
Algorithms 7
contributed
Thu, 29 Jan 2026, 15:00 - 15:00
- Efficient implementation of sequential quantum processes with group symmetryDmitry Grinko (University of Amsterdam and QuSoft); Satoshi Yoshida (The University of Tokyo); Mio Murao (The University of Tokyo); Maris Ozols (University of Amsterdam and QuSoft)[abstract]Abstract: Symmetry plays a crucial role in the design and analysis of quantum protocols. This result shows a canonical circuit decomposition of a quantum comb with $G\times H$ symmetry for compact groups $G$ and $H$ using the corresponding Clebsch--Gordan transforms. By using this circuit decomposition, we propose a parametrized quantum comb with group symmetry, and derive the optimal quantum comb which transforms an unknown unitary operation $U\in \SU(d)$ to its inverse $U^\dagger$ or transpose $U^\mathsf{T}$. From numerics, we find a deterministic and exact unitary transposition protocol for $d=3$ with $7$ queries to $U$, which is improved over the protocol shown in [Y.-A. Chen et al., arXiv:2403.04704], which requires $13$ queries to $U$. We also provide the simulation of random unitaries for any compact group $G$ using the compressed oracle, which can be implemented efficiently for the unitary group. The precision of our simulation for the unitary group is improved over the path-recording oracle introduced in [F. Ma and H.-Y. Huang, arXiv:2410.10116].
- Quantum algorithms for Uhlmann transformationTakeru Utsumi (The University of Tokyo); Yoshifumi Nakata (Kyoto University); Qisheng Wang (The University of Edinburgh); Ryuji Takagi (The University of Tokyo)[abstract]Abstract: Uhlmann's theorem is a central result in quantum information theory, associating the closeness of two quantum states with that of their purifications. This theorem well characterizes the fundamental task of transforming a quantum state into another state via local operations on its subsystem. The optimal transformation for this task is called the Uhlmann transformation, which has broad applications in various fields; however, its quantum circuit implementation and computational cost have remained unclear. In this work, we fill this gap by proposing quantum query and sample algorithms that realize the Uhlmann transformation in the form of quantum circuits. These algorithms achieve exponential improvements in computational costs, including query and sample complexities, over naive approaches based on state measurements such as quantum state tomography, under certain computational models. We apply our algorithms to the square root fidelity estimation task and particularly show that our approach attains a better query complexity than the prior state-of-the-art. Furthermore, we discuss applications to several information-theoretic tasks, specifically, entanglement transmission, quantum state merging, and algorithmic implementation of the Petz recovery map, providing a comprehensive evaluation of the computational costs.
- Efficient Quantum Hermite TransformSiddhartha Jain (UT Austin, Google Quantum AI); Vishnu Iyer (UT Austin); Rolando Somma (Google Quantum AI); Ning Bao (Northeastern, Brookhaven National Laboratory); Stephen Jordan (Google Quantum AI)[abstract]Abstract: We present a new primitive for quantum algorithms that implements a discrete Hermite transform efficiently, in time that depends logarithmically in both the dimension and the inverse of the allowable error. This transform, which maps basis states to states whose amplitudes are proportional to the Hermite functions, can be interpreted as the Gaussian analogue of the Fourier transform. Our algorithm is based on a method to exponentially fast forward the evolution of the quantum harmonic oscillator, which significantly improves over prior art. We apply this Hermite transform to give examples of provable quantum query advantage in property testing and learning. In particular, we show how to efficiently test the property of being close to a low-degree in the Hermite basis when inputs are sampled from the Gaussian distribution, and how to solve a Gaussian analogue of the Goldreich-Levin learning task efficiently. We also comment on other potential uses of this transform to simulating time dynamics of quantum systems in the continuum.
- Quantum Circuit Complexity of Matrix-Product UnitariesGeorgios Styliaris (Max Planck Institute of Quantum Optics); Rahul Trivedi (Max Planck Institute of Quantum Optics); J. Ignacio Cirac (Max Planck Institute of Quantum Optics)[abstract]Abstract: Matrix-product unitaries (MPUs) are many-body unitary operators that, as a consequence of their tensor-network structure, preserve the entanglement area law in 1D systems. However, it is unknown how to implement an MPU as a quantum circuit since the individual tensors describing the MPU are not unitary. In this paper, we show that a large class of MPUs can be implemented with a polynomial-depth quantum circuit. For an $N$-site MPU built from a repeated bulk tensor with open boundary, we explicitly construct a quantum circuit of polynomial depth $T = O(N^{\alpha})$ realizing the MPU, where the constant $\alpha$ depends only on the bulk and boundary tensor and not the system size $N$. We show that this class includes nontrivial unitaries that generate long-range entanglement and, in particular, contains a large class of unitaries constructed from representations of $C^*$-weak Hopf algebras. Furthermore, we also adapt our construction to nonuniform translationally-varying MPUs and show that they can be implemented by a circuit of depth $O(N^{\beta} \, \mathrm{poly}\, D)$ where $\beta \le 1 + \log_2 \sqrt{D}/ s_{\min}$, with $D$ being the bond dimension and $s_{\min}$ is the smallest nonzero Schmidt value of the normalized Choi state corresponding to the MPU.