paper-with-me

Papers

Beyond Ansätze: Learning Quantum Circuits as Unitary Operators

2022-03-01 · Bálint Máté, Bertrand Le Saux, Maxwell Henderson

This paper explores the advantages of optimizing quantum circuits on $N$ wires as operators in the unitary group $U(2^N)$. We run gradient-based optimization in the Lie algebra $\mathfrak u(2^N)$ and use the exponential map to parametrize unitary matrices. We argue that $U(2^N)$ is not only more general than the search space induced by an ansatz, but in ways easier to work with on classical computers. The resulting approach is quick, ansatz-free and provides an upper bound on performance over all ans\"atze on $N$ wires.

📄 PDF Abstract BibTeX arXiv:2203.00601

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Structured Unitary Tensor Network Representations for Circuit-Efficient Quantum Data Encoding

2026-02-18 · Guang Lin, Toshihisa Tanaka, Qibin Zhao arxiv

Encoding classical data into quantum states is a central bottleneck in quantum machine learning: many widely used encodings are circuit-inefficient, requiring deep circuits and substantial quantum resources, which limits…

Quantum Machine Learning

Architectures and random properties of symplectic quantum circuits

2024-05-16 · Diego García-Martín, Paolo Braccia, M. Cerezo

Parametrized and random unitary (or orthogonal) $n$-qubit circuits play a central role in quantum information. As such, one could naturally assume that circuits implementing symplectic transformation would attract simila…

Gaussian Processes

Learning unitaries with quantum statistical queries

2023-10-03 · Armando Angrisani

We propose several algorithms for learning unitary operators from quantum statistical queries (QSQs) with respect to their Choi-Jamiolkowski state. Quantum statistical queries capture the capabilities of a learner with l…

Quantum Machine Learning

Parameterized quantum comb and simpler circuits for reversing unknown qubit-unitary operations

2024-03-06 · Yin Mo, Lei Zhang, Yu-Ao Chen, Yingjian Liu 외

Quantum combs play a vital role in characterizing and transforming quantum processes, with wide-ranging applications in quantum information processing. However, obtaining the explicit quantum circuit for the desired quan…

Quantum Machine Learning

Super Quantum Mechanics

2025-01-25 · Mikhail Gennadievich Belov, Victor Victorovich Dubov, Vadim Konstantinovich Ivanov, Alexander Yurievich Maslov 외

We introduce Super Quantum Mechanics (SQM) as a theory that considers states in Hilbert space subject to multiple quadratic constraints. Traditional quantum mechanics corresponds to a single quadratic constraint of wavef…