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Found 14 papers, 9 papers with code
A quantum-classical performance separation in nonconvex optimization
In this paper, we identify a family of nonconvex continuous optimization instances, each $d$-dimensional instance with $2^d$ local minima, to demonstrate a quantum-classical performance separation.
Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels
In this work, we study the dynamics of QNNs and show that contrary to popular belief it is qualitatively different from that of any kernel regression: due to the unitarity of quantum operations, there is a non-negligible deviation from the tangent kernel regression derived at the random initialization.
Quantum Hamiltonian Descent
We propose Quantum Hamiltonian Descent (QHD), which is derived from the path integral of dynamical systems referring to the continuous-time limit of classical gradient descent algorithms, as a truly quantum counterpart of classical gradient methods where the contribution from classically-prohibited trajectories can significantly boost QHD's performance for non-convex optimization.
Differentiable Quantum Programming with Unbounded Loops
The emergence of variational quantum applications has led to the development of automatic differentiation techniques in quantum computing.
Differentiable Analog Quantum Computing for Optimization and Control
We formulate the first differentiable analog quantum computing framework with a specific parameterization design at the analog signal (pulse) level to better exploit near-term quantum devices via variational methods.
A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers
The Variational Quantum Eigensolver (VQE) is a promising candidate for quantum applications on near-term Noisy Intermediate-Scale Quantum (NISQ) computers.
Exponentially Many Local Minima in Quantum Neural Networks
Specifically, we show for typical under-parameterized QNNs, there exists a dataset that induces a loss function with the number of spurious local minima depending exponentially on the number of parameters.
Sublinear classical and quantum algorithms for general matrix games
We give a sublinear classical algorithm that can interpolate smoothly between these two cases: for any fixed $q\in (1, 2]$, we solve the matrix game where $\mathcal{X}$ is a $\ell_{q}$-norm unit ball within additive error $\epsilon$ in time $\tilde{O}((n+d)/{\epsilon^{2}})$.
On the Principles of Differentiable Quantum Programming Languages
We also conduct a case study of training a VQC instance with controls, which shows the advantage of our scheme over existing auto-differentiation for quantum circuits without controls.
A Verified Optimizer for Quantum Circuits
Optimizations and other transformations are expressed as Coq functions, which are proved correct with respect to a semantics of SQIR programs.
Programming Languages Emerging Technologies Logic in Computer Science Quantum Physics
Quantum Wasserstein Generative Adversarial Networks
The study of quantum generative models is well-motivated, not only because of its importance in quantum machine learning and quantum chemistry but also because of the perspective of its implementation on near-term quantum machines.
Verified Optimization in a Quantum Intermediate Representation
We present sqire, a low-level language for quantum computing and verification.
Logic in Computer Science Emerging Technologies Programming Languages Quantum Physics
Sublinear quantum algorithms for training linear and kernel-based classifiers
We design sublinear quantum algorithms for the same task running in $\tilde{O}(\sqrt{n} +\sqrt{d})$ time, a quadratic improvement in both $n$ and $d$.
Simulating large quantum circuits on a small quantum computer
The tensor network of such a circuit can be decomposed into clusters of size at most $d$ with at most $K$ qubits of inter-cluster quantum communication.
Quantum Physics
