arXiv subjects

Ioana Moflic

Publications and source records attributed to Ioana Moflic.

8 recordsLinked to original sources

arXiv2026

On the Constant Depth Implementation of Pauli Exponentials

We decompose, under the very restrictive linear nearest-neighbour connectivity, $Z^{\otimes n}$ exponentials of arbitrary length into circuits of constant depth using $\mathcal{O}(n)$ ancillae and two-body XX and ZZ interactions. Consequently, a similar method works for arbitrary Pauli exponentials. We prove the correctness of our approach, after introducing novel rewrite rules for circuits which benefit from qubit recycling. The decomposition has a wide variety of applications ranging from the efficient implementation of practical fault-tolerant lattice surgery computations, to expressing arbitrary stabilizer circuits via two-body interactions only and parallel decoding of quantum error-correcting computations.

quant-ph↗

arXiv2026

Superconducting qubits in the millions: the potential and limitations of modularity

The development of fault-tolerant quantum computers (FTQCs) is receiving increasing attention within the quantum computing community. Like conventional digital computers, FTQCs, which utilize error correction and millions of physical qubits, have the potential to address some of humanity's grand challenges. However, accurate estimates of the tangible scale of future FTQCs, based on transparent assumptions, are uncommon. How many physical qubits are necessary to solve a practical problem intractable for classical hardware? What costs arise from distributing quantum computation across multiple machines? This paper presents an architectural model of a potential FTQC based on superconducting qubits, divided into discrete modules and interconnected via coherent links. We employ a resource estimation framework and software tool to assess the physical resources required to execute specific quantum algorithms compiled into their graph-state form and arranged onto a modular superconducting hardware architecture. Our tool can predict the size, power consumption, and execution time of these algorithms based on explicit assumptions about the system's physical layout, thermal load, and modular connectivity. We assess the resources needed for quantum computation examples that serve as building blocks of proposed applications, quantifying the architectural bottlenecks and trade-offs that remain to be addressed to deliver utility.

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arXiv2025

QASER: Breaking the Depth vs. Accuracy Trade-Off for Quantum Architecture Search

Quantum computing faces a key challenge: balancing the need for low circuit depth (crucial for fault tolerance) with the high accuracy required for complex computations like quantum chemistry and error correction, which typically require deeper circuits. We overcome this trade-off by introducing a novel reinforcement learning approach featuring engineered reward functions, called \textbf{QASER}, that take into account seemingly contradictory optimization goals. This reward enables the compilation of circuits with lower depth and higher accuracy, significantly outperforming state-of-the-art techniques. Benchmarks on quantum chemistry state preparation circuits demonstrate stable compilations. We achieve up to 50\% improved accuracy, while reducing 2-qubit gate counts and depths by 20\%. This advancement enables more efficient and reliable quantum compilation.

quant-ph↗

arXiv2025

Ultra-Large-Scale Compilation and Manipulation of Quantum Circuits with Pandora

There is an enormous gap between what quantum circuit sizes can be compiled and manipulated with the current generation of quantum software and the sizes required by practical applications such as quantum chemistry or Shor's algorithm. We present Pandora, an efficient, open-source, multithreaded, high-performance-computing-enabled tool based on circuit rewrites. Pandora can be used for quantum circuit equivalence checking, full compilations of large circuits, and scalable, streaming quantum resource estimation frameworks. Pandora can easily handle billions of gates and can stream circuit partitions in resource estimation pipelines at very high rates. We utilized Pandora for full compilations of Fermi-Hubbard 100x100 and 1024-bit Shor's algorithm circuits. Compared to TKET and Qiskit, we determine a performance advantage for manipulating circuits of more than 10000 gates. For equivalence checking tasks, Pandora outperforms MQT.QCEC on specific circuits that have more than 32 qubits. The performance and versatility of Pandora open novel paths in quantum software.

quant-ph↗

arXiv2025

Quantum Circuit Caches and Compressors for Low Latency, High Throughput Computing

Utility-scale quantum programs contain operations on the order of $>10^{15}$ which must be prepared and piped from a classical co-processor to the control unit of the quantum device. The latency of this process significantly increases with the size of the program: existing high-level classical representations of quantum programs are typically memory intensive and do not naïvely efficiently scale to the degree required to execute utility-scale programs in real-time. To combat this limitation, we propose the utilization of high-level quantum circuit caches and compressors. The first save on the time associated with repetitive tasks and sub-circuits, and the latter are useful for representing the programs/circuits in memory-efficient formats. We present numerical evidence that caches and compressors can offer five orders of magnitude lower latencies during the automatic transpilation of extremely large quantum circuits.

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arXiv2023

Cost Explosion for Efficient Reinforcement Learning Optimisation of Quantum Circuits

Large scale optimisation of quantum circuits is a computationally challenging problem. Reinforcement Learning (RL) is a recent approach for learning strategies to optimise quantum circuits by increasing the reward of an optimisation agent. The reward is a function of the quantum circuit costs, such as gate and qubit counts, or circuit depth. Our goal is to improve the agent's optimization strategy, by including hints about how quantum circuits are optimized manually: there are situations when the cost of a circuit should be allowed to temporary explode, before applying optimisations which significantly reduce the circuit's cost. We bring numerical evidence, using Bernstein-Vazirani circuits, to support the advantage of this strategy. Our results are preliminary, and show that allowing cost explosions offers significant advantages for RL training, such as reaching optimum circuits. Cost explosion strategies have the potential to be an essential tool for RL of large-scale quantum circuit optimisation.

quant-ph↗

arXiv2023

Towards Faster Reinforcement Learning of Quantum Circuit Optimization: Exponential Reward Functions

Reinforcement learning for the optimization of quantum circuits uses an agent whose goal is to maximize the value of a reward function that decides what is correct and what is wrong during the exploration of the search space. It is an open problem how to formulate reward functions that lead to fast and efficient learning. We propose an exponential reward function which is sensitive to structural properties of the circuit. We benchmark our function on circuits with known optimal depths, and conclude that our function is reducing the learning time and improves the optimization. Our results are a next step towards fast, large scale optimization of quantum circuits.

quant-ph↗

arXiv2023

Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation

Reinforcement learning (RL) is a promising method for quantum circuit optimisation. However, the state space that has to be explored by an RL agent is extremely large when considering all the possibilities in which a quantum circuit can be transformed through local rewrite operations. This state space explosion slows down the learning of RL-based optimisation strategies. We present for the first time how to use graph neural network (GNN) autoencoders for the optimisation of quantum circuits. We construct directed acyclic graphs from the quantum circuits, encode the graphs and use the encodings to represent RL states. We illustrate our proof of concept implementation on Bernstein-Vazirani circuits and, from preliminary results, we conclude that our autoencoder approach: a) maintains the optimality of the original RL method; b) reduces by 20 \% the size of the table that encodes the learned optimisation strategy. Our method is the first realistic first step towards very large scale RL quantum circuit optimisation.

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