F2: Offline Reinforcement Learning Compiles Hamiltonian Simulation Circuits with Free-Fermionic Subroutines, Stabilizing Value Learning
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Quantum simulation promises to unlock breakthroughs in materials science and drug discovery, but creating the necessary quantum circuits remains a significant hurdle, often limited by the number of operations and the time they take to execute. Ethan Decker, Christopher Watson, Junyu Zhou, and colleagues from the University of Pennsylvania and Pacific Northwest National Laboratory present F2, a new framework that dramatically improves the compilation of these circuits using a technique called offline reinforcement learning. F2 focuses on exploiting the underlying mathematical structure of quantum systems, specifically free-fermionic properties, to design more efficient circuits for simulating how these systems evolve over time. Across a range of challenging problems, including modelling complex materials and protein fragments, F2 reduces the number of quantum operations by 47% and the circuit execution time by 38% compared to existing methods, all while maintaining a high degree of accuracy, demonstrating a powerful new approach to scalable quantum computation.
Quantum Circuit Optimisation for NISQ Devices Research in quantum computing increasingly focuses on optimising quantum circuits, a crucial step for running algorithms on near-term quantum hardware, known as NISQ devices, which have limited resources. A dominant theme is reducing circuit complexity, specifically the number of gates and the circuit’s depth, while preserving functionality. This optimisation is vital for tackling complex problems in fields like quantum chemistry and materials science. Scientists are also actively simulating quantum systems to understand material properties and molecular behaviour. A growing trend involves utilising Reinforcement Learning (RL) to automate and improve quantum circuit design, routing, and optimisation. Simultaneously, researchers address the impact of errors on noisy quantum hardware through error mitigation and correction techniques. Current research concentrates on developing techniques to optimise quantum circuits, ranging from general optimisation strategies to those leveraging RL for learning optimal circuit transformations. Specific techniques include topological quantum compiling and circuit decomposition methods. Several frameworks and compilers, such as t|ket⟩, Pcoast, and Qiskit, are being developed to aid in quantum circuit design and optimisation.
This research is driven by the need to make quantum algorithms practical for NISQ devices, focusing on techniques that minimise resource requirements and enhance performance. Quantum chemistry and materials science provide concrete problems that motivate the development of new algorithms and techniques.
Offline Reinforcement Learning for Quantum Compilation Scientists have pioneered F2, an offline reinforcement learning framework that efficiently compiles quantum circuits for Hamiltonian simulation. This addresses limitations in existing methods that rely on hand-engineered classical heuristics. Researchers constructed a reinforcement learning environment focused on classically simulatable free-fermionic subroutines, a key innovation that mitigates the exponential complexity typically associated with quantum system representation and circuit optimisation. To stabilise learning in this complex environment, the team developed a compositional action encoder coupled with a learned inductive bias within the critic objective, tackling the challenges of a large, hybrid discrete-continuous action space and allowing for more reliable value estimation. Furthermore, scientists harnessed the time-reversibility of quantum circuits to generate abundant synthetic trajectories, creating a dataset of guaranteed-successful transitions for offline reinforcement learning. Experiments employed benchmarks spanning lattice models, protein fragments, and crystalline materials, encompassing systems with 12 to 222 qubits. The study demonstrates that F2 achieves a 47% reduction in gate count and a 38% reduction in circuit depth, on average, when compared to strong baseline compilers, including Qiskit and Cirq/OpenFermion, while maintaining average errors of only 10 -7. Aligning deep reinforcement learning with the algebraic structure of quantum dynamics significantly enhances quantum circuit synthesis and offers a promising path toward scalable, learning-based quantum compilation. F2 Framework Dramatically Simplifies Quantum Simulations Researchers have achieved substantial improvements in quantum circuit synthesis using a novel framework named F2, designed for Hamiltonian simulation. F2 leverages the algebraic structure of free-fermionic systems, enabling significant reductions in both gate count and circuit depth. Experiments demonstrate that F2 reduces the total gate count by an average of 47% across benchmarks ranging from 12 to 222 qubits, encompassing lattice models, protein fragments, and crystalline materials, and simultaneously achieves a 38% reduction in circuit depth. Importantly, these optimisations are achieved while maintaining remarkably low average errors of 10 -7, demonstrating the precision of the method. The core of F2 is a reinforcement learning environment specifically tailored to classically simulatable free-fermionic subroutines, mitigating the exponential complexity typically associated with quantum system representation. Furthermore, the team developed a synthetic trajectory generation mechanism that consistently produces abundant, guaranteed-successful data for offline reinforcement learning. These advancements position F2 as a promising direction for scalable, learning-based quantum compilation, potentially unlocking more complex simulations on future quantum hardware.
Optimized Quantum Circuits via Reinforcement Learning This work demonstrates a significant advancement in the compilation of quantum circuits for Hamiltonian simulation, achieving substantial reductions in both gate count and circuit depth. By introducing an offline reinforcement learning framework, researchers have successfully optimised circuits across a range of benchmarks, including lattice models, protein fragments, and crystalline materials. On average, the new method reduces gate count by 47% and circuit depth by 38% when compared to existing approaches, while maintaining a high level of accuracy with errors around 10^(-7). The key to this improvement lies in aligning deep reinforcement learning with the underlying algebraic structure of quantum dynamics, specifically leveraging free-fermionic structure within the circuits. This approach allows for efficient optimisation through a reinforcement learning environment and a reversible synthetic-trajectory generation mechanism that provides abundant training data. While acknowledging that the method currently focuses on classically tractable subroutines, the authors suggest several promising avenues for future research, including extending the paradigm to other efficiently tractable routines and exploring the incorporation of continuous parameters into the model. 👉 More information 🗞 F2: Offline Reinforcement Learning for Hamiltonian Simulation via Free-Fermionic Subroutine Compilation 🧠 ArXiv: https://arxiv.org/abs/2512.08023 Tags: