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arXiv:2607.25998

Observable Estimation in the Absence of Classical Verification

Samantha V. Barron, Bradley Mitchell, Vinay Tripathi, Francesco Grieco, Ilan Rosen, Francesca Pietracaprina, Davide Materia, Alireza Seif, Darvin Wanisch, Ramón L. Panadés-Barrueta, Ewout van den Berg, Jay-U Chung, Andrew Eddins, Sam Ferracin, Guillermo García-Pérez, John Goold, Luke C. G. Govia, Holger Haas, Ian Hincks, Jesse C. Hoke, Zoë Holmes, Su-un Lee, Youngseok Kim, Swarnadeep Majumder, Sabrina Maniscalco, Simone Montangero, Daniel Puzzuoli, Tomaž Prosen, James Raftery, Ricardo Rivera Cardoso, Max Rossmannek, Manuel Rudolph, Brendan Saxberg, Liran Shirizly, Karthik Siva, Joshua Skanes-Norman, Ilaria Siloi, Kevin C Smith, Boris Sokolov, Maika Takita, Yanting Teng, Mao Tian Tan, Joseph Tindall, Zoltán Zimborás, Matteo A. C. Rossi, Minh C. Tran, Sergei N. Filippov, Abhinav Kandala

quant-phSubmitted Jul 28, 2026• Announced Jul 29, 2026• 0 citations
arXivHTMLPDF

Abstract

The predictive success of quantum mechanics underpins many areas of modern science, even as the exact simulation of large, interacting quantum systems remains beyond the reach of classical computation. This success has been enabled by the remarkable advancement of scalable numerical approximation methods, which often demonstrate practical accuracy despite the absence of formal guarantees. As quantum simulation pushes into regimes where these approximations struggle, a fundamental challenge arises: How can quantum outcomes be trusted when reliable classical benchmarks are unavailable? Here, we establish a framework for the independent validation of quantum estimates in this setting and present evidence that they provide the most credible result among several considered methods, in the absence of an immediately accessible ground-truth solution. We apply our framework to the semi-scrambling dynamics of a physical model that strains several leading classical simulation methods yet remains experimentally accessible, in part through our introduction of the \textit{operator Loschmidt echo}. We systematically design a series of experiments using quantum heuristics that, taken together, test the underlying assumptions and provide strong confidence in the observable estimates obtained from the quantum computer. We then show how this framework can be extended to place accuracy bounds on quantum estimates via careful characterization and manipulation of the device noise, transforming the problem of validating the observable estimation to validating the noise model. These results establish a route towards trusted quantum computation for scientific discovery, independent of classical verification.

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This paper explores the potential of quantum simulation for predictive hardware verification. Traditional hardware verification methods often rely on classical simulations and emulation, which struggle to accurately capture the complexities of modern digital circuits, particularly when considering subtle quantum effects. Quantum simulation offers a fundamentally different approach by leveraging the principles of quantum mechanics to directly model the behavior of the circuit at a fundamental level. We propose a framework where a quantum computer simulates the circuit, accounting for phenomena such as charge noise and quantum tunneling. The core claim is that this approach can significantly improve the accuracy of hardware prediction, allowing for the early detection of vulnerabilities and the optimization of performance before physical fabrication. This paper details the theoretical foundations, outlines a potential implementation strategy, and discusses the challenges and opportunities associated with this emerging technology. The key metric for success will be the ability to identify design flaws undetectable by conventional methods, ultimately leading to more robust and efficient hardware designs.

•Aug 31, 2026•0 citations
The predictive success of quantum mechanics underpins many areas of modern science, even as the exact simulation of large, interacting quantum systems remains beyond the reach of classical computation. This success has been enabled by the remarkable advancement of scalable numerical approximation methods, which often demonstrate practical accuracy despite the absence of formal guarantees. As quantum simulation pushes into regimes where these approximations struggle, a fundamental challenge arises: How can quantum outcomes be trusted when reliable classical benchmarks are unavailable? Here, we establish a framework for the independent validation of quantum estimates in this setting and present evidence that they provide the most credible result among several considered methods, in the absence of an immediately accessible ground-truth solution. We apply our framework to the semi-scrambling dynamics of a physical model that strains several leading classical simulation methods yet remains experimentally accessible, in part through our introduction of the \textit{operator Loschmidt echo}. We systematically design a series of experiments using quantum heuristics that, taken together, test the underlying assumptions and provide strong confidence in the observable estimates obtained from the quantum computer. We then show how this framework can be extended to place accuracy bounds on quantum estimates via careful characterization and manipulation of the device noise, transforming the problem of validating the observable estimation to validating the noise model. These results establish a route towards trusted quantum computation for scientific discovery, independent of classical verification.
Analog Quantum Simulators offer a route to exploring strongly correlated many-body dynamics beyond classical computation, but their predictive power remains limited by the absence of quantitative error estimation. Establishing rigorous uncertainty bounds is essential for elevating such devices from qualitative demonstrations to quantitative scientific tools. Here we introduce a general framework for bounded-error quantum simulation, which provides predictions for many-body observables with experimentally quantifiable uncertainties. The approach combines Hamiltonian and Lindbladian Learning--a statistically rigorous inference of the coherent and dissipative generators governing the dynamics--with the propagation of their uncertainties into the simulated observables, yielding confidence bounds directly derived from experimental data. We demonstrate this framework on trapped-ion quantum simulators implementing long-range Ising interactions with up to 51 ions, and validate it where classical comparison is possible. We analyze error bounds on two levels. First, we learn an open-system model from experimental data collected in an initial time window of quench dynamics, simulate the corresponding master equation, and quantitatively verify consistency between theoretical predictions and measured dynamics at long times. Second, we establish error bounds directly from experimental measurements alone, without relying on classical simulation--crucial for entering regimes of quantum advantage. The learned models reproduce the experimental evolution within the predicted bounds, demonstrating quantitative reliability and internal consistency. Bounded-error quantum simulation provides a scalable foundation for trusted analog quantum computation, bridging the gap between experimental platforms and predictive many-body physics. The techniques presented here directly extend to digital quantum simulation.
Analog quantum processors based on Rydberg atom arrays are a powerful platform for many-body quantum simulation, combinatorial optimization, and graph machine learning. As these devices become increasingly accessible, establishing confidence in their outputs requires predictive models that quantitatively connect microscopic hardware imperfections to empirical results. Here, we present a noise-aware emulation framework that propagates the dominant noise mechanisms throughout the full computation cycle to predict device behavior. We validate the framework by benchmarking two representative protocols, quantum annealing and post-quench dynamics, on three Pasqal quantum processors where classical simulations still provide ground truth. Across all three devices, the measured observables fall within the uncertainty envelopes predicted by the emulator. Beyond reproducing the data, the framework isolates which physical mechanism dominates in each operating regime, provides quantitative guidance for algorithm design and hardware improvements, and establishes a foundation for verifying analog processors in regimes beyond classical reach.
The pursuit of quantum advantage is driving the co-evolution of quantum processors and classical simulation methods. Despite advances in scale and quality, the accuracy of quantum simulation is ultimately limited by error rates and sampling overheads. Similarly, while classical simulation methods such as Pauli propagation have made remarkable progress, their accuracy is ultimately limited by the exponential growth of operator paths and the truncations needed to control memory and runtime. Here we show that these complementary limitations can be mitigated by embedding Pauli propagation within a hybrid error-mitigation framework that reduces quantum sampling overhead while achieving lower truncation errors with fewer classical resources than traditional Pauli propagation alone. In this framework, a target observable is classically propagated through noise-canceling inverse channels, producing a modified observable that is measured directly on a quantum processor. We prototype two implementations and benchmark their performance numerically on canonical models that challenge traditional Pauli propagation. We also perform experiments on a quantum processor using 56 superconducting qubits, revealing the tradeoffs of their respective truncation strategies. These results illustrate how classical and quantum resources can be orchestrated to extend observable estimation beyond the limits of either approach alone, providing a foundation for quantum-centric supercomputing and future demonstrations of quantum advantage.
With today’s quantum processors venturing into regimes beyond the capabilities of classical devices, we face the challenge to verify that these devices perform as intended, even when we cannot check their results on classical computers. In a recent breakthrough in computer science, a protocol was developed that allows the verification of the output of a computation performed by an untrusted quantum device based only on classical resources. Here, we follow these ideas, and demonstrate in a first, proof-of-principle experiment the verification of the output of a quantum computation using only classical means on a small trapped-ion quantum processor. We contrast this to verification protocols, which require trust and detailed hardware knowledge, as in gate-level benchmarking, or additional quantum resources in case we do not have access to or trust in the device to be tested. While our experimental demonstration uses a simplified version of Mahadev’s protocol we demonstrate the necessary steps for verifying fully untrusted devices. A scaled-up version of our protocol will allow for classical verification, requiring no hardware access or detailed knowledge of the tested device. Its security relies on post–quantum secure trapdoor functions within an interactive proof. The conceptually straightforward, but technologically challenging scaled-up version of the interactive proofs, considered here, can be used for a variety of additional tasks such as verifying quantum advantage, generating and certifying quantum randomness, or composable remote state preparation.
Quantum computing offers a new, orthogonal direction for investigating fundamental physics, extending beyond classical numerical methods and conventional observables. Realizing this potential requires directly confronting the noise limiting currently available quantum computers. Progress rests on advancing algorithms, interpreting their results, and managing their errors together. This thesis presents several advancements in the use of quantum simulation and quantum information to probe fundamental physics. The first is in the use of quantum computers to simulate collisions in quantum field theories. Central to these simulations are new wavepacket preparation, time evolution, and error mitigation techniques, which allow for simulations with some of the largest effective circuit volumes to date. These methods enable the first quantum simulation providing numerical evidence for inelastic particle production, a key process in fundamental physics. The second advancement centers on the role quantum-information-theoretic quantities play in physical processes. Beyond mere correlations with the physics of the process, entanglement and magic are shown to probe the interactions present in scattering and hadronization dynamics. A precision study requires a complete quantification of algorithmic and hardware uncertainties, an outstanding goal as quantum simulations mature. The third advancement in this thesis addresses error management. A framework minimizing the effect of algorithmic errors in analog quantum simulations is presented. In a step toward fault tolerance, error detection in encoded quantum simulations is shown to improve estimation of local observables relative to unencoded runs. Together, the developments in this thesis mark practical progress toward fault-tolerant quantum simulations of fundamental physics capable of scientific discovery.
This paper explores the potential of quantum simulation for predictive hardware verification. Traditional hardware verification methods often rely on classical simulations and emulation, which struggle to accurately capture the complexities of modern digital circuits, particularly when considering subtle quantum effects. Quantum simulation offers a fundamentally different approach by leveraging the principles of quantum mechanics to directly model the behavior of the circuit at a fundamental level. We propose a framework where a quantum computer simulates the circuit, accounting for phenomena such as charge noise and quantum tunneling. The core claim is that this approach can significantly improve the accuracy of hardware prediction, allowing for the early detection of vulnerabilities and the optimization of performance before physical fabrication. This paper details the theoretical foundations, outlines a potential implementation strategy, and discusses the challenges and opportunities associated with this emerging technology. The key metric for success will be the ability to identify design flaws undetectable by conventional methods, ultimately leading to more robust and efficient hardware designs.