Decomposition of multi-qutrit gates generated by Weyl-Heisenberg strings
- URL: http://arxiv.org/abs/2507.09781v1
- Date: Sun, 13 Jul 2025 20:33:48 GMT
- Title: Decomposition of multi-qutrit gates generated by Weyl-Heisenberg strings
- Authors: Daniele Trisciani, Marco Cattaneo, Zoltán Zimborás,
- Abstract summary: We introduce an algorithm that decomposes the exponential of an arbitrary tensor product of Weyl-Heisenberg operators into single- and two-qutrit gates.<n>We extend this approach to unitaries generated by Gell-Mann string (i.e., a tensor product of Gell-Mann matrices)<n>In particular, we generalize the Steiner-Gauss method, originally developed to reduce CNOT counts in qubit circuit, to optimize gate routing in qutrit-based systems.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Decomposing unitary operations into native gates is an essential step for implementing quantum algorithms. For qubit-based devices, where native gates are typically single- and two-qubit operations, a range of decomposition techniques have been developed. In particular, efficient algorithms exist for decomposing exponentials of Pauli strings while taking hardware topology in account. Motivated by the growing interest in qutrit-based quantum computing, we develop analogous decomposition methods for qutrit systems. Specifically, we introduce an algorithm that decomposes the exponential of an arbitrary tensor product of Weyl-Heisenberg operators (plus their Hermitian conjugation) into single- and two-qutrit gates. We further extend this approach to unitaries generated by Gell-Mann string (i.e., a tensor product of Gell-Mann matrices). Since both Gell-Mann matrices and Weyl-Heisenberg operators form (together with identity) complete operator bases of qutrit operators, we can use this result also to decompose any multi-qutrit gate that is diagonal up to single-qutrit rotations. As a practical application, we use our method to decompose the layers of the quantum approximate optimization algorithm for qutrit-based implementations of the graph k-coloring problem. For values of $k$ well-suited to qutrit architectures (e.g., $k=3$ or in general $k=3^n$), our approach yields significantly shallower circuits compared to qubit-based implementations, an advantage that grows with problem size, while also requiring a smaller total Hilbert space dimension. Finally, we also address the routing challenge in qutrit architectures that arises due to the limited connectivity of the devices. In particular, we generalize the Steiner-Gauss method, originally developed to reduce CNOT counts in qubit circuit, to optimize gate routing in qutrit-based systems.
Related papers
- Provably optimal exact gate synthesis from a discrete gate set [0.0]
We propose a method for exact circuit synthesizing using a discrete gate set.<n>Our approach translates the problem of a gate specified by its unitary matrix into a satisfiability (SAT) instance.
arXiv Detail & Related papers (2025-03-19T17:32:29Z) - Efficient compilation of quantum circuits using multi-qubit gates [0.0]
We present a compilation scheme which implements a general-circuit decomposition to a sequence of Ising-type, long-range, multi-qubit entangling gates.<n>We numerically test our compilation and show that, compared to conventional realizations with two-qubit gates, our compilations improves the logarithm of quantum volume by $20%$ to $25%$.
arXiv Detail & Related papers (2025-01-28T19:08:13Z) - On the Constant Depth Implementation of Pauli Exponentials [49.48516314472825]
We decompose arbitrary exponentials into circuits of constant depth using $mathcalO(n)$ ancillae and two-body XX and ZZ interactions.
We prove the correctness of our approach, after introducing novel rewrite rules for circuits which benefit from qubit recycling.
arXiv Detail & Related papers (2024-08-15T17:09:08Z) - Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs [93.82811501035569]
We introduce a new data efficient and highly parallelizable operator learning approach with reduced memory requirement and better generalization.
MG-TFNO scales to large resolutions by leveraging local and global structures of full-scale, real-world phenomena.
We demonstrate superior performance on the turbulent Navier-Stokes equations where we achieve less than half the error with over 150x compression.
arXiv Detail & Related papers (2023-09-29T20:18:52Z) - Near-optimal quantum circuit construction via Cartan decomposition [4.900041609957432]
We show the applicability of the Cartan decomposition of Lie algebras to quantum circuits.
This approach can be used to synthesize circuits that can efficiently implement any desired unitary operation.
arXiv Detail & Related papers (2022-12-25T17:01:13Z) - Automatic and effective discovery of quantum kernels [41.61572387137452]
Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data.<n>We present an approach to this problem, which employs optimization techniques, similar to those used in neural architecture search and AutoML.<n>The results obtained by testing our approach on a high-energy physics problem demonstrate that, in the best-case scenario, we can either match or improve testing accuracy with respect to the manual design approach.
arXiv Detail & Related papers (2022-09-22T16:42:14Z) - Fourier-based quantum signal processing [0.0]
Implementing general functions of operators is a powerful tool in quantum computation.
Quantum signal processing is the state of the art for this aim.
We present an algorithm for Hermitian-operator function design from an oracle given by the unitary evolution.
arXiv Detail & Related papers (2022-06-06T18:02:30Z) - Software mitigation of coherent two-qubit gate errors [55.878249096379804]
Two-qubit gates are important components of quantum computing.
But unwanted interactions between qubits (so-called parasitic gates) can degrade the performance of quantum applications.
We present two software methods to mitigate parasitic two-qubit gate errors.
arXiv Detail & Related papers (2021-11-08T17:37:27Z) - Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems.
By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
arXiv Detail & Related papers (2021-08-03T17:49:09Z) - Efficient decomposition of unitary matrices in quantum circuit compilers [0.0]
Unitary decomposition is a widely used method to map quantum algorithms to an arbitrary set of quantum gates.
We show that our implementation generates circuits with half the number of CNOT gates and a third of the total circuit length.
In addition to that, it is also up to 10 times as fast.
arXiv Detail & Related papers (2021-01-08T12:54:27Z) - Improving the Performance of Deep Quantum Optimization Algorithms with
Continuous Gate Sets [47.00474212574662]
Variational quantum algorithms are believed to be promising for solving computationally hard problems.
In this paper, we experimentally investigate the circuit-depth-dependent performance of QAOA applied to exact-cover problem instances.
Our results demonstrate that the use of continuous gate sets may be a key component in extending the impact of near-term quantum computers.
arXiv Detail & Related papers (2020-05-11T17:20:51Z) - Simulating nonnative cubic interactions on noisy quantum machines [65.38483184536494]
We show that quantum processors can be programmed to efficiently simulate dynamics that are not native to the hardware.
On noisy devices without error correction, we show that simulation results are significantly improved when the quantum program is compiled using modular gates.
arXiv Detail & Related papers (2020-04-15T05:16:24Z) - Lagrangian Decomposition for Neural Network Verification [148.0448557991349]
A fundamental component of neural network verification is the computation of bounds on the values their outputs can take.
We propose a novel approach based on Lagrangian Decomposition.
We show that we obtain bounds comparable with off-the-shelf solvers in a fraction of their running time.
arXiv Detail & Related papers (2020-02-24T17:55:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.