Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IEEE International Symposium on Hardware Oriented Security and Trust (HOST)

All Your Base Are Belong To Us: Stealing VRP Secrets from Quantum Circuit Structures

Jessie Chen, Jakub M. Szefer

Published May 6, 2024• 2 citations
Publisher

Abstract

The security and confidentiality of sensitive information processed by quantum computers are of paramount concerns, especially given quantum computers' potential to efficiently solve classically-hard optimization problems. At the heart of many transport optimization tasks lies the Vehicle Routing Problem (VRP), a complex combinatorial optimization issue classified as NP-hard. However, a promising avenue for approximating solutions to VRP is found in the Quantum Approximate Optimization Algorithm (QAOA). This paper demonstrates that analyzing the QAOA quantum circuit structure enables inference of the problem being solved, such as the location or connection of military bases in routing optimization scenarios. By exploiting information leakage, say from side channels, during the QAOA execution, attackers can potentially breach security and retrieve sensitive VRP details, posing profound implications for civilian and national security. This study underscores the need to address and mitigate side-channel vulnerabilities in quantum computing systems to safeguard sensitive information.

DOI
10.1109/host55342.2024.10545404

Related papers

Counting Bases from Number of Qubits: Inferring VRP from Quantum Circuits

Jessie Chen, Jakub M. Szefer

The security and confidentiality of sensitive information processed by quantum computers are of paramount importance, especially given quantum computers' potential to efficiently solve classically-hard optimization problems. At the heart of many transport optimization tasks lies the Vehicle Routing Problem (VRP), a complex combinatorial optimization problem classified as NP-hard. However, a promising avenue for approximating solutions to VRP is found in the Quantum Approximate Optimization Algorithm (QAOA). This paper demonstrates how leaking and learning simple parameters from QAOA quantum circuit structures, enables attackers to learn about the problem being solved. In routing optimization scenarios used by the military, for example, the attacker can learn location or connection of military bases. By exploiting information leakage during the QAOA execution, attackers can potentially breach security and retrieve sensitive VRP details, posing profound implications for civilian and national security.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 15, 2024•0 citations

Variational Quantum Algorithms for Solving Vehicle Routing Problem

Muhammad Alsaiyari, Muhamad Felemban

Vehicle Routing Problem (VRP) is considered one of the most challenging problems with application in several domains, including transportation and logistics distribution. VRP is known to be NP-Hard problem. Several algorithms have been proposed to solve VRP in polynomial time. However, these algorithms are inefficient if the VRP instance increases. Recently, researchers investigated how quantum computing can be used to solve VRP. In particular, two promising variational quantum algorithms have been studied, i.e., Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA). In this paper, we implement both algorithms on IBM Qiskit and compare their evaluation in solving several instances of VRP. We observe that current Noisy-Intermediate Scale Quantum (NISQ) devices cannot solve VRP instances beyond 5 nodes and 2 vehicles. Furthermore, we observe that classical optimizers still provide better results. However, we believe that with the rapid advancement of quantum computing manufacturing, VQE and QAOA can provide better performance as compared to classical computing,

International Conference on Smart Computing and Application (ICSCA)•Feb 5, 2023•8 citations

Quantum-Assisted Vehicle Routing: Realizing QAOA-based Approach on Gate-Based Quantum Computer

Talha Azfar, Osama M. Raisuddin, Ruimin Ke +1 more

The Vehicle Routing Problem (VRP) is a fundamental combinatorial optimization challenge with broad applications in logistics and transportation. In this work, we present a quantum-assisted framework that integrates the Quantum Approximate Optimization Algorithm (QAOA) with a link-based formulation of VRP. Our approach encodes flow conservation and subtour elimination directly into the cost Hamiltonian, preserving graph structure while minimizing resource requirements for practical hardware implementation. We design and implement the full pipeline on a gate-based quantum computer, including problem formulation, encoding, circuit synthesis, and execution on IBM Quantum System One. Experimental results on small VRP instances highlight the effects of penalty scaling, coefficient normalization, and circuit depth on solution feasibility under hardware noise. While scalability remains constrained by circuit complexity and decoherence, the study demonstrates a practical pathway for implementing VRP on quantum hardware and identifies methodological directions for advancing near-term quantum optimization.

arXiv:2505.01614•May 2, 2025•3 citations

32 - Nested Two Level Decomposition for Quantum Computing

Maciejunes, Andrew, Stenger, John, Gunlycke, Dan +1 more

Abstract—We present a two-level decomposition strategy for solving the Vehicle Routing Problem (VRP) using the Quantum Approximate Optimization Algorithm (QAOA). A Problem-Level Decomposition (PLD) partitions a 9-node (72-qubit) VRP into smaller Traveling Salesman Problem (TSP) instances. Each TSP is then further simplified via Circuit-Level Decomposition (CLD), enabling execution on near-term quantum devices. Our approach achieves up to 90% reductions in circuit depth and qubit count. These results demonstrate the feasibility of solving VRPs previously too complex for quantum simulators and provide early evidence of potential quantum utility.

•Apr 4, 2025•0 citations

Solving Vehicle Routing Problem Using Quantum Approximate Optimization Algorithm

Utkarsh Azad, Bikash K. Behera, Emad A. Ahmed +2 more

Intelligent transportation systems (ITS) are a critical component of Industry 4.0 and 5.0, particularly having applications in logistic management. One of their crucial utilization is in supply-chain management and scheduling for optimally routing transportation of goods by vehicles at a given set of locations. This paper discusses the broader problem of vehicle traffic management, more popularly known as the Vehicle Routing Problem (VRP), and investigates the possible use of near-term quantum devices for solving it. For this purpose, we give the Ising formulation for VRP and some of its constrained variants. Then, we present a detailed procedure to solve VRP by minimizing its corresponding Ising Hamiltonian using a hybrid quantum-classical heuristic called Quantum Approximate Optimization Algorithm (QAOA), implemented on the IBM Qiskit platform. We compare the performance of QAOA with classical solvers such as CPLEX on problem instances of up to 15 qubits. We find that performance of QAOA has a multifaceted dependence on the classical optimization routine used, the depth of the ansatz parameterized by$p$, initialization of variational parameters, and problem instance itself.

arXiv:2002.01351•IEEE Transactions on Intelligent Transportation Systems•May 13, 2022•149 citations

Hybrid Quantum Reinforcement Learning with QAOA for Improved Vehicle Routing Optimization

T. Satyanarayana Murthy, B. Swathi Sowmya, Santhosh Voruganti +3 more

Vehicle Routing Problem (VRP) is one of the most complex NP-hard combinatorial optimization problem in transportation and logistics that requires a dynamic solution approach. In this paper we present a new hybrid approach that combines the Quantum Approximate Optimization Algorithm (QAOA) into the QRL policy network, instead of the usual variational layers, QAOA mixing and cost Hamiltonian layers. This enhancement enables the agent to exploit problem specific particular quantum correlations when learning policies, and so richer exploration of the routing solution space. The QAOA-augmented QRL framework shows quicker convergence in training and can tackle larger VRP instances that are beyond the reach of Grover's Adaptive Search (GAS) and Quantum Reinforcement Learning (QRL) approaches. Experiments on standard VRP instances demonstrate better solutions, fewer episodes to converge and good memory usage on near term quantum hardware simulators. These findings demonstrate QAOA- integrated QRL as a viable approach to scalable, high quality quantum-assisted combinatorial optimization.

arXiv:2605.01574•May 2, 2026•0 citations
The security and confidentiality of sensitive information processed by quantum computers are of paramount concerns, especially given quantum computers' potential to efficiently solve classically-hard optimization problems. At the heart of many transport optimization tasks lies the Vehicle Routing Problem (VRP), a complex combinatorial optimization issue classified as NP-hard. However, a promising avenue for approximating solutions to VRP is found in the Quantum Approximate Optimization Algorithm (QAOA). This paper demonstrates that analyzing the QAOA quantum circuit structure enables inference of the problem being solved, such as the location or connection of military bases in routing optimization scenarios. By exploiting information leakage, say from side channels, during the QAOA execution, attackers can potentially breach security and retrieve sensitive VRP details, posing profound implications for civilian and national security. This study underscores the need to address and mitigate side-channel vulnerabilities in quantum computing systems to safeguard sensitive information.
The security and confidentiality of sensitive information processed by quantum computers are of paramount importance, especially given quantum computers' potential to efficiently solve classically-hard optimization problems. At the heart of many transport optimization tasks lies the Vehicle Routing Problem (VRP), a complex combinatorial optimization problem classified as NP-hard. However, a promising avenue for approximating solutions to VRP is found in the Quantum Approximate Optimization Algorithm (QAOA). This paper demonstrates how leaking and learning simple parameters from QAOA quantum circuit structures, enables attackers to learn about the problem being solved. In routing optimization scenarios used by the military, for example, the attacker can learn location or connection of military bases. By exploiting information leakage during the QAOA execution, attackers can potentially breach security and retrieve sensitive VRP details, posing profound implications for civilian and national security.
Vehicle Routing Problem (VRP) is considered one of the most challenging problems with application in several domains, including transportation and logistics distribution. VRP is known to be NP-Hard problem. Several algorithms have been proposed to solve VRP in polynomial time. However, these algorithms are inefficient if the VRP instance increases. Recently, researchers investigated how quantum computing can be used to solve VRP. In particular, two promising variational quantum algorithms have been studied, i.e., Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA). In this paper, we implement both algorithms on IBM Qiskit and compare their evaluation in solving several instances of VRP. We observe that current Noisy-Intermediate Scale Quantum (NISQ) devices cannot solve VRP instances beyond 5 nodes and 2 vehicles. Furthermore, we observe that classical optimizers still provide better results. However, we believe that with the rapid advancement of quantum computing manufacturing, VQE and QAOA can provide better performance as compared to classical computing,
The Vehicle Routing Problem (VRP) is a fundamental combinatorial optimization challenge with broad applications in logistics and transportation. In this work, we present a quantum-assisted framework that integrates the Quantum Approximate Optimization Algorithm (QAOA) with a link-based formulation of VRP. Our approach encodes flow conservation and subtour elimination directly into the cost Hamiltonian, preserving graph structure while minimizing resource requirements for practical hardware implementation. We design and implement the full pipeline on a gate-based quantum computer, including problem formulation, encoding, circuit synthesis, and execution on IBM Quantum System One. Experimental results on small VRP instances highlight the effects of penalty scaling, coefficient normalization, and circuit depth on solution feasibility under hardware noise. While scalability remains constrained by circuit complexity and decoherence, the study demonstrates a practical pathway for implementing VRP on quantum hardware and identifies methodological directions for advancing near-term quantum optimization.
Abstract—We present a two-level decomposition strategy for solving the Vehicle Routing Problem (VRP) using the Quantum Approximate Optimization Algorithm (QAOA). A Problem-Level Decomposition (PLD) partitions a 9-node (72-qubit) VRP into smaller Traveling Salesman Problem (TSP) instances. Each TSP is then further simplified via Circuit-Level Decomposition (CLD), enabling execution on near-term quantum devices. Our approach achieves up to 90% reductions in circuit depth and qubit count. These results demonstrate the feasibility of solving VRPs previously too complex for quantum simulators and provide early evidence of potential quantum utility.
Intelligent transportation systems (ITS) are a critical component of Industry 4.0 and 5.0, particularly having applications in logistic management. One of their crucial utilization is in supply-chain management and scheduling for optimally routing transportation of goods by vehicles at a given set of locations. This paper discusses the broader problem of vehicle traffic management, more popularly known as the Vehicle Routing Problem (VRP), and investigates the possible use of near-term quantum devices for solving it. For this purpose, we give the Ising formulation for VRP and some of its constrained variants. Then, we present a detailed procedure to solve VRP by minimizing its corresponding Ising Hamiltonian using a hybrid quantum-classical heuristic called Quantum Approximate Optimization Algorithm (QAOA), implemented on the IBM Qiskit platform. We compare the performance of QAOA with classical solvers such as CPLEX on problem instances of up to 15 qubits. We find that performance of QAOA has a multifaceted dependence on the classical optimization routine used, the depth of the ansatz parameterized byppp, initialization of variational parameters, and problem instance itself.
Vehicle Routing Problem (VRP) is one of the most complex NP-hard combinatorial optimization problem in transportation and logistics that requires a dynamic solution approach. In this paper we present a new hybrid approach that combines the Quantum Approximate Optimization Algorithm (QAOA) into the QRL policy network, instead of the usual variational layers, QAOA mixing and cost Hamiltonian layers. This enhancement enables the agent to exploit problem specific particular quantum correlations when learning policies, and so richer exploration of the routing solution space. The QAOA-augmented QRL framework shows quicker convergence in training and can tackle larger VRP instances that are beyond the reach of Grover's Adaptive Search (GAS) and Quantum Reinforcement Learning (QRL) approaches. Experiments on standard VRP instances demonstrate better solutions, fewer episodes to converge and good memory usage on near term quantum hardware simulators. These findings demonstrate QAOA- integrated QRL as a viable approach to scalable, high quality quantum-assisted combinatorial optimization.