Quantum machine learning uses quantum states and dynamics to represent and process data. In quantum reservoir computing, the physical system is typically fixed and a classical readout is trained. Inspired by the formation, strengthening and pruning of synapses during biological learning, we propose quantum neuroplastic learning (QNL), which reorganizes the couplings among the qubits of a quantum reservoir, much as experience rewires connections among neurons. Eligibility traces guide local edits to couplings, fields, gate angles and evolution time, and an edit is retained when a statistical bound on its improvement exceeds a complexity cost. We evaluate QNL under restarting and online monitored protocols on short- and long-term memory, nonlinear autoregressive processing and forecasting, and channel equalization tasks. In simulation, QNL enhances memory capacity, develops long-term recall and sustains adaptation across multiple tasks, with the largest benefit for tasks that require extended memory. On the IBM superconducting processor \texttt{ibm\_fez}, with six seven-qubit reservoirs running in parallel on 42 qubits (1.28 million shots in total), configurations learned in simulation and frozen before execution reduced the channel-equalization symbol error rate from 9.7\% to 2.2\% and achieved a lower error rate than a matched-budget tuned baseline.
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Can editing a quantum reservoir’s physical configuration improve temporal processing beyond training its readout? Quantum neuroplastic learning (QNL) combines a refitted linear readout with gradient-free edits to couplers, fields, gate angles and evolution time. Decaying eligibility traces bias proposals; an edit is accepted when a nominal Student-$t$ lower confidence bound on paired loss reduction exceeds a cost for added couplers. Qubit count, measurement protocol and readout architecture remain fixed.
Simulations of five-qubit monitored reservoirs show summed short-term-memory capacity gains of $4.92$–$5.94$ with exact expectation values. Training for delays $15$–$30$ raises long-term-memory capacity from near zero to $3.46$ under restarting and $3.40$ under weak monitoring, but only $0.62$ under stronger monitoring. Adaptation continues across 16 task switches; NARMA forecasting errors decrease descriptively by approximately $10$–$20\%$. Channel-equalization simulations reach errors comparable to random search over the same candidate count, with QNL requiring $25\%$ more stream evaluations.
Seven-qubit configurations learned in simulation and frozen before IBM hardware execution reduce symbol error from $9.7\%$ to $2.2\%$ in an initial run. A chain-balanced comparison gives $4.2\%$, versus $5.8\%$ for tuned random search and $13.0\%$ for the reference, using 600 rather than 720 CZ gates per message.
Limitations include small reservoirs, few realizations, two runs on one device, and no test of earlier-task retention. Hardware learning required approximately $10^9$ simulated shots. Exact-feature memory gains required large measurement ensembles; noise models omit effects including gate errors and crosstalk, and admission bounds rely on approximate normality.
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Error found: The abstract's 'matched-budget tuned baseline' matches candidate counts but excludes QNL's incumbent reevaluations, which give QNL 25% more learning-stream evaluations, so the hardware results do not establish superiority at equal total learning budget.
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Quantum machine learning uses quantum states and dynamics to represent and process data. In quantum reservoir computing, the physical system is typically fixed and a classical readout is trained. Inspired by the formation, strengthening and pruning of synapses during biological learning, we propose quantum neuroplastic learning (QNL), which reorganizes the couplings among the qubits of a quantum reservoir, much as experience rewires connections among neurons. Eligibility traces guide local edits to couplings, fields, gate angles and evolution time, and an edit is retained when a statistical bound on its improvement exceeds a complexity cost. We evaluate QNL under restarting and online monitored protocols on short- and long-term memory, nonlinear autoregressive processing and forecasting, and channel equalization tasks. In simulation, QNL enhances memory capacity, develops long-term recall and sustains adaptation across multiple tasks, with the largest benefit for tasks that require extended memory. On the IBM superconducting processor \texttt{ibm_fez}, with six seven-qubit reservoirs running in parallel on 42 qubits (1.28 million shots in total), configurations learned in simulation and frozen before execution reduced the channel-equalization symbol error rate from 9.7% to 2.2% and achieved a lower error rate than a matched-budget tuned baseline.
Can editing a quantum reservoir’s physical configuration improve temporal processing beyond training its readout? Quantum neuroplastic learning (QNL) combines a refitted linear readout with gradient-free edits to couplers, fields, gate angles and evolution time. Decaying eligibility traces bias proposals; an edit is accepted when a nominal Student-t lower confidence bound on paired loss reduction exceeds a cost for added couplers. Qubit count, measurement protocol and readout architecture remain fixed.
Simulations of five-qubit monitored reservoirs show summed short-term-memory capacity gains of 4.92–5.94 with exact expectation values. Training for delays 15–30 raises long-term-memory capacity from near zero to 3.46 under restarting and 3.40 under weak monitoring, but only 0.62 under stronger monitoring. Adaptation continues across 16 task switches; NARMA forecasting errors decrease descriptively by approximately 10–20%. Channel-equalization simulations reach errors comparable to random search over the same candidate count, with QNL requiring 25% more stream evaluations.
Seven-qubit configurations learned in simulation and frozen before IBM hardware execution reduce symbol error from 9.7% to 2.2% in an initial run. A chain-balanced comparison gives 4.2%, versus 5.8% for tuned random search and 13.0% for the reference, using 600 rather than 720 CZ gates per message.
Limitations include small reservoirs, few realizations, two runs on one device, and no test of earlier-task retention. Hardware learning required approximately 109 simulated shots. Exact-feature memory gains required large measurement ensembles; noise models omit effects including gate errors and crosstalk, and admission bounds rely on approximate normality.
Over the last decade, researchers have studied the synergy between quantum computing (QC) and classical machine learning (ML) algorithms. However, measurements in QC often disturb or destroy quantum states, requiring multiple repetitions of data processing to estimate observable values. In particular, this prevents online (i.e., real-time, single-shot) processing of temporal data as measurements are commonly performed during intermediate stages. Recently, it was proposed to sidestep this issue by focusing on tasks with quantum output, thereby removing the need for detectors. Inspired by reservoir computers, a model was proposed where only a subset of the internal parameters are optimized while keeping the others fixed at random values. Here, we also process quantum time series, but we do so using a quantum optical recurrent neural network (QORNN) of which all internal interactions can be trained. As expected, this approach yields higher performance, as long as training the QORNN is feasible. We further show that our model can enhance the transmission rate of quantum channels that experience certain memory effects. Moreover, it can counteract similar memory effects if they are unwanted, a task that could previously only be solved when redundantly encoded input signals were available. Finally, we run a small-scale version of this last task on the photonic processor Borealis, demonstrating that our QORNN can be constructed using currently existing hardware.
Forecasting neural activity from short recordings remains a fundamental challenge. Reservoir computing may offer an efficient paradigm for temporal prediction, however classical reservoirs typically underperform in small-data regimes. Here we investigate whether quantum reservoir computing (QRC) can help overcome this limitation. Building on recent advances, we introduce a quantum reservoir based on a transverse-field Ising model, combined with heterogeneous quantum measurements and polynomial ridge regression. On a standard benchmark task, results show that the quantum reservoir outperforms a classical counterpart overall, with prediction accuracy strongly dependent on reservoir parameters. We further demonstrate feasibility by running the same task on quantum hardware. To assess performance on biological signals, we evaluate QRC on simulated human electroencephalography (EEG) data with a parallel reservoir architecture. On this challenging task, the tested quantum reservoir did not match the performance of the classical one, but it produced stable, convergent predictions. This is a meaningful first step toward forecasting of biologically realistic neural data using a quantum reservoir. Overall, our findings indicate that although current quantum hardware and parallel reservoir architectures do not yet surpass classical methods on complex neural signals, QRC can be executed on near-term devices and does converge with realistic EEG-like data. This work establishes a practical baseline for future algorithmic and hardware developments aimed at clinical time-series forecasting with quantum systems.
Quantum machine learning could uncover statistical structure beyond the reach of classical models, but this requires quantum models large and expressive enough to be useful and cheap enough to read out. Most approaches optimize many quantum parameters and are thus limited by expensive training loops. Here we report a quantum machine-learning model implemented on a programmable superconducting quantum annealer that processes temporal data at large scale using up to 4,500 qubits-the largest quantum machine-learning experiment performed to date. Following the quantum reservoir computing paradigm, the untrained native many-body dynamics generated by reverse annealing is directly used to process temporal data. We prove that the interactions produced during annealing are indispensable-a non-interacting reservoir retains no memory of its input. We evaluate our model experimentally on standard memory benchmarks and demonstrate that it can successfully forecast chaotic time series. These results establish quantum annealers as a scalable platform for large-scale quantum machine learning.
Over the last decade, researchers have studied the interplay between quantum computing and classical machine learning algorithms. However, measurements often disturb or destroy quantum states, requiring multiple repetitions of data processing to estimate observable values. In particular, this prevents online (real-time, single-shot) processing of temporal data as measurements are commonly performed during intermediate stages. Recently, it was proposed to sidestep this issue by focusing on tasks with quantum output, eliminating the need for detectors. Inspired by reservoir computers, a model was proposed where only a subset of the internal parameters are trained while keeping the others fixed at random values. Here, we also process quantum time series, but we do so using a Recurrent Gaussian Quantum Network (RGQN) of which all internal interactions can be trained. As expected, this increased flexibility yields higher performance in benchmark tasks. Building on this, we show that the RGQN can tackle two quantum communication tasks, while also removing some hardware restrictions of the currently available methods. First, our approach is more resource efficient to enhance the transmission rate of quantum channels that experience certain memory effects. Second, it can counteract similar memory effects if they are unwanted, a task that could previously only be solved when redundantly encoded input signals could be provided. Finally, we run a small-scale version of the last task on Xanadu's photonic processor Borealis.
Quantum neural networks (QNNs) -- parameterised quantum circuits trained to perform pattern recognition and predictive tasks -- represent a prominent but contested direction in quantum machine learning, offering potential parameter efficiency and expressibility benefits over classical neural networks for specific problem structures while suffering from barren plateau trainability limitations at scale. The 2024 issue of this journal established SNAD LSAD-structured ansatz (Silva et al., 2024) as the critical enabler of scalable QNN training, and QHDC (Bianchi et al., 2024) showed that QNN advantage is dataset-dependent. This paper advances the QNN field with the Quantum Neural Network Design and Evaluation (QNNDE) framework, a systematic methodology for QNN architecture design, training optimisation, and performance evaluation across four pattern recognition and prediction task classes: temporal sequence modelling (quantum recurrent circuits), spatial pattern recognition (quantum convolutional circuits), generative modelling (quantum generative adversarial networks), and time-series regression (quantum reservoir computing). QNNDE is evaluated on eight benchmark tasks spanning physics simulation, molecular property prediction, quantum state tomography, financial time-series, medical signal classification, and quantum circuit synthesis on IBM Quantum Eagle (127-qubit) and IonQ Forte (35-qubit). Quantum recurrent circuits achieve 12.4pp accuracy improvement over classical LSTMs for quantum state evolution prediction. Quantum convolutional circuits match classical CNNs on image-derived molecular patterns at 68.4% parameter reduction. Quantum GANs generate molecular conformations with 8.4% lower FID than classical GAN at equal network size. Quantum reservoir computing outperforms classical echo state networks by 18.4% on quantum-process time-series prediction.
This study explores quantum and classical hybrid architectures for financial time-series fore casting, focusing on Quantum Long Short-Term Memory (QLSTM) networks and Quantum Reservoir Computing (QRC), using univariate and multivariate lag structures on real financial data. We assess how lag embeddings affect predictive accuracy and robustness. Data are en coded into quantum states via amplitude encoding, enabling efficient representation of normalized lagged observations under realistic qubit constraints. The recurrent dynamics of QLSTM and the reservoir of QRC are implemented as parameterized quantum circuits, while classical optimizers train the readout and, where applicable, variational circuit parameters. We benchmark quantum models against classical LSTM and reservoir computing using common error like metrics. Our results show that, with suitable lag selection and amplitude encoding, quantum-enhanced archi tectures match classical baselines in univariate settings and can modestly outperform them in multivariate regimes with correlated inputs, where expressive encodings are most beneficial.