Paper Citation Record · LEDGER

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis

As of 21 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2504.15990.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2504.15990 v1

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

Observation 7f4b88a8-65f6-495d-9f02-c3f8a219f7d4 · outbound

This paper cites Quantum logic gate synthesis as a markov decision process,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Quantum logic gate synthesis as a markov decision process,

Reference 1

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This paper cites Artificial intelligence for quantum computing,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Artificial intelligence for quantum computing,

Reference 2

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This paper cites Arqtic: A full-stack software package for simulating materials on quantum computers,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Arqtic: A full-stack software package for simulating materials on quantum computers,

Reference 3

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This paper cites Learning high-accuracy error decoding for quantum processors,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Learning high-accuracy error decoding for quantum processors,

Reference 4

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This paper cites Bellman, Dynamic Programming , 1st ed.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Bellman, Dynamic Programming , 1st ed

Reference 5

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This paper cites Efficient synthesis of probabilistic quantum circuits with fallback.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Efficient synthesis of probabilistic quantum circuits with fallback

Reference 6

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This paper cites Efficient synthesis of universal Repeat-Until-Success circuits.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Efficient synthesis of universal Repeat-Until-Success circuits

Reference 7

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Observation 8b2c06db-d4c6-48be-bf72-b1a24206c4fb · outbound

This paper cites Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning,

Reference 8

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This paper cites Program synthesis using deduction-guided reinforcement learning,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Program synthesis using deduction-guided reinforcement learning,

Reference 9

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This paper cites Towards optimal topology aware quantum circuit synthesis,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Towards optimal topology aware quantum circuit synthesis,

Reference 10

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Observation 8f637ac6-59f0-4cf4-a226-2c32b49ec9ed · outbound

This paper cites A Quantum Approximate Optimization Algorithm.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis A Quantum Approximate Optimization Algorithm

Reference 11

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Observation 63c2ee0f-fca2-4bb5-9344-5622658a093b · outbound

This paper cites An algorithm for the T-count.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis An algorithm for the T-count

Reference 12

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Observation b6f8ccfa-999e-476e-ac35-f7c10d34bf71 · outbound

This paper cites Quantum measurements and the abelian stabilizer problem,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Quantum measurements and the abelian stabilizer problem,

Reference 13

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This paper cites Quantum computations: algorithms and error correction,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Quantum computations: algorithms and error correction,

Reference 14

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This paper cites Fast and efficient exact synthesis of single qubit unitaries generated by Clifford and T gates.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Fast and efficient exact synthesis of single qubit unitaries generated by Clifford and T gates

Reference 15

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This paper cites Deep Neural Network Probabilistic Decoder for Stabilizer Codes.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Deep Neural Network Probabilistic Decoder for Stabilizer Codes

Reference 16

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This paper cites AI methods for approximate compiling of unitaries.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis AI methods for approximate compiling of unitaries

Reference 17

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Observation 04a8b752-624c-4637-99b5-2c736efffc34 · outbound

This paper cites Quarl: A learning-based quantum circuit optimizer,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Quarl: A learning-based quantum circuit optimizer,

Reference 18

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This paper cites Nerf: Representing scenes as neural radiance fields for view synthesis,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Nerf: Representing scenes as neural radiance fields for view synthesis,

Reference 19

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This paper cites Playing atari with deep reinforcement learn- ing,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Playing atari with deep reinforcement learn- ing,

Reference 20

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Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Quantum compiling by deep reinforcement learning,

Reference 21

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This paper cites Learning to Decode Linear Codes Using Deep Learning.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Learning to Decode Linear Codes Using Deep Learning

Reference 22

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Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Unresolved cited work

Reference 23

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This paper cites Synthetiq: Fast and versatile quantum circuit synthesis,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Synthetiq: Fast and versatile quantum circuit synthesis,

Reference 24

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Observation 462a104f-6b53-4930-bfea-690423054fcd · outbound

This paper cites A variational eigenvalue solver on a photonic quantum processor,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis A variational eigenvalue solver on a photonic quantum processor,

Reference 25

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Observation cbf7116a-44ae-494f-8425-6a158f7875e7 · outbound

This paper cites Compiler Optimization for Quantum Computing Using Reinforcement Learning,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Compiler Optimization for Quantum Computing Using Reinforcement Learning,

Reference 26

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This paper cites MQT Bench: Benchmark- ing Software and Design Automation Tools for Quantum Computing,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis MQT Bench: Benchmark- ing Software and Design Automation Tools for Quantum Computing,

Reference 27

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Observation dd82d9cb-5d71-4cde-a786-2dccafd0cc74 · outbound

This paper cites On the spectral bias of neural networks,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis On the spectral bias of neural networks,

Reference 28

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Observation ce9cccfc-c1bc-4b1c-bb58-43ae16209752 · outbound

This paper cites Unitary synthesis of clifford+t circuits with reinforcement learning,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Unitary synthesis of clifford+t circuits with reinforcement learning,

Reference 29

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Observation 8cbc39d1-2f4e-4ba5-8e32-9881280df7db · outbound

This paper cites The perceptron: A probabilistic model for information storage and organization in the brain,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis The perceptron: A probabilistic model for information storage and organization in the brain,

Reference 30

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This paper cites Optimal ancilla-free clifford+t approxima- tion of z-rotations,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Optimal ancilla-free clifford+t approxima- tion of z-rotations,

Reference 31

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Observation 30d57304-f6a1-48c0-be5d-edd8edf04dc5 · outbound

This paper cites Benchmarking Language Models for Code Syntax Understanding.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Benchmarking Language Models for Code Syntax Understanding

Reference 32

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This paper cites Synthesis of quantum- logic circuits,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Synthesis of quantum- logic circuits,

Reference 33

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This paper cites Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer,

Reference 34

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Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Co-domain symmetry for complex- valued deep learning,

Reference 35

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This paper cites Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models

Reference 36

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This paper cites Leap: Scaling numerical optimization based synthesis using an incremental approach,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Leap: Scaling numerical optimization based synthesis using an incremental approach,

Reference 37

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Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Unresolved cited work

Reference 38

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This paper cites Complex-valued deep learning with applications to magnetic resonance image synthesis,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Complex-valued deep learning with applications to magnetic resonance image synthesis,

Reference 39

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This paper cites A trace inequality for unitary matrices,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis A trace inequality for unitary matrices,

Reference 40

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Observation 7aec1806-c4e5-4bae-8971-d92e8d54cb8c · outbound

This paper cites Improving quantum circuit synthesis with machine learning,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Improving quantum circuit synthesis with machine learning,

Reference 41

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This paper cites High- precision multi-qubit clifford+t synthesis by unitary diagonalization,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis High- precision multi-qubit clifford+t synthesis by unitary diagonalization,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:19:11.576736Z

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Observation 09aae7e9-d1f4-4281-b7d0-455e3cf96be0 · outbound

This paper cites Frequency principle: Fourier analysis sheds light on deep neural networks,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Frequency principle: Fourier analysis sheds light on deep neural networks,

Reference 43

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This paper cites Berkeley quantum synthesis toolkit (bqskit) v1,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Berkeley quantum synthesis toolkit (bqskit) v1,

Reference 44

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This paper cites Topological quantum compiling with reinforcement learning,.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Topological quantum compiling with reinforcement learning,

Reference 45

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This paper cites Available: http://www.jstor.org/stable/2974909.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis Available: http://www.jstor.org/stable/2974909

Reference 1994

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Observation 968c7ddf-ec12-4075-9434-6d60cf208721 · outbound

This paper cites High-Precision Multi-Qubit Clifford+T Synthesis by Unitary Diagonalization.

Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis High-Precision Multi-Qubit Clifford+T Synthesis by Unitary Diagonalization

Reference 2025

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Pith citing papers

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