arXiv subjects

Luke C. G. Govia

Publications and source records attributed to Luke C. G. Govia.

At least 19 recordsLinked to original sources

arXiv2026

Balancing Expressivity and Overfitting in Quantum Gaussian Process Regression

Active learning is a paradigm of machine learning that can be utilized to model expensive black-box functions by training a surrogate model from actively queried training points. The performance of this framework depends heavily on the choice of the surrogate model. When the surrogate is Gaussian Process Regression (GPR), its performance is largely determined by the expressivity of the underlying kernel. In this work, we investigate the peculiarities of using a quantum kernel to change the computational dynamics of active learning with GPR, focusing on the sensitivity to regularization by hyperparameter tuning. While generic, unstructured kernels suffer from exponential concentration at large scale, we empirically demonstrate that even at small scale overfitting can collapse GPR performance. Kernel regularization can be used to counteract this effect, but due to the smoothness of the quantum fidelity kernel, regularization must be carefully chosen to balance expressivity and overfitting. Our qualitative results transfer to the practical application of restricted quantum kernels designed to avoid exponential concentration and also present the kinds of noise that may be valuable to kernel training in near-term quantum devices.

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arXiv2026

Observable Estimation in the Absence of Classical Verification

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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arXiv2026

Symmetries of Pauli Noise from Lindbladian Dynamics

Characterizing noise in quantum circuits is fundamentally limited by gauge degrees of freedom; certain parameters, such as the individual contributions of state preparation and measurement (SPAM) errors, are in principle unlearnable from any experiment within the gate set. Here, we show that the physical structure of realistic noise processes imposes approximate symmetry constraints on the Pauli fidelities of gate noise channels. These symmetries relate the fidelity of a Pauli $P$ and its gate-conjugate $U_g P U_g ^{\dagger}$, and can be used to fix the gauge using only knowledge of the error type and not its magnitude. Using Lindbladian perturbation theory, we analyze a broad class of Clifford gates, including $ZZ_{π/2}$, CZ, CNOT, iSWAP, and SWAP, and demonstrate that coherent errors do not induce first-order asymmetry, while only a restricted set of predominantly off-diagonal dissipative errors can break the symmetry at first order, for which we derive simple selection rules. Notably, common single-qubit noise sources such as $T_1$-relaxation and $T_{2ϕ}$-pure-dephasing can only cause asymmetry at second order. Leveraging these symmetries to fix the gauge enables systematic identification of SPAM errors, simplifying error characterization and mitigation. We validate our results numerically and experimentally on IBM Kingston.

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arXiv2026

Indoor Occupancy Classification using a Compact Hybrid Quantum-Classical Model Enabled by a Physics-Informed Radar Digital Twin

Indoor occupancy classification enables privacy-preserving monitoring in settings such as remote elder care, where presence information helps triage alarms without cameras or wearables. Radar suits this role by sensing motion through occlusions and in darkness. Modern deep-learning pipelines are the standard for interpreting radar returns effectively; however, they are often parameter-heavy and sensitive at low signal-to-noise ratios (SNR), motivating compact alternatives like Hybrid Quantum Neural Networks (HQNNs). A two-qubit HQNN is benchmarked against convolutional neural networks (CNNs) using a physics-informed 60GHz digital twin and real radar measurements under matched training protocols. In clean conditions, the HQNN achieves high accuracy (99.7% synthetic; 97.0% real) with up to 170x fewer parameters (0.066M). Its parameter efficiency is shown to be structural, as an ablation of the parameterized quantum circuit (PQC) causes sharp performance drops on real data (to 68.5% and 31.5% for the control heads). A domain-dependent sensitivity emerges under additive-noise evaluation, where the HQNN begins recovery earlier in synthetic data while CNNs recover more steeply and peak higher on real measurements. In label-fraction ablations, CNNs prove more sample-efficient on real Range-Doppler Maps (RDMs), with the performance gap being most pronounced (at 50% labels, BA 0.89-0.99 vs. HQNN 0.75). On synthetic data, this gap narrows significantly, largely vanishing by the 50% label mark. Overall, the HQNN's value lies in parameter efficiency and a compact inductive bias that shapes its distinct sensitivity profile; this work establishes a rigorous baseline for hybrid quantum models in privacy-preserving radar occupancy sensing.

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arXiv2026

Disambiguating Pauli noise in quantum computers

To successfully perform quantum computations, it is often necessary to first accurately characterize the noise in the underlying hardware. However, it is well known that fundamental limitations prevent the unique identification of the noise. This raises the question of whether these limitations impact the ability to predict noisy dynamics and mitigate errors. Here, we show, both theoretically and experimentally, that when learnable parameters are self-consistently characterized, the unlearnable (gauge) degrees of freedom do not impact predictions of noisy dynamics or error mitigation. We use the recently introduced framework of gate set Pauli noise learning to efficiently and self-consistently characterize and mitigate noise of a complete gate set, including state preparation, measurements, single-qubit gates and multi-qubit entangling Clifford gates. We validate our approach through experiments with up to 92 qubits and show that while the gauge choice does not affect error-mitigated observable values, optimizing it reduces sampling overhead. Our findings address an outstanding issue involving the ambiguities in characterizing and mitigating quantum noise.

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arXiv2026

Single snapshot non-Markovianity of Pauli channels

Pauli channels are widely used to describe errors in quantum computers, particularly when noise is shaped via Pauli twirling. A common assumption is that such channels admit a Markovian generator, namely a Pauli-Lindblad model with non-negative rates, but the validity of this assumption has not been systematically examined. Here, using CP-indivisibility as our criterion for non-Markovianity, we study multi-qubit Pauli channels from a single snapshot of the dynamics. We find that while the generator always has the same structure as the standard Pauli-Lindblad model, the rates may be negative or complex. We show that random Pauli channels are almost always non-Markovian, with the probability of encountering a negative rate converging doubly exponentially to unity with the number of qubits. For physically motivated noise models shaped by Pauli twirling, including single-qubit over-rotations and two-qubit amplitude damping errors, we find that negative rates are generic, even when the underlying physical noise is Markovian. We generalize probabilistic error amplification and cancellation to non-Markovian generators, and quantify the sampling overhead introduced by negative and complex rates. Experiments on superconducting qubits confirm that allowing negative rates in the learned noise model yields more accurate predictions than restricting to non-negative rates.

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arXiv2025

Order from chaos with adaptive circuits on quantum hardware

Programmable quantum devices provide a platform to control the coherent dynamics of quantum wavefunctions. Here we experimentally realize adaptive monitored quantum circuits, which incorporate conditional feedback into non-unitary evolution, to control quantum chaotic dynamics using a combination of local mid-circuit measurements and resets. The experiments are performed with an IBM superconducting quantum processor using up to 100 qubits that samples a quantum version of the classically chaotic Bernoulli map. This map scrambles quantum information, while local measurements and feedback attempt to steer the dynamics toward a state that is a fixed point of the map. This competition drives a dynamical phase transition between quantum and classical dynamics that we observe experimentally and describe theoretically using noisy simulations, matrix product states, and mappings to statistical mechanics models. Estimates of the universal critical properties are obtained to high accuracy on the quantum computer thanks to the large number of qubits utilized in the calculation. By successfully applying up to nearly 5000 entangling gates and 5000 non-unitary mid-circuit operations on systems up to 100 qubits, this experiment serves as a signpost on the route towards fault tolerance.

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arXiv2025

When Clifford benchmarks are sufficient; estimating application performance with scalable proxy circuits

The goal of benchmarking is to determine how far the output of a noisy system is from its ideal behavior; this becomes exceedingly difficult for large quantum systems where classical simulations become intractable. A common approach is to turn to circuits comprised of elements of the Clifford group (e.g., CZ, CNOT, $π$ and $π/2$ gates), which probe quantum behavior but are nevertheless efficient to simulate classically. However, there is some concern that these circuits may overlook error sources that impact the larger Hilbert space. In this manuscript, we show that for a broad class of error models these concerns are unwarranted. In particular, we show that, for error models that admit noise tailoring by Pauli twirling, the diamond norm and fidelity of any generic circuit is well approximated by the fidelities of proxy circuits composed only of Clifford gates. We discuss methods for extracting the fidelities of these Clifford proxy circuits in a manner that is robust to errors in state preparation and measurement and demonstrate these methods in simulation and on IBM Quantum's fleet of deployed heron devices.

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arXiv2025

Error mitigation with stabilized noise in superconducting quantum processors

Pre-fault tolerant quantum computers have already demonstrated the ability to estimate observable values accurately, at a scale beyond brute-force classical computation. This has been enabled by error mitigation techniques that often rely on a representative model on the device noise. However, learning and maintaining these models is complicated by fluctuations in the noise over unpredictable time scales, for instance, arising from resonant interactions between superconducting qubits and defect two-level systems (TLS). Such interactions affect the stability and uniformity of device performance as a whole, but also affect the noise model accuracy, leading to incorrect observable estimation. Here, we experimentally demonstrate that tuning of the qubit-TLS interactions helps reduce noise instabilities and consequently enables more reliable error-mitigation performance. These experiments provide a controlled platform for studying the performance of error mitigation in the presence of quasi-static noise. We anticipate that the capabilities introduced here will be crucial for the exploration of quantum applications on solid-state processors at non-trivial scales.

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arXiv2025

Efficient Lindblad synthesis for noise model construction

Effective noise models are essential for analyzing and understanding the dynamics of quantum systems, particularly in applications like quantum error mitigation and correction. However, even when noise processes are well-characterized in isolation, the effective noise channels impacting target quantum operations can differ significantly, as different gates experience noise in distinct ways. Here, we present a noise model construction method that builds an effective model from a Lindbladian description of the physical noise processes acting simultaneously to the desired gate operation. It employs the Magnus expansion and Dyson series, and can be utilized for both low-order symbolic and high-order numerical approximations of the noise channel of a multi-qubit quantum gate. We envision multiple use cases of our noise construction method such as (i) computing the corresponding noise channel from a learned Lindbladian, and (ii) generating the noise channel starting with physically motivated Lindbladians for a given hardware architecture. In doing so, we close the gap between physical Lindbladians and operational level noise model parameters. We demonstrate a strong agreement between our symbolic noise construction and full numerical Lindblad simulations for various two-qubit gates, in isolation and in three- and four-qubit scenarios, for a variety of physically motivated noise sources. Our symbolic construction provides a useful breakdown of how noise model parameters depend on the underlying physical noise parameters, which gives qualitative insight into the structure of errors. For instance, our theory provides insight into the interplay of Lindblad noise with the intended gate operations, and can predict how local Lindblad noise can effectively spread into multi-qubit error.

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arXiv2024

High-fidelity gates in a transmon using bath engineering for passive leakage reset

Leakage, the occupation of any state not used in the computation, is one of the of the most devastating errors in quantum error correction. Transmons, the most common superconducting qubits, are weakly anharmonic multilevel systems, and are thus prone to this type of error. Here we demonstrate a device which reduces the lifetimes of the leakage states in the transmon by three orders of magnitude, while protecting the qubit lifetime and the single-qubit gate fidelties. To do this we attach a qubit through an on-chip seventh-order Chebyshev filter to a cold resistor. The filter is engineered such that the leakage transitions are in its passband, while the qubit transition is in its stopband. Dissipation through the filter reduces the lifetime of the transmon's $f$ state, the lowest energy leakage state, by three orders of magnitude to 33 ns, while simultaneously keeping the qubit lifetime to greater than 100 $μ$s. Even though the $f$ state is transiently populated during a single qubit gate, no negative effect of the filter is detected with errors per gate approaching 1e-4. Modelling the filter as coupled linear harmonic oscillators, our theoretical analysis of the device corroborate our experimental findings. This leakage reduction unit turns leakage errors into errors within the qubit subspace that are correctable with traditional quantum error correction. We demonstrate the operation of the filter as leakage reduction unit in a mock-up of a single-qubit quantum error correcting cycle, showing that the filter increases the seepage rate back to the qubit subspace.

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arXiv2024

Randomized Benchmarking Protocol for Dynamic Circuits

Dynamic circuit operations -- measurements with feedforward -- are important components for future quantum computing efforts, but lag behind gates in the availability of characterization methods. Here we introduce a series of dynamic circuit benchmarking routines based on interleaving dynamic circuit operation blocks $F$ in one-qubit randomized benchmarking sequences of data qubits. $F$ spans between the set of data qubits and a measurement qubit and may include feedforward operations based on the measurement. We identify six candidate operation blocks, such as preparing the measured qubit in $|0\rangle$ and performing a $Z$-Pauli on the data qubit conditioned on a measurement of `1'. Importantly, these blocks provide a methodology to accumulate readout assignment errors in a long circuit sequence. We also show the importance of dynamic-decoupling in reducing ZZ crosstalk and measurement-induced phase errors during dynamic circuit blocks. When measured on an IBM Eagle device with appropriate dynamical decoupling, the results are consistent with measurement assignment error and the decoherence of the data qubit as the leading error sources.

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arXiv2023

Benchmarking Quantum Processor Performance at Scale

As quantum processors grow, new performance benchmarks are required to capture the full quality of the devices at scale. While quantum volume is an excellent benchmark, it focuses on the highest quality subset of the device and so is unable to indicate the average performance over a large number of connected qubits. Furthermore, it is a discrete pass/fail and so is not reflective of continuous improvements in hardware nor does it provide quantitative direction to large-scale algorithms. For example, there may be value in error mitigated Hamiltonian simulation at scale with devices unable to pass strict quantum volume tests. Here we discuss a scalable benchmark which measures the fidelity of a connecting set of two-qubit gates over $N$ qubits by measuring gate errors using simultaneous direct randomized benchmarking in disjoint layers. Our layer fidelity can be easily related to algorithmic run time, via $γ$ defined in Ref.\cite{berg2022probabilistic} that can be used to estimate the number of circuits required for error mitigation. The protocol is efficient and obtains all the pair rates in the layered structure. Compared to regular (isolated) RB this approach is sensitive to crosstalk. As an example we measure a $N=80~(100)$ qubit layer fidelity on a 127 qubit fixed-coupling "Eagle" processor (ibm\_sherbrooke) of 0.26(0.19) and on the 133 qubit tunable-coupling "Heron" processor (ibm\_montecarlo) of 0.61(0.26). This can easily be expressed as a layer size independent quantity, error per layered gate (EPLG), which is here $1.7\times10^{-2}(1.7\times10^{-2})$ for ibm\_sherbrooke and $6.2\times10^{-3}(1.2\times10^{-2})$ for ibm\_montecarlo.

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arXiv2023

Readout-induced suppression and enhancement of superconducting qubit lifetimes

It has long been known that the lifetimes of superconducting qubits suffer during readout, increasing readout errors. We show that this degradation is due to the anti-Zeno effect, as readout-induced dephasing broadens the qubit so that it overlaps 'hot spots' of strong dissipation, likely due to two-level systems in the qubit's bath. Using a flux-tunable qubit to probe the qubit's frequency dependent loss, we accurately predict the change in lifetime during readout with a new self-consistent master equation that incorporates the modification to qubit relaxation due to measurement-induced dephasing. Moreover, we controllably demonstrate both the Zeno and anti-Zeno effects, which explain suppression and the rarer enhancement of qubit lifetimes during readout.

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arXiv2022

Time-dependent Schrieffer-Wolff-Lindblad Perturbation Theory: measurement-induced dephasing and second-order Stark shift in dispersive readout

We develop a time-dependent Schrieffer-Wolff-Lindblad perturbation theory to study effective interactions for driven open quantum systems. The starting point of our analysis is a given Lindblad equation, based on which we obtain an effective (averaged) map that describes the renormalization of both the Hamiltonian and collapse operators due to the drive. As a case study, we apply this method to the dispersive readout of a transmon qubit and derive an effective disperive map that describes measurement-induced dephasing and Stark shift for the transmon. The effective map we derive is completely positive and trace-preserving under adiabatic resonator response. To benchmark our method, we demonstrate good agreement with a numerical computation of the effective rates via the Lindbladian spectrum. Our results are also in agreement with, and extend upon, an earlier derivation of such effects by Gambetta et al. (Phys. Rev. A 74, 042318 (2006)) using the positive P-representation for the resonator field.

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arXiv2022

Miniaturizing transmon qubits using van der Waals materials

Quantum computers can potentially achieve an exponential speedup versus classical computers on certain computational tasks, as recently demonstrated in systems of superconducting qubits. However, these qubits have large footprints due to their large capacitor electrodes needed to suppress losses by avoiding dielectric materials. This tactic hinders scaling by increasing parasitic coupling among circuit components, degrading individual qubit addressability, and limiting the spatial density of qubits. Here, we take advantage of the unique properties of the van der Waals (vdW) materials to reduce the qubit area by a factor of $>1000$ while preserving the required capacitance without increasing substantial loss. Our qubits combine conventional aluminum-based Josephson junctions with parallel-plate capacitors composed of crystalline layers of superconducting niobium diselenide (NbSe$_2$) and insulating hexagonal-boron nitride (hBN). We measure a vdW transmon $T_1$ relaxation time of 1.06 $μ$s, which demonstrates a path to achieve high-qubit-density quantum processors with long coherence times, and illustrates the broad utility of layered heterostructures in low-loss, high-coherence quantum devices.

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arXiv2022

Effective qubit dephasing induced by spectator-qubit relaxation

In many leading architectures for quantum computing, it remains to be understood if we can equate single-qubit coherence times measured in isolation, to that of coherence times measured in multi-qubit devices. On a multi-qubit superconducting circuit platform we show an increase in the dephasing rate of a control qubit due to the spontaneous relaxation of spectator qubits coupled to the control qubit. We attribute this increased dephasing to random in time Z-phase kicks on the control qubit due to the interplay between spectator relaxation and the control-spectator ZZ- interaction. We measure the magnitude of this extra dephasing using Ramsey decay experiments, show how it can be corrected via dynamical decoupling pulse sequences, and demonstrate that randomized benchmarking is insensitive to the effect. Our experimental results are supported by a robust theoretical model that captures an arbitrary number of spectator qubits, and gives a simple, intuitive picture for the mechanism behind the enhanced dephasing.

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arXiv2021

Symmetry-Aware Reservoir Computing

We demonstrate that matching the symmetry properties of a reservoir computer (RC) to the data being processed dramatically increases its processing power. We apply our method to the parity task, a challenging benchmark problem that highlights inversion and permutation symmetries, and to a chaotic system inference task that presents an inversion symmetry rule. For the parity task, our symmetry-aware RC obtains zero error using an exponentially reduced neural network and training data, greatly speeding up the time to result and outperforming hand crafted artificial neural networks. When both symmetries are respected, we find that the network size $N$ necessary to obtain zero error for 50 different RC instances scales linearly with the parity-order $n$. Moreover, some symmetry-aware RC instances perform a zero error classification with only $N=1$ for $n\leq7$. Furthermore, we show that a symmetry-aware RC only needs a training data set with size on the order of $(n+n/2)$ to obtain such performance, an exponential reduction in comparison to a regular RC which requires a training data set with size on the order of $n2^n$ to contain all $2^n$ possible $n-$bit-long sequences. For the inference task, we show that a symmetry-aware RC presents a normalized root-mean-square error three orders-of-magnitude smaller than regular RCs. For both tasks, our RC approach respects the symmetries by adjusting only the input and the output layers, and not by problem-based modifications to the neural network. We anticipate that generalizations of our procedure can be applied in information processing for problems with known symmetries.

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