Exponential improvement in precision for simulating sparse Hamiltonians

被引:233
作者
Berry, Dominic W. [1 ]
Childs, Andrew M. [2 ,3 ]
Cleve, Richard [3 ,4 ]
Kothari, Robin [3 ,4 ]
Somma, Rolando D. [5 ]
机构
[1] Macquarie Univ, Dept Phys & Astron, N Ryde, NSW, Australia
[2] Univ Waterloo, Dept Combinator & Optimizat, Waterloo, ON, Canada
[3] Univ Waterloo, Inst Quantum Comp, Waterloo, ON, Canada
[4] Univ Waterloo, Cheriton Sch Comp Sci, Waterloo, ON, Canada
[5] Los Alamos Natl Lab, Div Theoret, Los Alamos, NM USA
来源
STOC'14: PROCEEDINGS OF THE 46TH ANNUAL 2014 ACM SYMPOSIUM ON THEORY OF COMPUTING | 2014年
基金
加拿大自然科学与工程研究理事会; 澳大利亚研究理事会;
关键词
Hamiltonian simulation; quantum algorithms; QUANTUM; FORMULA;
D O I
10.1145/2591796.2591854
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
We provide a quantum algorithm for simulating the dynamics of sparse Hamiltonians with complexity sublogarithmic in the inverse error, an exponential improvement over previous methods. Specifically, we show that a d -sparse Hamiltonian H on n qubits can be simulated for time t with precision E using 0(ylog(T/E)/log log(y/E)) queries and 0 ( ynlog 2 (T/E) /log log (7- /)) additional 2-qubit gates, where T = d2 111111m axt. Unlike previous approaches based on product formulas, the query complexity is independent of the number of qubits acted on, and for time -varying Hamiltonians, the gate complexity is logarithmic in the norm of the derivative of the Hamiltonian. Our algorithm is based on a significantly improved simulation of the continuous and fractional -query models using discrete quantum queries, showing that the former models are not much more powerful than the discrete model even for very small error. We also significantly simplify the analysis of this conversion, avoiding the need for a complex fault correction procedure. Our simplification relies on a new form of "oblivious amplitude amplification" that can be applied even though the reflection about the input state is unavailable. Finally, we prove new lower bounds showing that our algorithms are optimal as a function of the error.
引用
收藏
页码:283 / 292
页数:10
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