Intro Sentence: UCLA researchers in the Division of Physical Sciences & Engineering have developed an efficient and cost-effective method for the improved simulation of open quantum systems.
Background: Open quantum systems are quantum systems that interact with their surroundings and possess dynamics that can be influenced by these interactions, leading to issues such as decoherence and dissipation. All areas of quantum mechanics face these issues of environmental interaction, as quantum systems generally exist in an open state. Existing methods of simulating open quantum systems require complex and computationally heavy approaches, which introduce noise and error to the system. Quantum simulation of non-unitary operators further introduces exponential costs. Current methods are also inefficient in simulating time series and large quantum systems, limiting their scalability and hardware-level integration. There remains an unmet need for an efficient technology for the simulation of open quantum systems.
Innovation:
UCLA researchers, led by Professor Prineha Narang, have come up with a new way to simulate open quantum systems. They use a technique called low-depth convex unitary evolutions. This method simplifies the process by breaking down the representation of the quantum state in a straightforward manner. Their approach has several benefits. First, it doesn't rely on complex frameworks, which helps to reduce errors and makes the calculations simpler. Second, they've found a clever way to make the simulations more efficient by adding a random sampling technique directly into the hardware. This means they can run simulations faster and handle larger quantum systems without overwhelming the computer. One tricky thing about simulating quantum systems is that they can grow very quickly in complexity. However, the UCLA team has found a workaround. They limit the number of simulations they need to run by using a specific number of sample circuits. This helps to manage the computational challenges and keeps the simulations from becoming too overwhelming.
Potential Applications: - Quantum computing algorithm design - Quantum chemistry simulations - Information processing - Quantum machine learning - Quantum network computing
Advantages: - Reduced computational complexity - Lower computational cost - Lower simulation noise - Real-time calibration
Development To Date: First demonstration of technology is complete as of March 2023.
Publications:
Joseph Peetz, Scott E. Smart, Spyros Tserkis, Prineha Narang, Simulation of Open Quantum Systems via Low-Depth Convex Unitary Evolutions, July 2023
Reference: UCLA Case No. 2023-183
Lead Inventor: Prineha Narang