arXiv subjects

Pablo Andrés-Martínez

Publications and source records attributed to Pablo Andrés-Martínez.

8 recordsLinked to original sources

arXiv2025

Scalable Pattern Matching in Computation Graphs

Graph rewriting is a popular tool for the optimisation and modification of graph expressions in domains such as compilers, machine learning and quantum computing. The underlying data structures are often port graphs - graphs with labels at edge endpoints. A pre-requisite for graph rewriting is the ability to find graph patterns. We propose a new solution to pattern matching in port graphs. Its novelty lies in the use of a pre-computed data structure that makes the pattern matching runtime complexity independent of the number of patterns. This offers a significant advantage over existing solutions for use cases with large sets of small patterns. Our approach is particularly well-suited for quantum superoptimisation. We provide an implementation and benchmarks showing that our algorithm offers a 20x speedup over current implementations on a dataset of 10000 real world patterns describing quantum circuits.

cs.DS↗

arXiv2025

Categories of sets with infinite addition

We consider sets with infinite addition, called $Σ$-monoids, and contribute to their literature in three ways. First, our definition subsumes those from previous works and allows us to relate them in terms of adjuctions between their categories. In particular, we discuss $Σ$-monoids with additive inverses. Second, we show that every Hausdorff commutative monoid is a $Σ$-monoid, and that there is a free Hausdorff commutative monoid for each $Σ$-monoid. Third, we prove that $Σ$-monoids have well-defined tensor products, unlike topological abelian groups.

math.CT↗

arXiv2024

Realizing Quantum Kernel Models at Scale with Matrix Product State Simulation

Data representation in quantum state space offers an alternative function space for machine learning tasks. However, benchmarking these algorithms at a practical scale has been limited by ineffective simulation methods. We develop a quantum kernel framework using a Matrix Product State (MPS) simulator and employ it to perform a classification task with 165 features and 6400 training data points, well beyond the scale of any prior work. We make use of a circuit ansatz on a linear chain of qubits with increasing interaction distance between qubits. We assess the MPS simulator performance on CPUs and GPUs and, by systematically increasing the qubit interaction distance, we identify a crossover point beyond which the GPU implementation runs faster. We show that quantum kernel model performance improves as the feature dimension and training data increases, which is the first evidence of quantum model performance at scale.

quant-ph↗

arXiv2024

The Subgraph Isomorphism Problem for Port Graphs and Quantum Circuits

We study a variant of the subgraph isomorphism problem that is of high interest to the quantum computing community. Our results give an algorithm to perform pattern matching in quantum circuits for many patterns simultaneously, independently of the number of patterns. After a pre-computation step in which the patterns are compiled into a decision tree, the running time is linear in the size of the input quantum circuit. More generally, we consider connected port graphs, in which every edge $e$ incident to $v$ has a label $L_v(e)$ unique in $v$. Jiang and Bunke showed that the subgraph isomorphism problem $H \subseteq G$ for such graphs can be solved in time $O(|V(G)| \cdot |V(H)|)$. We show that if in addition the graphs are directed acyclic, then the subgraph isomorphism problem can be solved for an unbounded number of patterns simultaneously. We enumerate all $m$ pattern matches in time $O(P)^{P+3/2} \cdot |V(G)| + O(m)$, where $P$ is the number of vertices of the largest pattern. In the case of quantum circuits, we can express the bound obtained in terms of the maximum number of qubits $N$ and depth $δ$ of the patterns : $O(N)^{N + 1/2} \cdot δ\log δ\cdot |V(G)| + O(m)$.

quant-ph↗

arXiv2023

Entanglement-efficient bipartite-distributed quantum computing

In noisy intermediate-scale quantum computing, the limited scalability of a single quantum processing unit (QPU) can be extended through distributed quantum computing (DQC), in which one can implement global operations over two QPUs by entanglement-assisted local operations and classical communication. To facilitate this type of DQC in experiments, we need an entanglement-efficient protocol. To this end, we extend the protocol in [Eisert et. al., PRA, 62:052317(2000)] implementing each nonlocal controlled-unitary gate locally with one maximally entangled pair to a packing protocol, which can pack multiple nonlocal controlled-unitary gates locally using one maximally entangled pair. In particular, two types of packing processes are introduced as the building blocks, namely the distributing processes and embedding processes. Each distributing process distributes corresponding gates locally with one entangled pair. The efficiency of entanglement is then enhanced by embedding processes, which merge two non-sequential distributing processes and hence save the entanglement cost. We show that the structure of distributability and embeddability of a quantum circuit can be fully represented by the corresponding packing graphs and conflict graphs. Based on these graphs, we derive heuristic algorithms for finding an entanglement-efficient packing of distributing processes for a given quantum circuit to be implemented by two parties. These algorithms can determine the required number of local auxiliary qubits in the DQC. We apply these algorithms for bipartite DQC of unitary coupled-cluster circuits and find a significant reduction of entanglement cost through embeddings. This method can determine a constructive upper bound on the entanglement cost for the DQC of quantum circuits.

quant-ph↗

arXiv2022

Unbounded loops in quantum programs: categories and weak while loops

Control flow of quantum programs is often divided into two different classes: classical and quantum. Quantum programs with classical control flow have their conditional branching determined by the classical outcome of measurements, and these collapse quantum data. Conversely, quantum control flow is coherent, i.e. it does not perturb quantum data; quantum walk-based algorithms are practical examples where coherent quantum feedback plays a major role. This dissertation has two main contributions: (i) a categorical study of coherent quantum iteration and (ii) the introduction of weak while loops. (i) The objective is to endow categories of quantum processes with a traced monoidal structure capable of modelling iterative quantum loops. To this end, the trace of a morphism is calculated via the execution formula, which adds up the contribution of all possible paths of the control flow. Haghverdi's unique decomposition categories are generalised to admit additive inverses and equipped with convergence criteria using basic topology. In this setting, it is possible to prove the validity of the execution formula as a categorical trace on certain categories of quantum processes. (ii) A weak while loop is a classical control flow primitive that offers a trade-off between the collapse caused on each iteration and the amount of information gained. The trade-off may be adjusted by tuning a parameter and, in certain situations, it is possible to set its value so that we may control the algorithm without sacrificing its quantum speed-up. As an example, it is shown that Grover's search problem can be implemented using a weak while loop, maintaining the same time complexity as the standard Grover's algorithm (as previously shown by Mizel).

quant-ph↗

arXiv2022

Weakly measured while loops: peeking at quantum states

A while loop tests a termination condition on every iteration. On a quantum computer, such measurements perturb the evolution of the algorithm. We define a while loop primitive using weak measurements, offering a trade-off between the perturbation caused and the amount of information gained per iteration. This trade-off is adjusted with a parameter set by the programmer. We provide sufficient conditions that let us determine, with arbitrarily high probability, a worst-case estimate of the number of iterations the loop will run for. As an example, we solve Grover's search problem using a while loop and prove the quadratic quantum speed-up is maintained.

quant-ph↗

arXiv2019

Automated distribution of quantum circuits via hypergraph partitioning

Quantum algorithms are usually described as monolithic circuits, becoming large at modest input size. Near-term quantum architectures can only manage a small number of qubits. We develop an automated method to distribute quantum circuits over multiple agents, minimising quantum communication between them. We reduce the problem to hypergraph partitioning and then solve it with state-of-the-art optimisers. This makes our approach useful in practice, unlike previous methods. Our implementation is evaluated on five quantum circuits of practical relevance.

quant-ph↗