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Open problems in Quantum machine learning

Lack of provable performance guarantees in quantum machine learning2 papers raise it · 2 address it
Without guarantees on trainability, inference efficiency, and generalization, quantum models risk being unreliable or impractical for real-world use.
- Supervised Learning with Quantum Hamiltonian Embeddingsraises · addresses
- Margin-Based Generalization for Quantum Machine Learning with Classical Data Encodingraises · addresses
Scalable and efficient quantum data embedding optimization1 paper raises it · 1 addresses it
Pairwise fidelity evaluations in embedding optimization create computational bottlenecks, limiting scalability for large datasets.
- Prototype-Guided Optimization of Quantum Data Embeddings with Linear Costraises · addresses
Noise-aware quantum software testing methodologies1 paper raises it · 0 address it
Most quantum software testing approaches ignore hardware noise, leading to unreliable models when deployed on NISQ devices.
- Noise-Awareness in Quantum Software Testing: An Empirical Landscape Analysisraises
Task-aligned inductive biases in quantum circuits1 paper raises it · 1 addresses it
Without task-specific architectural biases, quantum circuits may fail to learn efficiently from limited data or generalize well.
- Task-aligned spectral inductive biases in parameterized quantum circuitsraises · addresses
Quantum error-correcting code discovery with favorable parameters1 paper raises it · 1 addresses it
Existing algebraic constructions for QECCs struggle to balance high encoding rates with sufficient distance, limiting fault-tolerant quantum computation.
- LiftDiff: Guided Discrete Diffusion for Quantum Code Constructionraises · addresses

Named by an AI from these papers' abstracts.

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quantum machine learning · quantum neural networks · learning quantum124 papers23 this week

Topics in this field

Active contributors · 90 days

Joongheon KimA Tutorial on Quantum Amplitude Embedding with Data Re-Uploading4
Daniel K. ParkMargin-Based Generalization for Quantum Machine Learning with Classical Data Encoding3
Tak HurMargin-Based Generalization for Quantum Machine Learning with Classical Data Encoding3
Muhammad UsmanWhen do quantum views help? Complementary classical-quantum representations in multi-view fusion3
Hyojun AhnA Tutorial on Quantum Amplitude Embedding with Data Re-Uploading3
Alberto MarchisioQBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing3

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arXiv quant-phOct 17
Margin-Based Generalization for Quantum Machine Learning with Classical Data Encoding
Ju-Young Ryu, Tak Hur, Daniel K. Park
Understanding generalization is essential for assessing the practical viability of quantum machine learning. In many applications, quantum models operate on classical data that must first be encoded into quantum states, making the encoding part of the effective hypothesis class. We develop a margin-based generalization framework for this setting, covering both fixed and trainable quantum data encodings. For fixed encodings, the existing margin bound transfers unchanged. For trainable encodings, we derive a bound using a two-scale covering argument that combines the original margin-dependent term with an additive encoding-capacity term of order $\tilde O(\sqrt{L/m})$, where $L$ is the number of trainable encoding parameters and $m$ is the training-sample size. Across 19,440 simulated training runs spanning various image datasets, circuit architectures, and optimization settings, the inverse margin performs best among five baseline and two bound-derived complexity measures under conditional-independence and 90th-percentile sign-error criteria, within bootstrap uncertainty. These results provide a unified margin-based view of generalization in quantum machine learning with classical…
PDF arXiv
arXiv quant-phOct 17
Prototype-Guided Optimization of Quantum Data Embeddings with Linear Cost
Seokhoon Jeong, Daniel K. Park
In supervised quantum machine learning with classical data, classification performance critically depends on the geometry induced by quantum data embedding. Existing data-adaptive approaches rely on pairwise fidelity evaluations between samples, which scale as $O(B^2)$ for $B$ samples and create a computational bottleneck in iterative embedding optimization. To address this issue, we propose a prototype-guided framework for efficient data-adaptive quantum embedding. Using class-prototype separation and sample-to-prototype compactness, we derive a measurable lower certificate for class-ensemble distinguishability and construct a prototype-based objective requiring only $O(B)$ fidelity evaluations. We integrate this objective into an end-to-end pipeline that combines task-specific quantum circuit architecture optimization with continuous refinement of the data-embedding parameters. Across five benchmark datasets with up to 20 qubits, our method retains competitive quantum kernel support vector machine (SVM) performance while reducing fidelity-evaluation cost, with particularly strong gains over budget-matched pairwise optimization on T-MNIST. Geometric analyses examine how the…
PDF arXiv
arXiv quant-phOct 16
Task-aligned spectral inductive biases in parameterized quantum circuits
Felix Frohnert, Adrián Pérez-Salinas, Sebastian Lange, Marcus Schwaebisch, Evert van Nieuwenburg +1
Parameterized quantum circuits are widely investigated in quantum machine learning, yet how their architecture can provide task-aligned inductive biases remains poorly understood. We study data reuploading quantum circuits with two distinct inputs: a signal that varies between samples and a fixed, task-defined spectral query that specifies how the signal should be probed. We show that encoding the query can change the circuit's hypothesis class over the signal by modulating how its existing signal frequencies are combined, without introducing new sample-dependent information. This mechanism matches tasks built on nonuniform Fourier transforms, whose evaluation frequencies specify the query and whose sampling positions guide its encoding. We examine this connection in a classification task inspired by synthetic aperture radar, where acquisition geometry determines a query for classifying unfocused spectral patches before image formation. Across small-scale spectral and radar-like tasks, aligned queries improve learning from limited data over parameter-matched circuits with mismatched or absent queries. These results provide a route from known spectral task structure to the design…
PDF arXiv
arXiv quant-phOct 16
Quality-of-Transmission Estimation in Quantum Networks: Compute, Simulate, or Learn?
Anis Bekri, Amar Abane, Van Sy Mai, Abdella Battou, Saddek Bensalem
Future quantum networks will rely on control-plane decisions such as routing, scheduling, entanglement provisioning, purification, and memory allocation. Each decision depends on a timely and reliable estimate of the quality of the entanglement that a candidate path can deliver, with end-to-end fidelity as a central metric. Inspired by the optical-networking experience with quality-of-transmission (QoT) estimation, this article asks when a quantum controller should compute fidelity analytically, simulate it, or learn a surrogate model. The optical analogy is useful but incomplete: optical QoT estimation often predicts a scalar transmission margin, whereas quantum QoT estimation must account for probabilistic entanglement generation, random memory waiting times, decoherence, state consumption, and measurement constraints that can make fidelity a stochastic target. We propose a compute/simulate/learn framework for quantum QoT estimation and illustrate it with a simulation-based proof of concept. Our study shows that analytical composition can be sufficient in simple regimes, that simulation becomes necessary when stochastic memory effects shape the delivered fidelity distribution,…
PDF arXiv
arXiv quant-phOct 16
Expressivity of IQP Feature-Map Quantum Kernels via Reproducing Kernel Hilbert Spaces
Valeria Repetto, Stefano Melchionna, Romina D'Aurizio
Quantum kernels are among the most promising methods in quantum machine learning, providing a natural way to embed data into high-dimensional Hilbert spaces via quantum circuits and capturing non-linear relationships in data. Their learning behaviour is governed by the function class they represent, which is formally characterized by the structure of the associated Reproducing Kernel Hilbert Space (RKHS). However, a theoretical characterization of this function class, and of how it depends on the choice of feature map and its hyperparameters, is still lacking. In this work, we focus on the Instantaneous Quantum Polynomial (IQP) feature map and derive a Fourier representation of the associated RKHS generators as finite sums of trigonometric functions evaluated on polynomially transformed input variables, with a finite set of accessible frequencies determined by the circuit construction and scaled by the bandwidth hyperparameter. This representation identifies three bandwidth regimes with qualitatively distinct expressive properties from localised, through multimodal, to periodic. Numerical experiments on synthetic datasets with controlled periodic and multimodal structure, across…
PDF arXiv
arXiv quant-phOct 16
SpinControl: Control Software for Prototyping and Benchmarking of Spin-Qubit Devices and Measurement Protocols
Harold B. Meerwaldt, Daniël P. van der Velde, Pieter T. Eendebak, Michiel J. Haye
Quantum computers require software to control the states of the qubits. As the actions on qubits grow in complexity with circuit execution, calibration, and benchmarking, the complexity of the control software grows in tandem. Spin-qubit quantum computers are at a stage where experimentalists need low-level access for tuning and calibration, and quantum algorithm researchers want high-level circuit-level access. In this article, we present the software design of SpinControl. SpinControl integrates three architectural metrics: 1) customizability, to allow prototyping devices and measurement protocols, is achieved by separating data and behaviour; 2) modularity and extensibility are achieved by separating functionalities into different components; and 3) robustness, to reliably execute quantum circuits on many qubits, is achieved by CI pipelines and input validation. SpinControl allows different types of measurements while customizing the instruments used, the pulse or circuit program executed, and the data processing. It allows building standardized experiments from measurement and analysis building blocks and executing these for calibration in a custom and conditional order.…
PDF arXiv

Papers

Topics in this field

quantum machine learning · quantum neural networks · learning quantum124 papers23 this week

Recent papers

All in Research →

arXiv quant-phOct 17
Margin-Based Generalization for Quantum Machine Learning with Classical Data Encoding
Ju-Young Ryu, Tak Hur, Daniel K. Park
Understanding generalization is essential for assessing the practical viability of quantum machine learning. In many applications, quantum models operate on classical data that must first be encoded into quantum states, making the encoding part of the effective hypothesis class. We develop a margin-based generalization framework for this setting, covering both fixed and trainable quantum data encodings. For fixed encodings, the existing margin bound transfers unchanged. For trainable encodings, we derive a bound using a two-scale covering argument that combines the original margin-dependent term with an additive encoding-capacity term of order $\tilde O(\sqrt{L/m})$, where $L$ is the number of trainable encoding parameters and $m$ is the training-sample size. Across 19,440 simulated training runs spanning various image datasets, circuit architectures, and optimization settings, the inverse margin performs best among five baseline and two bound-derived complexity measures under conditional-independence and 90th-percentile sign-error criteria, within bootstrap uncertainty. These results provide a unified margin-based view of generalization in quantum machine learning with classical…
PDF arXiv
arXiv quant-phOct 17
Prototype-Guided Optimization of Quantum Data Embeddings with Linear Cost
Seokhoon Jeong, Daniel K. Park
In supervised quantum machine learning with classical data, classification performance critically depends on the geometry induced by quantum data embedding. Existing data-adaptive approaches rely on pairwise fidelity evaluations between samples, which scale as $O(B^2)$ for $B$ samples and create a computational bottleneck in iterative embedding optimization. To address this issue, we propose a prototype-guided framework for efficient data-adaptive quantum embedding. Using class-prototype separation and sample-to-prototype compactness, we derive a measurable lower certificate for class-ensemble distinguishability and construct a prototype-based objective requiring only $O(B)$ fidelity evaluations. We integrate this objective into an end-to-end pipeline that combines task-specific quantum circuit architecture optimization with continuous refinement of the data-embedding parameters. Across five benchmark datasets with up to 20 qubits, our method retains competitive quantum kernel support vector machine (SVM) performance while reducing fidelity-evaluation cost, with particularly strong gains over budget-matched pairwise optimization on T-MNIST. Geometric analyses examine how the…
PDF arXiv
arXiv quant-phOct 16
Task-aligned spectral inductive biases in parameterized quantum circuits
Felix Frohnert, Adrián Pérez-Salinas, Sebastian Lange, Marcus Schwaebisch, Evert van Nieuwenburg +1
Parameterized quantum circuits are widely investigated in quantum machine learning, yet how their architecture can provide task-aligned inductive biases remains poorly understood. We study data reuploading quantum circuits with two distinct inputs: a signal that varies between samples and a fixed, task-defined spectral query that specifies how the signal should be probed. We show that encoding the query can change the circuit's hypothesis class over the signal by modulating how its existing signal frequencies are combined, without introducing new sample-dependent information. This mechanism matches tasks built on nonuniform Fourier transforms, whose evaluation frequencies specify the query and whose sampling positions guide its encoding. We examine this connection in a classification task inspired by synthetic aperture radar, where acquisition geometry determines a query for classifying unfocused spectral patches before image formation. Across small-scale spectral and radar-like tasks, aligned queries improve learning from limited data over parameter-matched circuits with mismatched or absent queries. These results provide a route from known spectral task structure to the design…
PDF arXiv
arXiv quant-phOct 16
Quality-of-Transmission Estimation in Quantum Networks: Compute, Simulate, or Learn?
Anis Bekri, Amar Abane, Van Sy Mai, Abdella Battou, Saddek Bensalem
Future quantum networks will rely on control-plane decisions such as routing, scheduling, entanglement provisioning, purification, and memory allocation. Each decision depends on a timely and reliable estimate of the quality of the entanglement that a candidate path can deliver, with end-to-end fidelity as a central metric. Inspired by the optical-networking experience with quality-of-transmission (QoT) estimation, this article asks when a quantum controller should compute fidelity analytically, simulate it, or learn a surrogate model. The optical analogy is useful but incomplete: optical QoT estimation often predicts a scalar transmission margin, whereas quantum QoT estimation must account for probabilistic entanglement generation, random memory waiting times, decoherence, state consumption, and measurement constraints that can make fidelity a stochastic target. We propose a compute/simulate/learn framework for quantum QoT estimation and illustrate it with a simulation-based proof of concept. Our study shows that analytical composition can be sufficient in simple regimes, that simulation becomes necessary when stochastic memory effects shape the delivered fidelity distribution,…
PDF arXiv
arXiv quant-phOct 16
Expressivity of IQP Feature-Map Quantum Kernels via Reproducing Kernel Hilbert Spaces
Valeria Repetto, Stefano Melchionna, Romina D'Aurizio
Quantum kernels are among the most promising methods in quantum machine learning, providing a natural way to embed data into high-dimensional Hilbert spaces via quantum circuits and capturing non-linear relationships in data. Their learning behaviour is governed by the function class they represent, which is formally characterized by the structure of the associated Reproducing Kernel Hilbert Space (RKHS). However, a theoretical characterization of this function class, and of how it depends on the choice of feature map and its hyperparameters, is still lacking. In this work, we focus on the Instantaneous Quantum Polynomial (IQP) feature map and derive a Fourier representation of the associated RKHS generators as finite sums of trigonometric functions evaluated on polynomially transformed input variables, with a finite set of accessible frequencies determined by the circuit construction and scaled by the bandwidth hyperparameter. This representation identifies three bandwidth regimes with qualitatively distinct expressive properties from localised, through multimodal, to periodic. Numerical experiments on synthetic datasets with controlled periodic and multimodal structure, across…
PDF arXiv
arXiv quant-phOct 16
SpinControl: Control Software for Prototyping and Benchmarking of Spin-Qubit Devices and Measurement Protocols
Harold B. Meerwaldt, Daniël P. van der Velde, Pieter T. Eendebak, Michiel J. Haye
Quantum computers require software to control the states of the qubits. As the actions on qubits grow in complexity with circuit execution, calibration, and benchmarking, the complexity of the control software grows in tandem. Spin-qubit quantum computers are at a stage where experimentalists need low-level access for tuning and calibration, and quantum algorithm researchers want high-level circuit-level access. In this article, we present the software design of SpinControl. SpinControl integrates three architectural metrics: 1) customizability, to allow prototyping devices and measurement protocols, is achieved by separating data and behaviour; 2) modularity and extensibility are achieved by separating functionalities into different components; and 3) robustness, to reliably execute quantum circuits on many qubits, is achieved by CI pipelines and input validation. SpinControl allows different types of measurements while customizing the instruments used, the pulse or circuit program executed, and the data processing. It allows building standardized experiments from measurement and analysis building blocks and executing these for calibration in a custom and conditional order.…
PDF arXiv

Active contributors · 90 days

Joongheon KimA Tutorial on Quantum Amplitude Embedding with Data Re-Uploading4
Daniel K. ParkMargin-Based Generalization for Quantum Machine Learning with Classical Data Encoding3
Tak HurMargin-Based Generalization for Quantum Machine Learning with Classical Data Encoding3
Muhammad UsmanWhen do quantum views help? Complementary classical-quantum representations in multi-view fusion3
Hyojun AhnA Tutorial on Quantum Amplitude Embedding with Data Re-Uploading3
Alberto MarchisioQBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing3

Activity · 12 weeks

Coming with more sources: repos

Fields · AI · updated 44m ago

Quantum machine learning

Found by the topic model: quantum machine learning.

23papers this week−23.3% vs last week

All papers · 124 With code · 0

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116 papers this month

Open problems in Quantum machine learning

Lack of provable performance guarantees in quantum machine learning2 papers raise it · 2 address it
Without guarantees on trainability, inference efficiency, and generalization, quantum models risk being unreliable or impractical for real-world use.
- Supervised Learning with Quantum Hamiltonian Embeddingsraises · addresses
- Margin-Based Generalization for Quantum Machine Learning with Classical Data Encodingraises · addresses
Scalable and efficient quantum data embedding optimization1 paper raises it · 1 addresses it
Pairwise fidelity evaluations in embedding optimization create computational bottlenecks, limiting scalability for large datasets.
- Prototype-Guided Optimization of Quantum Data Embeddings with Linear Costraises · addresses
Noise-aware quantum software testing methodologies1 paper raises it · 0 address it
Most quantum software testing approaches ignore hardware noise, leading to unreliable models when deployed on NISQ devices.
- Noise-Awareness in Quantum Software Testing: An Empirical Landscape Analysisraises
Task-aligned inductive biases in quantum circuits1 paper raises it · 1 addresses it
Without task-specific architectural biases, quantum circuits may fail to learn efficiently from limited data or generalize well.
- Task-aligned spectral inductive biases in parameterized quantum circuitsraises · addresses
Quantum error-correcting code discovery with favorable parameters1 paper raises it · 1 addresses it
Existing algebraic constructions for QECCs struggle to balance high encoding rates with sufficient distance, limiting fault-tolerant quantum computation.
- LiftDiff: Guided Discrete Diffusion for Quantum Code Constructionraises · addresses

Named by an AI from these papers' abstracts.