Exponential improvement in precision for simulating sparse Hamiltonians (1312.1414v2)

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.

• The paper introduces a quantum simulation algorithm that exponentially improves precision by reducing query complexity to sublogarithmic in the inverse error.
• It exploits advanced quantum query models and oblivious amplitude amplification to achieve optimal error scaling independent of the qubit count.
• The results set new lower bounds for Hamiltonian simulation and open avenues for more efficient quantum simulations in physics and quantum chemistry.

Exponential Improvement in Precision for Simulating Sparse Hamiltonians

The paper "Exponential improvement in precision for simulating sparse Hamiltonians," authored by Dominic W. Berry, Andrew M. Childs, Richard Cleve, Robin Kothari, and Rolando D. Somma, presents a significant advancement in quantum algorithms specifically aimed at simulating the dynamics governed by sparse Hamiltonians. The presented research introduces a quantum simulation algorithm with complexity that is sublogarithmic in the inverse error, achieving an exponential improvement over previous methodologies.

Background and Motivation

Simulating quantum systems accurately is a quintessential objective in quantum computation, tracing back to Feynman's original motivations for quantum computers. Sparse Hamiltonians represent a broad class of systems, relevant in numerous physical simulations and quantum algorithms. Traditional approaches to simulating such systems often rely on product formulas, which, despite their applicability, scale poorly with precision requirements.

Main Contributions and Results

The paper provides a new algorithm for simulating a $d$-sparse Hamiltonian $H$ acting on $n$ qubits over a time duration $t$ with an error $\epsilon$. The algorithm's complexity in terms of queries is expressed as $$O\left(\tau \frac{\log(\tau/\epsilon)}{\log\log(\tau/\epsilon)}\right)$$, where $\tau = d^2 \|H\|_{\max} t$. For gate complexities, the algorithm requires $$O\left(\tau \frac{\log^2(\tau/\epsilon)}{\log\log(\tau/\epsilon)}n\right)$$ additional 2-qubit gates. The notable aspect here is that the query complexity of the algorithm does not depend on the number of qubits involved, which marks a distinct departure from many existing algorithms.

Methodology

The authors achieve this performance by linking Hamiltonian simulation to an improved version of the continuous- and fractional-query models using discrete queries. This connection leverages a form of quantum query known as oblivious amplitude amplification, which does not require a costly fault correction procedure inherent in previous similar tasks. Additionally, the paper includes simpler and efficient analysis techniques to support these improved methods.

Lower Bound and Optimality

The authors establish new lower bounds showing that their algorithms are optimal concerning the error parameter $\epsilon$. Specifically, they show that for precision $\epsilon$, any algorithm performing Hamiltonian simulation must use $$\Omega\left(\frac{\log(1/\epsilon)}{\log\log(1/\epsilon)}\right)$$ queries. This result solidifies the significance of their algorithmic improvement, achieving an upper bound that is tight with their derived lower bound within these logarithmic factors.

Implications and Future Directions

These advances suggest several implications for both theoretical and practical quantum computing. From a theoretical perspective, the connection made between different quantum query models opens avenues for potentially exploring similar efficiencies in other areas of quantum algorithms and simulations. Practically, the improved precision in simulating sparse Hamiltonians could enhance the reliability and performance of quantum simulations of physical systems, which is a critical component of quantum computing applications in materials science and quantum chemistry.

The research presents a promising step forward, with potential extensions to explore the efficacy across other classes of Hamiltonians, and to further bridge discrete and continuous quantum computational models. It also lays the groundwork for future studies to investigate the interaction of such methods with error-correction schemes necessary for scalable quantum computation. Such explorations could significantly influence the development of quantum algorithms in various domain-specific applications.