Scientists globally are striving to create advanced quantum systems for sensing, communication, computing, and control that could surpass conventional technologies. Developing stable and discernible quantum states, which are crucial for these systems, poses significant challenges. Quantum states have unique traits that can be used to build new information processing systems, but achieving stability and distinguishability is difficult. The ability to extract information from quantum systems relies on the distinguishability of quantum states, a feature tied to orthogonality. However, Gaussian states, a well-known type of quantum state, are never orthogonal, leading to unavoidable errors in distinguishing them.<\/p>\n
Moreover, current quantum devices typically maintain stability for only a brief moment and require complex protocols for state differentiation. Researchers at MIT and the University of Ferrara have now developed a new method to create easily distinguishable states, which could facilitate the advancement of these quantum devices. This method is detailed in a paper published in the journal Physical Review A by Moe Z. Win and Peter L. Falb from MIT, along with Andrea Giani and Andrea Conti from the University of Ferrara. They have discovered a way to translate between quantum states of light and algebraic varieties, simplifying analysis through solvable mathematical equations.<\/p>\n
Win states, \u201cQuantum systems can provide performance that is significantly better than classical counterparts, but this doesn\u2019t come for free.\u201d For practical devices that produce and detect various states, \u201cone needs to carefully engineer the quantum states in which they encode information.\u201d Traditional computers use different voltages to encode data, while optical systems may use light pulses. In quantum devices, states could involve the spin of an atom or electron excitation levels. Win adds, \u201cWe have been studying how to design distinguishable quantum states, which translates directly into improved performance for sensing and communication.\u201d<\/p>\n
The research focused on energy levels of photons, or light particles. Giani explains they used photon variation operations, which include photon addition, raising photons to a higher energy state, or photon subtraction, removing photons from the system. These operations transform Gaussian into non-Gaussian states, which appear more beneficial according to the team. \u201cThe domain of non-Gaussian states is quite big,\u201d Giani notes, \u201cbut among them, we are looking into non-Gaussian states that are easier to implement with current technologies.\u201d<\/p>\n
Unlike some new technologies being explored for quantum applications, Giani mentions that \u201cthese kinds of photon-varied states have already been produced in the laboratory, and there is much interest in this kind of operation.\u201d Conti adds that these states are relatively new, necessitating a theoretical characterization, which the team has provided. This characterization, based on mathematical properties, allows for designing states with improved distinguishability.<\/p>\n
Win remarks, \u201cWe have a theory that gives us a blueprint to go design these non-Gaussian states, rather than just, \u2018try this and that, and let\u2019s hope they\u2019re somewhat distinguishable.\u2019 Our theory tells us exactly how to go about designing orthogonal non-Gaussian states.\u201d The findings link algebraic equations with underlying physics, which Win describes as an important interdisciplinary connection. Falb adds, \u201cThe equations to be solved for determining the orthogonality happened to be polynomial equations. It just happened that there was the appropriate mathematics to solve them.\u201d<\/p>\n
With these principles established, the researchers believe implementation should be straightforward. Some optical setups can already employ these states. Giani states, \u201cIn principle, you can just put the parameters that you find by solving these equations directly into your physical apparatuses and produce these kinds of states. I don\u2019t think this requires some more-advanced technology.\u201d Conti hopes that \u201cas soon as this paper is published, experimentalists can try these methods.\u201d<\/p>\n
Win emphasizes that this is just the start. \u201cWe are getting momentum, and it\u2019s very exciting,\u201d he says. \u201cThe approach that we are taking here is to ask more general questions than just, \u2018here\u2019s a particular setup, how do you tune it to get a performance gain?\u2019 Rather, we\u2019re looking at a class of signal design problems, and then finding keys that really unlock these, so that hopefully the answer will not just be applied to only one particular setup, but something significantly broader.\u201d<\/p>\n