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Yingrong Chen

Publications and source records attributed to Yingrong Chen.

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Clifford-efficient sparse state preparation for molecular wavefunctions

Sparse quantum state preparation concerns an $n$-qubit target state that is a superposition of only $d \ll 2^n$ computational basis states. Existing approaches exploit this sparsity by compressing these $d$ basis states and their amplitudes onto a smaller set of qubits, called the dense register, before expanding the prepared state to the full register. Rather than relying on the permutation-based compression used in prior work, we exploit affine relationships among the binary configurations over the finite field $\operatorname{GF}(2)$ to reduce both the non-Clifford gate count and the ancillary qubit count. Invertible affine transformations over $\operatorname{GF}(2)$, comprising Gaussian elimination and all-ones-row removal, first reduce the dense register from $n$ to the rank $r$ using only Clifford gates and no ancillary qubits. An optional binary encoding stage then trades additional Toffoli gates and ancillary qubits for further compression to the minimum $\lceil\log_2 d\rceil$ dense qubits needed to represent $d$ distinct configurations. For chemically relevant wavefunctions, such as those obtained from selected configuration interaction calculations, shared electronic excitation patterns produce many of these affine relationships, enabling substantial Clifford-only compression before binary encoding. Across the molecular benchmarks, our method requires the fewest ancillary qubits among the evaluated sparse state preparation methods while maintaining comparable non-Clifford gate counts when using binary encoding.

quant-ph

Accurate and scalable exchange-correlation with deep learning

Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable properties remains fundamentally limited by the need to approximate the unknown exchange-correlation (XC) functional. The traditional paradigm for improving accuracy has relied on increasingly elaborate hand-crafted functional forms. This approach has led to a longstanding trade-off between computational efficiency and accuracy, which remains insufficient for reliable predictive modelling of laboratory experiments. Here we introduce Skala, a deep learning-based XC functional that surpasses state-of-the-art hybrid functionals in accuracy across the main-group chemistry benchmark set GMTKN55 with an error of 2.8 kcal/mol, while retaining the lower computational cost characteristic of semi-local DFT. This demonstrated departure from the historical trade-off between accuracy and efficiency is enabled by learning non-local representations of electronic structure directly from data, bypassing the need for increasingly costly hand-engineered features. Leveraging an unprecedented volume of high-accuracy reference data from wavefunction-based methods, we establish that modern deep learning enables systematically improvable neural exchange-correlation models as training datasets expand, positioning first-principles simulations to become progressively more predictive.

physics.chem-ph

QDK/Chemistry: A Modular Toolkit for Quantum Chemistry Applications

We present QDK/Chemistry, a software toolkit for quantum chemistry workflows targeting quantum computers. The toolkit addresses a key challenge in the field: while quantum algorithms for chemistry have matured considerably, the infrastructure connecting classical electronic structure calculations to quantum circuit execution remains fragmented. QDK/Chemistry provides this infrastructure through a modular architecture that separates data representations from computational methods, enabling researchers to compose workflows from interchangeable components. In addition to providing native implementations of targeted algorithms in the quantum-classical pipeline, the toolkit builds upon and integrates with widely used open-source quantum chemistry packages and quantum computing frameworks through a plugin system, allowing users to combine methods from different sources without modifying workflow logic. This paper describes the design philosophy, current capabilities, and role of QDK/Chemistry as a foundation for reproducible quantum chemistry experiments.

quant-ph

Quantum Arithmetic Algorithms: Implementation, Resource Estimation, and Comparison

As quantum computing technology advances, the need for optimized arithmetic circuits continues to grow. This paper presents the implementation and resource estimation of a library of quantum arithmetic algorithms, including addition, multiplication, division, and modular exponentiation. Using the Azure Quantum Resource Estimator, we evaluate runtime, qubit usage, and space-time trade-offs and identify the best-performing algorithm for each arithmetic operation. We explore the design space for division, optimize windowed modular exponentiation, and identify the tipping point between multipliers, demonstrating effective applications of resource estimation in quantum research. Additionally, we highlight the impact of parallelization, reset operations, and uncomputation techniques on implementation and resource estimation. Our findings provide both a practical library and a valuable knowledge base for selecting and optimizing quantum arithmetic algorithms in real-world applications.

quant-ph