arXiv subjects

Timothée Goubault de Brugière

Publications and source records attributed to Timothée Goubault de Brugière.

13 recordsLinked to original sources

arXiv2025

Minimizing resource overhead in fusion-based quantum computation using hybrid spin-photon devices

We present three schemes for constructing a (2,2)-Shor-encoded 6-ring photonic resource state for fusion-based quantum computing, each relying on a different type of photon source. We benchmark these architectures by analyzing their ability to achieve the loss tolerance threshold for fusion-based quantum computation using the target resource state. More precisely, we estimate their minimum hardware requirements for fault-tolerant quantum computation in terms of the number of photon sources to achieve on-demand generation of resource states with a desired generation period. Notably, we find that a group of 12 deterministic single-photon sources containing a single matter qubit degree of freedom can produce the target resource state near-deterministically by exploiting entangling gates that are repeated until success. The approach is fully modular, eliminates the need for lossy large-scale multiplexing, and reduces the overhead for resource-state generation by several orders of magnitude compared to architectures using heralded single-photon sources and probabilistic linear-optical entangling gates. Our work shows that the use of deterministic single-photon sources embedding a qubit substantially shortens the path toward fault-tolerant photonic quantum computation.

quant-ph↗

arXiv2025

New designs of linear optical interferometers with minimal depth and component count

We adapt an algorithm for CNOT circuits synthesis based on the Bruhat decomposition to the design of linear optical circuits with Mach-Zehnder interferometers (MZI). The synthesis algorithm reduces to designing sorting networks with nearest neighbor swapping operations as elementary gates. We recover previous designs from the literature but with additional theoretical properties regarding the compiler that implements unitaries on the interferometer. Notably the compiler can always decide whether a unitary can be implemented on a given interferometer and, if so, returns the shallowest possible implementation. We also show natural extensions of our framework for boson sampling experiments and for the coupling of multiple integrated interferometers to design larger linear optical systems. In both cases, the designs are optimal in terms of number of optical components. Finally, we propose a greedy design which exploits the arbritrary-but-fixed coupling of separate integrated interferometers to perform shallow boson sampling. We discuss the optimal interferometer dimensions to maximize the transmission. Beyond boson sampling, our developed framework allows a resource-favourable implemention of any non-adaptive linear optical quantum algorithm, by providing the shallowest possible interferometer for implementing this algorithm.

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arXiv2025

Fast and memory efficient strong simulation of noisy adaptive linear optical circuits

Exactly computing the full output distribution of linear optical circuits remains a challenge, as existing methods are either time-efficient but memory-intensive or memory-efficient but slow. Moreover, any realistic simulation must account for noise, and any viable quantum computing scheme based on linear optics requires feedforward. In this paper, we propose an algorithm that models the output amplitudes as partial derivatives of a multivariate polynomial. The algorithm explores the lattice of all intermediate partial derivatives, where each derivative is used to compute more efficiently ones with higher degree. In terms of memory, storing one path from the root to the leaves is sufficient to iterate over all amplitudes and requires only $2^n$ elements, as opposed to $\binom{n+m-1}{n}$ for the fastest state of the art method. This approach effectively balances the time-memory trade-off while extending to both noisy and feedforward scenarios with negligible cost. To the best of our knowledge, this is the first approach in the literature to meet all these requirements. We demonstrate how this method enables the simulation of systems that were previously out of reach, while providing a concrete implementation and complexity analysis.

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arXiv2025

A graph-state based synthesis framework for Clifford isometries

We tackle the problem of Clifford isometry compilation, i.e, how to synthesize a Clifford isometry into an executable quantum circuit. We propose a simple framework for synthesis that only exploits the elementary properties of the Clifford group and one equation of the symplectic group. We highlight the versatility of our framework by showing that several normal forms of the literature are natural corollaries. We recover the state of the art two-qubit gate depth necessary for the execution of a Clifford circuit on an LNN architecture, concomitantly with another work. We also propose practical synthesis algorithms for Clifford isometries with a focus on Clifford operators, graph states and codiagonalization of Pauli rotations. Benchmarks show that in all three cases we improve the 2-qubit gate count and depth of random instances compared to the state-of-the-art methods. We also improve the execution of practical quantum chemistry experiments.

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arXiv2024

Faster and shorter synthesis of Hamiltonian simulation circuits

We devise greedy heuristics tailored for synthesizing quantum circuits that implement a specified set of Pauli rotations. Our heuristics are designed to minimize either the count of entangling gates or the depth of entangling gates, and they can be adjusted to either maintain or loosen the ordering of rotations. We present benchmark results demonstrating a depth reduction of up to a factor of 4 compared to the current state-of-the-art heuristics for synthesizing Hamiltonian simulation circuits. We also show that these heuristics can be used to optimize generic quantum circuits by decomposing and resynthesizing them.

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arXiv2023

Shallower CNOT circuits on realistic quantum hardware

We focus on the depth optimization of CNOT circuits on hardwares with limited connectivity. We adapt the algorithm from Kutin et al. that implements any $n$-qubit CNOT circuit in depth at most $5n$ on a Linear Nearest Neighbour (LNN) architecture. Our proposal is a block version of Kutin et al.'s algorithm that is scalable with the number of interactions available in the hardware: the more interactions we have the less the depth. We derive better theoretical upper bounds and we provide a simple implementation of the algorithm. Overall, we achieve better depth complexity for CNOT circuits on some realistic quantum hardware like a grid or a ladder. For instance the execution of a $n$-qubit CNOT circuit on a grid can be done in depth $4n$.

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arXiv2022

Architecture aware compilation of quantum circuits via lazy synthesis

Qubit routing is a key problematic related to quantum circuit compilation. It consists in rewriting a quantum circuit by adding the least possible number of instructions to make the circuit compliant with some architecture's connectivity constraints. Usually, this problem is tackled via either SWAP insertion techniques or re-synthesis of portions of the circuit using architecture aware synthesis algorithms. In this work, we propose a meta-heuristic that couples the iterative approach of SWAP insertion techniques with greedy architecture aware synthesis routines. We propose two new compilation algorithms based on this meta-heuristic and compare their performances to state-of-the-art quantum circuit compilation techniques for several standard classes of quantum circuits and show significant reduction in the entangling gate overhead due to compilation.

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arXiv2022

Decoding techniques applied to the compilation of CNOT circuits for NISQ architectures

Current proposals for quantum compilers require the synthesis and optimization of linear reversible circuits and among them CNOT circuits. Since these circuits represent a significant part of the cost of running an entire quantum circuit, we aim at reducing their size. In this paper we present a new algorithm for the synthesis of CNOT circuits based on the solution of the syndrome decoding problem. Our method addresses the case of ideal hardware with an all-to-all qubit connectivity and the case of near-term quantum devices with restricted connectivity. For both cases, we present benchmarks showing that our algorithm outperforms existing algorithms.

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arXiv2022

Reducing the Depth of Linear Reversible Quantum Circuits

In quantum computing the decoherence time of the qubits determines the computation time available and this time is very limited when using current hardware. In this paper we minimize the execution time (the depth) for a class of circuits referred to as linear reversible circuits, which has many applications in quantum computing (e.g., stabilizer circuits, CNOT+T circuits, etc.). We propose a practical formulation of a divide and conquer algorithm that produces quantum circuits that are twice as shallow as those produced by existing algorithms. We improve the theoretical upper bound of the depth in the worst case for some range of qubits. We also propose greedy algorithms based on cost minimization to find more optimal circuits for small or simple operators. Overall, we manage to consistently reduce the total depth of a class of reversible functions, with up to 92% savings in an ancilla-free case and up to 99% when ancillary qubits are available.

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arXiv2022

Gaussian Elimination versus Greedy Methods for the Synthesis of Linear Reversible Circuits

Linear reversible circuits represent a subclass of reversible circuits with many applications in quantum computing. These circuits can be efficiently simulated by classical computers and their size is polynomially bounded by the number of qubits, making them a good candidate to deploy efficient methods to reduce computational costs. We propose a new algorithm for synthesizing any linear reversible operator by using an optimized version of the Gaussian elimination algorithm coupled with a tuned LU factorization. We also improve the scalability of purely greedy methods. Overall, on random operators, our algorithms improve the state-of-the-art methods for specific ranges of problem sizes: the custom Gaussian elimination algorithm provides the best results for large problem sizes (n > 150) while the purely greedy methods provide quasi optimal results when n < 30. On a benchmark of reversible functions, we manage to significantly reduce the CNOT count and the depth of the circuit while keeping other metrics of importance (T-count, T-depth) as low as possible.

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arXiv2021

Phase polynomials synthesis algorithms for NISQ architectures and beyond

We present a framework for the synthesis of phase polynomials that addresses both cases of full connectivity and partial connectivity for NISQ architectures. In most cases, our algorithms generate circuits with lower CNOT count and CNOT depth than the state of the art or have a significantly smaller running time for similar performances. We also provide methods that can be applied to our algorithms in order to trade an increase in the CNOT count for a decrease in execution time, thereby filling the gap between our algorithms and faster ones.

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arXiv2020

Synthesizing quantum circuits via numerical optimization

We provide a simple framework for the synthesis of quantum circuits based on a numerical optimization algorithm. This algorithm is used in the context of the trapped-ions technology. We derive theoretical lower bounds for the number of quantum gates required to implement any quantum algorithm. Then we present numerical experiments with random quantum operators where we compute the optimal parameters of the circuits and we illustrate the correctness of the theoretical lower bounds. We finally discuss the scalability of the method with the number of qubits.

cs.ET↗

arXiv2020

Quantum circuit synthesis using Householder transformations

The synthesis of a quantum circuit consists in decomposing a unitary matrix into a series of elementary operations. In this paper, we propose a circuit synthesis method based on the QR factorization via Householder transformations. We provide a two-step algorithm: during the first step we exploit the specific structure of a quantum operator to compute its QR factorization, then the factorized matrix is used to produce a quantum circuit. We analyze several costs (circuit size and computational time) and compare them to existing techniques from the literature. For a final quantum circuit twice as large as the one obtained by the best existing method, we accelerate the computation by orders of magnitude.

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