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Generating 3 qubit quantum circuits with neural networks
Quantum Physics · Submitted 31 Mar 2017
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The two networks are able to return coefficients that are sufficiently close to the true coefficient values to validate this method as an approach for generating quantum circuits.
The compiled record
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The abstract, sentence by sentence
A new method for compiling quantum algorithms is proposed and tested for a three qubit system. The proposed method is to decompose a a unitary matrix U, into a product of simpler U j via a neural network. These U j can then be decomposed into product of known quantum gates. Key to the effectiveness of this approach is the restriction of the set of training data generated to paths which approximate minimal normal subRiemannian geodesics, as this removes unnecessary redundancy and ensures the products are unique. The two neural networks are shown to work effectively, each individually returning low loss values on validation data after relatively short training periods. The two networks are able to return coefficients that are sufficiently close to the true coefficient values to validate this method as an approach for generating quantum circuits. There is scope for more work in scaling this approach to larger quantum systems.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | A new method for compiling quantum algorithms is proposed and tested for a three qubit system. | contribution 1.00 | 0.84 | 0.11 | asserted 1.00 |
| s1 | The proposed method is to decompose a a unitary matrix U, into a product of simpler U j via a neural network. | method 0.94 | 0.75 | 0.06 | asserted 1.00 |
| s2 | These U j can then be decomposed into product of known quantum gates. | method 0.99 | 0.32 | 0.05 | asserted 0.87 |
| s3 | Key to the effectiveness of this approach is the restriction of the set of training data generated to paths which approximate minimal normal subRiemannian geodesics, as this removes unnecessary redundancy and ensures the products are unique. | method 0.97 | 0.77 | 0.07 | asserted 1.00 |
| s4 | The two neural networks are shown to work effectively, each individually returning low loss values on validation data after relatively short training periods. | result 1.00 | 0.95 | 0.12 | asserted 1.00 |
| s5 | The two networks are able to return coefficients that are sufficiently close to the true coefficient values to validate this method as an approach for generating quantum circuits. | interpretation 0.60 | 0.94 | 0.71 | asserted 0.96 |
| s6 | There is scope for more work in scaling this approach to larger quantum systems. | future work 0.94 | 0.20 | 0.05 | hedged possibility 0.91 |
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The paper
Original abstract
A new method for compiling quantum algorithms is proposed and tested for a three qubit system. The proposed method is to decompose a a unitary matrix U, into a product of simpler U j via a neural network. These U j can then be decomposed into product of known quantum gates. Key to the effectiveness of this approach is the restriction of the set of training data generated to paths which approximate minimal normal subRiemannian geodesics, as this removes unnecessary redundancy and ensures the products are unique. The two neural networks are shown to work effectively, each individually returning low loss values on validation data after relatively short training periods. The two networks are able to return coefficients that are sufficiently close to the true coefficient values to validate this method as an approach for generating quantum circuits. There is scope for more work in scaling this approach to larger quantum systems.
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How the literature uses it
4 arXiv papers cite this in 4 sentences
Papers citing it · 2 sentences without a confident label
Cites it as background 2 papers
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Neural networks have been proposed in the past for performing particular tasks necessary for quantum computation [ref] or representing aspects of quantum systems [ref] .
Emulating quantum computation with artificial neural networks · Introduction · 0.90
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Neural networks have been proposed for performing intermediate tasks for quantum computing [ref] , as finding the optimal decomposition of a unitary matrix in terms of some “basis matrices” or optimizing an ansatz for multi-qubit wave functions.
Quantum computing with classical bits · Introduction · 0.83
What it cites
Uses the method or tool of 2 papers
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The first is a Gated Recurrent Unit, GRU, network [ref] which factors a \(U\) into a product of \(U_j\), [equation] where each \(U_j\) is implementable in polynomially many gates, which we call global decomposition.
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling · Introduction · 0.97
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The first is a Gated Recurrent Unit, GRU, network [ref] which factors a \(U\) into a product of \(U_j\), [equation] where each \(U_j\) is implementable in polynomially many gates, which we call global decomposition.
Gated Feedback Recurrent Neural Networks · Introduction · 0.97
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