Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 134 tok/s
Gemini 2.5 Pro 41 tok/s Pro
GPT-5 Medium 33 tok/s Pro
GPT-5 High 32 tok/s Pro
GPT-4o 101 tok/s Pro
Kimi K2 174 tok/s Pro
GPT OSS 120B 434 tok/s Pro
Claude Sonnet 4.5 37 tok/s Pro
2000 character limit reached

Compact Circuits for Constrained Quantum Evolutions of Sparse Operators (2504.09133v3)

Published 12 Apr 2025 in quant-ph

Abstract: We introduce a general framework for constructing compact quantum circuits that implement the real-time evolution of Hamiltonians of the form $H = \sigma P_B$, where $\sigma$ is a Pauli string commuting with a projection operator $P_B$ onto a subspace of the computational basis. Such Hamiltonians frequently arise in quantum algorithms, including constrained mixers in QAOA, fermionic and excitation operators in VQE, and lattice gauge theory applications. Additionally, we construct transposition gates, widely used in quantum computing, that scale more efficiently than the best known constructions in literature. Our method emphasizes the minimization of non-transversal gates, particularly T-gates, critical for fault-tolerant quantum computing. We construct circuits requiring $\mathcal{O}(n|B|)$ CX gates and $\mathcal{O}(n |B| + \log(|B|) \log (1/\epsilon))$ T-gates, where $n$ is the number of qubits, $|B|$ the dimension of the projected subspace, and $\epsilon$ the desired approximation precision. For subspaces that are generated by Pauli X-orbits we further reduce complexity to $\mathcal{O}(n \log |B|)$ CX gates and $\mathcal{O}(n+\log(\frac{1}{\epsilon}))$ T gates. Our constructive proofs yield explicit algorithms and include several applications, such as improved transposition circuits, efficient implementations of fermionic excitations, and oracle operators for combinatorial optimization. In the sparse case, i.e. when $|B|$ is small, the proposed algorithms scale favourably when compared to direct Pauli evolution.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 2 tweets and received 1 like.

Upgrade to Pro to view all of the tweets about this paper:

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube