New criteria reveal VQE needs fault-tolerant quantum computers due to decoherence and QPE has exponentially suppressed success probability from orthogonality catastrophe in classical input states.
Barren plateaus in quantum neural network training landscapes
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
abstract
Many experimental proposals for noisy intermediate scale quantum devices involve training a parameterized quantum circuit with a classical optimization loop. Such hybrid quantum-classical algorithms are popular for applications in quantum simulation, optimization, and machine learning. Due to its simplicity and hardware efficiency, random circuits are often proposed as initial guesses for exploring the space of quantum states. We show that the exponential dimension of Hilbert space and the gradient estimation complexity make this choice unsuitable for hybrid quantum-classical algorithms run on more than a few qubits. Specifically, we show that for a wide class of reasonable parameterized quantum circuits, the probability that the gradient along any reasonable direction is non-zero to some fixed precision is exponentially small as a function of the number of qubits. We argue that this is related to the 2-design characteristic of random circuits, and that solutions to this problem must be studied.
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 8roles
background 2polarities
background 2representative citing papers
QuanForge introduces statistical mutation killing and nine post-training mutation operators for QNNs to distinguish test suites and localize vulnerable circuit regions.
Loss-aware natural gradient variants are introduced by embedding the loss hypersurface in a statistical manifold or using quantum state overlaps, yielding conformal updates that adjust effective step size.
Numerical study of five symmetry-preserving HVAs for Z2 gauge theory finds overparametrization eliminates local minima and loss decay rate scales linearly with number of parameters.
Investigates which quantum encodings of classical datasets preserve persistent homology so that quantum algorithms can extract topological features directly from the data.
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.
Proposes a fabless-foundry ecosystem for superconducting quantum chips built on certified PDKs, SPICE-Q multiphysics modeling, parameterized cells, Q-EDA automation, and a quantum-IP market.
Demonstrates FLOPs-aware neural architecture search for hybrid quantum-classical neural networks to produce accurate yet computationally efficient models suitable for NISQ hardware.
citing papers explorer
-
Feasibility of performing quantum chemistry calculations on quantum computers
New criteria reveal VQE needs fault-tolerant quantum computers due to decoherence and QPE has exponentially suppressed success probability from orthogonality catastrophe in classical input states.
-
QuanForge: A Mutation Testing Framework for Quantum Neural Networks
QuanForge introduces statistical mutation killing and nine post-training mutation operators for QNNs to distinguish test suites and localize vulnerable circuit regions.
-
Loss-aware state space geometry for quantum variational algorithms
Loss-aware natural gradient variants are introduced by embedding the loss hypersurface in a statistical manifold or using quantum state overlaps, yielding conformal updates that adjust effective step size.
-
Symmetries and overparametrization properties of Hamiltonian variational ansatzes for the $(1+1)$d $\mathbb{Z}_2$ lattice gauge theory
Numerical study of five symmetry-preserving HVAs for Z2 gauge theory finds overparametrization eliminates local minima and loss decay rate scales linearly with number of parameters.
-
Quantum encodings that preserve persistent homology
Investigates which quantum encodings of classical datasets preserve persistent homology so that quantum algorithms can extract topological features directly from the data.
-
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.
-
Fabless Quantum Chip Design and Commercial Production
Proposes a fabless-foundry ecosystem for superconducting quantum chips built on certified PDKs, SPICE-Q multiphysics modeling, parameterized cells, Q-EDA automation, and a quantum-IP market.
-
Hybrid Quantum-Classical Neural Architecture Search
Demonstrates FLOPs-aware neural architecture search for hybrid quantum-classical neural networks to produce accurate yet computationally efficient models suitable for NISQ hardware.