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ACM Conference on Post-Quantum and Quantum-based Security

Stealing Variational Secrets from Quantum Clouds: Physical QAOA Parameter Recovery

José Luis Delgado

Published Oct 26, 2026• 0 citations
Publisher

Abstract

In shared quantum-cloud systems, variational programs encode costly classical optimization in rotation angles that can leak through adjacent hardware. We present the first physical recovery of a useful variational parameter region from a complete workload. The attack observes a disjoint idle two-qubit register during a 24-qubit quantum approximate optimization algorithm (QAOA) execution, without victim outputs or model queries. A fixed Marrakesh decoder distinguishes the regions under identical circuits, reaching 85/96 accuracy on held-out jobs and ROC AUC 0.997. Tests over separate time periods, matched controls, and a placement retest confirm the response at one of 17 layouts; conditional subsampling gives 0.756 expected accuracy from 2,000 observations. A public workload census finds source separation in 92 of 2,100 QAOA workloads, including five of 420 held-out workloads. For continuous secrets, a Jacobian factorization separates victim sensitivity, physical transfer, and scheduling, then converts channel gain into parameter and utility bounds. Under conditionally independent affine leakage, observable terms form a binary linear code. Its shortest element determines the first leakage order, while its dimension determines how many random bits perfect secrecy requires. Fresh masks defer leakage to the shortest cycle, while mask reuse creates a second-order temporal channel; the same code yields maximum safe slot concurrency.

DOI
10.1145/3839338.3857276

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Introduction Hybrid Variational Quantum Algorithms (VQAs) present a highly viable pathway to near-term quantum utility; however, their performance is fundamentally bottlenecked by classical-quantum communication latency in Quantumas-a-Service (QaaS) environments. Methods This paper proposes an optimized classical-quantum orchestration architecture designed to minimize cloud-induced latency and maximize Quantum Processing Unit (QPU) active compute time. By implementing edge-colocated classical optimizers alongside batched parameter-shift gradient evaluations, the system circumvents stateless cloud API barriers. Results Benchmarking across parameterized quantum circuits ranging from 15 to 50 qubits demonstrates an 84% reduction in network-induced QPU idle time. The framework yields a 3.2 × speedup in overall convergence time for the Quantum Approximate Optimization Algorithm (QAOA) and up to a 98% reduction in classical API call overhead compared to standard RESTful QaaS execution models. Discussion These quantitative findings demonstrate that tightly coupled hybrid co-processing, physically adjacent to the control electronics, is critical for extending the computational bound of Noisy Intermediate-Scale Quantum (NISQ) devices.

Frontiers in Computer Science•Sep 3, 2026•0 citations
In shared quantum-cloud systems, variational programs encode costly classical optimization in rotation angles that can leak through adjacent hardware. We present the first physical recovery of a useful variational parameter region from a complete workload. The attack observes a disjoint idle two-qubit register during a 24-qubit quantum approximate optimization algorithm (QAOA) execution, without victim outputs or model queries. A fixed Marrakesh decoder distinguishes the regions under identical circuits, reaching 85/96 accuracy on held-out jobs and ROC AUC 0.997. Tests over separate time periods, matched controls, and a placement retest confirm the response at one of 17 layouts; conditional subsampling gives 0.756 expected accuracy from 2,000 observations. A public workload census finds source separation in 92 of 2,100 QAOA workloads, including five of 420 held-out workloads. For continuous secrets, a Jacobian factorization separates victim sensitivity, physical transfer, and scheduling, then converts channel gain into parameter and utility bounds. Under conditionally independent affine leakage, observable terms form a binary linear code. Its shortest element determines the first leakage order, while its dimension determines how many random bits perfect secrecy requires. Fresh masks defer leakage to the shortest cycle, while mask reuse creates a second-order temporal channel; the same code yields maximum safe slot concurrency.
On a cloud-accessed quantum processor the classical optimizer of a variational algorithm communicates with the device through submitted jobs, and each job carries a queueing and turnaround overhead that does not depend on how many circuits it contains. We study a simple consequence of this for the quantum approximate optimization algorithm (QAOA). An optimizer whose next set of trial parameters is known before any result returns can evaluate the whole set in one job, whereas a sequential optimizer such as COBYLA spends one job per function evaluation. We compare a batched pattern search with COBYLA on IBM's \ibmfez\ processor for cardinality-constrained portfolio selection with six to twelve assets, giving both optimizers the same number of jobs and the same number of shots. At every size the batched search reaches, after its first job, a parameter quality that the serial optimizer takes several jobs to match, and the two methods converge to comparable final values; repeating the eight-asset comparison from four starting points gives the same ordering each time. The instances are small enough to be solved exactly, which lets us check that the device's output distribution is correlated with the true ranking of portfolios and is clearly separated from a control circuit of the same depth with the cost layer removed. A short scan over circuit depth at classically optimal parameters shows that on this device the measured solution quality peaks at two or three QAOA layers. The protocol is described in enough detail to be reproduced, and the notebooks and raw counts are released.
The Quantum Approximate Optimization Algorithm (QAOA) is a leading approach for combinatorial optimization on near-term quantum devices, yet its scalability is limited by the difficulty of optimizing 2p2p2p variational parameters for a large number ppp of layers. Recent empirical studies indicate that optimal QAOA angles exhibit concentration and transferability across problem sizes. Leveraging this observation, we propose a schedule-learning framework that transfers spectral-gap-informed adiabatic control strategies from small-scale instances to larger systems. Our method extracts the spectral gap profile of small problems and constructs a continuous schedule governed by ∂ts=κgq(s)\partial_t s = κg^q(s)∂t​s=κgq(s), where g(s)g(s)g(s) is the instantaneous gap and (κ,q)(κ, q)(κ,q) are global hyperparameters. Discretizing this schedule yields closed-form expressions for all QAOA angles, reducing the classical optimization task from 2p2p2p parameters to only 222, independent of circuit depth. This drastic parameter compression mitigates classical optimization overhead and reduces sensitivity to barren plateau phenomena. Numerical simulations on random QUBO and 3-regular MaxCut instances demonstrate that the learnt schedules transfer effectively to larger systems while achieving competitive approximation ratios. Our results suggest that gap-informed schedule transfers provide a scalable and parameter-efficient strategy for QAOA.
Abstract Quantum algorithms on Noisy Intermediate-Scale Quantum (NISQ) devices require optimization strategies matched to circuit structure. We present two complementary approaches: VERMICULAR, achieving 93.0% success rates for Grover’s algorithm through strategic dynamical decoupling (DD) placement (vs. 18.3% baseline), and systematic QAOA parameter optimization achieving 94.5% approximation ratios for MaxCut (vs. 39.6% with theoretical parameters). Through >15,000 circuit executions across IQM Garnet, Rigetti Ankaa-3, and IonQ Forte-1 (total cost: €70), we demonstrate that circuit structure determines which strategy succeeds: DD helps algorithms with substantial idle time (Grover: 34% idle → 5× improvement) but degrades continuously-active circuits (QAOA: 2.4% idle → 50% degradation with DD). We systematically characterize algorithm noise resilience via critical noise thresholds (σc), finding Bell states achieve 4× higher σc than product states (0.200 vs 0.050, p < 0.001) and that σc predicts hardware performance (r = 0.94, p = 0.006). QAOA scaling studies (3-40 qubits) reveal exponential decay with 56% plateau beyond 20 qubits. Simulator-optimized parameters transfer effectively to hardware (85-98% efficiency), but hardware-to-hardware transfer requires architecture-specific tuning. We provide SigmaCSuite, an open-source validation framework, and complete experimental protocols. These findings establish that optimization strategy must match circuit structure: idle-time protection for search algorithms, parameter tuning for variational algorithms—achieving 2.5-5× improvements through targeted approaches.
Quantum cloud providers can identify a user's algorithm and secret problem structure without ever seeing actual quantum data, simply by analyzing routine metadata collected for billing and system management. Existing confidentiality tools such as blind quantum computation and quantum homomorphic encryption protect the quantum payload itself, but they do not protect this classical orchestration metadata. This leaves an unexplored security risk in the logs generated when a large quantum program is split into smaller pieces to fit onto limited hardware, a process known as circuit cutting. These fragments leak sensitive information through what we term the topological transpilation penalty: the unavoidable depth and gate inflation added when a compiler reorganizes a program for a restricted hardware topology. Tests on a 156-qubit production Quantum Processing Unit (QPU) show that traditional timing side-channels fail in this setting, since hardware control-plane delays mask actual quantum execution time. The unique shape of the transpilation penalty acts instead as a persistent structural fingerprint for the hidden workload. Using 12,000 circuit fragments across eight algorithm families, our attack recovers algorithm family and Hamiltonian k-locality with near-perfect accuracy, achieving instance-disjoint AUC = 1.000 for both. This leakage persists under size-holdout evaluation on unseen circuit scales, with AUC = 0.987 and 0.986 respectively. The cutting mechanism is inferred with AUC = 0.991, and hardware topology is recovered well above chance with AUC = 0.818. These results show that circuit cutting exposes algorithmic intent, and potentially proprietary problem structure, through metadata alone, without any need to observe quantum data.
We introduce a novel quantum optimization paradigm: the Fixed-Parameter-Count Quantum Approximate Optimization Algorithm (FPC-QAOA). It is a scalable variational framework that maintains a constant number of trainable parameters regardless of the number of qubits, Hamiltonian complexity, or circuit depth. By separating schedule function optimization from circuit digitization, FPC-QAOA enables accurate schedule approximations with minimal parameters while supporting arbitrarily deep digitized adiabatic evolutions, constrained only by NISQ hardware capabilities. This separation allows depth to scale without expanding the classical search space, mitigating overparameterization and optimization challenges typical of deep QAOA circuits, such as barren plateaus-like behaviors. We benchmark FPC-QAOA on random MaxCut instances and the Tail Assignment Problem, achieving performance comparable to or better than standard QAOA with nearly constant classical effort and significantly fewer quantum circuit evaluations. Experiments on the IBM Kingston superconducting processor with up to 50 qubits confirm robustness and hardware efficiency under realistic noise. These results position FPC-QAOA as a practical and scalable paradigm for variational quantum optimization on near-term quantum devices.
Introduction Hybrid Variational Quantum Algorithms (VQAs) present a highly viable pathway to near-term quantum utility; however, their performance is fundamentally bottlenecked by classical-quantum communication latency in Quantumas-a-Service (QaaS) environments. Methods This paper proposes an optimized classical-quantum orchestration architecture designed to minimize cloud-induced latency and maximize Quantum Processing Unit (QPU) active compute time. By implementing edge-colocated classical optimizers alongside batched parameter-shift gradient evaluations, the system circumvents stateless cloud API barriers. Results Benchmarking across parameterized quantum circuits ranging from 15 to 50 qubits demonstrates an 84% reduction in network-induced QPU idle time. The framework yields a 3.2 × speedup in overall convergence time for the Quantum Approximate Optimization Algorithm (QAOA) and up to a 98% reduction in classical API call overhead compared to standard RESTful QaaS execution models. Discussion These quantitative findings demonstrate that tightly coupled hybrid co-processing, physically adjacent to the control electronics, is critical for extending the computational bound of Noisy Intermediate-Scale Quantum (NISQ) devices.