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Andrew T. Sornborger

Publications and source records attributed to Andrew T. Sornborger.

At least 19 recordsLinked to original sources
arXiv2026

All On-Board: Fully On-Chip Neuromorphic Q-Learning with Embedded CartPole Simulation

As AI models grow in size and usage, their energy demands increase dramatically, raising sustainability and economic concerns. Neuromorphic hardware, inspired by the energy efficiency of the brain, seeks to address this challenge by offering low-power, fast-processing alternatives to conventional computing. Such hardware is particularly well-suited to control systems deployed in resource-constrained environments, which are best trained via reinforcement learning (RL). This contribution presents the design and implementation of a fully on-chip, closed-loop Loihi 2 RL agent. Our neuromorphic circuit consists of a fully embedded Q-learning algorithm and an on-chip simulation of the CartPole-v0 environment on Loihi 2. Our Q-learning algorithm trained the same number of successful agents as the CPU implementation in only half the execution time and with two orders of magnitude less dynamic power. These findings demonstrate the viability of RL on neuromorphic hardware and highlight its promise for building energy-efficient, real-time, embedded AI systems.

cs.NE↗
arXiv2026

Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring

Persistent acoustic monitoring can detect machine faults without physical contact, but always-on inference is constrained by power, latency, and deployment complexity. We demonstrate autoencoder-based acoustic anomaly detection on an Intel Loihi 2 neuromorphic processor under clean and noisy conditions. Log-mel features are computed off chip; normalization, autoencoder inference, L1 reconstruction scoring, and thresholding run on chip. In a clean, microphone-position-invariant ToyADMOS ToyCar benchmark, the on-chip model achieves 0.9959 AUC and 0.9785 standardized pAUC at maximum false-positive rate 0.1. In the DCASE 2026 Task 2 ToyCar noisy benchmark, the model achieves source AUC 0.7990, target AUC 0.6466, and pAUC 0.6426, exceeding reported baseline metrics. Power profiling on a 16-chip Loihi 2 VPX system shows real-time throughput with 0.0406$\unicode{x2013}$0.0426 mJ dynamic energy per sample, two orders of magnitude lower than both a CPU and GPU. These results support neuromorphic acoustic anomaly detection as a practical candidate for low-power, persistent machine monitoring.

cs.NE↗
arXiv2026

A Validation Framework for Quantum Simulation of Spin Dynamics against Inelastic Neutron Scattering and Classical Simulation

Quantitative validation of quantum simulations of dynamical spin response remains challenging because experiment, classical simulation, and quantum simulation do not produce the same native observables. This problem has become increasingly important as quantum simulation protocols for dynamical response have progressed from theory to hardware-level benchmarking against neutron-scattering data, while the longer term goal is validation in regimes that may eventually become classically intractable, including in future fault-tolerant implementations. Here, we develop a cross-pipeline validation framework for quantum simulation, using inelastic neutron scattering and classical many-body simulation as complementary experimental and computational anchors, based on explicit forward and inverse observable maps, covariance- or resampling-based uncertainty propagation, robustness tests for structured distortion, and a hierarchy of complementary metric families. The framework distinguishes stochastic uncertainty from robustness-induced distortion, carries both explicitly through the comparison chain, and uses the resulting metric-level uncertainty and distortion information to support layered validation at the pipeline, solver, and model levels. We also introduce actuator-aware feedback logic aimed at improving agreement without obscuring the physical origin of any remaining mismatch. We close by outlining future extensions of this methodology, including upstream uncertainty and distortion modeling, adaptive feedback, asymmetric validation beyond full classical benchmarking, fault-tolerant workflows, and community infrastructure for reproducible validation.

quant-ph↗
arXiv2026

Robust design under uncertainty in quantum error mitigation

Error mitigation techniques are crucial to achieving near-term quantum advantage. Classical post-processing of quantum computation outcomes is a popular approach for error mitigation, which includes methods such as Zero Noise Extrapolation, Virtual Distillation, and learning-based error mitigation. However, these techniques have limitations due to the propagation of uncertainty resulting from the finite shot number of a quantum measurement. In this work, we introduce general and unbiased methods for quantifying the uncertainty and error of error-mitigated observables based on the strategic sampling of error mitigation outcomes. We then extend our approach to demonstrate the optimization of performance and robustness of error mitigation under uncertainty. To illustrate our methods, we apply them to Zero Noise Extrapolation and Clifford Date Regression in the ground state of the XY model simulated using depolarizing and IBM Toronto noise models, respectively. In particular, we optimize the choice of noise levels and the allocation of shots for Zero Noise Extrapolation and the distribution of the training circuits for Clifford Data Regression. While our methods are readily applicable to any post-processing-based error mitigation approach, in practice they must not be prohibitively expensive -- even though they perform optimizations of the error mitigation hyperparameters requiring sampling of a statistical distribution of error mitigation outcomes. By leveraging surrogate-based optimization, we show that our methods can efficiently perform optimal design for a Zero Noise Extrapolation implementation. We then further demonstrate the transferability of learned Zero Noise Extrapolation hyperparameters to other similar circuits.

quant-ph↗
arXiv2026

Optimal Coherent Quantum Phase Estimation via Tapering

Due to its significance as a subroutine, in this work, we consider the coherent version of the quantum phase estimation problem, where given an arbitrary input state and black-box access to unitaries $U$ and controlled-$U$, the goal is to estimate the phases of $U$ in superposition. Most existing phase estimation algorithms involve intermediary measurements that disrupt coherence. Only a couple of algorithms, including the standard quantum phase estimation algorithm, consider this coherent setting. However, the standard algorithm only succeeds with a constant probability. To boost this success probability, one can employ the coherent median technique, resulting in an algorithm with asymptotically optimal query complexity (the total number of calls to $U$ and controlled-$U$). However, this coherent median technique requires a large number of ancilla qubits and a computationally expensive quantum sorting network. To address this, in this work, we propose an improved version of the standard algorithm called the tapered quantum phase estimation (tQPE) algorithm, which leverages tapering (or window) functions commonly used in classical signal processing. Our algorithm achieves the asymptotically optimal query complexity without requiring the expensive coherent median technique to boost success probability. Moreover, we find the absolutely optimal taper - not only in the asymptotic scaling but in terms of exact performance. We provide an efficiently preparable ancilla state based on an approximation of the optimal taper, which incurs at most a factor-of-two increase in the probability of error, thereby maintaining near-optimal performance in practice. In the appendices, we give an explicit construction of the taper state preparation circuit. Finally, we derive an error bound for coherent QPE when the phase estimate is used as a control and subsequently uncomputed.

quant-ph↗
arXiv2026

Thresholded Quantum Sensing with a Frustrated Kitaev Trimer

We investigate the response of a Ramsey interferometric quantum sensor based on a frustrated, three-spin system (a Kitaev trimer) to a classical time-dependent field (signal). The system eigenspectrum is symmetric about a critical point, $|\vec{b}| = 0$, with four of the spectral components varying approximately linearly with the magnetic field and four exhibiting a nonlinear dependence. Under the adiabatic approximation and for appropriate initial states, we show that the sensor's response to a zero-mean signal is such that below a threshold, $|\vec{b}| < b_\mathrm{th}$, the sensor does not respond to the signal, whereas above the threshold, the sensor acts as a detector that the signal has occurred. This thresholded response is approximately omnidirectional. Moreover, when deployed in an entangled multisensor configuration, the sensor achieves sensitivity at the Heisenberg limit. Such detectors could be useful both as stand-alone units for signal detection above a noise threshold and in two- or three-dimensional arrays, analogous to a quantum bubble chamber, for applications such as particle track detection and long-baseline telescopy.

quant-ph↗
arXiv2026

Background cancellation for frequency-selective quantum sensing

A key challenge in quantum sensing is the detection of weak time dependent signals, particularly those that arise as specific frequency perturbations over a background field. Conventional methods usually demand complex dynamical control of the quantum sensor and heavy classical post-processing. We propose a quantum sensor that leverages time independent interactions and entanglement to function as a passive, tunable, thresholded frequency filter. By encoding the frequency selectivity and thresholding behavior directly into the dynamics, the sensor is responsive only to a target frequency of choice whose amplitude is above a threshold. This approach circumvents the need for complex control schemes and reduces the post-processing overhead.

quant-ph↗
arXiv2025

Improving the efficiency of learning-based error mitigation

Error mitigation will play an important role in practical applications of near-term noisy quantum computers. Current error mitigation methods typically concentrate on correction quality at the expense of frugality (as measured by the number of additional calls to quantum hardware). To fill the need for highly accurate, yet inexpensive techniques, we introduce an error mitigation scheme that builds on Clifford data regression (CDR). The scheme improves the frugality by carefully choosing the training data and exploiting the symmetries of the problem. We test our approach by correcting long range correlators of the ground state of XY Hamiltonian on IBM Toronto quantum computer. We find that our method is an order of magnitude cheaper while maintaining the same accuracy as the original CDR approach. The efficiency gain enables us to obtain a factor of $10$ improvement on the unmitigated results with the total budget as small as $2\cdot10^5$ shots. Furthermore, we demonstrate orders of magnitude improvements in frugality for mitigation of energy of the LiH ground state simulated with IBM's Ourense-derived noise model.

quant-ph↗
arXiv2025

Efficient Online Quantum Circuit Learning with No Upfront Training

We propose a surrogate-based method for optimizing parameterized quantum circuits which is designed to operate with few calls to a quantum computer. We employ a computationally inexpensive classical surrogate to approximate the cost function of a variational quantum algorithm. An initial surrogate is fit to data obtained by sparse sampling of the true cost function using noisy quantum computers. The surrogate is iteratively refined by querying the true cost at the surrogate optima, then using radial basis function interpolation with existing and new true cost data. The use of radial basis function interpolation enables surrogate construction without hyperparameters to pre-train. Additionally, using the surrogate as an acquisition function focuses hardware queries in the vicinity of the true optima. For 16-qubit random 3-regular Max-Cut problems solved using the QAOA ansatz, we find that our method outperforms the prior state of the art. Furthermore, we demonstrate successful optimization of QAOA circuits for 127-qubit random Ising models on an IBM quantum processor using measurement counts of the order of $10^4-10^5$. The strong empirical performance of this approach is an important step towards the large-scale practical application of variational quantum algorithms and a clear demonstration of the effectiveness of classical-surrogate-based learning approaches.

quant-ph↗
arXiv2024

Large-scale simulations of Floquet physics on near-term quantum computers

Periodically driven quantum systems exhibit a diverse set of phenomena but are more challenging to simulate than their equilibrium counterparts. Here, we introduce the Quantum High-Frequency Floquet Simulation (QHiFFS) algorithm as a method to simulate fast-driven quantum systems on quantum hardware. Central to QHiFFS is the concept of a kick operator which transforms the system into a basis where the dynamics is governed by a time-independent effective Hamiltonian. This allows prior methods for time-independent simulation to be lifted to simulate Floquet systems. We use the periodically driven biaxial next-nearest neighbor Ising (BNNNI) model, a natural test bed for quantum frustrated magnetism and criticality, as a case study to illustrate our algorithm. We implemented a 20-qubit simulation of the driven two-dimensional BNNNI model on Quantinuum's trapped ion quantum computer. Our error analysis shows that QHiFFS exhibits not only a cubic advantage in driving frequency $ω$ but also a linear advantage in simulation time $t$ compared to~Trotterization.

quant-ph↗
arXiv2024

The cost of solving linear differential equations on a quantum computer: fast-forwarding to explicit resource counts

How well can quantum computers simulate classical dynamical systems? There is increasing effort in developing quantum algorithms to efficiently simulate dynamics beyond Hamiltonian simulation, but so far exact resource estimates are not known. In this work, we provide two significant contributions. First, we give the first non-asymptotic computation of the cost of encoding the solution to general linear ordinary differential equations into quantum states -- either the solution at a final time, or an encoding of the whole history within a time interval. Second, we show that the stability properties of a large class of classical dynamics allow their fast-forwarding, making their quantum simulation much more time-efficient. From this point of view, quantum Hamiltonian dynamics is a boundary case that does not allow this form of stability-induced fast-forwarding. In particular, we find that the history state can always be output with complexity $O(T^{1/2})$ for any stable linear system. We present a range of asymptotic improvements over state-of-the-art in various regimes. We illustrate our results with a family of dynamics including linearized collisional plasma problems, coupled, damped, forced harmonic oscillators and dissipative nonlinear problems. In this case the scaling is quadratically improved, and leads to significant reductions in the query counts after inclusion of all relevant constant prefactors.

quant-ph↗
arXiv2024

Challenges for quantum computation of nonlinear dynamical systems using linear representations

A number of recent studies have proposed that linear representations are appropriate for solving nonlinear dynamical systems with quantum computers, which fundamentally act linearly on a wave function in a Hilbert space. Linear representations, such as the Koopman representation and Koopman von Neumann mechanics, have regained attention from the dynamical-systems research community. Here, we aim to present a unified theoretical framework, currently missing in the literature, with which one can compare and relate existing methods, their conceptual basis, and their representations. We also aim to show that, despite the fact that quantum simulation of nonlinear classical systems may be possible with such linear representations, a necessary projection into a feasible finite-dimensional space will in practice eventually induce numerical artifacts which can be hard to eliminate or even control. As a result, a practical, reliable and accurate way to use quantum computation for solving general nonlinear dynamical systems is still an open problem.

quant-ph↗
arXiv2024

Gate-based quantum simulation of Gaussian bosonic circuits on exponentially many modes

We introduce a framework for simulating, on an $(n+1)$-qubit quantum computer, the action of a Gaussian Bosonic (GB) circuit on a state over $2^n$ modes. Specifically, we encode the initial bosonic state's expectation values over quadrature operators (and their covariance matrix) as an input qubit-state. This is then evolved by a quantum circuit that effectively implements the symplectic propagators induced by the GB gates. We find families of GB circuits and initial states leading to efficient quantum simulations. For this purpose, we introduce a dictionary that maps between GB and qubit gates such that particle- (non-particle-) preserving GB gates lead to real (imaginary) time evolutions at the qubit level. For the special case of particle-preserving circuits, we present a BQP-complete GB decision problem, indicating that GB evolutions of Gaussian states on exponentially many modes are as powerful as universal quantum computers. We also perform numerical simulations of an interferometer on $\sim8$ billion modes, illustrating the power of our framework.

quant-ph↗
arXiv2024

Quantum Tensor Product Decomposition from Choi State Tomography

The Schmidt decomposition is the go-to tool for measuring bipartite entanglement of pure quantum states. Similarly, it is possible to study the entangling features of a quantum operation using its operator-Schmidt, or tensor product decomposition. While quantum technological implementations of the former are thoroughly studied, entangling properties on the operator level are harder to extract in the quantum computational framework because of the exponential nature of sample complexity. Here we present an algorithm for unbalanced partitions into a small subsystem and a large one (the environment) to compute the tensor product decomposition of a unitary whose effect on the small subsystem is captured in classical memory while the effect on the environment is accessible as a quantum resource. This quantum algorithm may be used to make predictions about operator non-locality, effective open quantum dynamics on a subsystem, as well as for finding low-rank approximations and low-depth compilations of quantum circuit unitaries. We demonstrate the method and its applications on a time-evolution unitary of an isotropic Heisenberg model in two dimensions.

quant-ph↗
arXiv2023

Mixed Quantum-Semiclassical Simulation

We study the quantum simulation of mixed quantum-semiclassical (MQS) systems, of fundamental interest in many areas of physics, such as molecular scattering and gravitational backreaction. A basic question for these systems is whether quantum algorithms of MQS systems would be valuable at all, when one could instead study the full quantum-quantum system. We study MQS simulations in the context where a semiclassical system is encoded in a Koopman-von Neumann (KvN) Hamiltonian and a standard quantum Hamiltonian describes the quantum system. In this case, because KvN and quantum Hamiltonians are constructed with the same operators on a Hilbert space, standard theorems guaranteeing simulation efficiency apply. We show that, in this context, $\textit{many-body}$ MQS particle simulations give only nominal improvements in qubit resources over quantum-quantum simulations due to logarithmic scaling in the ratio, $S_q/S_c$, of actions between quantum and semiclassical systems. However, $\textit{field}$ simulations can give improvements proportional to the ratio of quantum to semiclassical actions, $S_q/S_c$. Of particular note, due to the ratio $S_q/S_c \sim 10^{-18}$ of particle and gravitational fields, this approach could be important for semiclassical gravity. We demonstrate our approach in a model of gravitational interaction, where a harmonic oscillator mediates the interaction between two spins. In particular, we demonstrate a lack of distillable entanglement generation between spins due to classical mediators, a distinct difference in dynamics relative to the fully quantum case.

quant-ph↗
arXiv2023

Inference-Based Quantum Sensing

In a standard Quantum Sensing (QS) task one aims at estimating an unknown parameter $θ$, encoded into an $n$-qubit probe state, via measurements of the system. The success of this task hinges on the ability to correlate changes in the parameter to changes in the system response $\mathcal{R}(θ)$ (i.e., changes in the measurement outcomes). For simple cases the form of $\mathcal{R}(θ)$ is known, but the same cannot be said for realistic scenarios, as no general closed-form expression exists. In this work we present an inference-based scheme for QS. We show that, for a general class of unitary families of encoding, $\mathcal{R}(θ)$ can be fully characterized by only measuring the system response at $2n+1$ parameters. This allows us to infer the value of an unknown parameter given the measured response, as well as to determine the sensitivity of the scheme, which characterizes its overall performance. We show that inference error is, with high probability, smaller than $δ$, if one measures the system response with a number of shots that scales only as $Ω(\log^3(n)/δ^2)$. Furthermore, the framework presented can be broadly applied as it remains valid for arbitrary probe states and measurement schemes, and, even holds in the presence of quantum noise. We also discuss how to extend our results beyond unitary families. Finally, to showcase our method we implement it for a QS task on real quantum hardware, and in numerical simulations.

quant-ph↗
arXiv2023

Out-of-distribution generalization for learning quantum dynamics

Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution. Here, we prove out-of-distribution generalization for the task of learning an unknown unitary. In particular, we show that one can learn the action of a unitary on entangled states having trained only product states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics on near term quantum hardware, and further opens up new methods for both the classical and quantum compilation of quantum circuits.

quant-ph↗
arXiv2023

Quantum Mixed State Compiling

The task of learning a quantum circuit to prepare a given mixed state is a fundamental quantum subroutine. We present a variational quantum algorithm (VQA) to learn mixed states which is suitable for near-term hardware. Our algorithm represents a generalization of previous VQAs that aimed at learning preparation circuits for pure states. We consider two different ansätze for compiling the target state; the first is based on learning a purification of the state and the second on representing it as a convex combination of pure states. In both cases, the resources required to store and manipulate the compiled state grow with the rank of the approximation. Thus, by learning a lower rank approximation of the target state, our algorithm provides a means of compressing a state for more efficient processing. As a byproduct of our algorithm, one effectively learns the principal components of the target state, and hence our algorithm further provides a new method for principal component analysis. We investigate the efficacy of our algorithm through extensive numerical implementations, showing that typical random states and thermal states of many body systems may be learnt this way. Additionally, we demonstrate on quantum hardware how our algorithm can be used to study hardware noise-induced states.

quant-ph↗