Spectral Born machines are Fourier-phase quantum generative models over Z_d^n that train classically via graph-spectral MMD and show reduced parameters plus apparent overfitting resistance on integer data.
IQPopt: Fast optimization of instantaneous quantum polynomial circuits in JAX
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
IQPopt is a software package designed to optimize large-scale instantaneous quantum polynomial circuits on classical hardware. By exploiting an efficient classical simulation algorithm for expectation value estimation, circuits with thousands of qubits and millions of gates can be optimized, provided the relevant objective function has an efficient description in terms of Pauli-Z type observables. Since sampling from instantaneous quantum polynomial circuits is widely believed to be hard for classical computers, this provides a method to identify powerful circuit instances before deployment and sampling on quantum hardware, where computational advantages may exist. The package leverages automatic differentiation in JAX, can be accelerated with access to hardware accelerators such as graphics processing units, and contains a dedicated module that can be used to train and evaluate quantum generative models via the maximum mean discrepancy.
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quant-ph 5years
2026 5roles
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Analytical lower bounds and Lipschitz concentration results show when Gaussian-initialized IQP QCBMs avoid exponential gradient concentration under MMD training.
Parity-moment supervision improves forward-KL fit and unseen high-value-state recovery over coordinate-wise MSE in a controlled 12-qubit IQP Born-machine benchmark.
Qudit extension of parameterized IQP circuits proposed for generative modeling of integer data, with loss function and covariance matrix, validated on electron shower energy deposits in CLIC electromagnetic calorimeter.
citing papers explorer
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Spectral Born machines: classically trainable quantum generative models for discrete data
Spectral Born machines are Fourier-phase quantum generative models over Z_d^n that train classically via graph-spectral MMD and show reduced parameters plus apparent overfitting resistance on integer data.
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Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization
Analytical lower bounds and Lipschitz concentration results show when Gaussian-initialized IQP QCBMs avoid exponential gradient concentration under MMD training.
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Parity Supervision as a Driver of Generalization in Quantum Generative Modeling
Parity-moment supervision improves forward-KL fit and unseen high-value-state recovery over coordinate-wise MSE in a controlled 12-qubit IQP Born-machine benchmark.
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Qudit extension of parameterized IQP circuits: A generative quantum machine learning approach to integer data
Qudit extension of parameterized IQP circuits proposed for generative modeling of integer data, with loss function and covariance matrix, validated on electron shower energy deposits in CLIC electromagnetic calorimeter.
- An IQP Born Machine for Calorimeter Image Generation at 64 Qubits with Compiled-IQP Deployment