Narayanan Rengaswamy: Tailoring Fault-Tolerance to Quantum Algorithms

Channel:
Subscribers:
2,470
Published on ● Video Link: https://www.youtube.com/watch?v=wDBYCK4Glug



Duration: 39:49
98 views
0


The standard approach to universal fault-tolerant quantum computing is to develop a general-purpose quantum error correction mechanism that can implement a universal set of logical gates fault-tolerantly. Given such a scheme, any quantum algorithm can be realized fault-tolerantly by composing the relevant logical gates from this set. However, we know that quantum computers provide a significant quantum advantage only for specific quantum algorithms. Hence, a universal quantum computer can likely gain from compiling such specific algorithms using tailored quantum error correction schemes. In this work, we take the first steps towards such algorithm-tailored quantum fault-tolerance. We consider Trotter circuits in quantum simulation, which is an important application of quantum computing. We develop a solve-and-stitch algorithm to systematically synthesize physical realizations of Clifford Trotter circuits on the well-known [[n,n−2,2]] error-detecting code family. Our analysis shows that this family implements Trotter circuits with optimal depth, thereby serving as an illuminating example of tailored quantum error correction. We achieve fault-tolerance for these circuits using flag gadgets, which add minimal overhead. The solve-and-stitch algorithm has the potential to scale beyond this specific example and hence provide a principled approach to tailored fault-tolerance in quantum computing.

The paper is available at: https://arxiv.org/abs/2404.11953. It is closely related to my prior work on the Logical Clifford Synthesis (LCS) algorithm: https://arxiv.org/abs/1907.00310, whose implementation is available at: https://github.com/nrenga/symplectic-arxiv18a.




Other Videos By QuICS


2024-10-28Hakan Türeci: Harnessing Quantum Dynamics for Inference on Data Embedded in Weak Signals
2024-10-28Junyi Liu
2024-10-27Quntao Zhuang: Dynamical Transition in Controllable Quantum Neural Networks with Large Depth
2024-10-27Vahagn Mkhitaryan: Quantum phases of Rydberg atoms on Shastry - Sutherland lattice
2024-10-27Hossein Sadeghi: Analog quantum computing with neutral atoms
2024-10-27Sheng-Tao Wang: Some Results on Aquila - QuEra’s neutral-atom analog quantum computer
2024-09-24Jonathan Conrad: GKP Codes: A Rosetta Stone for Quantum Error Correction
2024-08-20Ivan Rojkov: Stabilization of cat-state manifolds using nonlinear reservoir engineering
2024-07-09Marcos Crichigno: Quantum Spin Chains and Symmetric Functions
2024-05-28Kenneth Rudinger: QCVV: Making Quantum Computers Less Broken
2024-05-28Narayanan Rengaswamy: Tailoring Fault-Tolerance to Quantum Algorithms
2024-05-28Timur Tscherbul: JQI-QuICS Special Seminar
2024-02-19Anatoly Dymarsky: Classical and quantum codes, 2d CFTs and holography
2024-02-05Carleton Coffrin: Some Unexpected Applications of Analog Quantum Computers
2023-11-28Alexander Kwiatkowski: Optimized experiment design and analysis for fully randomized benchmarking
2023-11-09Roger Mong: Measurement Quantum Cellular Automata and Anomalies in Floquet Codes
2023-10-11Yu Tong: Recent progress in Hamiltonian learning
2023-10-06Ryan O'Donnell:New directions in quantum state learning and testing
2023-09-22PQCrypto 2023: Session VII: Faulting WOTS to forge LMS, XMSS, or SPHINCS+ signatures (A. Wagner)
2023-09-22PQCrypto 2023: Session VII: Side-Channel Analysis of Dilithium in Hardware (Georg Land)
2023-09-22PQCrypto 2023: Session VII: WrapQ: Side-Channel Secure Key Management (Markku-Juhani O. Saarinen)



Tags:
quantum computing