2}, KH, RbH, and CsH. |
| format | Article |
| id | doaj-art-b428879eb7a74ef6957ab82fb7af3dcc |
| institution | DOAJ |
| issn | 2643-1564 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj-art-b428879eb7a74ef6957ab82fb7af3dcc2025-08-20T03:21:31ZengAmerican Physical SocietyPhysical Review Research2643-15642025-06-017202327010.1103/msvx-1drxEnhancing the Harrow-Hassidim-Lloyd (HHL) algorithm in systems with large condition numbersPeniel Bertrand TsemoAkshaya JayashankarK. SugisakiNishanth BaskaranSayan ChakrabortyV. S. PrasannaaAlthough the Harrow-Hassidim-Lloyd (HHL) algorithm offers an exponential speedup in system size for treating linear equations of the form Ax[over ⃗]=b[over ⃗] on quantum computers when compared to their traditional counterparts, it faces a challenge related to the condition number (κ) scaling of the A matrix. In this work, we address the issue by introducing the postselection-improved HHL (Psi-HHL) framework that operates on a simple yet effective premise: subtracting mixed and wrong signals to extract correct signals while providing the benefit of optimal scaling in the condition number of A (denoted as κ) for large κ scenarios. This approach, which leads to minimal increase in circuit depth, has the important practical implication of having to use substantially fewer shots relative to the traditional HHL algorithm. The term “signal” refers to a feature of |x〉. We design circuits for overlap and expectation value estimation in the Psi-HHL framework. We demonstrate performance of Psi-HHL via numerical simulations. We carry out two sets of computations, where we go up to 26-qubit calculations, to demonstrate the ability of Psi-HHL to handle situations involving large-κ matrices via (a) a set of toy matrices, for which we go up to size 64×64 and κ values of up to ≈1×10^{6}, and (b) application to quantum chemistry, where we consider matrices up to size 256×256 that reach κ of about 393. The molecular systems that we consider are Li_{2}, KH, RbH, and CsH.http://doi.org/10.1103/msvx-1drx |
| spellingShingle | Peniel Bertrand Tsemo Akshaya Jayashankar K. Sugisaki Nishanth Baskaran Sayan Chakraborty V. S. Prasannaa Enhancing the Harrow-Hassidim-Lloyd (HHL) algorithm in systems with large condition numbers Physical Review Research |
| title | Enhancing the Harrow-Hassidim-Lloyd (HHL) algorithm in systems with large condition numbers |
| title_full | Enhancing the Harrow-Hassidim-Lloyd (HHL) algorithm in systems with large condition numbers |
| title_fullStr | Enhancing the Harrow-Hassidim-Lloyd (HHL) algorithm in systems with large condition numbers |
| title_full_unstemmed | Enhancing the Harrow-Hassidim-Lloyd (HHL) algorithm in systems with large condition numbers |
| title_short | Enhancing the Harrow-Hassidim-Lloyd (HHL) algorithm in systems with large condition numbers |
| title_sort | enhancing the harrow hassidim lloyd hhl algorithm in systems with large condition numbers |
| url | http://doi.org/10.1103/msvx-1drx |
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