Quantum Computation with Machine-Learning-Controlled Quantum Stuff

  title={Quantum Computation with Machine-Learning-Controlled Quantum Stuff},
  author={Lucien Hardy and Adam G. M. Lewis},
  journal={Mach. Learn. Sci. Technol.},
We describe how one may go about performing quantum computation with arbitrary "quantum stuff", as long as it has some basic physical properties. Imagine a long strip of stuff, equipped with regularly spaced wires to provide input settings and to read off outcomes. After showing how the corresponding map from settings to outcomes can be construed as a quantum circuit, we provide a machine learning algorithm to tomographically "learn" which settings implement the members of a universal gate set… 

Figures and Tables from this paper


An introduction to quantum machine learning
This contribution gives a systematic overview of the emerging field of quantum machine learning and presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.
Quantum machine learning
The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers.
QuCumber: wavefunction reconstruction with neural networks
An open-source software package called QuCumber is presented that uses machine learning to reconstruct a quantum state consistent with a set of projective measurements and uses a restricted Boltzmann machine to efficiently represent the quantum wavefunction for a large number of qubits.
Neural-network quantum state tomography
It is demonstrated that machine learning allows one to reconstruct traditionally challenging many-body quantities—such as the entanglement entropy—from simple, experimentally accessible measurements, and can benefit existing and future generations of devices.
Quantum Computation and Quantum Information (10th Anniversary edition)
Containing a wealth of figures and exercises, this well-known textbook is ideal for courses on the subject, and will interest beginning graduate students and researchers in physics, computer science, mathematics, and electrical engineering.
Randomized Benchmarking of Quantum Gates
A key requirement for scalable quantum computing is that elementary quantum gates can be implemented with sufficiently low error. One method for determining the error behavior of a gate
Quantum process tomography of a controlled-NOT gate.
We demonstrate complete characterization of a two-qubit entangling process--a linear optics controlled-NOT gate operating with coincident detection--by quantum process tomography. We use a
Reconstructing quantum states with generative models
The key insight is to reduce state tomography to an unsupervised learning problem of the statistics of an informationally complete quantum measurement, which constitutes a modern machine learning approach to the validation of complex quantum devices.
Complete Characterization of a Quantum Process: The Two-Bit Quantum Gate
We show how to fully characterize a quantum process in an open quantum system. We particularize the procedure to the case of a universal two-qubit gate in a quantum computer. We illustrate the method
Symmetrized Characterization of Noisy Quantum Processes
This work introduces a technique based on symmetrization that enables direct experimental measurement of some key properties of the decoherence affecting a quantum system and reduces the number of experiments required from exponential to polynomial in thenumber of subsystems.