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Circuit-centric quantum classifiers
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The current generation of quantum computing technologies call for quantum algorithms that require a limited number of qubits and quantum gates, and which are robust against errors. A suitable design approach are variational circuits where the parameters of gates are learnt, an approach that is particularly fruitful for applications in machine learning. In this paper, we propose a low-depth variational quantum algorithm for supervised learning. The input feature vectors are encoded into the amplitudes of a quantum system, and a quantum circuit of parametrised single and two-qubit gates together with a single-qubit measurement is used to classify the inputs. This circuit architecture ensures that the number of learnable parameters is poly-logarithmic in the input dimension. We propose a quantum-classical training scheme where the analytical gradients of the model can be estimated by running several slightly adapted versions of the variational circuit. We show with simulations that the circuit-centric quantum classifier performs well on standard classical benchmark datasets while requiring dramatically fewer parameters than other methods. We also evaluate sensitivity of the classification to state preparation and parameter noise, introduce a quantum version of dropout regularisation and provide a graphical representation of quantum gates as highly symmetric linear layers of a neural network.
Forward citations
Cited by 8 Pith papers
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When cheap gradients fail: the measurement cost of attacking quantum classifiers
Unbiased gradient extraction for attacking quantum classifiers costs at least Θ(d^{5/2}) shots under norm-concentration scaling, and ~d³ for tested deep circuits, so the attacker's relative cost diverges versus classi...
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Supervised learning with a quantum classifier using a multi-level system
A variational quantum classifier that encodes features into a single N-level quantum system and trains all samples of a class at once via a density-matrix loss, achieving moderate accuracy on four benchmark datasets.
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From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics
A hybrid quantum-classical classifier on simulated HH→bbγγ events claims 95% CL limits of 1.9–2.1×SM, but the gain over XGBoost is 21–29%, not the advertised factor of two.
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Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models
Noise, especially decoherent gate errors, systematically reduces Fourier coefficient magnitudes, expressibility, and entangling capability of quantum Fourier models, with circuit architecture and encoding modulating t...
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Effects of Quantum Noise on Quantum Approximate Optimization Algorithm
For dephasing, bit-flip, and depolarizing noise on a 7-qubit Max-Cut QAOA, fidelity, cost, and gradients decay like (1-p)^(αN), and fitted optimal parameters stay close to noiseless values for Np<0.5.
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Quantum Natural Gradient
Quantum Natural Gradient uses the Fubini-Study metric of quantum states to precondition gradient updates for variational quantum circuits, converging faster than standard optimizers in simulations.
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The Capacity of Quantum Neural Networks
The memory capacity of any quantum neural network is at most the information content of its trainable parameters, so classically-parameterized QNNs lack capacity advantage over classical NNs.
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How quantum computing can enhance biomarker discovery
A review argues that quantum computing, particularly quantum machine learning, could enhance biomarker discovery for small, high-dimensional, and noisy healthcare datasets.
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