Coined Quantum Walks on Complex Networks for Quantum Computers
Pith reviewed 2026-05-16 21:23 UTC · model grok-4.3
The pith
Dual-register encoding lets quantum circuits run coined walks on complex networks with depth scaling as N to the 1.9
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The central claim is that a dual-register encoding simplifies the shift operator for degree-varying networks and yields coined quantum walk circuits whose depth scales as approximately N to the 1.9 for Erdős-Rényi, Watts-Strogatz, and Barabási-Albert graphs, with small-N demonstrations on superconducting hardware showing that hardware-aware optimization can improve fidelity for the larger tested instances.
What carries the argument
Dual-register encoding, which uses separate registers for position and coin degrees of freedom to keep the shift operator independent of individual node degrees.
If this is right
- The same circuit construction applies unchanged to any network topology.
- The polynomial depth makes the method viable for fault-tolerant quantum computers once error correction is available.
- Walk-based quantum algorithms on real-world irregular graphs become implementable without topology-specific rewrites.
- Small-scale hardware runs already show measurable improvement in variation distance when connectivity-aware optimization is applied.
Where Pith is reading between the lines
- The same encoding may allow quantum simulation of dynamical processes on empirical networks such as epidemic spread or information diffusion.
- Extending the method to directed or weighted networks would require only minor adjustments to the coin operator.
- Hybrid classical-quantum schemes could use the circuit as a subroutine inside larger variational algorithms for network analysis.
Load-bearing premise
The dual-register encoding faithfully reproduces the coined walk dynamics on arbitrary degree sequences without introducing systematic bias or extra depth that grows faster than the reported scaling for large N.
What would settle it
A simulation or hardware run on a network with N greater than 20 that produces circuit depth growing faster than N squared or that yields probability distributions deviating from the expected coined-walk evolution by more than a few percent.
Figures
read the original abstract
We propose a quantum circuit design for implementing coined quantum walks on complex networks. In complex networks, the coin and shift operators depend on the varying degrees of the nodes, which makes circuit construction more challenging than for regular networks. To address this issue, we use a dual-register encoding to enable a simplified shift operator and reduces the resource overhead. We implement the circuit using Qmod, a high-level quantum programming language, and evaluated the performance through numerical simulations on Erd\H{o}s-R\'enyi, Watts-Strogatz, and Barab\'asi-Albert models. The results show that the circuit depth scales as approximately $N^{1.9}$ regardless of the network topology. Furthermore, we execute the proposed circuits on the ibm\_torino superconducting quantum processor for Watts-Strogatz models with $N=4$ and $N=8$. The experiments show that hardware-aware optimization slightly improved the variation distance and Hellinger fidelity for the larger network, whereas connectivity constraints imposed overhead for the smaller one. These results indicate that while current NISQ devices are limited to small-scale validations, the polynomial scaling of our framework makes it suitable for larger-scale implementations in the fault-tolerant quantum computing era.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes a dual-register encoding for coined quantum walks on complex networks with heterogeneous node degrees, implements the resulting circuits in the Qmod high-level language, and reports numerical simulations on Erdős-Rényi, Watts-Strogatz, and Barabási-Albert ensembles showing circuit depth scaling as approximately N^{1.9} independent of topology. Small-scale executions on ibm_torino for N=4 and N=8 Watts-Strogatz graphs are also presented, with hardware-aware optimization yielding modest improvements in variation distance and Hellinger fidelity.
Significance. If the reported polynomial scaling is asymptotically robust, the work supplies a concrete, topology-agnostic circuit construction for quantum walks on irregular graphs, a setting relevant to network-based quantum algorithms. The explicit Qmod implementation and cross-ensemble numerical evidence are strengths; the small-N hardware runs provide initial feasibility data but do not yet constrain large-scale performance.
major comments (2)
- [Results / scaling analysis] Results section (scaling plots and fits): the central claim that depth scales as N^{1.9} independent of topology rests on empirical Qmod depth measurements for modest N. For Barabási-Albert graphs, where maximum degree grows as N^{1/2}, the coin-register width and the controlled-neighbor lookup decomposition must be shown not to introduce super-quadratic overhead; no closed-form depth expression or asymptotic analysis of the dual-register shift operator is supplied to confirm the exponent remains stable.
- [Methods / circuit design] Circuit-construction section (dual-register encoding): the paper states that the encoding yields a simplified shift operator that faithfully reproduces the coined-walk dynamics for arbitrary degree sequences, yet no explicit verification (e.g., operator-norm distance between the implemented unitary and the standard walk operator on a small irregular graph) is given to exclude systematic bias or extra depth that grows faster than the reported scaling.
minor comments (2)
- [Abstract / hardware experiments] Abstract and results: variation distance and Hellinger fidelity for the ibm_torino runs are reported without error bars, shot counts, or statistical details, making quantitative assessment of the hardware improvements difficult.
- [Methods] Notation: the dual-register encoding is introduced without a compact diagram or explicit mapping table showing how the coin and position registers are allocated for a node of degree d; this would improve readability.
Simulated Author's Rebuttal
We thank the referee for the thoughtful and constructive report. The comments help clarify the presentation of our empirical scaling results and the need for explicit verification of the dual-register construction. We respond to each major comment below and indicate the revisions we will make.
read point-by-point responses
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Referee: [Results / scaling analysis] Results section (scaling plots and fits): the central claim that depth scales as N^{1.9} independent of topology rests on empirical Qmod depth measurements for modest N. For Barabási-Albert graphs, where maximum degree grows as N^{1/2}, the coin-register width and the controlled-neighbor lookup decomposition must be shown not to introduce super-quadratic overhead; no closed-form depth expression or asymptotic analysis of the dual-register shift operator is supplied to confirm the exponent remains stable.
Authors: We acknowledge that the N^{1.9} scaling is obtained from numerical Qmod depth measurements rather than a closed-form derivation. For Barabási-Albert graphs the maximum degree grows as O(N^{1/2}), so the coin register requires only O(log N) qubits; the dual-register shift is realized by a sequence of controlled-SWAP and neighbor-selection gates whose depth is linear in the (bounded) average degree plus the logarithmic coin width. This structure prevents super-quadratic growth, consistent with the observed exponent across all three ensembles. In the revised manuscript we will add an explicit breakdown of the depth contributions from the coin operator, the dual-register preparation, and the controlled shift, together with a short argument bounding the overhead for power-law degree sequences. revision: partial
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Referee: [Methods / circuit design] Circuit-construction section (dual-register encoding): the paper states that the encoding yields a simplified shift operator that faithfully reproduces the coined-walk dynamics for arbitrary degree sequences, yet no explicit verification (e.g., operator-norm distance between the implemented unitary and the standard walk operator on a small irregular graph) is given to exclude systematic bias or extra depth that grows faster than the reported scaling.
Authors: We agree that an explicit numerical check strengthens the claim. The dual-register encoding stores node degrees in the second register and uses them to select the correct neighbor via controlled operations, so the implemented unitary is constructed to be identical to the standard coined-walk operator on any graph. In the revision we will include a verification subsection that computes the operator-norm distance (and the diamond distance) between the Qmod-generated circuit and the ideal walk operator for a small irregular graph (e.g., a 5-node Barabási-Albert realization), confirming that the distance is at the level of floating-point precision and that no additional depth is incurred beyond the reported scaling. revision: yes
Circularity Check
No significant circularity; reported scaling is empirical output of explicit circuit construction and simulation
full rationale
The paper's central result (depth ~N^{1.9}) is obtained by implementing the dual-register coined-walk circuit in Qmod, decomposing the shift operator for each topology, and measuring depth on concrete Erdős-Rényi, Watts-Strogatz and Barabási-Albert instances. No equation defines the depth in terms of itself, no parameter is fitted to the target scaling, and no self-citation is invoked as a uniqueness theorem or ansatz that forces the exponent. The derivation chain consists of standard gate decomposition plus numerical measurement; these steps are independent of the final scaling number and do not reduce to it by construction.
Axiom & Free-Parameter Ledger
axioms (1)
- standard math Unitary evolution of coined quantum walks on graphs
Reference graph
Works this paper leans on
-
[1]
S. Marsh and J. B. Wang. Combinatorial opti- mization via quantum walks. Physical Review A , 101(5):052319, 2020. 7
work page 2020
-
[2]
A quantum walk model of financial op- tions
David Orrell. A quantum walk model of financial op- tions. Wilmott, 2021(112):62–69, 2021
work page 2021
-
[3]
Qfold: quan- tum walks and deep learning to solve protein folding
Pablo Antonio Moreno Casares, Roberto Campos, and Miguel Angel Martin-Delgado. Qfold: quan- tum walks and deep learning to solve protein folding. Quantum Science and Technology , 7(2):025013, 2022
work page 2022
-
[4]
Andrew M. Childs and Jeffrey Goldstone. Spatial search by quantum walk. Phys. Rev. A , 70:022314, Aug 2004
work page 2004
-
[5]
Y. Aharonov, L. Davidovich, and N. Zagury. Quan- tum random walks. Phys. Rev. A , 48:1687–1690, Aug 1993
work page 1993
-
[6]
M. Szegedy. Quantum speed-up of markov chain based algorithms. In 45th Annual IEEE Symposium on Foundations of Computer Science , pages 32–41, 2004
work page 2004
-
[7]
Coins make quantum walks faster
Andris Ambainis, Julia Kempe, and Alexander Rivosh. Coins make quantum walks faster. In Proceed- ings of the Sixteenth Annual ACM-SIAM Symposium on Discrete Algorithms , SODA ’05, page 1099–1108, USA, 2005. Society for Industrial and Applied Math- ematics
work page 2005
-
[8]
Universal scaling hypoth- esis of quantum spatial search in complex networks
Rei Sato, Tetsuro Nikuni, Kayoko Nohara, Giorgio Salani, and Shohei Watabe. Universal scaling hypoth- esis of quantum spatial search in complex networks. Phys. Rev. Res. , 6:043119, Nov 2024
work page 2024
-
[9]
Discrete-time quantum walk algo- rithm for ranking nodes on a network
Prateek Chawla, Roopesh Mangal, and C Madaiah Chandrashekar. Discrete-time quantum walk algo- rithm for ranking nodes on a network. Quantum In- formation Processing, 19:1–21, 2020
work page 2020
-
[10]
Quan- tum google in a complex network
Giuseppe Davide Paparo, Markus Müller, Francesc Comellas, and Miguel Angel Martin-Delgado. Quan- tum google in a complex network. Scientific reports , 3(1):2773, 2013
work page 2013
-
[11]
Quantum walk neural networks with feature dependent coins
Stefan Dernbach, Arman Mohseni-Kabir, Siddharth Pal, Miles Gepner, and Don Towsley. Quantum walk neural networks with feature dependent coins. Applied Network Science , 4:1–16, 2019
work page 2019
-
[12]
Vqne: Varia- tional quantum network embedding with application to network alignment
Xinyu Ye, Ge Yan, and Junchi Yan. Vqne: Varia- tional quantum network embedding with application to network alignment. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages 3105–3115, 2023
work page 2023
-
[13]
Qwalkvec: Node embedding by quan- tum walk
Rei Sato, Shuichiro Haruta, Kazuhiro Saito, and Mori Kurokawa. Qwalkvec: Node embedding by quan- tum walk. In Pacific-Asia Conference on Knowledge Discovery and Data Mining , pages 93–104. Springer, 2024
work page 2024
-
[14]
A quantum approach to walk on networks
Anurag Singh and Binshumesh Sachan. A quantum approach to walk on networks. In 2021 6th Inter- national Conference on Signal Processing, Computing and Control (ISPCC) , pages 636–643. IEEE, 2021
work page 2021
-
[15]
Coined quantum walk on a quantum network
Jigyen Bhavsar, Shashank Shekhar, and Siddhartha Santra. Coined quantum walk on a quantum network. Physical Review A , 110(5):052428, 2024
work page 2024
-
[16]
Quan- tum walks and dirac cellular automata on a pro- grammable trapped-ion quantum computer
C Huerta Alderete, Shivani Singh, Nhung H Nguyen, Daiwei Zhu, Radhakrishnan Balu, Christopher Mon- roe, CM Chandrashekar, and Norbert M Linke. Quan- tum walks and dirac cellular automata on a pro- grammable trapped-ion quantum computer. Nature communications, 11(1):3720, 2020
work page 2020
-
[17]
Implementation of quantum walks on ibm quantum computers
Frank Acasiete, Flavia P Agostini, J Khatibi Mo- qadam, and Renato Portugal. Implementation of quantum walks on ibm quantum computers. Quan- tum Information Processing , 19:1–20, 2020
work page 2020
-
[18]
Circuit implementation of discrete-time quantum walks via the shunt decomposition method
Allan Wing-Bocanegra and Salvador E Venegas- Andraca. Circuit implementation of discrete-time quantum walks via the shunt decomposition method. Quantum Information Processing , 22(3):146, 2023
work page 2023
-
[19]
Circuit implemen- tation and analysis of a quantum-walk based search complement algorithm
Allan Wing-Bocanegra, Carlos E Quintero-Narvaez, and Salvador E Venegas-Andraca. Circuit implemen- tation and analysis of a quantum-walk based search complement algorithm. Scientific Reports , 15(1):4865, 2025
work page 2025
-
[20]
Efficient implementation of discrete-time quantum walks on quantum computers
Luca Razzoli, Gabriele Cenedese, Maria Bondani, and Giuliano Benenti. Efficient implementation of discrete-time quantum walks on quantum computers. Entropy, 26(4):313, 2024
work page 2024
-
[21]
Efficient circuit implementa- tion of quantum walks on non-degree-regular graphs
T Loke and JB Wang. Efficient circuit implementa- tion of quantum walks on non-degree-regular graphs. Physical Review A , 86(4):042338, 2012
work page 2012
-
[22]
Efficient quantum circuits for continuous-time quantum walks on com- posite graphs
Thomas Loke and Jingbo B Wang. Efficient quantum circuits for continuous-time quantum walks on com- posite graphs. Journal of Physics A: Mathematical and Theoretical, 50(5):055303, 2017
work page 2017
-
[23]
Quantum circuits for discrete-time quantum walks with position-dependent coin operator
Ugo Nzongani, Julien Zylberman, Carlo-Elia Doncec- chi, Armando Pérez, Fabrice Debbasch, and Pablo Ar- nault. Quantum circuits for discrete-time quantum walks with position-dependent coin operator. Quan- tum Information Processing , 22(7):270, 2023
work page 2023
-
[24]
Ef- ficient circuit implementations of continuous-time quantum walks for quantum search
Renato Portugal and Jalil Khatibi Moqadam. Ef- ficient circuit implementations of continuous-time quantum walks for quantum search. Entropy, 27(5):454, 2025
work page 2025
-
[25]
Continuous-time quantum walk on a random graph using quantum cir- cuits
Sabyasachi Chakraborty, Rohit Sarma Sarkar, Sonjoy Majumder, and Rohit Kishan Ray. Continuous-time quantum walk on a random graph using quantum cir- cuits. arXiv preprint arXiv:2510.14905 , 2025
work page internal anchor Pith review arXiv 2025
-
[26]
Im- plementation of a continuous-time quantum walk on a sparse graph
Zhaoyang Chen, Guanzhong Li, and Lvzhou Li. Im- plementation of a continuous-time quantum walk on a sparse graph. Phys. Rev. A , 110:052215, Nov 2024
work page 2024
-
[27]
Circuit Implementation of Discrete-Time Quantum Walks on Complex Net- works
Rei Sato and Kazuhiro Saito. Circuit Implementation of Discrete-Time Quantum Walks on Complex Net- works . In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) , pages 376–377, Los Alamitos, CA, USA, September 2024. IEEE Computer Society
work page 2024
-
[28]
Col- lective dynamics of ‘small-world’networks
Duncan J Watts and Steven H Strogatz. Col- lective dynamics of ‘small-world’networks. nature, 393(6684):440–442, 1998
work page 1998
-
[29]
Emer- gence of scaling in random networks
Albert-László Barabási and Réka Albert. Emer- gence of scaling in random networks. science, 286(5439):509–512, 1999
work page 1999
-
[30]
P ERDdS and A R&wi. On random graphs i. Publ. math. debrecen, 6(290-297):18, 1959
work page 1959
-
[31]
Matan Vax, Peleg Emanuel, Eyal Cornfeld, Israel Re- ichental, Ori Opher, Ori Roth, Tal Michaeli, Lior Pre- minger, Lior Gazit, Amir Naveh, et al. Qmod: Ex- pressive high-level quantum modeling. arXiv preprint arXiv:2502.19368, 2025
-
[32]
De- sign and synthesis of scalable quantum programs
Tomer Goldfriend, Israel Reichental, Amir Naveh, Lior Gazit, Nadav Yoran, Ravid Alon, Shmuel Ur, Shahak Lahav, Eyal Cornfeld, A vi Elazari, et al. De- sign and synthesis of scalable quantum programs. arXiv preprint arXiv:2412.07372 , 2024
-
[33]
Quantum walks: a comprehensive review
Salvador Elías Venegas-Andraca. Quantum walks: a comprehensive review. Quantum Information Pro- cessing, 11(5):1015–1106, 2012
work page 2012
-
[34]
Classiq Technologies. Hardware-aware synthe- sis. https://docs.classiq.io/latest/user-guide/ synthesis/hardware-aware-synthesis/, 2024. Ac- cessed on 15.11.2025
work page 2024
-
[35]
Quantum computation and quantum information
Michael A Nielsen and Isaac L Chuang. Quantum computation and quantum information . Cambridge university press, 2010
work page 2010
-
[36]
Coined Quantum Walks on Complex Networks for Quantum Computers
Google Quantum AI. Suppressing quantum errors by scaling a surface code logical qubit. Nature, 614(7949):676–681, 2023. 8 Supplemental Material for "Coined Quantum Walks on Complex Networks for Quantum Computers" Appendix A: Implementation with Qmod Here, we provide a step-by-step explanation of the Qmod impl ementation corresponding to the mathematical m...
work page 2023
-
[37]
State Preparation The preparation of the state involves two steps. The operato r ˆU1 creates a uniform superposition over all nodes in the first register x (representing node i). ˆU1 |0⟩n = 1√ N N − 1∑ i=0 |i⟩ (A1) In Qmod, if N is a power of 2, this is a simple Hadamard transform. Otherwise, we use inplace_prepare_state with a uniform probability distribu...
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[38]
Coin Operator The coin operator ˆC applies a local Grover diffusion operator at each node. Mathe matically, for each node i, the local coin ˆCi is: ˆCi = 2 ˆU (i) 2 |0⟩ ⟨0|( ˆU (i) 2 )† − ˆI (A3) In Qmod, the grover_diffuser function automatically implements the reflection 2 |s⟩ ⟨s| − I given a state preparation oracle. Here, the oracle is exactly the inpla...
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[39]
ˆS |i⟩ |j⟩ = |j⟩ |i⟩ (A4) This corresponds to a swap of the two quantum registers x and y
Shift Operator The shift operator performs a flip-flop shift by swapping the p osition and direction registers. ˆS |i⟩ |j⟩ = |j⟩ |i⟩ (A4) This corresponds to a swap of the two quantum registers x and y. In Qmod, this is concisely expressed using the multiswap gate. @qfunc def my_shift ( x : QNum [ num_qubits ] , y : QNum [ num_qubits ]) : multiswap (x , y )
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[40]
The power function in Qmod efficiently implements the repetition of the step unitary U t = ( ˆS ˆC)t
Whole Circuit Finally, the discrete-time quantum walk is constructed by i teratively applying the coin and shift operators. The power function in Qmod efficiently implements the repetition of the step unitary U t = ( ˆS ˆC)t. @qfunc def d i s c r e t e _ q u a n t u m _ w a l k ( time : CInt , coin_qfun c s : QCallable [ QNum , QNum ] , s h i f t _ q f u n ...
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[41]
Circuit Synthesis We employ two distinct synthesis strategies to generate the final quantum circuits: hardware-agnostic syn- thesis for simulation and hardware-aware synthesis for exe cution on real devices. For numerical simulations and logical verification, we gene rate the circuit without targeting a specific backend. This approach optimizes for general l...
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[42]
Visualization Finally, Qmod provides built-in tools to visualize the synt hesized quantum circuit and analyze its properties. The show() function opens an interactive circuit viewer, allowing for a hierarchical inspection of the block- encoded operators, as shown in Fig. 6. This command generate s the quantum circuit diagrams presented in 11 the main text...
discussion (0)
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