From liangyuc at chalmers.se Tue May 23 01:36:18 2023 From: liangyuc at chalmers.se (Liangyu Chen) Date: Mon, 22 May 2023 23:36:18 +0000 Subject: [All.non-board.wacqt] =?windows-1252?q?Seminar_=22Hardware_Effic?= =?windows-1252?q?ient_Machine_Learning_for_High-Fidelity_Qubit_Readout=94?= =?windows-1252?q?_by_Asst=2E_Prof=2E_Swamit_Tannu_on_May_24?= Message-ID: <94b72e7b475f4b36a8469599ee4820ef@chalmers.se> Dear all, You are very welcome to attend a seminar by Asst. Prof. Swamit Tannu from the University of Wisconsin-Madison on May 24 at 11.00 in Kollektorn (4th floor MC2 Building) or on Zoom via https://chalmers.zoom.us/j/65047510522, password: 2023. The seminar is titled "Hardware Efficient Machine Learning for High-Fidelity Qubit Readout" Below you can find the abstract of the talk. Swamit will visit Chalmers from 10:00 to 15:00 on May 24 within the framework of WACQT?s guest researcher programme, and please let me know if you are interested in a discussion during lunch or in the afternoon. Title: Hardware Efficient Machine Learning for High-Fidelity Qubit Readout Abstract: Multi-qubit readout is among the most error-prone operations in superconducting quantum computing systems. These errors occur due to crosstalk between the readout tones in a frequency-multiplexed readout scheme, spontaneous state transitions during the measurement, excitations caused by the readout pulse, and thermal noise added to the readout signal as it travels from the refrigerator to the room-temperature electronics. Prior works on reducing readout errors include machine learning-assisted readout, where a neural network is used for more robust discrimination by compensating for crosstalk errors. However, the neural network size can limit systems' scalability, especially if fast hardware discrimination is required. In this talk, I will discuss the need to incorporate "control scalability" as a metric when developing readout protocols and systems. Furthermore, I will outline our ongoing work on scalable approaches for mitigating single-shot readout errors by using a matched filter in conjunction with a significantly smaller and scalable neural network for qubit-state discrimination. Fast and accurate discrimination of qubit states is essential for deploying quantum error correction codes. To that end, we are investigating computationally efficient and scalable machine learning algorithms for enabling high-fidelity multi-quit readout that are readily implementable on FPGAs. Bio: Swamit is an Assistant Professor in the Computer Sciences Department at the University of Wisconsin-Madison. His research focuses on quantum computer architecture and quantum software. Swamit was inducted into the MICRO Hall of Fame (Class of 2022). In addition, his works were awarded the Stamatis Vassiliadis best paper award at the computing frontiers conference and the best presentation award at the International Memory System Symposium. Before joining the University of Wisconsin-Madison, Swamit received his Ph.D. from the Georgia Institute of Technology in 2020. Have a nice day and see you on Wednesday! CHALMERS Liangyu Chen Doctoral student Department of Microtechnology and Nanoscience Quantum Technology Laboratory | Room A730 +46(0)702 50 71 92 Chalmers University of Technology MC2 | Kemivägen 9 SE-412 96 Gothenburg Sweden -------------- next part -------------- En HTML-bilaga skiljdes ut... URL: From liangyuc at chalmers.se Wed May 24 10:13:37 2023 From: liangyuc at chalmers.se (Liangyu Chen) Date: Wed, 24 May 2023 08:13:37 +0000 Subject: [All.non-board.wacqt] =?windows-1252?q?Seminar_=22Hardware_Effic?= =?windows-1252?q?ient_Machine_Learning_for_High-Fidelity_Qubit_Readout=94?= =?windows-1252?q?_by_Asst=2E_Prof=2E_Swamit_Tannu_on_May_24?= In-Reply-To: <94b72e7b475f4b36a8469599ee4820ef@chalmers.se> References: <94b72e7b475f4b36a8469599ee4820ef@chalmers.se> Message-ID: <7dc0b731bc8f43559312802fafedf660@chalmers.se> Dear all, Just a reminder, the seminar titled ?Hardware Efficient Machine Learning for High-Fidelity Qubit Readout? by Asst. Prof. Swamit Tannu will happen TODAY at 11am in Kollektorn, or you can tune in on Zoom via https://chalmers.zoom.us/j/65047510522, password: 2023. Hope to see you there! CHALMERS Liangyu Chen Doctoral student Department of Microtechnology and Nanoscience Quantum Technology Laboratory | Room A730 +46(0)702 50 71 92 Chalmers University of Technology MC2 | Kemivägen 9 SE-412 96 Gothenburg Sweden www.chalmers.se From: Liangyu Chen Sent: Tuesday, May 23, 2023 1:36 AM To: equip at lists.chalmers.se; chalmers-mc2_aqpl ; chalmers-mc2_qdpl ; qtl.mc2 at lists.chalmers.se; hus.mc2 ; all.non-board.wacqt at lists.chalmers.se; Mats Granath ; Swamit Tannu ; Benjamin Lienhard Subject: Seminar "Hardware Efficient Machine Learning for High-Fidelity Qubit Readout? by Asst. Prof. Swamit Tannu on May 24 Importance: High Dear all, You are very welcome to attend a seminar by Asst. Prof. Swamit Tannu from the University of Wisconsin-Madison on May 24 at 11.00 in Kollektorn (4th floor MC2 Building) or on Zoom via https://chalmers.zoom.us/j/65047510522, password: 2023. The seminar is titled "Hardware Efficient Machine Learning for High-Fidelity Qubit Readout" Below you can find the abstract of the talk. Swamit will visit Chalmers from 10:00 to 15:00 on May 24 within the framework of WACQT?s guest researcher programme, and please let me know if you are interested in a discussion during lunch or in the afternoon. Title: Hardware Efficient Machine Learning for High-Fidelity Qubit Readout Abstract: Multi-qubit readout is among the most error-prone operations in superconducting quantum computing systems. These errors occur due to crosstalk between the readout tones in a frequency-multiplexed readout scheme, spontaneous state transitions during the measurement, excitations caused by the readout pulse, and thermal noise added to the readout signal as it travels from the refrigerator to the room-temperature electronics. Prior works on reducing readout errors include machine learning-assisted readout, where a neural network is used for more robust discrimination by compensating for crosstalk errors. However, the neural network size can limit systems' scalability, especially if fast hardware discrimination is required. In this talk, I will discuss the need to incorporate "control scalability" as a metric when developing readout protocols and systems. Furthermore, I will outline our ongoing work on scalable approaches for mitigating single-shot readout errors by using a matched filter in conjunction with a significantly smaller and scalable neural network for qubit-state discrimination. Fast and accurate discrimination of qubit states is essential for deploying quantum error correction codes. To that end, we are investigating computationally efficient and scalable machine learning algorithms for enabling high-fidelity multi-quit readout that are readily implementable on FPGAs. Bio: Swamit is an Assistant Professor in the Computer Sciences Department at the University of Wisconsin-Madison. His research focuses on quantum computer architecture and quantum software. Swamit was inducted into the MICRO Hall of Fame (Class of 2022). In addition, his works were awarded the Stamatis Vassiliadis best paper award at the computing frontiers conference and the best presentation award at the International Memory System Symposium. Before joining the University of Wisconsin-Madison, Swamit received his Ph.D. from the Georgia Institute of Technology in 2020. Have a nice day and see you on Wednesday! CHALMERS Liangyu Chen Doctoral student Department of Microtechnology and Nanoscience Quantum Technology Laboratory | Room A730 +46(0)702 50 71 92 Chalmers University of Technology MC2 | Kemivägen 9 SE-412 96 Gothenburg Sweden -------------- next part -------------- En HTML-bilaga skiljdes ut... URL: