REVIEW 8 cited by
Circuit-centric quantum classifiers
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Circuit-centric quantum classifiers
read the original abstract
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
-
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...
-
Quantum Kernels are Spectral Tensor Networks
Quantum kernels are spectral tensor networks because their Fourier coefficient tensors are matrix product operator factorizations, with kernel target alignment acting as Frobenius cosine similarity on frequency grids.
-
Quantum Machine Learning for State Tomography Using Classical Data
A variational quantum circuit trained solely on classical measurement outcomes reconstructs diverse quantum states including GHZ, spin-chain ground states, and random circuits with fidelities above 90% on simulators a...
-
PennyLane: Automatic differentiation of hybrid quantum-classical computations
PennyLane is a software library extending automatic differentiation to hybrid quantum-classical systems for variational quantum algorithms.
-
Evaluating quantum circuits in the reservoir computing paradigm
Brickwall quantum circuits with Haar-random, dual-unitary, and solvable two-qubit gates serve as effective reservoirs for temporal processing tasks, with performance correlated to circuit dynamics and validated on syn...
-
Evaluating quantum circuits in the reservoir computing paradigm
Brickwall circuits from Haar-random, dual-unitary, and solvable two-qubit gates are tested as quantum reservoirs, showing effective fading memory and prediction accuracy on synthetic time-series data.
-
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.
-
A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.