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1
Intro
2
Why do we care?
3
Quantum linear systems problem
4
Complexity scaling
5
Continuous adiabatic algorithm
6
Adiabatic approach to QLSP
7
Non-symmetric case
8
Adiabatic walk
9
Norm of differences
10
Multistep gap
11
Discrete adiabatic theorem
12
Summation by parts formula
13
Contour integrals for bounds
14
Multiple eigenvalues problem
15
Numerical testing for constant factor
16
Filtering solution
17
LCU with two qubits
18
Putting it all together
19
Lower bound
20
Conclusions
Description:
Explore an advanced quantum computing lecture on solving linear systems using a discrete adiabatic theorem approach. Delve into the development of an asymptotically optimal quantum algorithm with linear complexity in the condition number, matching known lower bounds. Examine the rigorous proof of the discrete adiabatic theorem, its application to quantum linear systems, and the algorithm's simplified implementation. Investigate the constant factors, gate count complexities, and potential applications. Compare this method to existing suboptimal approaches and understand its advantages in terms of precision and efficiency.

Optimal Scaling Quantum Linear Systems Solver via Discrete Adiabatic Theorem

Institute for Pure & Applied Mathematics (IPAM)
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