@article {2926, title = {Optimal scaling quantum linear systems solver via discrete adiabatic theorem}, year = {2021}, month = {11/15/2021}, abstract = {

Recently, several approaches to solving linear systems on a quantum computer have been formulated in terms of the quantum adiabatic theorem for a continuously varying Hamiltonian. Such approaches enabled near-linear scaling in the condition number κ of the linear system, without requiring a complicated variable-time amplitude amplification procedure. However, the most efficient of those procedures is still asymptotically sub-optimal by a factor of log(κ). Here, we prove a rigorous form of the adiabatic theorem that bounds the error in terms of the spectral gap for intrinsically discrete time evolutions. We use this discrete adiabatic theorem to develop a quantum algorithm for solving linear systems that is asymptotically optimal, in the sense that the complexity is strictly linear in κ, matching a known lower bound on the complexity. Our O(κlog(1/ε)) complexity is also optimal in terms of the combined scaling in κ and the precision ε. Compared to existing suboptimal methods, our algorithm is simpler and easier to implement. Moreover, we determine the constant factors in the algorithm, which would be suitable for determining the complexity in terms of gate counts for specific applications.\ 

}, url = {https://arxiv.org/abs/2111.08152}, author = {Pedro C. S. Costa and Dong An and Yuval R. Sanders and Yuan Su and Ryan Babbush and Dominic W. Berry} } @article {2402, title = {Time-dependent Hamiltonian simulation with L1-norm scaling}, journal = {Quantum}, volume = {4}, year = {2020}, month = {4/20/2020}, abstract = {

The difficulty of simulating quantum dynamics depends on the norm of the Hamiltonian. When the Hamiltonian varies with time, the simulation complexity should only depend on this quantity instantaneously. We develop quantum simulation algorithms that exploit this intuition. For the case of sparse Hamiltonian simulation, the gate complexity scales with the L1 norm \∫t0dτ||H(τ)||max, whereas the best previous results scale with tmaxτ\∈[0,t]||H(τ)||max. We also show analogous results for Hamiltonians that are linear combinations of unitaries. Our approaches thus provide an improvement over previous simulation algorithms that can be substantial when the Hamiltonian varies significantly. We introduce two new techniques: a classical sampler of time-dependent Hamiltonians and a rescaling principle for the Schr{\"o}dinger equation. The rescaled Dyson-series algorithm is nearly optimal with respect to all parameters of interest, whereas the sampling-based approach is easier to realize for near-term simulation. By leveraging the L1-norm information, we obtain polynomial speedups for semi-classical simulations of scattering processes in quantum chemistry.

}, doi = {https://doi.org/10.22331/q-2020-04-20-254}, url = {https://arxiv.org/abs/1906.07115}, author = {Dominic W. Berry and Andrew M. Childs and Yuan Su and Xin Wang and Nathan Wiebe} } @article {1923, title = {Quantum algorithm for linear differential equations with exponentially improved dependence on precision}, journal = {Communications in Mathematical Physics}, volume = {356}, year = {2017}, month = {2017/12/01}, pages = {1057-1081}, abstract = {

We present a quantum algorithm for systems of (possibly inhomogeneous) linear ordinary differential equations with constant coefficients. The algorithm produces a quantum state that is proportional to the solution at a desired final time. The complexity of the algorithm is polynomial in the logarithm of the inverse error, an exponential improvement over previous quantum algorithms for this problem. Our result builds upon recent advances in quantum linear systems algorithms by encoding the simulation into a sparse, well-conditioned linear system that approximates evolution according to the propagator using a Taylor series. Unlike with finite difference methods, our approach does not require additional hypotheses to ensure numerical stability.

}, url = {https://arxiv.org/abs/1701.03684}, author = {Dominic W. Berry and Andrew M. Childs and Aaron Ostrander and Guoming Wang} } @article {1247, title = {Hamiltonian simulation with nearly optimal dependence on all parameters}, journal = {Proceedings of the 56th IEEE Symposium on Foundations of Computer Science}, year = {2015}, month = {2015/01/08}, pages = {792-809}, abstract = { We present an algorithm for sparse Hamiltonian simulation that has optimal dependence on all parameters of interest (up to log factors). Previous algorithms had optimal or near-optimal scaling in some parameters at the cost of poor scaling in others. Hamiltonian simulation via a quantum walk has optimal dependence on the sparsity $d$ at the expense of poor scaling in the allowed error $\epsilon$. In contrast, an approach based on fractional-query simulation provides optimal scaling in $\epsilon$ at the expense of poor scaling in $d$. Here we combine the two approaches, achieving the best features of both. By implementing a linear combination of quantum walk steps with coefficients given by Bessel functions, our algorithm achieves near-linear scaling in $\tau := d \|H\|_{\max} t$ and sublogarithmic scaling in $1/\epsilon$. Our dependence on $\epsilon$ is optimal, and we prove a new lower bound showing that no algorithm can have sublinear dependence on $\tau$. }, doi = {10.1109/FOCS.2015.54}, url = {http://arxiv.org/abs/1501.01715}, author = {Dominic W. Berry and Andrew M. Childs and Robin Kothari} } @article {1265, title = {Simulating Hamiltonian dynamics with a truncated Taylor series}, journal = {Physical Review Letters}, volume = {114}, year = {2015}, month = {2015/03/03}, pages = {090502}, abstract = { We describe a simple, efficient method for simulating Hamiltonian dynamics on a quantum computer by approximating the truncated Taylor series of the evolution operator. Our method can simulate the time evolution of a wide variety of physical systems. As in another recent algorithm, the cost of our method depends only logarithmically on the inverse of the desired precision, which is optimal. However, we simplify the algorithm and its analysis by using a method for implementing linear combinations of unitary operations to directly apply the truncated Taylor series. }, doi = {10.1103/PhysRevLett.114.090502}, url = {http://arxiv.org/abs/1412.4687v1}, author = {Dominic W. Berry and Andrew M. Childs and Richard Cleve and Robin Kothari and Rolando D. Somma} } @article {1264, title = {Exponential improvement in precision for simulating sparse Hamiltonians}, journal = {Proceedings of the 46th ACM Symposium on Theory of Computing (STOC 2014)}, year = {2014}, month = {2014/05/31}, pages = {283-292}, abstract = { 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 $H$ acting on $n$ qubits can be simulated for time $t$ with precision $\epsilon$ using $O\big(\tau \frac{\log(\tau/\epsilon)}{\log\log(\tau/\epsilon)}\big)$ queries and $O\big(\tau \frac{\log^2(\tau/\epsilon)}{\log\log(\tau/\epsilon)}n\big)$ additional 2-qubit gates, where $\tau = d^2 \|{H}\|_{\max} t$. 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 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. }, isbn = {978-1-4503-2710-7}, doi = {10.1145/2591796.2591854}, url = {http://arxiv.org/abs/1312.1414v2}, author = {Dominic W. Berry and Andrew M. Childs and Richard Cleve and Robin Kothari and Rolando D. Somma} } @article {1220, title = {Black-box Hamiltonian simulation and unitary implementation}, year = {2009}, month = {2009/10/22}, abstract = { We present general methods for simulating black-box Hamiltonians using quantum walks. These techniques have two main applications: simulating sparse Hamiltonians and implementing black-box unitary operations. In particular, we give the best known simulation of sparse Hamiltonians with constant precision. Our method has complexity linear in both the sparseness D (the maximum number of nonzero elements in a column) and the evolution time t, whereas previous methods had complexity scaling as D^4 and were superlinear in t. We also consider the task of implementing an arbitrary unitary operation given a black-box description of its matrix elements. Whereas standard methods for performing an explicitly specified N x N unitary operation use O(N^2) elementary gates, we show that a black-box unitary can be performed with bounded error using O(N^{2/3} (log log N)^{4/3}) queries to its matrix elements. In fact, except for pathological cases, it appears that most unitaries can be performed with only O(sqrt{N}) queries, which is optimal. }, url = {http://arxiv.org/abs/0910.4157v4}, author = {Dominic W. Berry and Andrew M. Childs} }