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Journal of Physics Conference Series

Efficient Quantum Circuit Implementations of The qDRIFT Algorithm via Linear Combinations of Unitaries and Quantum Forking

I. J. David, Ilya Sinayskiy, Francesco Petruccione

Published Mar 1, 2025• 0 citations
Publisher

Abstract

Abstract Quantum computers can be used to simulate the evolution of quantum systems via the discretization of this evolution into unitary gate sequences. While Trotter-Suzuki (TS) methods stand as the most prevalent approach for quantum simulation, their effectiveness is contingent upon the sparsity of the Hamiltonian system. This is due to the precision and gate complexity of the TS method being dependent on the number of terms within the Hamiltonian. Despite attempts to mitigate this dependence through the random permutation of terms within TS methods, scalability issues persist. To address this, Campbell introduced the qDRIFT algorithm, leveraging random compilation to alleviate the reliance on the Hamiltonian’s term count. However, the initial implementation of this algorithm necessitated the involvement of classical computing resources for gate set compilation. Our study presents two novel quantum circuit implementations of the qDRIFT algorithm, eliminating the need for classical computing resources. We employ the Linear Combination of Unitaries (LCU) approach and the Quantum Forking (QF) procedure to construct efficient circuits to implement the qDRIFT algorithm. Furthermore, we conduct a comparative analysis of the gate complexities between our proposed circuits and TS methods, identifying the scenarios in which our circuits exhibit superior performance.

DOI
10.1088/1742-6596/2970/1/012004

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Abstract Quantum computers can be used to simulate the evolution of quantum systems via the discretization of this evolution into unitary gate sequences. While Trotter-Suzuki (TS) methods stand as the most prevalent approach for quantum simulation, their effectiveness is contingent upon the sparsity of the Hamiltonian system. This is due to the precision and gate complexity of the TS method being dependent on the number of terms within the Hamiltonian. Despite attempts to mitigate this dependence through the random permutation of terms within TS methods, scalability issues persist. To address this, Campbell introduced the qDRIFT algorithm, leveraging random compilation to alleviate the reliance on the Hamiltonian’s term count. However, the initial implementation of this algorithm necessitated the involvement of classical computing resources for gate set compilation. Our study presents two novel quantum circuit implementations of the qDRIFT algorithm, eliminating the need for classical computing resources. We employ the Linear Combination of Unitaries (LCU) approach and the Quantum Forking (QF) procedure to construct efficient circuits to implement the qDRIFT algorithm. Furthermore, we conduct a comparative analysis of the gate complexities between our proposed circuits and TS methods, identifying the scenarios in which our circuits exhibit superior performance.
The dynamics of a quantum system can be simulated using a quantum computer by breaking down the unitary into a quantum circuit of one and two qubit gates. The most established methods are the Trotter-Suzuki decompositions, for which rigorous bounds on the circuit size depend on the number of terms L in the system Hamiltonian and the size of the largest term in the Hamiltonian Λ. Consequently, the Trotter-Suzuki method is only practical for sparse Hamiltonians. Trotter-Suzuki is a deterministic compiler but it was recently shown that randomized compiling offers lower overheads. Here we present and analyze a randomized compiler for Hamiltonian simulation where gate probabilities are proportional to the strength of a corresponding term in the Hamiltonian. This approach requires a circuit size independent of L and Λ, but instead depending on λ the absolute sum of Hamiltonian strengths (the ℓ_{1} norm). Therefore, it is especially suited to electronic structure Hamiltonians relevant to quantum chemistry. Considering propane, carbon dioxide, and ethane, we observe speed-ups compared to standard Trotter-Suzuki of between 306× and 1591× for physically significant simulation times at precision 10^{-3}. Performing phase estimation at chemical accuracy, we report that the savings are similar.
In this work we propose an approach for implementing time-evolution of a quantum system using product formulas. The quantum algorithms we develop have provably better scaling (in terms of gate complexity and circuit depth) than a naive application of well-known Trotter formulas, for systems where the evolution is determined by a Hamiltonian with different energy scales (i.e., one part is "large" and another part is "small"). Our algorithms generate a decomposition of the evolution operator into a product of simple unitaries that are directly implementable on a quantum computer. Although the theoretical scaling is suboptimal compared with state-of-the-art algorithms (e.g., quantum signal processing), the performance of the algorithms we propose is highly competitive in practice. We illustrate this via extensive numerical simulations for several models. For instance, in the strong-field regime of the 1D transverse-field Ising model, our algorithms achieve an improvement of one order of magnitude in both the system size and evolution time that can be simulated with a fixed budget of 1000 arbitrary 2-qubit gates, compared with standard Trotter formulas.
When simulating the dynamics of open quantum systems with quantum computers, it is essential to accurately approximate the system's behaviour while preserving the physicality of its evolution. Traditionally, for Markovian open quantum systems, this has been achieved using first and second-order Trotter-Suzuki product formulas or probabilistic algorithms. In this work, we introduce novel non-probabilistic algorithms for simulating Markovian open quantum systems using randomisation. Our methods, including first and second-order randomised Trotter-Suzuki formulas and the QDRIFT channel, not only maintain the physicality of the system's evolution but also enhance the scalability and precision of quantum simulations. We derive error bounds and step count limits for these techniques, bypassing the need for the mixing lemma typically employed in Hamiltonian simulation proofs. Furthermore, we implement these randomised algorithms using Classical Sampling (CS), demonstrating their gate complexity advantages over deterministic TS product formulas. This work systematically extends powerful randomisation techniques from Hamiltonian simulation to the general setting of Markovian open quantum systems, highlighting their potential to enable faster and more accurate simulations.
We develop a technique to perform efficient unitary time evolution of a general spin Hamiltonian based on the system with identical qubits, as naturally realized by permutation symmetric atomic ensembles. We employ Cartan decomposition method to the associated Hamiltonian Lie algebra and construct the symmetry adapted generators to implement unitary operator of the ensemble Hamiltonian. This enables the compact circuit representations of the system dynamics in terms of experimentally accessible collective spin operations and exploits the Dicke basis structure of the ensembles as an effective qudit representation. We benchmark Cartan based evolution against both exact time evolution and the Suzuki Trotter product formula. Our results show that the Cartan protocol accurately reproduces the operator dynamics, spin expectation values, and fluctuation observables with zero error that are substantially smaller than those obtained through Trotterization while maintaining an optimized circuit structure. In addition, scaling analysis with the ensemble size demonstrates that the Cartan error remains independent of the atom number while the corresponding Trotter error increases with the system size, highlighting the improved efficiency of the Cartan approach for a larger system size.
In this paper we provide a framework for combining multiple quantum simulation methods, such as Trotter-Suzuki formulas and QDrift into a single Composite channel that builds upon older coalescing ideas for reducing gate counts. The central idea behind our approach is to use a partitioning scheme that allocates a Hamiltonian term to the Trotter or QDrift part of a channel within the simulation. This allows us to simulate small but numerous terms using QDrift while simulating the larger terms using a high-order Trotter-Suzuki formula. We prove rigorous bounds on the diamond distance between the Composite channel and the ideal simulation channel and show under what conditions the cost of implementing the Composite channel is asymptotically upper bounded by the methods that comprise it for both probabilistic partitioning of terms and deterministic partitioning. Finally, we discuss strategies for determining partitioning schemes as well as methods for incorporating different simulation methods within the same framework.
The simulation of time evolution of large quantum systems is a classically challenging and often intractable task, making it a promising application for quantum computation. A Trotter-Suzuki approximation yields an implementation thereof, where a certain desired accuracy can be achieved by raising the gate count adequately. In this work, we introduce a variational algorithm which uses solutions of classical optimizations to predict efficient quantum circuits for time evolution of large quantum systems. Our strategy improves on the Trotter-Suzuki ansatz in accuracy and gate count by several orders of magnitude. In a NISQ-friendly setting, we can either significantly reduce the approximation error while using the same amount of gates as the Trotter-Suzuki ansatz or achieve a comparable accuracy with significantly fewer gates. We find, that our strategy outperforms the Trotter-Suzuki benchmark in translation invariant systems as well as for open boundaries.