Frequency crowding is a fundamental limitation in superconducting quantum architectures, particularly in tunable-coupler systems. We present a framework that explicitly models both coherent spectator-induced errors and incoherent lifetime effects through an error budgeting approach. Using this model, we analyze how frequency crowding impacts gate fidelity as module size and connectivity scale, and formulate a constrained optimization problem to assign qubit and coupler frequencies under realistic separation and hardware constraints. We demonstrate scalable frequency allocation strategies that minimize spectator-induced errors. We further show that increasing qubit count and coupling density within a module leads to a fidelity-connectivity tradeoff. To explore the benefits at the system scale, we have developed a noise-aware transpilation approach called FINESSE, which minimizes error by selecting high-fidelity paths that satisfy connectivity via SWAP insertion while jointly optimizing downstream gate execution. We demonstrate this physics-informed architecture-transpilation co-design approach for a SNAIL-based third-order coupler that natively realizes the $\sqrt{iSWAP}$ basis with frequency aware gate fidelities. On SNAIL architectures, FINESSE achieves an average 8.9% reduction in log-infidelity cost and 6.8% reduction in circuit depth vs. SABRE. We also compare results on IBM Brisbane's architecture.
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14 pages, 16 figures, prepared initially for ASPLOS
Frequency crowding is a fundamental limitation in superconducting quantum architectures, particularly in tunable-coupler systems. We present a framework that explicitly models both coherent spectator-induced errors and incoherent lifetime effects through an error budgeting approach. Using this model, we analyze how frequency crowding impacts gate fidelity as module size and connectivity scale, and formulate a constrained optimization problem to assign qubit and coupler frequencies under realistic separation and hardware constraints. We demonstrate scalable frequency allocation strategies that minimize spectator-induced errors. We further show that increasing qubit count and coupling density within a module leads to a fidelity-connectivity tradeoff. To explore the benefits at the system scale, we have developed a noise-aware transpilation approach called FINESSE, which minimizes error by selecting high-fidelity paths that satisfy connectivity via SWAP insertion while jointly optimizing downstream gate execution. We demonstrate this physics-informed architecture-transpilation co-design approach for a SNAIL-based third-order coupler that natively realizes the iSWAP basis with frequency aware gate fidelities. On SNAIL architectures, FINESSE achieves an average 8.9% reduction in log-infidelity cost and 6.8% reduction in circuit depth vs. SABRE. We also compare results on IBM Brisbane's architecture.
This paper addresses frequency crowding in SNAIL-based superconducting quantum modules. First, we present design constraints by describing a physical model for realizable gates within a module, and building a fidelity model using error budgeting derived from device characteristics. Second, we tackle the allocation problem by analyzing the impact of frequency crowding on gate fidelity as the radix of the module increases. We explore whether the heuristic gate fidelity can be optimized with a discrete set of qubit frequencies while adhering to defined separation thresholds. By leveraging a combination of analytical and numerical techniques, we demonstrate scalable frequency allocation strategies that minimize spectator-induced errors. Our results further indicate that removing edges leads to improved gate fidelities while maintaining sufficient connectivity, suggesting that edge density is not a limiting factor for NISQ-scale benchmarks. The findings have implications for designing robust, high-fidelity quantum systems with practical constraints on hardware and connectivity.
Frequency crowding, the close spacing of qubit transitions, limits superconducting quantum processors through crosstalk, leakage, and spectator dependent errors. We introduce a Hamiltonian based framework that maps two- and three-body collision mechanisms onto a Max-k-Cut problem on qubit frequency allocation. In order to make larger lattices tractable, we introduce a cluster-stitching protocol that decomposes larger lattices into overlapping subgraphs and reconciles their local frequency assignments into globally consistent configurations. We tested our proposed framework across multiple qubit architectures to suppress dominant collision penalties by revealing a hardware residual crosstalk. We benchmark digitized counterdiabatic QAOA (DC-QAOA), standard QAOA, and simulated annealing. Counterdiabatic terms improve QAOA optimization near steep collision penalties, while simulated annealing achieves the lowest costs on the tested instances. These results indicate that our hardware-aware optimization connects microscopic interaction physics to processor-level frequency planning. These results enable a systematic solver for benchmarking of scalable software hardware co design.
One of the key challenges in current Noisy Intermediate-Scale Quantum (NISQ) computers is to control a quantum system with high-fidelity quantum gates. There are many reasons a quantum gate can go wrong - for superconducting transmon qubits in particular, one major source of gate error is the unwanted crosstalk between neighboring qubits due to a phenomenon called frequency crowding. We motivate a systematic approach for understanding and mitigating the crosstalk noise when executing near-term quantum programs on superconducting NISQ computers. We present a general software solution to alleviate frequency crowding by systematically tuning qubit frequencies according to input programs, trading parallelism for higher gate fidelity when necessary. The net result is that our work dramatically improves the crosstalk resilience of tunable-qubit, fixed-coupler hardware, matching or surpassing other more complex architectural designs such as tunable-coupler systems. On NISQ benchmarks, we improve worst-case program success rate by 13.3x on average, compared to existing traditional serialization strategies.
Fluxonium qubits combine long coherence times with strong anharmonicity, making them a promising platform for scalable superconducting quantum processors. Recent experiments have demonstrated high-fidelity operations in multi-qubit processors while suppressing stray qubit interactions using fluxonium-transmon-fluxonium (FTF) architectures. However, scaling such systems to larger arrays is constrained by a trade-off between achievable coupling strength, crosstalk suppression and qubit-qubit spacing required for wiring in a two-dimensional architecture. Multimode couplers, such as the double-transmon coupler (DTC), provide a promising pathway to overcome this limitation by enabling stronger interactions without compromising qubit spacing and isolation. Here, we develop a quantitative design framework for fluxonium-based quantum processors employing DTCs. Central to this work is a frequency-partitioned architecture that places qubit transitions, tunable-coupler excitations, and resonator modes in well-separated spectral regions. This structured allocation reduces parameter interdependence and enables the concurrent optimization of gate operations, readout, and qubit reset. By formulating device design as a multi-objective optimization problem under realistic experimental constraints and fabrication-induced disorder, we develop a tractable sequential workflow and determine a feasible parameter regime that simultaneously supports high-fidelity single- and two-qubit gates, fast qubit reset, and robust dispersive readout. These results establish a system-level architectural methodology that links circuit parameters to processor-level performance, and provide an experimentally actionable pathway toward scalable fluxonium quantum processors.
Superconducting circuits are a leading platform for scalable quantum computing, where parametric modulation is a widely used technique for implementing high-fidelity multi-qubit operations. A critical challenge, however, is that this modulation can induce a dense landscape of parasitic couplings, leading to detrimental frequency collisions that constrain processor performance. In this work, we develop a comprehensive numerical framework, grounded in Floquet theory, to systematically analyze and mitigate these collisions. Our approach integrates this numerical analysis with newly derived analytical models for both qubit-modulated and coupler-modulated schemes, allowing us to characterize the complete map of parasitic sideband interactions and their distinct error budgets. This analysis forms the basis of a constraint-based optimization methodology designed to identify parameter configurations that satisfy the derived physical constraints, thereby avoiding detrimental parasitic interactions. We illustrate the utility of this framework with applications to analog quantum simulation and gate design. Our work provides a predictive tool for co-engineering device parameters and control protocols, enabling the systematic suppression of crosstalk and paving the way for large-scale, high-performance quantum processors.
Abstract Crosstalk, caused by unwanted interactions from the surrounding environment, remains a fundamental challenge in existing superconducting quantum computers (SQCs). We propose a method for qubit placement, connectivity, and logical qubit allocation on tunable-coupler SQCs to eliminate unnecessary qubit connections and optimize resources while reducing crosstalk errors. Existing mitigation methods carry trade-offs, like increasing qubit connectivity or software-based gate scheduling. Our method, the Context-Aware COupler REconfiguration (CA-CORE) compilation method, aligns with application-specific design principles. It optimizes the qubit connections for improved SQC performance, leveraging tunable couplers. Through contextual analysis of qubit correlations, we configure an efficient coupling map considering SQC constraints. We then apply the SWAP-based Bidirectional Heuristic Search (SABRE) qubit mapping method and crosstalk-adaptive scheduling to further optimize the quantum circuit. Our architecture reduces depth by an average of 18% and 27%, and by up to 50% and 60%, compared to lattice and heavy-hex architectures, respectively. With crosstalk optimization through adaptive scheduling, we achieve performance improvements of 35%, 20%, and 160% on fully-enabled grid, lattice, and heavy-hex topologies, respectively.