Research group home page

Historically, our laboratory has dealt with experimental quantum optics. We have made sig­nificant contributions to this field and re­cently expanded our research horizons. Now we are solving problems at the intersection of experimental physics, machine learning and ro­botics.

Research

Quantum Optical Experiment

There are multiple quantum systems that have a potential as the basis for future quantum information technology, and it is not known at present, which one is the best. Research groups all over the world are investigating advantages and disadvantages of various candidates. Our group’s effort is concentrated on one such candidate – quantum light, and its fundamental particle – the photon.

The most important, unique advantage of quantum light is its ability to be an information carrier. No matter what future quantum computers will be built of, they will almost certainly communicate by means of photons. This means that developing quantum optical information technology is essential for our quantum future. This is the goal of our group.

We define the following five basic construction blocks of quantum optical technology:

  1. Preparation of quantum states of light
  2. Manipulating them in a controlled manner
  3. Measuring them (quantum tomography)
  4. Interfacing quantum information between light and stationary media
  5. Bringing photons into controlled interaction with each other

There are multiple quantum systems that have a potential as the basis for future quantum information technology, and it is not known at present, which one is the best. Research groups all over the world are investigating advantages and disadvantages of various candidates. Our group’s effort is concentrated on one such candidate – quantum light, and its fundamental particle – the photon.

The most important, unique advantage of quantum light is its ability to be an information carrier. No matter what future quantum computers will be built of, they will almost certainly communicate by means of photons. This means that developing quantum optical information technology is essential for our quantum future. This is the goal of our group.

We define the following five basic construction blocks of quantum optical technology:

  1. Preparation of quantum states of light
  2. Manipulating them in a controlled manner
  3. Measuring them (quantum tomography)
  4. Interfacing quantum information between light and stationary media
  5. Bringing photons into controlled interaction with each other

Machine learning has made enormous progress during recent years, entering almost all spheres of technology, economy and our everyday life. Machines perform comparably to, or even surpass humans in playing board and computer games, driving cars, recognizing images, reading and comprehension. These developments however impose growing demand on our computing capabilities, including both the size of neural networks and the processing rate. With data centers already consuming 2-3% of the electric power produced in the world, and this number growing exponentially, we are in dire need of a new paradigm to continue progressing this technology.

This new paradigm is offered by optical neural networks (ONNs): implementing artificial neural networks using optics rather than electronics. The processing of information in a neural network consists of linear operations (matrix multiplication) combined with nonlinear activation functions applied to individual units. Both these operations can be implemented optically using lenses, spatial light modulators and nonlinear optical elements. Because all these computations in an ONN layer are performed in parallel, the fundamental processing time is independent of the size of the layer. This enables processing speeds and power efficiencies orders of magnitude beyond electronic computing.

Machine learning has made enormous progress during recent years, entering almost all spheres of technology, economy and our everyday life. Machines perform comparably to, or even surpass humans in playing board and computer games, driving cars, recognizing images, reading and comprehension. These developments however impose growing demand on our computing capabilities, including both the size of neural networks and the processing rate. With data centers already consuming 2-3% of the electric power produced in the world, and this number growing exponentially, we are in dire need of a new paradigm to continue progressing this technology.

This new paradigm is offered by optical neural networks (ONNs): implementing artificial neural networks using optics rather than electronics. The processing of information in a neural network consists of linear operations (matrix multiplication) combined with nonlinear activation functions applied to individual units. Both these operations can be implemented optically using lenses, spatial light modulators and nonlinear optical elements. Because all these computations in an ONN layer are performed in parallel, the fundamental processing time is independent of the size of the layer. This enables processing speeds and power efficiencies orders of magnitude beyond electronic computing.

Optical Neural Networks

Intelligent Robotics

Today’s neural networks outperform humans in environments about which they have complete information. The next frontier is our everyday world. Allowing machines to enter the natural environment, touch, experience, learn and change it in a way that humans do will give rise to a new phase of machine learning technology: smart robotics. This technology will revolutionize society by fulfilling the dream of many generations of philosophers, engineers and visionaries: eliminating physical labour from the range of necessary human activities.

We are engaged in a variety of research activities towards smart robotics. This includes developing reinforcement learning algorithms that allow robots to adapt themselves to solving a wide class of problems, applying these algorithms to “conventional” mechanical robots as well as robotic assistants in quantum optical experiments. Finally, we use optics to develop a new generation of tactile sensors that would enable a robotic sense of touch that is comparable in its sensitivity and versatility to that of human fingers.

Today’s neural networks outperform humans in environments about which they have complete information. The next frontier is our everyday world. Allowing machines to enter the natural environment, touch, experience, learn and change it in a way that humans do will give rise to a new phase of machine learning technology: smart robotics. This technology will revolutionize society by fulfilling the dream of many generations of philosophers, engineers and visionaries: eliminating physical labour from the range of necessary human activities.

We are engaged in a variety of research activities towards smart robotics. This includes developing reinforcement learning algorithms that allow robots to adapt themselves to solving a wide class of problems, applying these algorithms to “conventional” mechanical robots as well as robotic assistants in quantum optical experiments. Finally, we use optics to develop a new generation of tactile sensors that would enable a robotic sense of touch that is comparable in its sensitivity and versatility to that of human fingers.

Quantum machine learning is an emerging interdisciplinary field that deals both with the application of quantum technology to accelerate the performance of neural networks, or, conversely, applying machine learning methods to solve problem in quantum physics.

We are interested in quantum variational optimization – the problem of finding the quantum state that best satisfies a certain criterion. Examples include determining the ground state of a certain Hamiltonian, quantum tomography (state estimation from measurements) and quantum chemistry. Hilbert space dimension, and hence the number of parameters describing the state of a quantum system, grows exponentially with its size and becomes unwieldy very quickly; hence the ability of machine learning algorithms to analyze and find regularities in large datasets is extremely useful.

The results of this research have a broad spectrum of applications, including drug and new material discovery, understanding biological processes, quantum computation and communications.

Quantum machine learning is an emerging interdisciplinary field that deals both with the application of quantum technology to accelerate the performance of neural networks, or, conversely, applying machine learning methods to solve problem in quantum physics.

We are interested in quantum variational optimization – the problem of finding the quantum state that best satisfies a certain criterion. Examples include determining the ground state of a certain Hamiltonian, quantum tomography (state estimation from measurements) and quantum chemistry. Hilbert space dimension, and hence the number of parameters describing the state of a quantum system, grows exponentially with its size and becomes unwieldy very quickly; hence the ability of machine learning algorithms to analyze and find regularities in large datasets is extremely useful.

The results of this research have a broad spectrum of applications, including drug and new material discovery, understanding biological processes, quantum computation and communications.

Quantum Machine Learning

Quantum-inspired superresolution imaging

Rayleigh’s criterion defines the minimum resolvable distance between two incoherent point sources as the diffraction-limited spot size. Enhancing the resolution beyond this limit has been a crucial outstanding problem for many years. A number of solutions that have been realized, such as those based on near-field imaging and nonlinear interactions, but they are expensive and not universally applicable. A recent theoretical breakthrough demonstrated that “Rayleigh’s curse” can be resolved by coherent detection of the image in certain transverse electromagnetic modes, rather than implementing the traditional imaging procedure.

To date, there exist proof-of-principle experimental results demonstrating the plausibility of this approach. Our goal is to test this approach in a variety of settings that are relevant for practical application, evaluate its advantages and limitations. If successful, it will result in a revolutionary imaging technology with a potential to change the faces of all fields of science and technology that involve optical imaging, including astronomy, biology, medicine and nanotechnology, as well as optomechanical industry.

Rayleigh’s criterion defines the minimum resolvable distance between two incoherent point sources as the diffraction-limited spot size. Enhancing the resolution beyond this limit has been a crucial outstanding problem for many years. A number of solutions that have been realized, such as those based on near-field imaging and nonlinear interactions, but they are expensive and not universally applicable. A recent theoretical breakthrough demonstrated that “Rayleigh’s curse” can be resolved by coherent detection of the image in certain transverse electromagnetic modes, rather than implementing the traditional imaging procedure.

To date, there exist proof-of-principle experimental results demonstrating the plausibility of this approach. Our goal is to test this approach in a variety of settings that are relevant for practical application, evaluate its advantages and limitations. If successful, it will result in a revolutionary imaging technology with a potential to change the faces of all fields of science and technology that involve optical imaging, including astronomy, biology, medicine and nanotechnology, as well as optomechanical industry.

Lab 5
Lab 4
Lab 3
Lab 2
Lab 1

Contacts

Group Leader

Alexander Lvovsky [web]
Alex.Lvovsky[at]physics.ox.ac.uk

Address

University of Oxford
Clarendon Laboratory
Parks Road
Oxford OX1 3PU, UK [map]

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