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Quantum supremacy using a programmable superconducting processor
Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute.
Characterizing quantum supremacy in near-term devices
A critical question for quantum computing in the near future is whether quantum devices without error correction can perform a well-defined computational task beyond the capabilities of…
Evidence for quantum annealing with more than one hundred qubits
Quantum annealing is expected to solve certain optimization problems more efficiently, but there are still open questions regarding the functioning of devices such as D-Wave One. A numerical and…
Defining and detecting quantum speedup
Here, it is shown how to define and measure quantum speedup and how to avoid pitfalls that might mask or fake such a speedup, and the subtle nature of the quantum speed up question is illustrated.
What is the Computational Value of Finite Range Tunneling
It is demonstrated how finite range tunneling can provide considerable computational advantage over classical processors for a crafted problem designed to have tall and narrow energy barriers separating local minima, the D-Wave 2X quantum annealer achieves significant runtime advantages relative to Simulated Annealing.
Quantum approximate optimization of non-planar graph problems on a planar superconducting processor
The application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA) is demonstrated and an approximation ratio is obtained that is independent of problem size and for the first time, that performance increases with circuit depth.
Simulation of low-depth quantum circuits as complex undirected graphical models
Near term quantum computers with a high quantity (around 50) and quality (around 0.995 fidelity for two-qubit gates) of qubits will approximately sample from certain probability distributions beyond…
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators.
Optimised simulated annealing for Ising spin glasses
- S. Isakov, I. Zintchenko, T. F. Ronnow, M. Troyer
- Computer Science, PhysicsComput. Phys. Commun.
- 6 January 2014
A generic code for any choice of couplings, an optimised code for bipartite graphs, and highly optimised implementations using multi-spin coding for graphs with small maximum degree and discrete couplings with a finite range are provided.
The ALPS project release 2.0: open source software for strongly correlated systems
We present release 2.0 of the ALPS (Algorithms and Libraries for Physics Simulations) project, an open source software project to develop libraries and application programs for the simulation of…