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Dynamic parameterized quantum circuits: expressive and barren-plateau free
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Classical optimization of parameterized quantum circuits is a widely studied methodology for the preparation of complex quantum states, as well as the solution of machine learning and optimization problems. However, it is well known that many proposed parameterized quantum circuit architectures suffer from drawbacks which limit their utility, such as their classical simulability or the hardness of optimization due to a problem known as "barren plateaus". We propose and study a class of dynamic parameterized quantum circuit architectures. These are parameterized circuits containing intermediate measurements and feedforward operations. In particular, we show that these architectures: 1. Provably do not suffer from barren plateaus. 2. Are expressive enough to describe arbitrarily deep unitary quantum circuits. 3. Are competitive with state of the art methods for preparing ground states and facilitating the representation of nontrivial thermal states. These features make the proposed architectures promising candidates for a variety of applications.
Forward citations
Cited by 7 Pith papers
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Drift-resilient mid-circuit measurement and state preparation error mitigation for dynamic circuits
Parity of repeated measurements realizes an amplified readout-error channel, enabling drift-resilient, characterization-free mitigation of mid-circuit and terminating measurement and preparation errors.
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Pitfalls when tackling the exponential concentration of parameterized quantum models
Exponentially concentrated measurement outcomes are statistically indistinguishable from fixed noise after polynomial shots, so classical post-processing cannot fix them, and common proposed remedies do not escape this.
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Scaling Quantum Algorithms via Dissipation: Avoiding Barren Plateaus
Dissipative quantum circuits with periodic qubit resets provably avoid both unitary and noise-induced barren plateaus for gates near the final measurement.
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Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning
A stochastic quantum spiking neuron with internal quantum memory and a local perturbation-based learning rule improves classification accuracy over prior quantum spiking networks without global backpropagation.
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A unifying account of warm start guarantees for patches of quantum landscapes
A new theorem shows that a patch of parameter space around any point with non-exponentially small curvature retains polynomially large loss variance, unifying and extending prior warm-start results for variational qua...
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Pauli Propagation: A Computational Framework for Simulating Quantum Systems
Pauli propagation, a classical method that evolves Pauli operators through quantum circuits, is presented as a unified algorithmic framework together with the Julia package PauliPropagation.jl that implements it.
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