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 Conference on Quantum Computing and Engineering (QCE)

Harnessing Environment-Mediated Crosstalk: Mutual Performance Enhancement in Parallel Quantum Annealing

Jargalsaikhan Artag, Koki Awaya, Takumi Kanezashi, Daisuke Tsukayama, Moe Shimada, Jun‐ichi Shirakashi

Published Aug 30, 2025• 0 citations
Publisher

Abstract

Parallel Quantum Annealing (PQA) is a technique to improve computational throughput by running multiple independent problems concurrently. However, the concurrent execution of independent problems on a shared physical device gives rise to the possibility of unintended crosstalk mediated by the hardware environment. This work investigates a novel strategy to not only probe but actively harness these interactions. We hypothesize that applying relative time offsets to the annealing schedules of parallel problems can control these environment-mediated dynamics. To show this, we executed two independent ferromagnetic Ising chains (up to$N=500$qubits) in parallel on a D-Wave quantum annealer, systematically varying the schedule offset of one chain relative to the other. Our experimental results reveal a strong, non-monotonic dependence of the Ground State Probability (GSP) on the applied offset. Most significantly, we discovered non-zero offset regimes where both concurrently running problems achieved higher GSPs than their respective single-run baselines. This counter-intuitive finding of mutual performance enhancement suggests a controllable, cooperative dynamic mediated by the shared quantum environment. This reframes environmental crosstalk from a simple source of error into a potential resource, opening new avenues for schedule optimization and for probing the complex open-system dynamics of near-term quantum devices.

DOI
10.1109/qce65121.2025.10456

Related papers

Parallel Quantum Annealing: A Novel Approach to Solving Multiple NP-Hard Problems Concurrently

Jargalsaikhan Artag, Moe Shimada, Jun‐ichi Shirakashi

This study introduces an innovative application of Parallel Quantum Annealing (PQA) to concurrently solve multiple NP-hard problems on a quantum annealer, leveraging unused qubits on D-Wave's Pegasus architecture to enhance efficiency and accuracy. While parallel execution of different problems on the same quantum annealing device has been previously explored, our application extends the current PQA method, which typically solves a single problem multiple times or different problems with minor embedded combinatorial optimization. This study primarily focuses on three NP-hard problems relevant to integrated circuit design: the Graph Coloring Problem (GCP), the Minimum Vertex Cover Problem (MVCP), and the Graph Partitioning Problem (GPP). After formulating these problems as Quadratic Unconstrained Binary Optimization (QUBO), they were embedded into the D-Wave quantum annealing machine, and the annealing process was run on the combined QUBO. The results were subsequently decoded into individual problems. Our results, quantified via the Time-To-Target (TTT) metric, showcased enhanced efficiency and accuracy in reaching target solutions, indicating a substantial improvement over traditional Quantum Annealing (QA) and Simulated Annealing (SA) methods. The research illustrates the potential of quantum computing for solving complex real-world problems more efficiently and emphasizes the importance of leveraging all available qubits in quantum annealers. The findings can significantly contribute to the development and optimization of quantum computing methods and have broad application implications, particularly in areas where NP-hard problems are prevalent.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 17, 2023•3 citations

Multi-tasking through quantum annealing

Jargalsaikhan Artag, Koki Awaya, Takumi Kanezashi +3 more

Quantum annealing approximately solves combinatorial optimization problems by leveraging the principles of adiabatic quantum systems. In this approach, the system's Hamiltonian evolves from an initial general state to a problem-specific state. This study introduces multi-tasking quantum annealing (MTQA), a method that enables the parallel processing of multiple optimization problems by embedding them into spatially distinct regions on quantum hardware. MTQA is evaluated using two NP-hard problems: the minimum vertex cover problem (MVCP) and the graph partitioning problem (GPP). This parallel approach optimizes quantum resource utilization by concurrently utilizing idle qubits. The findings demonstrate that MTQA achieves a solution quality comparable to single-problem quantum annealing and classical simulated annealing (SA), while notably reducing the time-to-solution (TTS) metrics. Eigenspectrum analysis further theoretically supports the hypothesis that parallel embedding preserves quantum coherence and does not increase computational complexity by efficiently utilizing available quantum hardware (e.g., qubits and couplers). MTQA enables efficient multitasking in quantum annealing, optimizing hardware utilization and improving throughput for concurrent tasks and demonstrating performance for problems up to 100 nodes in real-world applications.

quant-pharXiv:2603.09468•EPJ Quantum Technology•Mar 11, 2026•0 citations

Software Mitigation of Crosstalk on Noisy Intermediate-Scale Quantum Computers

Prakash Murali, David C. McKay, Margaret Martonosi +1 more

Crosstalk is a major source of noise in Noisy Intermediate-Scale Quantum (NISQ) systems and is a fundamental challenge for hardware design. When multiple instructions are executed in parallel, crosstalk between the instructions can corrupt the quantum state and lead to incorrect program execution. Our goal is to mitigate the application impact of crosstalk noise through software techniques. This requires (i) accurate characterization of hardware crosstalk, and (ii) intelligent instruction scheduling to serialize the affected operations. Since crosstalk characterization is computationally expensive, we develop optimizations which reduce the characterization overhead. On 3 20-qubit IBMQ systems, we demonstrate two orders of magnitude reduction in characterization time (compute time on the QC device) compared to all-pairs crosstalk measurements. Informed by these characterization, we develop a scheduler that judiciously serializes high crosstalk instructions balancing the need to mitigate crosstalk and exponential decoherence errors from serialization. On real-system runs on 3 IBMQ systems, our scheduler improves the error rate of application circuits by up to 5.6x, compared to the IBM instruction scheduler and offers near-optimal crosstalk mitigation in practice.

arXiv:2001.02826•International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)•Mar 9, 2020•252 citations

Cycle-Aware Parallel Optimization for Mitigating ZZ Crosstalk on Quantum Hardware

Jiayi Zhong, Yuxin Deng

ZZ crosstalk and decoherence hinder superconducting quantum computing. Mitigation strategies often require sequential gate execution, which restricts parallelism. We reformulate ZZ crosstalk mitigation as a parallel task scheduling problem by integrating quantum cycles and qubit interference. We then propose CYCO, a CYcle-aware ZZ Crosstalk Optimization algorithm, which uses a timing-based greedy strategy to schedule gates through cycles within quantum circuits. A novel data structure called Time and Distance Dependency Graph (TDDG) is designed to model gate dependencies and physical qubit distances. Based on TDDG, barrier punching is introduced to eliminate redundant synchronization barriers by merging independent gate groups, improving gate concurrency per cycle. Simulations show a reduction of up to 37.44% in quantum program cycle (14.19% on average) on 53- to 127-qubit NISQ devices, with up to 1.6 × higher parallelism than state-of-the-art methods. Real-device experiments demonstrate significant acceleration in quantum computing while maintaining fidelity.

Proceedings of the International Conference on Parallel Processing•Sep 8, 2025•0 citations

Simultaneous Execution of Quantum Circuits on Current and Near-Future NISQ Systems

Yasuhiro Ohkura, Takahiko Satoh, Rodney Van Meter

In the noisy intermediate-scale quantum (NISQ) era, the idea ofquantum multiprogramming, running multiple quantum circuits (QCs) simultaneously on the same hardware, helps to improve the throughput of quantum computation. However, the crosstalk, unwanted interference between qubits on NISQ processors, may cause performance degradation when using multiprogramming. To address this challenge, we introducepalloq(parallel allocation of QCs), a novel compilation protocol. Palloq improves the performance of quantum multiprogramming on NISQ processors, while paying attention to 1) the combination of QCs chosen for parallel execution and 2) the assignment of program qubit variables to physical qubits, to reduce unwanted interference among the active set of QCs. We also propose a software-based crosstalk detection protocol using a new combination of randomized benchmarking methods. Our method successfully characterizes the suitability of hardware for multiprogramming with relatively low detection costs. We found a tradeoff between the success rate and execution time of the multiprogramming. Our results will be of value when device throughput becomes a significant bottleneck. Until service providers have enough quantum processors available to more than meet demand, this approach will be attractive to the service providers and users who want to optimize job management and throughput of the processor.

arXiv:2112.07091•IEEE Transactions on Quantum Engineering•Jan 1, 2022•59 citations

Crosstalk In Contemporary Quantum Devices

Spiro Gicev, Ben Harper, Haiyue Kang +2 more

Crosstalk noise derives from phenomena in quantum devices which inhibit individual addressability or cause unintended interactions among qubits. It is widely considered one of the major problems to be solved for a quantum computing platform to operate at scales beyond one or two qubits. Despite this, detailed discussion of crosstalk is often neglected when quantum device performance is described both in the context of device benchmarking and individual algorithm execution. Additionally, while the potential for crosstalk exists in all quantum platforms, the mechanisms and severity of crosstalk between platforms varies significantly, increasing the barrier of entry associated with understanding and performing research on unfamiliar quantum platforms. While previous work focused on theoretical formalism or platform specific details, in this review article, we provide a comprehensive overview of crosstalk from quantum computing literature across a range of physical systems focusing on physical origins, methods of mitigation and known consequential security vulnerabilities. We describe multiple crosstalk mechanisms for all major quantum computing platforms, which are usually implicitly addressed through device design, tuning, and mitigation techniques. We also observe accelerating research regarding security implications, however with multiple avenues for further exploration, especially for non-superconducting systems. Together, this review provides a comprehensive entry point for researchers and industry engineers interested in understanding and addressing the challenges arising from crosstalk phenomena in modern quantum computing systems.

quant-pharXiv:2605.26528•May 27, 2026•0 citations
Parallel Quantum Annealing (PQA) is a technique to improve computational throughput by running multiple independent problems concurrently. However, the concurrent execution of independent problems on a shared physical device gives rise to the possibility of unintended crosstalk mediated by the hardware environment. This work investigates a novel strategy to not only probe but actively harness these interactions. We hypothesize that applying relative time offsets to the annealing schedules of parallel problems can control these environment-mediated dynamics. To show this, we executed two independent ferromagnetic Ising chains (up toN=500N=500N=500qubits) in parallel on a D-Wave quantum annealer, systematically varying the schedule offset of one chain relative to the other. Our experimental results reveal a strong, non-monotonic dependence of the Ground State Probability (GSP) on the applied offset. Most significantly, we discovered non-zero offset regimes where both concurrently running problems achieved higher GSPs than their respective single-run baselines. This counter-intuitive finding of mutual performance enhancement suggests a controllable, cooperative dynamic mediated by the shared quantum environment. This reframes environmental crosstalk from a simple source of error into a potential resource, opening new avenues for schedule optimization and for probing the complex open-system dynamics of near-term quantum devices.
This study introduces an innovative application of Parallel Quantum Annealing (PQA) to concurrently solve multiple NP-hard problems on a quantum annealer, leveraging unused qubits on D-Wave's Pegasus architecture to enhance efficiency and accuracy. While parallel execution of different problems on the same quantum annealing device has been previously explored, our application extends the current PQA method, which typically solves a single problem multiple times or different problems with minor embedded combinatorial optimization. This study primarily focuses on three NP-hard problems relevant to integrated circuit design: the Graph Coloring Problem (GCP), the Minimum Vertex Cover Problem (MVCP), and the Graph Partitioning Problem (GPP). After formulating these problems as Quadratic Unconstrained Binary Optimization (QUBO), they were embedded into the D-Wave quantum annealing machine, and the annealing process was run on the combined QUBO. The results were subsequently decoded into individual problems. Our results, quantified via the Time-To-Target (TTT) metric, showcased enhanced efficiency and accuracy in reaching target solutions, indicating a substantial improvement over traditional Quantum Annealing (QA) and Simulated Annealing (SA) methods. The research illustrates the potential of quantum computing for solving complex real-world problems more efficiently and emphasizes the importance of leveraging all available qubits in quantum annealers. The findings can significantly contribute to the development and optimization of quantum computing methods and have broad application implications, particularly in areas where NP-hard problems are prevalent.
Quantum annealing approximately solves combinatorial optimization problems by leveraging the principles of adiabatic quantum systems. In this approach, the system's Hamiltonian evolves from an initial general state to a problem-specific state. This study introduces multi-tasking quantum annealing (MTQA), a method that enables the parallel processing of multiple optimization problems by embedding them into spatially distinct regions on quantum hardware. MTQA is evaluated using two NP-hard problems: the minimum vertex cover problem (MVCP) and the graph partitioning problem (GPP). This parallel approach optimizes quantum resource utilization by concurrently utilizing idle qubits. The findings demonstrate that MTQA achieves a solution quality comparable to single-problem quantum annealing and classical simulated annealing (SA), while notably reducing the time-to-solution (TTS) metrics. Eigenspectrum analysis further theoretically supports the hypothesis that parallel embedding preserves quantum coherence and does not increase computational complexity by efficiently utilizing available quantum hardware (e.g., qubits and couplers). MTQA enables efficient multitasking in quantum annealing, optimizing hardware utilization and improving throughput for concurrent tasks and demonstrating performance for problems up to 100 nodes in real-world applications.
Crosstalk is a major source of noise in Noisy Intermediate-Scale Quantum (NISQ) systems and is a fundamental challenge for hardware design. When multiple instructions are executed in parallel, crosstalk between the instructions can corrupt the quantum state and lead to incorrect program execution. Our goal is to mitigate the application impact of crosstalk noise through software techniques. This requires (i) accurate characterization of hardware crosstalk, and (ii) intelligent instruction scheduling to serialize the affected operations. Since crosstalk characterization is computationally expensive, we develop optimizations which reduce the characterization overhead. On 3 20-qubit IBMQ systems, we demonstrate two orders of magnitude reduction in characterization time (compute time on the QC device) compared to all-pairs crosstalk measurements. Informed by these characterization, we develop a scheduler that judiciously serializes high crosstalk instructions balancing the need to mitigate crosstalk and exponential decoherence errors from serialization. On real-system runs on 3 IBMQ systems, our scheduler improves the error rate of application circuits by up to 5.6x, compared to the IBM instruction scheduler and offers near-optimal crosstalk mitigation in practice.
ZZ crosstalk and decoherence hinder superconducting quantum computing. Mitigation strategies often require sequential gate execution, which restricts parallelism. We reformulate ZZ crosstalk mitigation as a parallel task scheduling problem by integrating quantum cycles and qubit interference. We then propose CYCO, a CYcle-aware ZZ Crosstalk Optimization algorithm, which uses a timing-based greedy strategy to schedule gates through cycles within quantum circuits. A novel data structure called Time and Distance Dependency Graph (TDDG) is designed to model gate dependencies and physical qubit distances. Based on TDDG, barrier punching is introduced to eliminate redundant synchronization barriers by merging independent gate groups, improving gate concurrency per cycle. Simulations show a reduction of up to 37.44% in quantum program cycle (14.19% on average) on 53- to 127-qubit NISQ devices, with up to 1.6 × higher parallelism than state-of-the-art methods. Real-device experiments demonstrate significant acceleration in quantum computing while maintaining fidelity.
In the noisy intermediate-scale quantum (NISQ) era, the idea ofquantum multiprogramming, running multiple quantum circuits (QCs) simultaneously on the same hardware, helps to improve the throughput of quantum computation. However, the crosstalk, unwanted interference between qubits on NISQ processors, may cause performance degradation when using multiprogramming. To address this challenge, we introducepalloq(parallel allocation of QCs), a novel compilation protocol. Palloq improves the performance of quantum multiprogramming on NISQ processors, while paying attention to 1) the combination of QCs chosen for parallel execution and 2) the assignment of program qubit variables to physical qubits, to reduce unwanted interference among the active set of QCs. We also propose a software-based crosstalk detection protocol using a new combination of randomized benchmarking methods. Our method successfully characterizes the suitability of hardware for multiprogramming with relatively low detection costs. We found a tradeoff between the success rate and execution time of the multiprogramming. Our results will be of value when device throughput becomes a significant bottleneck. Until service providers have enough quantum processors available to more than meet demand, this approach will be attractive to the service providers and users who want to optimize job management and throughput of the processor.
Crosstalk noise derives from phenomena in quantum devices which inhibit individual addressability or cause unintended interactions among qubits. It is widely considered one of the major problems to be solved for a quantum computing platform to operate at scales beyond one or two qubits. Despite this, detailed discussion of crosstalk is often neglected when quantum device performance is described both in the context of device benchmarking and individual algorithm execution. Additionally, while the potential for crosstalk exists in all quantum platforms, the mechanisms and severity of crosstalk between platforms varies significantly, increasing the barrier of entry associated with understanding and performing research on unfamiliar quantum platforms. While previous work focused on theoretical formalism or platform specific details, in this review article, we provide a comprehensive overview of crosstalk from quantum computing literature across a range of physical systems focusing on physical origins, methods of mitigation and known consequential security vulnerabilities. We describe multiple crosstalk mechanisms for all major quantum computing platforms, which are usually implicitly addressed through device design, tuning, and mitigation techniques. We also observe accelerating research regarding security implications, however with multiple avenues for further exploration, especially for non-superconducting systems. Together, this review provides a comprehensive entry point for researchers and industry engineers interested in understanding and addressing the challenges arising from crosstalk phenomena in modern quantum computing systems.