arXiv subjects

Matthew Sutcliffe

Publications and source records attributed to Matthew Sutcliffe.

6 recordsLinked to original sources

arXiv2026

ABSTRACTS: Amsterdam Benchmark Suite for the Time and Resource Analysis of Clifford+T Simulators

Recent years have seen a rapid growth in literature presenting new methods for simulating non-Clifford quantum circuits with classical hardware. These methods span a range of approaches, including stabiliser decomposition and tensor contraction techniques, varying in efficiency depending on circuit class, depth, non-Clifford gate count, and other metrics. A notable limitation of this literature is the lack of a standardised approach to benchmarking, with each new paper outlining its own specification, simulating its own set of circuits on the authors' own hardware. This paper seeks to address this issue by presenting a standardised and canonical benchmark suite and infrastructure for quantifying the efficiency of non-Clifford classical simulators, with a consistent dataset of circuits and providing consistent (virtual) hardware, thereby enabling a fair comparison of results.

quant-ph↗

arXiv2026

Towards Quantum Optimised Malware Containment

The containment of malware in computing networks may be naturally formulated as a network influence minimisation problem, in which one seeks to limit the expected spread of an infection while balancing the operational cost of disabling network connections. Classical approaches often rely on Monte Carlo simulation of stochastic diffusion processes and greedy optimisation over candidate edge removals, resulting in significant computational overhead due to repeated influence evaluations. In this work, we propose a hybrid quantum approach which combines Quantum Amplitude Estimation (QAE) and Grover Minimum Finding (GMF) to provide quadratic improvements in both the estimation and optimisation components of the problem. Specifically, QAE replaces classical Monte Carlo simulation, reducing the sampling complexity of influence estimation from $O(1/\varepsilon^2)$ to $O(1/\varepsilon)$ for a target additive error $\varepsilon \ll 1$, while GMF reduces the number of candidate evaluations required to identify optimal edge removals from $O(|E_C|)$ to $O(\sqrt{|E_C|})$. We present a formal problem definition, describe the construction of the corresponding quantum oracles, and analyse the resulting complexity improvements under standard oracle assumptions. Preliminary experiments, including classical simulation of QAE and small-scale execution of Grover search on real quantum hardware, support the expected theoretical scaling. While practical implementation at scale requires fault-tolerant quantum devices, our results demonstrate that quantum algorithms offer a promising long-term direction for accelerating stochastic network optimisation problems such as malware containment.

quant-ph↗

arXiv2024

Dynamic T-decomposition for classical simulation of quantum circuits

It is known that a quantum circuit may be simulated with classical hardware via stabilizer state (T-)decomposition in $O(2^{\alpha t})$ time, given $t$ non-Clifford gates and a decomposition efficiency $\alpha$. The past years have seen a number of papers presenting new decompositions of lower $\alpha$ to reduce this runtime and enable simulation of ever larger circuits. More recently, it has been demonstrated that well placed applications of apparently weaker (higher $\alpha$) decompositions can in fact result in better overall efficiency when paired with the circuit simplification strategies of ZX-calculus. In this work, we take the most generalized T-decomposition (namely vertex cutting), which achieves a poor efficiency of $\alpha=1$, and identify common structures to which applying this can, after simplification via ZX-calculus rewriting, yield very strong effective efficiencies $\alpha_{\text{eff}}\ll1$. By taking into account this broader scope of the ZX-diagram and incorporating the simplification facilitated by the well-motivated cuts, we derive a handful of efficient T-decompositions whose applicabilities are relatively frequent. In benchmarking these new 'dynamic' decompositions against the existing alternatives, we observe a significant reduction in overall $\alpha$ and hence overall runtime for classical simulation, particularly for certain common circuit classes.

quant-ph↗

arXiv2024

Smarter k-Partitioning of ZX-Diagrams for Improved Quantum Circuit Simulation

We introduce a novel method for strong classical simulation of quantum circuits based on optimally k-partitioning ZX-diagrams, reducing each part individually, and then efficiently cross-referencing their results to conclude the overall probability amplitude of the original circuit. We then analyse how this method fares against the alternatives for circuits of various size, shape, and interconnectedness and demonstrate how it is often liable to outperform those alternatives in speed by orders of magnitude.

quant-ph↗

arXiv2024

Procedurally Optimised ZX-Diagram Cutting for Efficient T-Decomposition in Classical Simulation

A quantum circuit may be strongly classically simulated with the aid of ZX-calculus by decomposing its $t$ T-gates into a sum of $2^{\alpha t}$ classically computable stabiliser terms. In this paper, we introduce a general procedure to find an optimal pattern of vertex cuts in a ZX-diagram to maximise its T-count reduction at the cost of the fewest cuts. Rather than reducing a Clifford+T diagram based on a fixed routine of decomposing its T-gates directly (as is the conventional approach), we focus instead on taking advantage of certain patterns and structures common to such circuits to, in effect, design by automatic procedure an arrangement of spider decompositions that is optimised for the particular circuit. In short, this works by assigning weights to vertices based on how many T-like gates they are blocking from fusing/cancelling and then appropriately propagating these weights through any neighbours which are then blocking weighted vertices from fusing, and so on. Ultimately, this then provides a set of weightings on relevant nodes, which can then each be cut, starting from the highest weighted down. While this is a heuristic approach, we show that, for circuits small enough to verify, this method achieves the most optimal set of cuts possible $71\%$ of the time. Furthermore, there is no upper bound for the efficiency achieved by this method, allowing, in principle, an effective decomposition efficiency $\alpha\rightarrow0$ for highly structured circuits. Even applied to random pseudo-structured circuits (produced from CNOTs, phase gates, and Toffolis), we record the number of stabiliser terms required to reduce all T-gates, via our method as compared to that of the more conventional T-decomposition approaches (namely \cite{kissinger21}, with $\alpha\approx0.47$), and show consistent improvements of orders of magnitude, with an effective efficiency $0.1\lesssim\alpha\lesssim0.2$.

quant-ph↗

arXiv2024

Fast Classical Simulation of Quantum Circuits via Parametric Rewriting in the ZX-Calculus

The ZX-calculus is an algebraic formalism that allows quantum computations to be simplified via a small number of simple graphical rewrite rules. Recently, it was shown that, when combined with a family of "sum-over-Cliffords" techniques, the ZX-calculus provides a powerful tool for classical simulation of quantum circuits. However, for several important classical simulation tasks, such as computing the probabilities associated with many measurement outcomes of a single quantum circuit, this technique results in reductions over many very similar diagrams, where much of the same computational work is repeated. In this paper, we show that the majority of this work can be shared across branches, by developing reduction strategies that can be run parametrically on diagrams with boolean free parameters. As parameters only need to be fixed after the bulk of the simplification work is already done, we show that it is possible to perform the final stage of classical simulation quickly utilising a high degree of GPU parallelism. Using these methods, we demonstrate an average speedup factor of 78.3+-10.2 for certain classical simulation tasks vs. the non-parametric approach.

quant-ph↗